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https://api.github.com/repos/pandas-dev/pandas/issues/11203 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11203/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11203/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11203/events | https://github.com/pandas-dev/pandas/pull/11203 | 108,946,012 | MDExOlB1bGxSZXF1ZXN0NDYzMjMzOTY= | 11,203 | Fixed pandas.DataFrame().join() function. | {
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| null | 6 | 2015-09-29T19:51:34Z | 2015-10-05T11:05:55Z | 2015-10-05T11:05:55Z | CONTRIBUTOR | null | Added on_right and on_left keywords.
Now a right.join(left,on=key) works as expected.
Old behavior is archived by right.join(left,left_on=key)
Tested multi-index funtionallity.
```
import pandas as pd
from copy import copy
print pd.__file__
left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3','A4'],
'key': ['K0', 'K1', 'K3', 'K4','K5']})
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'key': ['K0', 'K2', 'K1', 'K3']},
index=[4,5,6,7])
print left.join(right,on='key',how="outer",rsuffix="_r")
```
Old result:
```
A key C key_r
0 A0 K0 NaN NaN
1 A1 K1 NaN NaN
2 A2 K3 NaN NaN
3 A3 K4 NaN NaN
4 A4 K5 NaN NaN
4 NaN 4 C0 K0
4 NaN 5 C1 K2
4 NaN 6 C2 K1
4 NaN 7 C3 K3
```
New result:
```
A key C
0 A0 K0 C0
1 A1 K1 C2
2 A2 K3 C3
3 A3 K4 NaN
4 A4 K5 NaN
5 NaN K2 C1
```
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| null | 5 | 2015-09-29T20:49:38Z | 2015-09-29T21:57:30Z | 2015-09-29T21:57:30Z | NONE | null | Using Pandas 0.16.2, NumPy 1.9.2.
This pairwise operation is completed without errors.
``` python
df0=pd.DataFrame(np.random.normal(size=(10000,100)))
df1=df0.div(df0.sum(axis=1))
```
This one involves 10 times more rows. Memory never exceeds 50%, and yet a MemoryError comes up.
``` python
df0=pd.DataFrame(np.random.normal(size=(100000,100)))
df1=df0.div(df0.sum(axis=1))
```
Same issue if I use the (now deprecated) method:
``` python
df0=pd.DataFrame(np.random.normal(size=(100000,100)))
df1=df0/df0.sum(axis=1)
```
Thanks
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} | 4 | 2015-09-30T00:03:30Z | 2015-10-04T17:01:51Z | 2015-10-02T02:15:07Z | CONTRIBUTOR | null | This is a follow-up to https://github.com/pydata/pandas/pull/10744. In that, vectorized versions of some offsets were implemented, mostly by changing to periods and back.
The case of shifting by years/months (which is actually most useful to me) required some extra hoops and had poorer performance - this PR implements a special cython routine for that, for about a 10x improvement.
```
In [3]: s = pd.Series(pd.date_range('1900-1-1', periods=100000))
# Master
In [4]: %timeit s + pd.DateOffset(months=1)
1 loops, best of 3: 140 ms per loop
# PR
In [2]: %timeit s + pd.DateOffset(months=1)
100 loops, best of 3: 14.2 ms per loo
```
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} | 0 | 2015-09-30T11:21:29Z | 2015-11-20T14:51:21Z | 2015-11-20T14:51:21Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/32863674/python-pandas-isnull-does-not-work-on-nat-in-object-dtype)
this should work
```
Out[3]: array([11, 22, 33, nan, numpy.datetime64('NaT')], dtype=object)
In [4]: pd.isnull(ser)
Out[4]:
0 False
1 False
2 False
3 True
4 False
Name: my_series, dtype: bool
```
This could be updated: https://github.com/pydata/pandas/blob/master/pandas/tslib.pyx#L711, then `lib.isnullobj` then could use for null detection
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| null | 15 | 2015-09-30T17:11:45Z | 2020-09-06T18:44:21Z | 2015-11-01T16:35:56Z | CONTRIBUTOR | null | Aims to eventually close #10954 , alternative to #10973
In the interest of advancing the discussion on this, here's a skeleton of a different copy on write implementation.
**Currently**
When setting on a view, converts to copy.
```
df = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
intermediate = df.loc[1:1,]
intermediate['col1'] = -99
intermediate
Out[8]:
col1 col2
1 -99 4
df
Out[9]:
col1 col2
0 1 3
1 2 4
```
Chained-indexing will ALWAYS fail.
```
df = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
df.loc[1:1,]['col1'] = -99
df
Out[11]:
col1 col2
1 2 4
```
`SettingWithCopy` warning disabled, thought not fully removed (figure this is too early stage to really chase that down).
Forward Protection for `loc` setting (proof of concept, added oct 2):
```
df = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
intermediate = df.loc[1:1,]
df.loc[1,'col1'] = -99
intermediate
Out[11]:
col1 col2
0 1 3
1 2 4
```
**Goals / Places I'd love help**
- We've discussed keeps full-column slices as views, since we can guarantee these will always be views. I created an attribute called `_is_column_view` that can be set to `True` when a slice is a full column (e.g. `new = df['col1']`, but I'm not sure how to actually figure out if a slice is a full column, so right now it's initialized as `False` and just stays that way.
- This has no protection against forward propagation -- situations where one sets values on a Frame and they propagate to views of that Frame. There IS a framework to address it, but it's incomplete. As it stands:
- Frames now have a `_children` list attribute to keep references to their child `views`.
- Before setting, there's a call to a function to try and convert the children from `views` to `copies`.
But as it stand, I don't know how to really manage those references (i.e. how to put references to children in that `_children` list).
@jreback
@shoyer
@JanSchulz
@TomAugspurger
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} | 1 | 2015-09-30T19:40:45Z | 2015-09-30T19:56:15Z | 2015-09-30T19:56:03Z | CONTRIBUTOR | null | Per [this SO question](http://stackoverflow.com/q/32873402/467366), using `apply()` to convert multiple `DataFrame` columns to categorical does not work unless _all_ columns are categorical.
An MWE demonstrating the issue can be found at [this gist](https://gist.github.com/pganssle/4d0b4c326f0f8e80ba50) for easy copy-paste.
Using this for example data:
``` python
import pandas as pd
pdf = pd.DataFrame(dict(name= ('Earl', 'Eve', 'Alan', 'Randall', 'Danielle'),
age= ( 29, 17, 73, 31, 62),
gender= ( 'M', 'F', 'M', 'M', 'F'),
nationality=( 'US', 'UK', 'CAN', 'CAN', 'US'),
height= ( 182.9, 167.6, 175.3, 170.2, 172.8)),
columns=('name', 'age', 'gender', 'nationality', 'height'))
pdf = pdf.set_index('name')
>>> print(pdf)
age gender nationality height
name
Earl 29 M US 182.9
Eve 17 F UK 167.6
Alan 73 M CAN 175.3
Randall 31 M CAN 170.2
Danielle 62 F US 172.8
```
I tried to use `pdf.apply()` to convert `'gender'` and `'nationality'` to categorical columns:
``` python
cat_list = {'gender', 'nationality'}
set_cat_list = lambda x: x.astype('category') if x.name in cat_list else x
dfa = pdf.apply(set_cat_list)
>>> print('Applied to subset: dtype={}'.format(dfa['gender'].dtype))
Applied to subset: dtype=object
```
To make sure that the problem isn't just that I'm never reaching the `x.astype('category')` branch of the `lambda` expression, I added in an alert:
``` python
in_cl = lambda x: x.name in cat_list
set_cat_list_alert = lambda x: (set_cat_list(x),
sys.stdout.write('{}: {}\n'.format(x.name, in_cl(x))))[0]
dfa = pdf.apply(set_cat_list_alert)
>>> print('Applied to subset: dtype={}'.format(dfa['gender'].dtype))
age: False
age: False
gender: True
nationality: True
height: False
Applied to subset: dtype=object
```
To verify that it's not just a problem setting _any_ columns as categorical, I tried setting _all_ columns to categorical, which works just fine:
``` python
set_cat = lambda x: x.astype('category')
dfb = pdf.apply(set_cat)
>>> print('Applied to whole frame: dtype={}'.format(dfb['gender'].dtype))
Applied to whole frame: dtype=category
```
Finally, I tried just using a for loop to duplicate the final result, to make sure that mixed categorical / non-categorical columns can coexist like this:
``` python
dfc = pdf.copy()
for cat in cat_list:
dfc[cat] = pdf[cat].astype('category')
>>> print('For loop: dtype={}'.format(dfc['gender'].dtype))
For loop: dtype=category
```
Is this the desired behavior?
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| null | 8 | 2015-09-30T21:35:26Z | 2015-10-02T15:25:42Z | 2015-10-02T15:25:42Z | CONTRIBUTOR | null | - Modify `read_gbq()` to allow users to redirect the query results to a destination table via the `destination_table` parameter
- Modify `read_gbq()` to allow users to allow users to set the `'allowLargeResults'` option in the BigQuery job configuration via the `allow_large_results` parameter
cc @aaront
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} | 5 | 2015-09-30T22:04:59Z | 2016-07-21T15:43:59Z | 2015-11-13T19:03:22Z | CONTRIBUTOR | null | These are occasionally failing on travis: https://github.com/pydata/pandas/commit/2f59a027a28ef50aeedb7753420a7605a2036aaa
don't fail for me locally, so not sure what's up.
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} | 29 | 2015-09-30T22:59:02Z | 2017-01-18T13:36:08Z | 2015-11-18T20:12:21Z | NONE | null | …eIndex
closes #8038
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} | 21 | 2015-09-30T23:23:51Z | 2015-11-20T14:51:30Z | 2015-11-20T14:51:21Z | CONTRIBUTOR | null | closes #11206
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} | 4 | 2015-10-01T00:32:51Z | 2015-10-01T17:33:05Z | 2015-10-01T10:43:19Z | NONE | null | python 3.4, pandas version 0.16.2
this works:
```
monthly = {'datetime_col_to_use':['START DATE'],
'colnames': ["TYPE", "START DATE", "END DATE", "USAGE", "UNITS", "COST", "NOTES"],
'header_size':4,
'concat_datetime_cols': False}
monthly_df = pd.DataFrame.from_dict(monthly, orient='index')
```
but this doesn't:
```
hourly = {'datetime_col_to_use': ['START TIME', 'DATE'],
'colnames': ["TYPE", "DATE", "START TIME", "END TIME", "USAGE", "UNITS", "COST", "NOTES"],
'header_size':4,
'concat_datetime_cols': True}
hourly_df = pd.DataFrame.from_dict(hourly, orient='index')
```
Full traceback:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-66cef4447f7e> in <module>()
----> 1 hourly_df = pd.DataFrame.from_dict(hourly, orient='index')
2 #hourly_df.columns=['hourly']
3 #hourly_df.head()
/home/vagrant/venv/lib/python3.4/site-packages/pandas/core/frame.py in from_dict(cls, data, orient, dtype)
681 raise ValueError('only recognize index or columns for orient')
682
--> 683 return cls(data, index=index, columns=columns, dtype=dtype)
684
685 @deprecate_kwarg(old_arg_name='outtype', new_arg_name='orient')
/home/vagrant/venv/lib/python3.4/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
249 if len(data) > 0:
250 if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1:
--> 251 arrays, columns = _to_arrays(data, columns, dtype=dtype)
252 columns = _ensure_index(columns)
253
/home/vagrant/venv/lib/python3.4/site-packages/pandas/core/frame.py in _to_arrays(data, columns, coerce_float, dtype)
4917 if isinstance(data[0], (list, tuple)):
4918 return _list_to_arrays(data, columns, coerce_float=coerce_float,
-> 4919 dtype=dtype)
4920 elif isinstance(data[0], collections.Mapping):
4921 return _list_of_dict_to_arrays(data, columns,
/home/vagrant/venv/lib/python3.4/site-packages/pandas/core/frame.py in _list_to_arrays(data, columns, coerce_float, dtype)
4998 else:
4999 # list of lists
-> 5000 content = list(lib.to_object_array(data).T)
5001 return _convert_object_array(content, columns, dtype=dtype,
5002 coerce_float=coerce_float)
pandas/src/inference.pyx in pandas.lib.to_object_array (pandas/lib.c:58728)()
TypeError: object of type 'bool' has no len()
```
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} | 7 | 2015-10-01T11:02:20Z | 2017-11-10T16:12:40Z | 2017-11-10T09:00:41Z | CONTRIBUTOR | null | xref #11205
https://github.com/pydata/pandas/blob/master/pandas/src/offsets.pyx
is in the repo but is not included in the build, nor updated in 2+ years.
Looks like their might be some cython code to speed up `DateOffset` apply in the non-vectorized cases.
Further might look to move _some_ routines from `tslib.pyx` for these types of things (and obviously would need to be included in the build).
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} | 6 | 2015-10-02T01:55:57Z | 2017-01-16T10:56:11Z | 2015-10-03T15:48:50Z | CONTRIBUTOR | null | - When we don't specifiy a chunksize we get an object dtype which is ok
- We create a propery datetime64[ns, tz] type, but its a pytz.FixedOffset(....)
which ATM is not really a useful/palatable type and is mostly confusing for now.
In the future could attempt to coerce this to a nice tz, e.g. US/Eastern, ,not sure if
this is possible.
- Note that this is w/o parse_dates specified
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} | 25 | 2015-10-02T08:12:50Z | 2020-03-31T05:14:54Z | 2020-03-31T05:14:53Z | CONTRIBUTOR | null | I'm using `resample()` to aggregate data in a timeframe.
``` python
>>> s = pandas.Series((3, 4), (pandas.Timestamp("2014-1-1"), pandas.Timestamp("2015-1-1")))
>>> x = s.resample("1s")
>>> len(x)
31536001
```
When doing such a call, `resample` fills with NaN all the (31536001 - 2) inexistent values, which ends up creating thousands of points and making Python using 500M+ RAM. The thing is that I don't care about the NaN point, so I would like to _not_ fill them in the `Series` and having so much memory used. AFAICS `resample` does not offer such as `fill_method`.
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| null | 2 | 2015-10-02T11:36:23Z | 2015-10-02T11:59:27Z | 2015-10-02T11:59:27Z | CONTRIBUTOR | null | The docs (contributing.rst and CONTRIBUTING.md) suggest that I should install a conda env using `ci/requirements_dev.txt`, but that file is missing.
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} | 15 | 2015-10-02T12:34:30Z | 2015-10-12T16:01:43Z | 2015-10-12T15:49:37Z | CONTRIBUTOR | null | This closes #7615 and represents work that started in #2636.
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} | 2 | 2015-10-02T13:43:43Z | 2019-12-12T21:05:09Z | 2019-12-12T21:05:09Z | CONTRIBUTOR | null | Whether `np.datetime64('NaT')` is printed as `NaT` or `NaN` depends on other types present in the column. This is consistent with `pd.NaT`, whose display also depends on other types. But when all the other scalars are `None`, `pd.NaT` and `None` are both printed as `NaT`, whereas in a column of `np.datetime64('NaT')` and `None`, it depends on the order of the entries (last two lines).
```
In [44]: pd.DataFrame([[np.datetime64('NaT')], [None]])
Out[44]:
0
0 NaN
1 None
In [45]: pd.DataFrame([[pd.NaT], [None]])
Out[45]:
0
0 NaT
1 NaT
In [46]: pd.DataFrame([[np.datetime64('NaT')], [pd.NaT]])
Out[46]:
0
0 NaT
1 NaT
In [47]: pd.DataFrame([[pd.NaT], [np.datetime64('NaT')]])
Out[47]:
0
0 NaT
1 NaT
In [81]: pd.DataFrame([[None],[np.datetime64('NaT')]])
Out[81]:
0
0 NaT
1 NaT
In [82]: pd.DataFrame([[np.datetime64('NaT')],[None]])
Out[82]:
0
0 NaN
1 None
In [86]: pd.DataFrame([[None],[np.datetime64('NaT')]]).dtypes
Out[86]:
0 datetime64[ns]
dtype: object
In [87]: pd.DataFrame([[np.datetime64('NaT')],[None]]).dtypes
Out[87]:
0 object
dtype: object
```
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} | 46 | 2015-10-02T14:16:01Z | 2017-07-18T11:26:45Z | 2017-07-18T11:26:45Z | CONTRIBUTOR | null | xref #11173
or IMHO simply replace by use of `pd.to_datetime,pd.to_timedelta,pd.to_numeric`.
Having an auto-guesser is ok, but when you try to forcefully coerce things can easily go awry.
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| null | 5 | 2015-10-02T15:26:03Z | 2015-10-02T16:24:21Z | 2015-10-02T15:28:40Z | NONE | null | Hi, I am new here just started some tutorials learning python, can you plz help me
'vw_bits_amazon.csv'
```
bits loss
25 0.151731
24 0.151809
23 0.151951
22 0.151809
```
```
import pandas as pd
from matplotlib import pyplot as plt
input_file = 'vw_bits_amazon.csv'
d = pd.read_csv(input_file)
plt.plot(d.bits, d.loss)
plt.show()
```
I got
`AttributeError: 'DataFrame' object has no attribute 'bits'`
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} | 2 | 2015-10-02T17:56:33Z | 2015-11-15T18:41:29Z | 2015-11-15T18:41:29Z | CONTRIBUTOR | null | In [here](http://pandas-docs.github.io/pandas-docs-travis/groupby.html#groupby-with-multiindex) object `s` is used in the example but not created on this page (at least in this form).
Above it is created as series
``` python
In [8]: s = pd.Series([1, 2, 3, 10, 20, 30], lst)
```
once under [here](http://pandas-docs.github.io/pandas-docs-travis/groupby.html#groupby-with-multiindex)
``` python
In [27]: s
Out[27]:
first second
bar one -0.575247
two 0.254161
baz one -1.143704
two 0.215897
foo one 1.193555
two -0.077118
qux one -0.408530
two -0.862495
dtype: float64
```
is completely different thing.
I find referring to objects created on other manual pages very confusing.
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| null | 9 | 2015-10-02T18:00:01Z | 2015-10-02T20:00:01Z | 2015-10-02T18:53:10Z | NONE | null | If a dataframe has a single row for a given index entry, it returns a Series. If it
has two rows for that index, it returns a DataFrame. I believe that it should return a
DataFrame in either case for consistency.
Image attached, small dataframe and notebook exhibiting the problem attached. OK, so
I can't attach either the dataframe or the notebook, even suffixing them with .txt (github barfs).
So I'm pasting the text fragment after the image..
<img width="959" alt="pandasinconsistenttypes" src="https://cloud.githubusercontent.com/assets/1727968/10252096/e0e7ed72-68fa-11e5-809c-a125c44a8fbf.png">
dataframe = '''\
Locus,Decision,Group,Var,Region,Gene,Rows,Mutation,Profile
chr01:0018961727,Homopolymer,VS,CA,exonic,PAX7,1.0,synonymous SNV,000000010010001000000000001101
chr01:0027057772,Bad,IR-PM-VS,CA,exonic,ARID1A,1.0,nonsynonymous SNV,000000000000000000001000100000
chr01:0027057772,Bad,IR-PM-VS,CA,exonic,ARID1A,1.0,nonsynonymous SNV,000000000000000000011001110100
chr01:0027057772,Bad,IR-PM-VS,CA,exonic,ARID1A,1.0,nonsynonymous SNV,100000000001010000010001110110\
'''
df = [line.split(',') for line in txt.split('\n')]
tdf = pd.DataFrame.from_records(df, index=(0,))
tdf
type(tdf.loc['chr01:0018961727']), type(tdf.loc['chr01:0027057772'])
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} | 4 | 2015-10-02T19:43:34Z | 2015-10-02T21:26:59Z | 2015-10-02T21:26:58Z | CONTRIBUTOR | null | Their service was slowing down the loading of our docs. I've added a defer tag so that our docs load first.
Or we can delete that entirely, anyone know why it's in there to begin with?
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} | 1 | 2015-10-02T20:44:21Z | 2016-02-13T13:48:30Z | 2016-02-13T13:48:30Z | CONTRIBUTOR | null | ```
In [2]: df = pd.DataFrame(data=[[0, 2, 1], [np.nan, 5, 4]], index=pd.date_range('2014-01-01', '2014-01-2', name='timestamp'), columns=pd.MultiIndex.from_product([['value'], [24, 3766, 5061]], names=[None, 'sid']))
In [3]: df
Out[3]:
value
sid 24 3766 5061
timestamp
2014-01-01 0 2 1
2014-01-02 NaN 5 4
In [4]: assets = [3766,5061,123]
In [5]: df.reindex(columns=assets,level=1)
Out[5]:
value
sid 3766 5061
timestamp
2014-01-01 2 1
2014-01-02 5 4
```
So this should include 123 as the last column (and all nans) as well.
The indexer returned in `np.array([1,2])`, but should be `np.array([1,2,-1])`, see [here](https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L2406)
only 1 test I could find actually tests this behavior, see [here](https://github.com/pydata/pandas/blob/master/pandas/tests/test_multilevel.py#L141)
but only tests that the SAME values are returned, not less or more (which is the point of this issue)
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} | 7 | 2015-10-03T15:47:50Z | 2021-01-22T21:42:34Z | 2021-01-22T21:42:34Z | CONTRIBUTOR | null | This occurs on 32-bit linux, a slightly different ordering is returned from the hashtable. Only guess is that it is because the indexing is `Py_ssize_t` and this is hashed and has differing values. So the test should be slightly different for those platforms.
see test skipping here: https://github.com/pydata/pandas/commit/d6c7a3a884232948a198ba4a9475325cacb5ca98
Not a big deal, but here's the question. Should we guarantee these types of orderings, IOW, use a `int64` instead of `Py_ssize_t` for indexing (on all platforms)?
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| null | 8 | 2015-10-03T17:04:06Z | 2021-04-20T05:26:33Z | null | NONE | null | In pandas 0.17.0.rc1 there is an inconsistency in the color arguments: once it is "color", other times it is "colors":
df.plot(kind='bar', color= ....)
df.plot(kind='pie', colors= ...)
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} | 1 | 2015-10-03T18:04:36Z | 2015-10-09T13:15:20Z | 2015-10-09T13:15:20Z | CONTRIBUTOR | null | ``` python
In [77]: empty=pd.DataFrame(pd.np.empty(shape=(0,1)))
In [78]: empty
Out[78]:
Empty DataFrame
Columns: [0]
Index: []
In [79]: empty.squeeze()
Out[79]:
Empty DataFrame
Columns: [0]
Index: []
```
...but should return a `Series`
PR coming now
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} | 9 | 2015-10-03T18:05:48Z | 2015-12-08T03:52:34Z | 2015-10-09T13:15:07Z | CONTRIBUTOR | null | fixes: https://github.com/pydata/pandas/issues/11229.
fixes: https://github.com/pydata/pandas/issues/8999.
Also a better implementation that avoids `ix`
Should I add to What's new for 0.17? Or is that closed now?
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} | 8 | 2015-10-03T23:23:35Z | 2015-10-08T05:09:42Z | 2015-10-07T15:52:05Z | MEMBER | null | Follow-up for #11102. Added note because doc output may look incorrect depending on font.
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} | 4 | 2015-10-04T05:34:59Z | 2015-10-06T18:04:08Z | 2015-10-06T18:04:08Z | CONTRIBUTOR | null | Building and testing OSX wheels for 0.17.0, I am getting these errors on Python 3.3 (not 2.7 or 3.4 or 3.5):
```
======================================================================
FAIL: test_isin (pandas.tseries.tests.test_timedeltas.TestTimedeltaIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/travis/build/MacPython/pandas-wheels/venv/lib/python3.3/site-packages/pandas/tseries/tests/test_timedeltas.py", line 1406, in test_isin
self.assertTrue(result.all())
AssertionError: False is not true
======================================================================
FAIL: test_isin (pandas.tseries.tests.test_timeseries.TestDatetimeIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/travis/build/MacPython/pandas-wheels/venv/lib/python3.3/site-packages/pandas/tseries/tests/test_timeseries.py", line 2923, in test_isin
self.assertTrue(result.all())
AssertionError: False is not true
======================================================================
FAIL: test_isin_with_i8 (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/travis/build/MacPython/pandas-wheels/venv/lib/python3.3/site-packages/pandas/tests/test_series.py", line 7268, in test_isin_with_i8
assert_series_equal(result, expected)
File "/Users/travis/build/MacPython/pandas-wheels/venv/lib/python3.3/site-packages/pandas/util/testing.py", line 925, in assert_series_equal
check_less_precise, obj='{0}'.format(obj))
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)
File "pandas/src/testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)
File "/Users/travis/build/MacPython/pandas-wheels/venv/lib/python3.3/site-packages/pandas/util/testing.py", line 798, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series are different
Series values are different (40.0 %)
[left]: [False, False, False, False, False]
[right]: [True, True, False, False, False]
```
Full log at https://s3.amazonaws.com/archive.travis-ci.org/jobs/83497520/log.txt
I think I'm testing with the same parameters as the Linux test runs. Am I missing something?
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} | 2 | 2015-10-04T06:55:29Z | 2015-10-09T13:04:03Z | 2015-10-09T13:03:57Z | CONTRIBUTOR | null | closes #10660
first try at contributing to pandas. feedbacks will be greatly appreciated.
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} | 7 | 2015-10-04T15:18:43Z | 2015-10-09T13:33:13Z | 2015-10-09T13:33:13Z | NONE | null | When I try to save some non-ascii character like é and then load it again, I end up with UnicodeDecodeError. If you add some more data to the string (like 'aée'), the data gets stored and retrieved without error, but the result is missing the last character.
``` python
import pandas as pd
df = pd.DataFrame(columns=["A"])
toAppend = {"A": "é"}
df = df.append(toAppend, ignore_index = True)
store = pd.HDFStore(r'thiswillcrash.h5')
store.put('df', df, format='table', encoding="utf-8")
d = store["df"]
print(d)
store.close()
```
Versions
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.3
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.7
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11235/events | https://github.com/pandas-dev/pandas/issues/11235 | 109,686,658 | MDU6SXNzdWUxMDk2ODY2NTg= | 11,235 | Dataframe.eval(): Negative number in list passed to 'in'-expression causes crash on python 3.4.0 | {
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} | 5 | 2015-10-04T15:30:08Z | 2017-05-15T22:59:47Z | 2015-10-23T20:41:12Z | CONTRIBUTOR | null | The following crashes on python 3.4.0. It works fine on Python 2.7.5.
```
>>> import pandas
>>> from io import StringIO
>>> data = "foo,bar\n11,12"
>>> df = pandas.read_csv(StringIO(data))
>>> df.eval('foo in [11, 32]')
0 True
dtype: bool
>>> df.eval('foo in [11, -32]')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/core/frame.py", line 1987, in eval
return _eval(expr, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/eval.py", line 230, in eval
truediv=truediv)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 635, in __init__
self.terms = self.parse()
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 652, in parse
return self._visitor.visit(self.expr)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 320, in visit_Module
return self.visit(expr, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 323, in visit_Expr
return self.visit(node.value, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 560, in visit_Compare
return self.visit(binop)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 404, in visit_BinOp
op, op_class, left, right = self._possibly_transform_eq_ne(node)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 357, in _possibly_transform_eq_ne
right = self.visit(node.right, side='right')
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 430, in visit_List
name = self.env.add_tmp([self.visit(e).value for e in node.elts])
File "/home/tobias/Envs/qcache-py3/lib/python3.4/site-packages/pandas/computation/expr.py", line 430, in <listcomp>
name = self.env.add_tmp([self.visit(e).value for e in node.elts])
AttributeError: 'UnaryOp' object has no attribute 'value'
>>>
```
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} | 6 | 2015-10-04T18:21:15Z | 2015-10-11T23:36:11Z | 2015-10-10T00:23:39Z | CONTRIBUTOR | null | closes #11007
closes #10970 (data in wrong order)
closes #10982 (columns lost).
Using the approach suggested [here](https://github.com/pydata/pandas/issues/11007#issuecomment-141019207)
All three occurred when using `to_excel` with duplicate columns in the `DataFrame`
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} | 9 | 2015-10-04T21:27:09Z | 2020-06-25T22:56:46Z | 2020-06-25T22:56:46Z | NONE | null | The `HDFStore.append_to_multiple` passes on its entire `min_itemsize` argument to every sub-append. Because not all columns are in every append, it fails when it tries to set a min_itemsize for a certain column when appending to a table that doesn't use that column.
Simple example:
```
>>> store.append_to_multiple({
... 'index': ["IX"],
... 'nums': ["Num", "BigNum", "RandNum"],
... "strs": ["Str", "LongStr"]
... }, d.iloc[[0]], 'index', min_itemsize={"Str": 10, "LongStr": 100})
Traceback (most recent call last):
File "<pyshell#52>", line 5, in <module>
}, d.iloc[[0]], 'index', min_itemsize={"Str": 10, "LongStr": 100})
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\io\pytables.py", line 1002, in append_to_multiple
self.append(k, val, data_columns=dc, **kwargs)
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\io\pytables.py", line 920, in append
**kwargs)
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\io\pytables.py", line 1265, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\io\pytables.py", line 3773, in write
**kwargs)
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\io\pytables.py", line 3460, in create_axes
self.validate_min_itemsize(min_itemsize)
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\io\pytables.py", line 3101, in validate_min_itemsize
"data_column" % k)
ValueError: min_itemsize has the key [LongStr] which is not an axis or data_column
```
This apparently means that you can't use `min_itemsize` without manually creating and appending to each separate table beforehand, which is the kind of thing `append_to_multiple` is supposed to shield you from.
I think `append_to_multiple` should special-case `min_itemsize` and split it into separate dicts for each sub-append. I don't know if there are other potential kwargs that need to be "allocated" separately to sub-appends, but if there are it might be good to split them too.
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} | 0 | 2015-10-04T21:53:18Z | 2021-04-20T05:26:50Z | null | NONE | null | `HDFStore.append_to_multiple` only lets your specify `data_columns` for the "selector" table; all other tables you make with it have no data columns. Is there a reason it needs to be that way?
I think the `append_to_multiple`/`select_as_multiple` workflow would be somewhat more flexible if we didn't view the set of tables being appended/selected as a monolithic grouping that must always be used together, with the "selector" (queryable) table specified up front. For write operations it makes sense to append together to keep them in sync, but for read operations, is there any particular reason why you always need to be querying on the same table?
I am working with some census data that has many columns representing different sorts of data. Some are geographic identifiers like FIPS codes, some are overall population counts, some are detailed population counts for subcategories like racial/age groups, some may be other kinds of data like housing units or income. It would be nice if you could use `append_to_multiple` to write a row of all the data to all the tables, but then later still be able to query or retrieve some of the data depending on what you need.
For instance, suppose you have a dataset with columns GEO1, GEO2, POP1, POP2, INCOME1, INCOME2. Sometimes I might need the population columns but not income, or maybe the income columns but not the population ones. It would be nice if I could use `append_to_multiple` to write my dataset to three separate tables, but then do something like `select_as_multiple(['GeoTable', 'PopTable'], where=['POP1>1000'], selector='PopTable')` or, on the same data, `select_as_multiple(['GeoTable', 'IncomeTable', where=['INCOME1>20000'], selector='IncomeTable')`. In other words, I want to specify the selector table at query time, not write time.
I think just modifying `append_to_multiple` to allow setting data columns on any table, not just the "selector" table. In fact, there's no need to specify a "selector" table at write time at all (although it could still be done for convenience). This would mean that every one of the sub-tables created by `append_to_multiple` would be nicely queryable on its own. As far as I can tell, there's nothing in the existing implementations of append/select that would preclude this; they just don't expose any interface for it.
For now this would probably stick to allowing just one selector table per query (e.g., in the above example you can query on population columns or income columns, but not both in one query). But in the future it could be possible to allow multi-selector queries by intersecting the resulting row sets of each sub-query.
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} | 2 | 2015-10-05T00:15:36Z | 2015-10-09T13:33:02Z | 2015-10-09T13:33:02Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/11234
Failure came when the maximum length of the unencoded string
was smaller than the maximum encoded length.
Need to run a perf check still. We end up having to call `_convert_string_array` twice, once before we know the min_itemsize, and a second time just before appending once we do know the min itemsize.
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} | 1 | 2015-10-05T14:18:58Z | 2015-10-08T12:25:38Z | 2015-10-08T12:25:34Z | CONTRIBUTOR | null | As talked in #11217.
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} | 3 | 2015-10-05T15:37:20Z | 2015-10-06T11:18:26Z | 2015-10-06T11:18:06Z | NONE | null | read_stata(buf) needs a buf which implements both read() and seek(). read() is not enough. For example, the object returned from zipfile.ZipFile.open('filename') only provides read() and not seek() and cannot be used.
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} | 2 | 2015-10-05T17:25:06Z | 2020-01-04T17:25:15Z | 2020-01-04T17:25:15Z | NONE | null | Pandas 0.16.2, Python 3.4.
``` python
test = pd.DataFrame({
pd.Timestamp('2012-01-01 00:00:00'): ['a', 'b'],
pd.Timestamp('2012-01-02 00:00:00'): ['c', 'd'],
'name': ['e', 'e'],
'aaaa': ['f', 'g']
})
print(test)
print(test.groupby('name').first())
```
Fails with:
```
File "/usr/lib64/python3.4/site-packages/pandas/core/groupby.py", line 106, in f
self._set_selection_from_grouper()
File "/usr/lib64/python3.4/site-packages/pandas/core/groupby.py", line 489, in _set_selection_from_grouper
self._group_selection = ax.difference(Index(groupers)).tolist()
File "/usr/lib64/python3.4/site-packages/pandas/core/index.py", line 1506, in difference
theDiff = sorted(set(self) - set(other))
File "pandas/tslib.pyx", line 836, in pandas.tslib._Timestamp.__richcmp__ (pandas/tslib.c:15612)
TypeError: Cannot compare type 'Timestamp' with type 'str'
```
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} | 16 | 2015-10-05T18:41:09Z | 2015-10-11T15:17:56Z | 2015-10-11T15:17:51Z | CONTRIBUTOR | null | I ran into some strange behavior with a series of dtype datetime64[ns] where I called max and got back a `nan`. I think the correct behavior here is to return `nat`. I looked through test_nanops but I am not sure where the correct place to put the test for this is.
The new behavior is:
``` python
In [1]: pd.Series(dtype='datetime64[ns]').max()
Out[1]: NaT
```
where the old behavior was:
``` python
In [1]: pd.Series(dtype='datetime64[ns]').max()
Out[1]: nan
```
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} | 0 | 2015-10-05T20:33:35Z | 2015-10-06T07:55:46Z | 2015-10-06T07:55:46Z | CONTRIBUTOR | null | In the new warning. `to_timestamp` should be `to_timedelta`
```
df.convert_objects()
...
FutureWarning: convert_objects is deprecated.
Use the data-type specific converters pd.to_datetime, pd.to_timestamp and pd.to_numeric.
```
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} | 10 | 2015-10-05T21:59:17Z | 2015-10-16T16:40:40Z | 2015-10-16T16:40:40Z | NONE | null | My original code used:
```
data = df.to_dict(orient='records')
```
but I had to replace with a different to_dict conversion because to_dict(orient='records') doesn't properly convert numpy.datetime64 to Timestamps but rather returns numpy.datetime64 values which are hard to work with downstream. If you include any other non-datetime fields in the dataframe the conversion to a Timestamp happens with 'records' mode, so I think this is some kind of edge-case where converters aren't being triggered when everything is a datetime.
```
import pandas as pd
df = pd.DataFrame({
'c1': pd.to_datetime(['1-1-2015', '1-2-2015', '1-3-2015']),
'c2': pd.to_datetime(['2-2-2015', '2-3-2015', '2-4-2015']),
})
print('raw dataframe')
print(df)
print('\nconverted to records')
print(df.to_dict(orient='records'))
print('\nconverted to dict')
print(df.to_dict(orient='dict'))
print('\nconverted to list')
print(df.to_dict(orient='list'))
```
produces
```
raw dataframe
c1 c2
0 2015-01-01 2015-02-02
1 2015-01-02 2015-02-03
2 2015-01-03 2015-02-04
converted to records
[{'c2': numpy.datetime64('2015-02-01T16:00:00.000000000-0800'), 'c1': numpy.datetime64('2014-12-31T16:00:00.000000000-0800')}, {'c2': numpy.datetime64('2015-02-02T16:00:00.000000000-0800'), 'c1': numpy.datetime64('2015-01-01T16:00:00.000000000-0800')}, {'c2': numpy.datetime64('2015-02-03T16:00:00.000000000-0800'), 'c1': numpy.datetime64('2015-01-02T16:00:00.000000000-0800')}]
converted to dict
{'c2': {0: Timestamp('2015-02-02 00:00:00'), 1: Timestamp('2015-02-03 00:00:00'), 2: Timestamp('2015-02-04 00:00:00')}, 'c1': {0: Timestamp('2015-01-01 00:00:00'), 1: Timestamp('2015-01-02 00:00:00'), 2: Timestamp('2015-01-03 00:00:00')}}
converted to list
{'c2': [Timestamp('2015-02-02 00:00:00'), Timestamp('2015-02-03 00:00:00'), Timestamp('2015-02-04 00:00:00')], 'c1': [Timestamp('2015-01-01 00:00:00'), Timestamp('2015-01-02 00:00:00'), Timestamp('2015-01-03 00:00:00')]}
```
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} | 1 | 2015-10-05T23:24:45Z | 2015-10-06T23:58:39Z | 2015-10-06T07:55:46Z | CONTRIBUTOR | null | closes #11246
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} | 14 | 2015-10-06T03:21:46Z | 2021-04-20T05:27:53Z | null | NONE | null | Here is sample code that finds duplicate columns in a DataFrame based on their values (useful for cleaning data):
``` python
def duplicate_columns(frame):
groups = frame.columns.to_series().groupby(frame.dtypes).groups
dups = []
for t, v in groups.items():
dcols = frame[v].to_dict(orient="list")
vs = dcols.values()
ks = dcols.keys()
lvs = len(vs)
for i in range(lvs):
for j in range(i+1,lvs):
if vs[i] == vs[j]:
dups.append(ks[i])
break
return dups
```
I've seen others suggest something like `df.T.drop_duplicates().T`. However, transposing is a bad idea when working with large DataFrames.
I would add a pull request, but I'm not sure I even know what that means.
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| null | 2 | 2015-10-06T07:12:59Z | 2015-10-07T07:07:46Z | 2015-10-06T11:03:48Z | NONE | null | In python3 when I import pandas from the interactive session, if there is a file called `parser.py` in the current working directory the file is imported as part of the process. I am running Mac OS 10.10.5 with python 3.4.3 and using pandas 0.16.2 installed with homebrew.
pandas should not be importing random files when it is imported. The only reason I noticed this is that the file had a syntax error. If my `parser.py` had been doing file operations on import this could have causes serious harm.
My full version of pandas is
>>> pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.16.2
nose: 1.3.6
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
To see this I wrote a small file called `parser.py` and put it in \ ran python (with -v) then imported pandas. Below is a condesed part of the output.
Reids-MBP:/ Reid$ echo "Print('You are in my parser.py')" > parser.py
Reids-MBP:/ Reid$ echo "Fail here" >> parser.py
Reids-MBP:/ Reid$ python3 -v
...
>>> import pandas
...
'/usr/local/Cellar/python3/3.4.3/Frameworks/Python.framework/Versions/3.4/lib/python3.4/xml/parsers/**pycache**/expat.cpython-34.pyc'
# extension module loaded from '/usr/local/Cellar/python3/3.4.3/Frameworks/Python.framework/Versions/3.4/lib/python3.4/lib-dynload/pyexpat.so'
import 'xml.parsers.expat' # <_frozen_importlib.SourceFileLoader object at 0x10c1024e0>
import 'plistlib' # <_frozen_importlib.SourceFileLoader object at 0x10be529b0>
# destroy pkg_resources
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/usr/local/lib/python3.4/site-packages/pandas/__init__.py", line 7, in <module>
from pandas import hashtable, tslib, lib
File "<frozen importlib._bootstrap>", line 2284, in _handle_fromlist
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1191, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1161, in _load_backward_compatible
File "<frozen importlib._bootstrap>", line 539, in _check_name_wrapper
File "<frozen importlib._bootstrap>", line 1715, in load_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "pandas/tslib.pyx", line 50, in init pandas.tslib (pandas/tslib.c:73946)
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2212, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/usr/local/lib/python3.4/site-packages/pytz/__init__.py", line 29, in <module>
from pkg_resources import resource_stream
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1191, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1161, in _load_backward_compatible
File "/usr/local/lib/python3.4/site-packages/setuptools-15.2-py3.4.egg/pkg_resources/__init__.py", line 77, in <module>
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1467, in exec_module
File "<frozen importlib._bootstrap>", line 1572, in get_code
File "<frozen importlib._bootstrap>", line 1532, in source_to_code
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/parser.py", line 2
Fail here
^
SyntaxError: invalid syntax
>>>
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} | 1 | 2015-10-06T11:07:25Z | 2018-01-22T18:34:53Z | 2015-10-06T18:04:08Z | CONTRIBUTOR | null | closes #11232
PERF: use np.in1d on larger isin sizes
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| null | 21 | 2015-10-06T15:58:42Z | 2015-11-23T00:11:48Z | 2015-11-23T00:11:47Z | NONE | null | Added a method random_split() for NDFrames, to split the Frame into several frames according to one axis.
Basic use: for train/test or train/validation/test splitting of a dataframe.
Note: I'm not sure this feature fits well in NDFrame. If you think this feature can be added, I'll add more tests and docs.
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| null | 5 | 2015-10-06T21:12:16Z | 2015-10-06T21:44:06Z | 2015-10-06T21:44:06Z | CONTRIBUTOR | null | I got this ANN email from the PyData mailing list from @jreback:
> I'm pleased to announce the availability of the second release candidate of Pandas 0.17.0.
> ...
> Best way to get this is to install via conda from our development channel. Builds for osx-64,linux-64,win-64 for Python 2.7, Python 3.4, and Python 3.5 (for osx/linux) are all available.
>
> `conda install pandas -c pandas`
which is awesome! I can't wait for the new version to be out. I'd love to help with debugging, but when I tried to install the RC, I still got `0.16.2` in Python 3.5. See below:
```
$ conda install pandas -c pandas
Fetching package metadata: ........
Solving package specifications: ............
Package plan for installation in environment /Users/avazquez/anaconda3/envs/py35:
The following NEW packages will be INSTALLED:
pandas: 0.16.2-np19py35_0
Proceed ([y]/n)? y
Linking packages ...
[ COMPLETE]
```
Same thing if I uninstall pandas first and then try `conda install pandas -c pandas` again. Any thoughts on what could be wrong?
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| null | 12 | 2015-10-06T23:25:18Z | 2015-10-16T15:07:19Z | 2015-10-10T14:26:08Z | NONE | null | When iterating through several dimension columns and doing a groupby function, it often works. However, for certain columns it will not and instead I get an error. The error is:
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
The only thing different about these specific columns is that they are unique. However, we won't often be aware if they are unique. I'm hoping this isn't the issue and if it is, is there a work around?
Thanks!
Below is the full traceback.
---
ValueError Traceback (most recent call last)
<ipython-input-113-07075c74519b> in <module>()
----> 1 df_grp = df.groupby('post_id').sum()
/Users/Dev/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze)
3157 axis = self._get_axis_number(axis)
3158 return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
-> 3159 sort=sort, group_keys=group_keys, squeeze=squeeze)
3160
3161 def asfreq(self, freq, method=None, how=None, normalize=False):
/Users/Dev/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in groupby(obj, by, *_kwds)
1197 raise TypeError('invalid type: %s' % type(obj))
1198
-> 1199 return klass(obj, by, *_kwds)
1200
1201
/Users/Dev/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in **init**(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze)
386 if grouper is None:
387 grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis,
--> 388 level=level, sort=sort)
389
390 self.obj = obj
/Users/Dev/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort)
2152 in_axis, name = False, None
2153
-> 2154 if is_categorical_dtype(gpr) and len(gpr) != len(obj):
2155 raise ValueError("Categorical dtype grouper must have len(grouper) == len(data)")
2156
/Users/Dev/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in **nonzero**(self)
712 raise ValueError("The truth value of a {0} is ambiguous. "
713 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
--> 714 .format(self.**class**.**name**))
715
716 **bool** = **nonzero**
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
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} | 3 | 2015-10-07T11:17:45Z | 2017-01-10T13:18:21Z | 2017-01-10T13:18:21Z | NONE | null | read_fwf can automatically detect column widths, but that does not seem to take into account rows to be skipped (parameter skiprows).
So, if the file contains text before the data, column detection fails. It could be solved by using only the actual data lines (and header) to infer column widths.
I worked around that issue by opening the text file, calling readline as many times as necessary (= skiprows), and then calling read_fwf on that buffer (with skiprows=0)
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} | 3 | 2015-10-07T11:19:56Z | 2018-12-06T21:13:45Z | 2015-10-07T21:42:44Z | MEMBER | null | This PR includes some hacks to numpydoc to get rid of a bunch of warnings (split of from https://github.com/pydata/pandas/pull/11069 as I want these merged for 0.17.0). There is some more explanation in the commit messages.
Closes #6100
The disadvantage of this is that this causes a deviation from numpydoc upstream, and makes it more difficult to update to that one.
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} | 10 | 2015-10-07T18:34:33Z | 2021-04-06T15:13:52Z | 2015-10-09T07:08:49Z | NONE | null | Pandas is very helpful and I have been using it heavily recently. However, I have run into an issue where this code works:
```
from sqlalchemy import *
import pandas as pd
orcl_connect_string = 'oracle+cx_oracle://system:[email protected]:1521/?service_name=orcl'
engine = create_engine(orcl_connect_string)
pd.read_sql_query('SELECT * FROM INT_PROP', engine)
```
but this code fails:
```
from sqlalchemy import *
import pandas as pd
orcl_connect_string = 'oracle+cx_oracle://system:[email protected]:1521/?service_name=orcl'
engine = create_engine(orcl_connect_string)
read_sql_table('INT_PROP', engine)
```
Error message I get back:
```
/System/Library/Frameworks/Python.framework/Versions/2.7/bin/python2.7 "/Users/me/Google Drive/Me Projects/Pycharm/auto-profiler/profile_pandas.py"
Traceback (most recent call last):
File "/Users/me/Google Drive/Me Projects/Pycharm/auto-profiler/profile_pandas.py", line 13, in <module>
df = pd.read_sql_table(table_name_str, engine)
File "/Library/Python/2.7/site-packages/pandas/io/sql.py", line 340, in read_sql_table
raise ValueError("Table %s not found" % table_name)
ValueError: Table INT_PROP not found
```
I can't think of any other extenuating circumstances -- it's a table, not a view, the userid logging in has full rights to the table in question. I've also tried adding the schema name and I get the same error back.
Versions:
```
Mac OS X 10.11 El Capitan (recently upgraded but had the same issue on 10.10 Yosemite)
Oracle 12c
Python 2.7.6
SQLAlchemy 1.0.8
Pandas 0.16.2
```
Problem occurs whether running from within Pycharm CE or running at the terminal console with python.
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| null | 3 | 2015-10-07T20:24:56Z | 2021-04-20T05:28:43Z | null | CONTRIBUTOR | null | Currently:
- `corr` on a `DataFrame` requires another `DataFrame`, and fails on a `Series`
- `corrwith` on a `DataFrame` takes a `Series`
Is there a good reason these are separate? Should `corr` do whatever `corrwith` does when passed a `Series`, and `corrwith` could be deprecated?
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} | 1 | 2015-10-08T01:56:48Z | 2015-10-08T03:00:41Z | 2015-10-08T03:00:41Z | CONTRIBUTOR | null | This documentation appears to not be consistent with the function usage, i.e. halflife can also be specified as an alternative to center of mass or span.
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} | 0 | 2015-10-08T02:42:15Z | 2015-10-08T03:00:33Z | 2015-10-08T03:00:33Z | CONTRIBUTOR | null | xref #11187
conda packages are not yet updated, so need to fix to 1.9.3 for now
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} | 6 | 2015-10-08T03:29:23Z | 2015-10-21T22:42:05Z | 2015-10-17T14:48:10Z | CONTRIBUTOR | null | This is a WIP, but far enough along I thought I'd share and see if the approach was reasonable.
This releases the GIL on most vectorized field accessors (e.g. `dt.year`) and conversion to and from `Period`. May be places it could be done - obviously would be nice for parsing, but I'm not sure that's possible.
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https://api.github.com/repos/pandas-dev/pandas/issues/11264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11264/events | https://github.com/pandas-dev/pandas/issues/11264 | 110,399,531 | MDU6SXNzdWUxMTAzOTk1MzE= | 11,264 | add set_index_ncol(ncol) to set first n columns as indices | {
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| null | 2 | 2015-10-08T08:42:43Z | 2015-10-08T11:18:45Z | 2015-10-08T11:00:02Z | NONE | null | ```
df = pd.read_sql('select idx1,idx2,metric1 from table1 ', dbcon)
df.set_index(list(df.columns)[:ncol]).T.to_dict('list')
```
such operations are very frequent in product
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| null | 7 | 2015-10-08T16:14:59Z | 2018-06-20T13:18:48Z | 2015-10-08T17:38:31Z | NONE | null | The output is so long but it seems that something goes wrong with the compiling, is there something wrong with the complier I use?
```
> pip install pandas
Collecting pandas
Using cached pandas-0.16.2.tar.gz
Requirement already satisfied (use --upgrade to upgrade): python-dateutil>=2 in c:\program files\python 3.5\lib\site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): pytz>=2011k in c:\program files\python 3.5\lib\site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): numpy>=1.7.0 in c:\program files\python 3.5\lib\site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): six>=1.5 in c:\program files\python 3.5\lib\site-packages (from python-dateutil>=2->pandas)
Installing collected packages: pandas
Running setup.py install for pandas
Complete output from command "c:\program files\python 3.5\python.exe" -c "import setuptools, tokenize;__file__='C:\\Users\\SLAPaper\\AppData\\Local\\Temp\\pip-build-xy08hyhc\\pandas\\setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record C:\Users\SLAPaper\AppData\Local\Temp\pip-1v1jv6y0-record\install-record.txt --single-version-externally-managed --compile:
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.5
creating build\lib.win-amd64-3.5\pandas
copying pandas\info.py -> build\lib.win-amd64-3.5\pandas
copying pandas\version.py -> build\lib.win-amd64-3.5\pandas
copying pandas\__init__.py -> build\lib.win-amd64-3.5\pandas
creating build\lib.win-amd64-3.5\pandas\compat
copying pandas\compat\chainmap.py -> build\lib.win-amd64-3.5\pandas\compat
copying pandas\compat\chainmap_impl.py -> build\lib.win-amd64-3.5\pandas\compat
copying pandas\compat\openpyxl_compat.py -> build\lib.win-amd64-3.5\pandas\compat
copying pandas\compat\pickle_compat.py -> build\lib.win-amd64-3.5\pandas\compat
copying pandas\compat\__init__.py -> build\lib.win-amd64-3.5\pandas\compat
creating build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\align.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\api.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\common.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\engines.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\eval.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\expr.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\expressions.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\ops.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\pytables.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\scope.py -> build\lib.win-amd64-3.5\pandas\computation
copying pandas\computation\__init__.py -> build\lib.win-amd64-3.5\pandas\computation
creating build\lib.win-amd64-3.5\pandas\computation\tests
copying pandas\computation\tests\test_eval.py -> build\lib.win-amd64-3.5\pandas\computation\tests
copying pandas\computation\tests\__init__.py -> build\lib.win-amd64-3.5\pandas\computation\tests
creating build\lib.win-amd64-3.5\pandas\core
copying pandas\core\algorithms.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\api.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\base.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\categorical.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\common.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\config.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\config_init.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\datetools.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\format.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\frame.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\generic.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\groupby.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\index.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\indexing.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\internals.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\matrix.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\nanops.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\ops.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\panel.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\panel4d.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\panelnd.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\reshape.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\series.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\sparse.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\strings.py -> build\lib.win-amd64-3.5\pandas\core
copying pandas\core\__init__.py -> build\lib.win-amd64-3.5\pandas\core
creating build\lib.win-amd64-3.5\pandas\io
copying pandas\io\api.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\auth.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\clipboard.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\common.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\data.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\date_converters.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\excel.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\ga.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\gbq.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\html.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\json.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\packers.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\parsers.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\pickle.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\pytables.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\sql.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\stata.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\wb.py -> build\lib.win-amd64-3.5\pandas\io
copying pandas\io\__init__.py -> build\lib.win-amd64-3.5\pandas\io
creating build\lib.win-amd64-3.5\pandas\rpy
copying pandas\rpy\base.py -> build\lib.win-amd64-3.5\pandas\rpy
copying pandas\rpy\common.py -> build\lib.win-amd64-3.5\pandas\rpy
copying pandas\rpy\mass.py -> build\lib.win-amd64-3.5\pandas\rpy
copying pandas\rpy\vars.py -> build\lib.win-amd64-3.5\pandas\rpy
copying pandas\rpy\__init__.py -> build\lib.win-amd64-3.5\pandas\rpy
creating build\lib.win-amd64-3.5\pandas\sandbox
copying pandas\sandbox\qtpandas.py -> build\lib.win-amd64-3.5\pandas\sandbox
copying pandas\sandbox\__init__.py -> build\lib.win-amd64-3.5\pandas\sandbox
creating build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\api.py -> build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\array.py -> build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\frame.py -> build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\list.py -> build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\panel.py -> build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\scipy_sparse.py -> build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\series.py -> build\lib.win-amd64-3.5\pandas\sparse
copying pandas\sparse\__init__.py -> build\lib.win-amd64-3.5\pandas\sparse
creating build\lib.win-amd64-3.5\pandas\sparse\tests
copying pandas\sparse\tests\test_array.py -> build\lib.win-amd64-3.5\pandas\sparse\tests
copying pandas\sparse\tests\test_libsparse.py -> build\lib.win-amd64-3.5\pandas\sparse\tests
copying pandas\sparse\tests\test_list.py -> build\lib.win-amd64-3.5\pandas\sparse\tests
copying pandas\sparse\tests\test_sparse.py -> build\lib.win-amd64-3.5\pandas\sparse\tests
copying pandas\sparse\tests\__init__.py -> build\lib.win-amd64-3.5\pandas\sparse\tests
creating build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\api.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\common.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\fama_macbeth.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\interface.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\math.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\misc.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\moments.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\ols.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\plm.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\var.py -> build\lib.win-amd64-3.5\pandas\stats
copying pandas\stats\__init__.py -> build\lib.win-amd64-3.5\pandas\stats
creating build\lib.win-amd64-3.5\pandas\util
copying pandas\util\clipboard.py -> build\lib.win-amd64-3.5\pandas\util
copying pandas\util\decorators.py -> build\lib.win-amd64-3.5\pandas\util
copying pandas\util\doctools.py -> build\lib.win-amd64-3.5\pandas\util
copying pandas\util\misc.py -> build\lib.win-amd64-3.5\pandas\util
copying pandas\util\print_versions.py -> build\lib.win-amd64-3.5\pandas\util
copying pandas\util\terminal.py -> build\lib.win-amd64-3.5\pandas\util
copying pandas\util\testing.py -> build\lib.win-amd64-3.5\pandas\util
copying pandas\util\__init__.py -> build\lib.win-amd64-3.5\pandas\util
creating build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_algos.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_base.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_categorical.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_common.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_compat.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_config.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_expressions.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_format.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_frame.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_generic.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_graphics.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_groupby.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_index.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_indexing.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_internals.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_lib.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_multilevel.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_nanops.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_panel.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_panel4d.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_panelnd.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_reshape.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_rplot.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_series.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_stats.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_strings.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_testing.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_tseries.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\test_util.py -> build\lib.win-amd64-3.5\pandas\tests
copying pandas\tests\__init__.py -> build\lib.win-amd64-3.5\pandas\tests
creating build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_buffer.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_case.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_except.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_format.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_obj.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_pack.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_read_size.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_seq.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_sequnpack.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_subtype.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\test_unpack_raw.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
copying pandas\tests\test_msgpack\__init__.py -> build\lib.win-amd64-3.5\pandas\tests\test_msgpack
creating build\lib.win-amd64-3.5\pandas\tools
copying pandas\tools\merge.py -> build\lib.win-amd64-3.5\pandas\tools
copying pandas\tools\pivot.py -> build\lib.win-amd64-3.5\pandas\tools
copying pandas\tools\plotting.py -> build\lib.win-amd64-3.5\pandas\tools
copying pandas\tools\rplot.py -> build\lib.win-amd64-3.5\pandas\tools
copying pandas\tools\tile.py -> build\lib.win-amd64-3.5\pandas\tools
copying pandas\tools\util.py -> build\lib.win-amd64-3.5\pandas\tools
copying pandas\tools\__init__.py -> build\lib.win-amd64-3.5\pandas\tools
creating build\lib.win-amd64-3.5\pandas\tools\tests
copying pandas\tools\tests\test_merge.py -> build\lib.win-amd64-3.5\pandas\tools\tests
copying pandas\tools\tests\test_pivot.py -> build\lib.win-amd64-3.5\pandas\tools\tests
copying pandas\tools\tests\test_tile.py -> build\lib.win-amd64-3.5\pandas\tools\tests
copying pandas\tools\tests\test_util.py -> build\lib.win-amd64-3.5\pandas\tools\tests
copying pandas\tools\tests\__init__.py -> build\lib.win-amd64-3.5\pandas\tools\tests
creating build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\api.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\base.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\common.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\converter.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\frequencies.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\holiday.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\index.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\interval.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\offsets.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\period.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\plotting.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\resample.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\tdi.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\timedeltas.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\tools.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\util.py -> build\lib.win-amd64-3.5\pandas\tseries
copying pandas\tseries\__init__.py -> build\lib.win-amd64-3.5\pandas\tseries
creating build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_base.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_converter.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_daterange.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_frequencies.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_holiday.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_offsets.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_period.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_plotting.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_resample.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_timedeltas.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_timeseries.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_timeseries_legacy.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_timezones.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_tslib.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\test_util.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
copying pandas\tseries\tests\__init__.py -> build\lib.win-amd64-3.5\pandas\tseries\tests
creating build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\generate_legacy_pickles.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_clipboard.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_common.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_cparser.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_data.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_date_converters.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_excel.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_ga.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_gbq.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_html.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_json_norm.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_packers.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_parsers.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_pickle.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_pytables.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_sql.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_stata.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\test_wb.py -> build\lib.win-amd64-3.5\pandas\io\tests
copying pandas\io\tests\__init__.py -> build\lib.win-amd64-3.5\pandas\io\tests
creating build\lib.win-amd64-3.5\pandas\io\tests\test_json
copying pandas\io\tests\test_json\test_pandas.py -> build\lib.win-amd64-3.5\pandas\io\tests\test_json
copying pandas\io\tests\test_json\test_ujson.py -> build\lib.win-amd64-3.5\pandas\io\tests\test_json
copying pandas\io\tests\test_json\__init__.py -> build\lib.win-amd64-3.5\pandas\io\tests\test_json
creating build\lib.win-amd64-3.5\pandas\stats\tests
copying pandas\stats\tests\common.py -> build\lib.win-amd64-3.5\pandas\stats\tests
copying pandas\stats\tests\test_fama_macbeth.py -> build\lib.win-amd64-3.5\pandas\stats\tests
copying pandas\stats\tests\test_math.py -> build\lib.win-amd64-3.5\pandas\stats\tests
copying pandas\stats\tests\test_moments.py -> build\lib.win-amd64-3.5\pandas\stats\tests
copying pandas\stats\tests\test_ols.py -> build\lib.win-amd64-3.5\pandas\stats\tests
copying pandas\stats\tests\test_var.py -> build\lib.win-amd64-3.5\pandas\stats\tests
copying pandas\stats\tests\__init__.py -> build\lib.win-amd64-3.5\pandas\stats\tests
creating build\lib.win-amd64-3.5\pandas\io\tests\data
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_hdf
copying pandas\io\tests\data\legacy_hdf\legacy.h5 -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_hdf
copying pandas\io\tests\data\legacy_hdf\legacy_0.10.h5 -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_hdf
copying pandas\io\tests\data\legacy_hdf\legacy_table.h5 -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_hdf
copying pandas\io\tests\data\legacy_hdf\legacy_table_0.11.h5 -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_hdf
copying pandas\io\tests\data\legacy_hdf\pytables_native.h5 -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_hdf
copying pandas\io\tests\data\legacy_hdf\pytables_native2.h5 -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_hdf
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.10.1
copying pandas\io\tests\data\legacy_pickle\0.10.1\AMD64_windows_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.10.1
copying pandas\io\tests\data\legacy_pickle\0.10.1\x86_64_linux_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.10.1
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.11.0
copying pandas\io\tests\data\legacy_pickle\0.11.0\0.11.0_x86_64_linux_3.3.0.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.11.0
copying pandas\io\tests\data\legacy_pickle\0.11.0\x86_64_linux_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.11.0
copying pandas\io\tests\data\legacy_pickle\0.11.0\x86_64_linux_3.3.0.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.11.0
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.12.0
copying pandas\io\tests\data\legacy_pickle\0.12.0\0.12.0_AMD64_windows_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.12.0
copying pandas\io\tests\data\legacy_pickle\0.12.0\0.12.0_x86_64_linux_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.12.0
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_AMD64_windows_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_i686_linux_2.6.5.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_i686_linux_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_i686_linux_3.2.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_x86_64_darwin_2.7.5.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_x86_64_darwin_2.7.6.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_x86_64_linux_2.7.3.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_x86_64_linux_2.7.8.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
copying pandas\io\tests\data\legacy_pickle\0.13.0\0.13.0_x86_64_linux_3.3.0.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.13.0
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.14.0
copying pandas\io\tests\data\legacy_pickle\0.14.0\0.14.0_x86_64_darwin_2.7.6.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.14.0
copying pandas\io\tests\data\legacy_pickle\0.14.0\0.14.0_x86_64_linux_2.7.8.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.14.0
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.14.1
copying pandas\io\tests\data\legacy_pickle\0.14.1\0.14.1_x86_64_linux_2.7.8.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.14.1
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.15.0
copying pandas\io\tests\data\legacy_pickle\0.15.0\0.15.0_x86_64_linux_2.7.8.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.15.0
creating build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.15.2
copying pandas\io\tests\data\legacy_pickle\0.15.2\0.15.2_x86_64_darwin_2.7.9.pickle -> build\lib.win-amd64-3.5\pandas\io\tests\data\legacy_pickle\0.15.2
copying pandas\io\tests\data\banklist.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\iris.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata3.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata5.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata6.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test1.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test1.csv.bz2 -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test1.csv.gz -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test2.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\tips.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\unicode_series.csv -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata10_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata10_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata11_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata11_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata12_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata1_114.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata1_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata1_encoding.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata2_113.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata2_114.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata2_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata2_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata3_113.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata3_114.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata3_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata3_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata4_113.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata4_114.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata4_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata4_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata5_113.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata5_114.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata5_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata5_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata6_113.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata6_114.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata6_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata6_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata7_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata7_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata8_113.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata8_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata8_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata9_115.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\stata9_117.dta -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\gbq_fake_job.txt -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\utf16_ex.txt -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test.xls -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test2.xls -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test3.xls -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test_converters.xls -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test_types.xls -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\times_1900.xls -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\times_1904.xls -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test.xlsx -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test2.xlsx -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test_converters.xlsx -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test_multisheet.xlsx -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test_types.xlsx -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\test.xlsm -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\salary.table -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\banklist.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\computer_sales_page.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\macau.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\nyse_wsj.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\spam.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\valid_markup.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\wikipedia_states.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\yahoo_options1.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\yahoo_options2.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
copying pandas\io\tests\data\yahoo_options3.html -> build\lib.win-amd64-3.5\pandas\io\tests\data
creating build\lib.win-amd64-3.5\pandas\io\tests\data\html_encoding
copying pandas\io\tests\data\html_encoding\chinese_utf-16.html -> build\lib.win-amd64-3.5\pandas\io\tests\data\html_encoding
copying pandas\io\tests\data\html_encoding\chinese_utf-32.html -> build\lib.win-amd64-3.5\pandas\io\tests\data\html_encoding
copying pandas\io\tests\data\html_encoding\chinese_utf-8.html -> build\lib.win-amd64-3.5\pandas\io\tests\data\html_encoding
copying pandas\io\tests\data\html_encoding\letz_latin1.html -> build\lib.win-amd64-3.5\pandas\io\tests\data\html_encoding
creating build\lib.win-amd64-3.5\pandas\io\tests\test_json\data
copying pandas\io\tests\test_json\data\tsframe_iso_v012.json -> build\lib.win-amd64-3.5\pandas\io\tests\test_json\data
copying pandas\io\tests\test_json\data\tsframe_v012.json -> build\lib.win-amd64-3.5\pandas\io\tests\test_json\data
creating build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\categorical_0_14_1.pickle -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\categorical_0_15_2.pickle -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\mindex_073.pickle -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\multiindex_v1.pickle -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\s1-0.12.0.pickle -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\s2-0.12.0.pickle -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\iris.csv -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\tips.csv -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tests\data\unicode_series.csv -> build\lib.win-amd64-3.5\pandas\tests\data
copying pandas\tools\tests\cut_data.csv -> build\lib.win-amd64-3.5\pandas\tools\tests
creating build\lib.win-amd64-3.5\pandas\tseries\tests\data
copying pandas\tseries\tests\data\cday-0.14.1.pickle -> build\lib.win-amd64-3.5\pandas\tseries\tests\data
copying pandas\tseries\tests\data\dateoffset_0_15_2.pickle -> build\lib.win-amd64-3.5\pandas\tseries\tests\data
copying pandas\tseries\tests\data\daterange_073.pickle -> build\lib.win-amd64-3.5\pandas\tseries\tests\data
copying pandas\tseries\tests\data\frame.pickle -> build\lib.win-amd64-3.5\pandas\tseries\tests\data
copying pandas\tseries\tests\data\series.pickle -> build\lib.win-amd64-3.5\pandas\tseries\tests\data
copying pandas\tseries\tests\data\series_daterange0.pickle -> build\lib.win-amd64-3.5\pandas\tseries\tests\data
running build_ext
building 'pandas.tslib' extension
creating build\temp.win-amd64-3.5
creating build\temp.win-amd64-3.5\Release
creating build\temp.win-amd64-3.5\Release\pandas
creating build\temp.win-amd64-3.5\Release\pandas\src
creating build\temp.win-amd64-3.5\Release\pandas\src\datetime
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas\tslib.c /Fobuild\temp.win-amd64-3.5\Release\pandas\tslib.obj
tslib.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src\datetime_helper.h(7): warning C4005: 'PyInt_AS_LONG': macro redefinition
pandas\tslib.c(145): note: see previous definition of 'PyInt_AS_LONG'
c:\users\slapaper\appdata\local\temp\pip-build-xy08hyhc\pandas\pandas\src\klib\khash.h(573): warning C4090: 'function': different 'const' qualifiers
pandas/src/klib\khash_python.h(13): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(38): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(44): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(49): warning C4090: 'function': different 'const' qualifiers
pandas\tslib.c(13646): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\tslib.c(13692): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\tslib.c(23160): warning C4013: 'cmp_pandas_datetimestruct' undefined; assuming extern returning int
pandas\tslib.c(41981): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'npy_int32', possible loss of data
pandas\tslib.c(41990): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'npy_int32', possible loss of data
pandas\tslib.c(44233): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\tslib.c(44859): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\tslib.c(51475): warning C4244: '=': conversion from 'npy_int64' to '__pyx_t_5numpy_int32_t', possible loss of data
pandas\tslib.c(52439): warning C4244: '=': conversion from 'npy_int64' to '__pyx_t_5numpy_int32_t', possible loss of data
pandas\tslib.c(53401): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_int32_t', possible loss of data
pandas\tslib.c(53569): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(54536): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\tslib.c(54889): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(55313): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(55737): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(56175): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(56613): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(57029): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(57492): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(58775): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\tslib.c(59982): warning C4244: 'function': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\tslib.c(60039): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas\tslib.c(65018): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\tslib.c(65226): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/datetime/np_datetime.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime.obj
np_datetime.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/datetime/np_datetime.c(267): warning C4244: '=': conversion from 'npy_int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(852): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(866): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(881): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(897): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(914): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(931): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(949): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(959): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(976): warning C4244: 'function': conversion from 'npy_datetime' to 'int', possible loss of data
pandas/src/datetime/np_datetime.c(1002): warning C4244: 'function': conversion from 'npy_datetime' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/datetime/np_datetime_strings.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime_strings.obj
np_datetime_strings.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/datetime/np_datetime_strings.c(224): warning C4244: '=': conversion from 'NPY_TIME_T' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/period_helper.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/period_helper.obj
period_helper.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/period_helper.c(85): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(87): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(91): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(93): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(225): warning C4244: '=': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(236): warning C4244: '=': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(529): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(600): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(601): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(649): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(650): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(726): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(977): warning C4244: '=': conversion from 'npy_int64' to 'double', possible loss of data
pandas/src/period_helper.c(1217): warning C4267: '+=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/period_helper.c(1228): warning C4267: '+=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/period_helper.c(1246): warning C4267: 'initializing': conversion from 'size_t' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO "/LIBPATH:c:\program files\python 3.5\libs" "/LIBPATH:c:\program files\python 3.5\PCbuild\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10150.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" /EXPORT:PyInit_tslib build\temp.win-amd64-3.5\Release\pandas\tslib.obj build\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime.obj build\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime_strings.obj build\temp.win-amd64-3.5\Release\pandas/src/period_helper.obj /OUT:build\lib.win-amd64-3.5\pandas\tslib.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\pandas\tslib.cp35-win_amd64.lib
tslib.obj : warning LNK4197: export 'PyInit_tslib' specified multiple times; using first specification
Creating library build\temp.win-amd64-3.5\Release\pandas\tslib.cp35-win_amd64.lib and object build\temp.win-amd64-3.5\Release\pandas\tslib.cp35-win_amd64.exp
Generating code
Finished generating code
building 'pandas.hashtable' extension
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas\hashtable.c /Fobuild\temp.win-amd64-3.5\Release\pandas\hashtable.obj
hashtable.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
c:\users\slapaper\appdata\local\temp\pip-build-xy08hyhc\pandas\pandas\src\klib\khash.h(573): warning C4090: 'function': different 'const' qualifiers
pandas/src/klib\khash_python.h(13): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(38): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(44): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(49): warning C4090: 'function': different 'const' qualifiers
pandas\hashtable.c(5668): warning C4244: 'function': conversion from 'Py_ssize_t' to 'khint32_t', possible loss of data
pandas\hashtable.c(6316): warning C4267: '=': conversion from 'size_t' to '__pyx_t_5numpy_int32_t', possible loss of data
pandas\hashtable.c(16493): warning C4244: 'function': conversion from 'Py_ssize_t' to 'khint_t', possible loss of data
pandas\hashtable.c(16762): warning C4018: '<': signed/unsigned mismatch
pandas\hashtable.c(17015): warning C4244: 'function': conversion from 'Py_ssize_t' to 'khint_t', possible loss of data
pandas\hashtable.c(17320): warning C4018: '<': signed/unsigned mismatch
pandas\hashtable.c(17759): warning C4018: '<': signed/unsigned mismatch
pandas\hashtable.c(17779): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\hashtable.c(18062): warning C4018: '<': signed/unsigned mismatch
pandas\hashtable.c(18082): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO "/LIBPATH:c:\program files\python 3.5\libs" "/LIBPATH:c:\program files\python 3.5\PCbuild\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10150.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" /EXPORT:PyInit_hashtable build\temp.win-amd64-3.5\Release\pandas\hashtable.obj /OUT:build\lib.win-amd64-3.5\pandas\hashtable.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\pandas\hashtable.cp35-win_amd64.lib
hashtable.obj : warning LNK4197: export 'PyInit_hashtable' specified multiple times; using first specification
Creating library build\temp.win-amd64-3.5\Release\pandas\hashtable.cp35-win_amd64.lib and object build\temp.win-amd64-3.5\Release\pandas\hashtable.cp35-win_amd64.exp
Generating code
Finished generating code
building 'pandas.index' extension
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas\index.c /Fobuild\temp.win-amd64-3.5\Release\pandas\index.obj
index.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
c:\users\slapaper\appdata\local\temp\pip-build-xy08hyhc\pandas\pandas\src\klib\khash.h(573): warning C4090: 'function': different 'const' qualifiers
pandas/src/klib\khash_python.h(13): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(38): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(44): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(49): warning C4090: 'function': different 'const' qualifiers
pandas/src\datetime_helper.h(7): warning C4005: 'PyInt_AS_LONG': macro redefinition
pandas\index.c(145): note: see previous definition of 'PyInt_AS_LONG'
pandas\index.c(4339): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\index.c(6281): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\index.c(6407): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\index.c(6435): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\index.c(7648): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\index.c(8232): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\index.c(12527): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\index.c(12735): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/datetime/np_datetime.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime.obj
np_datetime.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/datetime/np_datetime.c(267): warning C4244: '=': conversion from 'npy_int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(852): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(866): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(881): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(897): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(914): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(931): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(949): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(959): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(976): warning C4244: 'function': conversion from 'npy_datetime' to 'int', possible loss of data
pandas/src/datetime/np_datetime.c(1002): warning C4244: 'function': conversion from 'npy_datetime' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/datetime/np_datetime_strings.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime_strings.obj
np_datetime_strings.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/datetime/np_datetime_strings.c(224): warning C4244: '=': conversion from 'NPY_TIME_T' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO "/LIBPATH:c:\program files\python 3.5\libs" "/LIBPATH:c:\program files\python 3.5\PCbuild\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10150.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" /EXPORT:PyInit_index build\temp.win-amd64-3.5\Release\pandas\index.obj build\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime.obj build\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime_strings.obj /OUT:build\lib.win-amd64-3.5\pandas\index.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\pandas\index.cp35-win_amd64.lib
index.obj : warning LNK4197: export 'PyInit_index' specified multiple times; using first specification
Creating library build\temp.win-amd64-3.5\Release\pandas\index.cp35-win_amd64.lib and object build\temp.win-amd64-3.5\Release\pandas\index.cp35-win_amd64.exp
Generating code
Finished generating code
building 'pandas.lib' extension
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas\lib.c /Fobuild\temp.win-amd64-3.5\Release\pandas\lib.obj
lib.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src\datetime_helper.h(7): warning C4005: 'PyInt_AS_LONG': macro redefinition
pandas\lib.c(145): note: see previous definition of 'PyInt_AS_LONG'
pandas\lib.c(5534): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(5611): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(5623): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(5759): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(5830): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(5842): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(11291): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\lib.c(11382): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\lib.c(17176): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data
pandas\lib.c(17441): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(18159): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data
pandas\lib.c(18235): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(18266): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(23627): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\lib.c(26102): warning C4133: 'function': incompatible types - from 'PySliceObject *' to 'PyObject *'
pandas\lib.c(26326): warning C4133: 'function': incompatible types - from 'PySliceObject *' to 'PyObject *'
pandas\lib.c(32797): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\lib.c(39169): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\lib.c(39179): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\lib.c(39421): warning C4244: 'function': conversion from '__pyx_t_5numpy_int64_t' to 'int', possible loss of data
pandas\lib.c(40001): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(59034): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\lib.c(60141): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\lib.c(60349): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO "/LIBPATH:c:\program files\python 3.5\libs" "/LIBPATH:c:\program files\python 3.5\PCbuild\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10150.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" /EXPORT:PyInit_lib build\temp.win-amd64-3.5\Release\pandas\lib.obj /OUT:build\lib.win-amd64-3.5\pandas\lib.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\pandas\lib.cp35-win_amd64.lib
lib.obj : warning LNK4197: export 'PyInit_lib' specified multiple times; using first specification
Creating library build\temp.win-amd64-3.5\Release\pandas\lib.cp35-win_amd64.lib and object build\temp.win-amd64-3.5\Release\pandas\lib.cp35-win_amd64.exp
Generating code
Finished generating code
building 'pandas._period' extension
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas\src/period.c /Fobuild\temp.win-amd64-3.5\Release\pandas\src/period.obj
period.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
c:\users\slapaper\appdata\local\temp\pip-build-xy08hyhc\pandas\pandas\src\datetime_helper.h(7): warning C4005: 'PyInt_AS_LONG': macro redefinition
pandas\src/period.c(145): note: see previous definition of 'PyInt_AS_LONG'
pandas\src/period.c(2448): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas\src/period.c(3958): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
pandas\src/period.c(7807): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\src/period.c(8329): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas\src/period.c(8606): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas\src/period.c(8961): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\src/period.c(8985): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas\src/period.c(9117): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas\src/period.c(17324): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\src/period.c(17532): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/datetime/np_datetime.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime.obj
np_datetime.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/datetime/np_datetime.c(267): warning C4244: '=': conversion from 'npy_int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(852): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(866): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(881): warning C4244: '=': conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(897): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(914): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(931): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(949): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(959): warning C4244: '=': conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(976): warning C4244: 'function': conversion from 'npy_datetime' to 'int', possible loss of data
pandas/src/datetime/np_datetime.c(1002): warning C4244: 'function': conversion from 'npy_datetime' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/datetime/np_datetime_strings.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime_strings.obj
np_datetime_strings.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/datetime/np_datetime_strings.c(224): warning C4244: '=': conversion from 'NPY_TIME_T' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/period_helper.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/period_helper.obj
period_helper.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/period_helper.c(85): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(87): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(91): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(93): warning C4244: 'return': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(225): warning C4244: '=': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(236): warning C4244: '=': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(529): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(600): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(601): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(649): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(650): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(726): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas/src/period_helper.c(977): warning C4244: '=': conversion from 'npy_int64' to 'double', possible loss of data
pandas/src/period_helper.c(1217): warning C4267: '+=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/period_helper.c(1228): warning C4267: '+=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/period_helper.c(1246): warning C4267: 'initializing': conversion from 'size_t' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO "/LIBPATH:c:\program files\python 3.5\libs" "/LIBPATH:c:\program files\python 3.5\PCbuild\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10150.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" /EXPORT:PyInit__period build\temp.win-amd64-3.5\Release\pandas\src/period.obj build\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime.obj build\temp.win-amd64-3.5\Release\pandas/src/datetime/np_datetime_strings.obj build\temp.win-amd64-3.5\Release\pandas/src/period_helper.obj /OUT:build\lib.win-amd64-3.5\pandas\_period.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\pandas\src\_period.cp35-win_amd64.lib
period.obj : warning LNK4197: export 'PyInit__period' specified multiple times; using first specification
Creating library build\temp.win-amd64-3.5\Release\pandas\src\_period.cp35-win_amd64.lib and object build\temp.win-amd64-3.5\Release\pandas\src\_period.cp35-win_amd64.exp
Generating code
Finished generating code
building 'pandas.parser' extension
creating build\temp.win-amd64-3.5\Release\pandas\src\parser
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas\parser.c /Fobuild\temp.win-amd64-3.5\Release\pandas\parser.obj
parser.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
c:\users\slapaper\appdata\local\temp\pip-build-xy08hyhc\pandas\pandas\src\klib\khash.h(573): warning C4090: 'function': different 'const' qualifiers
pandas/src/klib\khash_python.h(13): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(38): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(44): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(49): warning C4090: 'function': different 'const' qualifiers
pandas\parser.c(7001): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(7200): warning C4018: '>': signed/unsigned mismatch
pandas\parser.c(7293): warning C4018: '<': signed/unsigned mismatch
pandas\parser.c(7305): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas\parser.c(7361): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas\parser.c(9903): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(11291): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(11499): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(11707): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(11792): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(11955): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(12283): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(12307): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(12332): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(12380): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(12404): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(12429): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(17100): warning C4018: '<': signed/unsigned mismatch
pandas\parser.c(17528): warning C4018: '<': signed/unsigned mismatch
pandas\parser.c(17967): warning C4018: '<': signed/unsigned mismatch
pandas\parser.c(23385): warning C4018: '<': signed/unsigned mismatch
pandas\parser.c(23497): warning C4018: '<': signed/unsigned mismatch
pandas\parser.c(23583): warning C4018: '<': signed/unsigned mismatch
pandas\parser.c(24174): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\parser.c(24309): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/parser/tokenizer.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/parser/tokenizer.obj
tokenizer.c
pandas/src/klib\khash.h(573): warning C4090: 'function': different 'const' qualifiers
pandas/src/parser/tokenizer.c(305): warning C4267: 'function': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(335): warning C4267: 'function': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(372): warning C4267: 'function': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(455): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(448): warning C4267: 'initializing': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(613): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(1690): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(1724): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(1732): warning C4267: '-=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(1762): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(1781): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(1800): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
pandas/src/parser/tokenizer.c(2178): warning C4554: '&': check operator precedence for possible error; use parentheses to clarify precedence
pandas/src/parser/tokenizer.c(2345): warning C4554: '&': check operator precedence for possible error; use parentheses to clarify precedence
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas/src/parser/io.c /Fobuild\temp.win-amd64-3.5\Release\pandas/src/parser/io.obj
io.c
pandas/src/klib\khash.h(573): warning C4090: 'function': different 'const' qualifiers
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO "/LIBPATH:c:\program files\python 3.5\libs" "/LIBPATH:c:\program files\python 3.5\PCbuild\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10150.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" /EXPORT:PyInit_parser build\temp.win-amd64-3.5\Release\pandas\parser.obj build\temp.win-amd64-3.5\Release\pandas/src/parser/tokenizer.obj build\temp.win-amd64-3.5\Release\pandas/src/parser/io.obj /OUT:build\lib.win-amd64-3.5\pandas\parser.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\pandas\parser.cp35-win_amd64.lib
parser.obj : warning LNK4197: export 'PyInit_parser' specified multiple times; using first specification
Creating library build\temp.win-amd64-3.5\Release\pandas\parser.cp35-win_amd64.lib and object build\temp.win-amd64-3.5\Release\pandas\parser.cp35-win_amd64.exp
Generating code
Finished generating code
building 'pandas.algos' extension
E:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ipandas/src/klib -Ipandas/src "-Ic:\program files\python 3.5\lib\site-packages\numpy\core\include" "-Ic:\program files\python 3.5\include" "-Ic:\program files\python 3.5\include" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IE:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10150.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tcpandas\algos.c /Fobuild\temp.win-amd64-3.5\Release\pandas\algos.obj
algos.c
c:\program files\python 3.5\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
c:\users\slapaper\appdata\local\temp\pip-build-xy08hyhc\pandas\pandas\src/headers/math.h(6): error C2059: syntax error: 'sizeof'
pandas/src\skiplist.h(118): warning C4244: '=': conversion from 'double' to 'int', possible loss of data
pandas/src\datetime_helper.h(7): warning C4005: 'PyInt_AS_LONG': macro redefinition
pandas\algos.c(145): note: see previous definition of 'PyInt_AS_LONG'
c:\users\slapaper\appdata\local\temp\pip-build-xy08hyhc\pandas\pandas\src\klib\khash.h(573): warning C4090: 'function': different 'const' qualifiers
pandas/src/klib\khash_python.h(13): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(38): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(44): warning C4244: '=': conversion from 'Py_hash_t' to 'khint_t', possible loss of data
pandas/src/klib\khash_python.h(49): warning C4090: 'function': different 'const' qualifiers
pandas\algos.c(9487): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
pandas\algos.c(9628): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(9681): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(9734): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(9787): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(9849): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(10580): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
pandas\algos.c(10605): warning C4244: 'function': conversion from '__pyx_t_5numpy_int64_t' to 'double', possible loss of data
pandas\algos.c(10721): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(10774): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(10827): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(10880): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(10942): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(12090): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
pandas\algos.c(12256): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(12319): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(12382): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(12445): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(12517): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(13460): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
pandas\algos.c(13490): warning C4244: 'function': conversion from '__pyx_t_5numpy_int64_t' to 'double', possible loss of data
pandas\algos.c(13626): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(13689): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(13752): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(13815): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(13887): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(15158): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
pandas\algos.c(15271): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(15324): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(15410): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(16939): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
pandas\algos.c(17129): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(17192): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(17288): warning C4244: '=': conversion from 'Py_ssize_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(23999): warning C4244: '=': conversion from 'long' to '__pyx_t_5numpy_int8_t', possible loss of data
pandas\algos.c(24183): warning C4244: '=': conversion from 'long' to '__pyx_t_5numpy_int16_t', possible loss of data
pandas\algos.c(24735): warning C4244: '=': conversion from 'long' to '__pyx_t_5numpy_uint8_t', possible loss of data
pandas\algos.c(24919): warning C4244: '=': conversion from 'long' to '__pyx_t_5numpy_uint16_t', possible loss of data
pandas\algos.c(25471): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(25857): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(26441): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(26553): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(26603): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(26633): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(26693): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(26756): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(26778): warning C4244: '+=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(27863): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(27912): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(28592): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(28611): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(28710): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(31508): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(34172): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(34726): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(34738): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(35725): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(35829): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(36916): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(37020): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(38411): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(38712): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(38741): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(44726): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(44787): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(45718): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(45737): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(46727): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(46746): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(48347): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(48366): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(53144): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(53878): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(54610): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(55385): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(56119): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(56853): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(57588): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(58340): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(59090): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(59888): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(60640): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(61392): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(62059): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(62437): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(62816): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(63205): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(63583): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(63961): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(64338): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(64714): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(65091): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(65478): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(65854): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(66230): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(66675): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(67149): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(67624): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(68109): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(68583): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(69057): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(69530): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(70002): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(70475): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(70958): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(71430): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(71902): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(83421): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(93663): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(93999): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(104935): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(105292): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(113410): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(116600): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(116681): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(116774): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(116855): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(117226): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(117307): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(117400): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(117481): warning C4244: '=': conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(118478): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(118559): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(118652): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(118733): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(120211): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(121757): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(123259): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(124805): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(126556): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(128386): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(129962): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(130754): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(130788): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(130812): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(131723): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(131757): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(131781): warning C4244: '+=': conversion from 'Py_ssize_t' to 'long', possible loss of data
pandas\algos.c(131874): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(134982): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(137210): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(139292): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(141303): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(143638): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(146139): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(148624): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(151131): warning C4244: '=': conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(152440): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(152907): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(154786): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(155242): warning C4244: '=': conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(178634): warning C4244: 'function': conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(178842): warning C4244: 'function': conversion from 'npy_int64' to 'int', possible loss of data
pandas\algos.c(200205): warning C4244: '=': conversion from 'float' to '__pyx_t_5numpy_float16_t', possible loss of data
pandas\algos.c(200453): warning C4244: '=': conversion from 'float' to '__pyx_t_5numpy_float16_t', possible loss of data
error: command 'E:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\amd64\\cl.exe' failed with exit status 2
----------------------------------------
Command ""c:\program files\python 3.5\python.exe" -c "import setuptools, tokenize;__file__='C:\\Users\\SLAPaper\\AppData\\Local\\Temp\\pip-build-xy08hyhc\\pandas\\setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record C:\Users\SLAPaper\AppData\Local\Temp\pip-1v1jv6y0-record\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in C:\Users\SLAPaper\AppData\Local\Temp\pip-build-xy08hyhc\pandas
```
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| null | 4 | 2015-10-09T07:45:54Z | 2015-10-09T14:45:57Z | 2015-10-09T14:45:57Z | CONTRIBUTOR | null | When I run `df.to_hdf('filename', complib='blosc')` in windows , with anaconda python3.4. Python would crash. When I used anaconda python2.7, it didn' t crash. It doesn' t crash with anaconda python 3 in linux.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.3
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
Some other people also experience this: http://stackoverflow.com/questions/32897866/using-blosc-compression-in-pandas-causes-heap-corruption .
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} | 7 | 2015-10-09T08:02:26Z | 2020-10-17T01:51:36Z | 2020-10-17T01:51:36Z | NONE | null | `MultiIndex` cannot be subclassed because in several spots, including `__new__`, the output class is hardcoded as `MultiIndex` rather than `cls` or `self.__class__` or the like.
This code illustrates the problem:
``` python
$ python
Python 2.7.10 (default, Sep 13 2015, 20:30:50)
[GCC 5.2.1 20150911] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> class MyMultiIndex(pandas.MultiIndex):
... pass
...
>>> multiindex = MyMultiIndex([['a'], ['b']],
... [[0], [0]])
>>> print type(multiindex)
<class 'pandas.core.index.MultiIndex'>
```
The last line should instead read
``` python
<class '__main__.MyMultiIndex'>
```
I have a patch in the works.
``` python
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.16.2.dev
nose: 1.3.6
Cython: 0.23.2
numpy: 1.9.2
scipy: 0.14.1
statsmodels: 0.6.1
IPython: 2.3.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.2
pytz: 2012c
bottleneck: None
tables: 3.1.1
numexpr: 2.4.3
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 11 | 2015-10-09T08:26:43Z | 2015-11-18T20:15:11Z | 2015-11-18T20:15:11Z | NONE | null | closes #11267
MultiIndex had several places where the output class was hard-coded to
MultiIndex rather than cls, self.**class**, or the like. These have
been replaced.
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} | 7 | 2015-10-09T11:42:27Z | 2015-10-28T11:13:27Z | 2015-10-28T11:13:27Z | NONE | null | I propose that `itertuples()` should return `collections.namedtuple` objects, a drop-in replacement for the standard tuple but with the benefit of having named fields. I have tested the following with Python 3.4 (only slight changes compared to the current implementation).
``` python
def itertuples(self, index=True):
arrays = []
if index:
arrays.append(self.index)
fields = ["Index"] + list(self.columns)
else:
fields = self.columns
itertuple = collections.namedtuple("Itertuple", fields, rename=True)
# use integer indexing because of possible duplicate column names
arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
return (itertuple(*row) for row in zip(*arrays))
```
Example
```
In [3]: df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]}, index=['a', 'b'])
In [4]: for row in df.itertuples():
...: print(row)
...:
Itertuple(Index='a', col1=1, col2=0.10000000000000001)
Itertuple(Index='b', col1=2, col2=0.20000000000000001)
In [5]: row.Index, row.col1, row.col2
Out[5]: ('b', 2, 0.20000000000000001)
```
There is no performance overhead. I'm not sure about the compatibility for older versions of Python, though. The `rename` parameter is needed for renaming disallowed field names and duplicate identifiers to standard position-based identifiers, and this feature was added in Python 2.7/3.1.
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} | 11 | 2015-10-09T13:13:37Z | 2018-10-29T10:51:32Z | 2018-10-29T10:51:32Z | NONE | null | So, my issue was that pandas was throwing an error (about not having implemented `_constructor`) when I tried to use a custom object as a label for a column in `DataFrame`, even though that object's class was not a part of the pandas hierarchy. After some digging I have realized that pandas thinks this object is an instance of `ABCGeneric`, which I eventually tracked down to https://github.com/pydata/pandas/blob/master/pandas/core/common.py#L111, where it can be seen that any object implementing a `_data` attribute is considered an instance of `ABCGeneric`.
The simplest code to recreate the issue is this:
``` python
import pandas
class Foo(object):
_data = None
foo = Foo()
df = pandas.DataFrame({foo: [1,2,3,4,5]})
df[foo]
```
This is clearly broken, as (like in my case) there are perfectly legitimate cases when other, non-pandas objects might have a `_data` attribute. Also, at least in this particular case `_data` is not the only item required from the object, as obviously it needs a `_constructor` as well. I would recommend changing this implementation either not to rely on the presence of `_data` only, or even better to look for an actual superclass, or, possibly the best, to use duck typing instead of relying on `isinstance`.
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} | 0 | 2015-10-09T13:41:05Z | 2015-10-15T22:21:38Z | 2015-10-15T22:21:38Z | CONTRIBUTOR | null | {
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} | 18 | 2015-10-09T16:08:15Z | 2015-11-04T21:04:53Z | 2015-11-04T20:28:15Z | CONTRIBUTOR | null | The GIL was removed around the tokenizer functions and the conversion function(_string_convert excluded).
## Benchmark:
### Data Generation:
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(1000000,10))
df.to_csv('test.csv')
```
### Benchmark Code:
``` python
import pandas as pd
from pandas.util.testing import test_parallel
def f():
for i in range(4):
pd.read_csv('test.csv', index_col=0)
@test_parallel(4)
def g():
pd.read_csv('test.csv', index_col=0)
```
### Before:
```
In [4]: %timeit pd.read_csv('test.csv', index_col=0)
1 loops, best of 3: 2.3 s per loop
In [7]: %timeit f()
1 loops, best of 3: 9.15 s per loop
In [8]: %timeit g()
1 loops, best of 3: 9.25 s per loop
```
### After:
``` pycon
In [6]: %timeit pd.read_csv('test.csv', index_col=0)
1 loops, best of 3: 2.35 s per loop
In [9]: %timeit f()
1 loops, best of 3: 9.55 s per loop
In [10]: %timeit g()
1 loops, best of 3: 4.38 s per loop
```
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} | 3 | 2015-10-09T18:08:39Z | 2015-10-14T00:25:46Z | 2015-10-14T00:25:46Z | CONTRIBUTOR | null | the `engine` kw and older code: https://github.com/pydata/pandas/blob/master/pandas/core/format.py#L1364
let's deprecate in 0.17.1 and just remove in 0.18.0
This was never advertised / doc-ed so prob safe to remove
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| null | 16 | 2015-10-09T18:36:32Z | 2015-10-09T19:03:38Z | 2015-10-09T19:00:23Z | CONTRIBUTOR | null | I noticed the comment on the announcement that "pypi is not accepting 3.5 wheels".
I've uploaded quite a few 3.5 wheels to pypi in the last couple of weeks, e.g.:
https://pypi.python.org/pypi/scipy
https://pypi.python.org/pypi/matplotlib
https://pypi.python.org/pypi/Cython
Do you have any more information on that problem?
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} | 9 | 2015-10-09T20:30:11Z | 2017-04-03T07:24:39Z | 2017-04-02T22:55:23Z | NONE | null | `pandas.DatetimeIndex` supports a method called `to_julian_date()` would it be easily possible to support a function/method doing the opposite, i.e. `from_julian_date()`?
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} | 1 | 2015-10-09T21:13:48Z | 2015-10-10T10:10:18Z | 2015-10-10T10:10:18Z | CONTRIBUTOR | null | Fix documentation for index_col parameter to read_sql, read_sql_table and read_sql_query to reflect that a list can be passed.
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} | 1 | 2015-10-09T23:25:59Z | 2020-05-03T16:09:14Z | 2020-05-03T16:09:14Z | CONTRIBUTOR | null | ``` python
In [2]: index = pd.period_range(start='2000', periods=20, freq='B')
In [5]: series = pd.Series(range(20), index=index)
In [6]: series
Out[6]:
2000-01-03 0
2000-01-04 1
2000-01-05 2
...
2000-01-26 17
2000-01-27 18
2000-01-28 19
Freq: B, dtype: int64
In [7]: series.loc['2000-01-14']
Out[7]: 9
```
Supplying a list of Periods as the indexer works as expected:
``` python
In [15]: series[[pd.Period(d, freq='B') for d in ['2000-01-14', '2000-01-18']]]
Out[15]:
2000-01-14 9
2000-01-18 11
Freq: B, dtype: int64
```
But not with strings:
``` python
In [10]: series.loc[['2000-01-14', '2000-01-15']]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-10-e922e8a9ed74> in <module>()
----> 1 series.loc[['2000-01-14', '2000-01-15']]
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1187 return self._getitem_tuple(key)
1188 else:
-> 1189 return self._getitem_axis(key, axis=0)
1190
1191 def _getitem_axis(self, key, axis=0):
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1321 raise ValueError('Cannot index with multidimensional key')
1322
-> 1323 return self._getitem_iterable(key, axis=axis)
1324
1325 # nested tuple slicing
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
931 def _getitem_iterable(self, key, axis=0):
932 if self._should_validate_iterable(axis):
--> 933 self._has_valid_type(key, axis)
934
935 labels = self.obj._get_axis(axis)
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1269
1270 raise KeyError("None of [%s] are in the [%s]" %
-> 1271 (key, self.obj._get_axis_name(axis)))
1272
1273 return True
KeyError: "None of [['2000-01-14', '2000-01-15']] are in the [index]"
```
It also gives `NaN` without the `loc` indexer:
``` python
In [16]: series[['2000-01-14', '2000-01-18']]
Out[16]:
2000-01-14 NaN
2000-01-18 NaN
dtype: float64
```
When supplied with the `int`s behind the `PeriodIndex`, it doesn't resolve a single int, but will resolve a list:
``` python
In [4]: index.values
Out[4]:
array([7827, 7828, 7829, 7830, 7831, 7832, 7833, 7834, 7835, 7836, 7837,
7838, 7839, 7840, 7841, 7842, 7843, 7844, 7845, 7846])
In [9]: series.loc[[7829, 7830]]
Out[9]:
7829 2
7830 3
dtype: int64
In [8]: series.loc[7829]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-b6f5fe23a769> in <module>()
----> 1 series.loc[7829]
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1187 return self._getitem_tuple(key)
1188 else:
-> 1189 return self._getitem_axis(key, axis=0)
1190
1191 def _getitem_axis(self, key, axis=0):
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1331
1332 # fall thru to straight lookup
-> 1333 self._has_valid_type(key, axis)
1334 return self._get_label(key, axis=axis)
1335
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1283
1284 try:
-> 1285 key = self._convert_scalar_indexer(key, axis)
1286 if not key in ax:
1287 error()
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/indexing.py in _convert_scalar_indexer(self, key, axis)
161 ax = self.obj._get_axis(min(axis, self.ndim - 1))
162 # a scalar
--> 163 return ax._convert_scalar_indexer(key, kind=self.name)
164
165 def _convert_slice_indexer(self, key, axis):
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/tseries/base.py in _convert_scalar_indexer(self, key, kind)
333
334 if kind in ['loc'] and lib.isscalar(key) and (is_integer(key) or is_float(key)):
--> 335 self._invalid_indexer('index',key)
336
337 return super(DatetimeIndexOpsMixin, self)._convert_scalar_indexer(key, kind=kind)
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/index.py in _invalid_indexer(self, form, key)
942 klass=type(self),
943 key=key,
--> 944 kind=type(key)))
945
946 def get_duplicates(self):
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11279 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11279/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11279/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11279/events | https://github.com/pandas-dev/pandas/pull/11279 | 110,758,984 | MDExOlB1bGxSZXF1ZXN0NDczMzQwNDk= | 11,279 | PERF: use .values in index difference | {
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| null | 14 | 2015-10-10T01:16:40Z | 2016-01-06T17:35:24Z | 2016-01-06T17:18:33Z | CONTRIBUTOR | null | The existing `.difference` method 'unboxed' all the objects, which has a severe performance impact on `PeriodIndex` in particular.
``` python
In [3]: long_index = pd.period_range(start='2000', freq='s', periods=1000)
In [4]: empty_index = pd.PeriodIndex([],freq='s')
In [24]: %timeit long_index.difference(empty_index)
# existing:
1 loops, best of 1: 1.02 s per loop
# updated:
1000 loops, best of 3: 538 µs per loop
```
...so around 2000x
I haven't worked with asv or the like - is this a case where a test like that is required?
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} | 0 | 2015-10-10T10:12:33Z | 2015-10-10T10:12:54Z | 2015-10-10T10:12:54Z | MEMBER | null | Some fixes I applied to the v0.17.0 docs I uploaded (older whatsnew was failing due to changes in 0.17)
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https://api.github.com/repos/pandas-dev/pandas/issues/11281 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11281/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11281/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11281/events | https://github.com/pandas-dev/pandas/issues/11281 | 110,797,705 | MDU6SXNzdWUxMTA3OTc3MDU= | 11,281 | Saving datetime64 with timezone to HDF5 | {
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| null | 2 | 2015-10-10T14:30:23Z | 2015-11-17T10:18:46Z | 2015-10-10T14:47:39Z | NONE | null | An attempt to store a dataframe with datetime64+tz column (introduced in pandas 0.17) results in a TypeError exception
``` python
import pandas as pd
```
``` python
df = pd.DataFrame(pd.date_range('2015-01-01', '2015-01-02', freq='T'), columns=['time'])
df.time = df.time.dt.tz_localize('UTC')
df.dtypes
```
```
time datetime64[ns, UTC]
dtype: object
```
``` python
df.to_hdf('/tmp/data.h5', 'data')
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-871011f2131e> in <module>()
----> 1 df.to_hdf('/tmp/data.h5', 'data')
/usr/lib/python3.5/site-packages/pandas/core/generic.py in to_hdf(self, path_or_buf, key, **kwargs)
936
937 from pandas.io import pytables
--> 938 return pytables.to_hdf(path_or_buf, key, self, **kwargs)
939
940 def to_msgpack(self, path_or_buf=None, **kwargs):
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
268 with HDFStore(path_or_buf, mode=mode, complevel=complevel,
269 complib=complib) as store:
--> 270 f(store)
271 else:
272 f(path_or_buf)
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in <lambda>(store)
263 f = lambda store: store.append(key, value, **kwargs)
264 else:
--> 265 f = lambda store: store.put(key, value, **kwargs)
266
267 if isinstance(path_or_buf, string_types):
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in put(self, key, value, format, append, **kwargs)
825 format = get_option("io.hdf.default_format") or 'fixed'
826 kwargs = self._validate_format(format, kwargs)
--> 827 self._write_to_group(key, value, append=append, **kwargs)
828
829 def remove(self, key, where=None, start=None, stop=None):
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1263
1264 # write the object
-> 1265 s.write(obj=value, append=append, complib=complib, **kwargs)
1266
1267 if s.is_table and index:
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in write(self, obj, **kwargs)
2803 # I have no idea why, but writing values before items fixed #2299
2804 blk_items = data.items.take(blk.mgr_locs)
-> 2805 self.write_array('block%d_values' % i, blk.values, items=blk_items)
2806 self.write_index('block%d_items' % i, blk_items)
2807
/usr/lib/python3.5/site-packages/pandas/io/pytables.py in write_array(self, key, value, items)
2584 self.group, key)._v_attrs.value_type = 'timedelta64'
2585 else:
-> 2586 self._handle.create_array(self.group, key, value)
2587
2588 getattr(self.group, key)._v_attrs.transposed = transposed
/usr/lib/python3.5/site-packages/tables/file.py in create_array(self, where, name, obj, title, byteorder, createparents, atom, shape)
1135 strides=(0,)*len(shape))
1136 else:
-> 1137 flavor = flavor_of(obj)
1138 # use a temporary object because converting obj at this stage
1139 # breaks some test. This is soultion performs a double,
/usr/lib/python3.5/site-packages/tables/flavor.py in flavor_of(array)
194 raise TypeError(
195 "objects of type ``%s`` are not supported in this context, sorry; "
--> 196 "supported objects are: %s" % (type_name, supported_descs))
197
198
TypeError: objects of type ``DatetimeIndex`` are not supported in this context, sorry; supported objects are: NumPy array, record or scalar; homogeneous list or tuple, integer, float, complex or bytes
```
``` python
pd.show_versions()
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.2-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: pl_PL.utf8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.3.2
Cython: 0.23.3
numpy: 1.9.3
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.5
lxml: 3.4.4
bs4: 4.4.0
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11282 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11282/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11282/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11282/events | https://github.com/pandas-dev/pandas/issues/11282 | 110,807,301 | MDU6SXNzdWUxMTA4MDczMDE= | 11,282 | few fresh failed tests on big-endian platforms (0.17.0+git8-gcac4ad2) | {
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} | 15 | 2015-10-10T17:35:46Z | 2021-04-20T05:32:22Z | 2021-04-20T05:32:21Z | CONTRIBUTOR | null | for details see https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=powerpc&ver=0.17.0%2Bgit8-gcac4ad2-1&stamp=1444485115 but here is the excerpt of few failed tests:
```
======================================================================
FAIL: pandas.io.tests.test_packers.TestMsgpack.test_msgpack('0.16.2',)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_packers.py", line 623, in read_msgpacks
self.compare(vf, version)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_packers.py", line 595, in compare
check_arbitrary(result, expected)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_packers.py", line 42, in check_arbitrary
assert_series_equal(a, b)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 903, in assert_series_equal
obj='{0}.index'.format(obj))
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 669, in assert_index_equal
obj=obj, lobj=left, robj=right)
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)
File "pandas/src/testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 798, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series.index are different
Series.index values are different (80.0 %)
[left]: Int64Index([ 0, 72057594037927936, 144115188075855872,
216172782113783808, 288230376151711744],
dtype='int64')
[right]: Int64Index([0, 1, 2, 3, 4], dtype='int64')
======================================================================
FAIL: pandas.io.tests.test_packers.TestMsgpack.test_msgpack('0.16.0',)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_packers.py", line 623, in read_msgpacks
self.compare(vf, version)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_packers.py", line 595, in compare
check_arbitrary(result, expected)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_packers.py", line 44, in check_arbitrary
assert_index_equal(a, b)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 665, in assert_index_equal
raise_assert_detail(obj, msg, left, right)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 798, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Index are different
Index values are different (100.0 %)
[left]: DatetimeIndex(['1970-01-03 12:45:03.392593170',
'1970-01-01 06:33:35.598089490',
'1970-01-02 06:33:18.485329170',
'1970-01-03 06:51:25.162321170',
'1970-01-01 00:39:57.351040530',
'1970-01-02 00:58:04.028032530',
'1970-01-03 00:57:46.915272210',
'1970-01-04 01:15:53.575487250',
'1970-01-01 19:04:25.780983570',
'1970-01-02 19:04:08.668223250'],
dtype='datetime64[ns]', freq='D')
[right]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08',
'2013-01-09', '2013-01-10'],
dtype='datetime64[ns]', freq='D')
======================================================================
FAIL: test_NaT_cast (pandas.tseries.tests.test_timeseries.TestSeriesDatetime64)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_timeseries.py", line 3498, in test_NaT_cast
assert_series_equal(result, expected)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 925, in assert_series_equal
check_less_precise, obj='{0}'.format(obj))
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)
File "pandas/src/testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 798, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series are different
Series values are different (100.0 %)
[left]: [2262-04-10T00:12:45.292707840+0000]
[right]: [NaT]
======================================================================
FAIL: test_where_datetime (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_series.py", line 2156, in test_where_datetime
assert_series_equal(rs, expected)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 925, in assert_series_equal
check_less_precise, obj='{0}'.format(obj))
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)
File "pandas/src/testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)
File "/«BUILDDIR»/pandas-0.17.0+git8-gcac4ad2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 798, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series are different
Series values are different (50.0 %)
[left]: [1970-01-01T00:00:00.000000010+0000, 2262-04-10T00:12:45.292707840+0000]
[right]: [1970-01-01T00:00:00.000000010+0000, NaT]
----------------------------------------------------------------------
Ran 8936 tests in 754.808s
FAILED (SKIP=248, failures=4)
```
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} | 3 | 2015-10-10T23:11:39Z | 2015-10-13T11:49:26Z | 2015-10-13T11:49:26Z | NONE | null | in BinOp.conform in pandas.computation,pytables there is this code:
```
def conform(self, rhs):
""" inplace conform rhs """
if not com.is_list_like(rhs):
rhs = [rhs]
if hasattr(self.rhs, 'ravel'):
rhs = rhs.ravel()
return rhs
```
I think this is wrong. The first test checks `rhs`, but the second checks `self.rhs`. They should both check one or the other. I _think_ both should check local `rhs` but I'm not really familiar with all the nuts and bolts here so I'm not sure.
This causes failures when doing HDF5 queries making use of a local variable whose value is a single Numpy value (e.g., a float64). Because even a single numpy value has `ravel`, but it is not list-like, both `if` blocks execute, meaning that it sets `rhs` to a list and then tries to call `ravel` on it. This leads to a difficult-to-debug error because the exception is caught at a higher level and replaced with an "Invalid query syntax" error message.
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| null | 5 | 2015-10-10T23:23:55Z | 2020-04-30T05:51:49Z | null | NONE | null | With PyTables, you can do queries like `someTable.where("X**2 + Y**2 < 1")` (as described [here](http://www.pytables.org/usersguide/condition_syntax.html)). It seems, though, that pandas imposes its own more restrictive query syntax which allows only a very limited set of query operations. This is confusing to users who expect to be able to query a Pandas HDFStore in the same way that they would query the underlying PyTables table.
Comments by @jreback on [this Stackoverflow question](http://stackoverflow.com/questions/33056227/arithmetic-in-pandas-hdf5-queries) suggest the pandas query handling is needed to handle complex queries and/or queries involving datetimes. However, it's rather extreme to block _all_ queries from using complex expressions, even when the same query would work fine on the Pytable itself. (The SO question gives a simple example of that.) I suggest the following:
1. The [documentation](http://pandas.pydata.org/pandas-docs/stable/io.html#querying-a-table) should make it very clear that the examples provided there exhaustively describe the types of possible queries. (If they don't, then we need to come up with a comprehensive explanation of what is allowed.) They should also document that this query syntax is a subset of what is allowed with Pytables.
2. It would be nice to provide some sort of override flag (or perhaps a separate function) saying "Just pass this query through to Pytables", to stop pandas from messing with the query. There is still work to be done in terms of wrapping the query result in a DataFrame, but this is on the output side and doesn't require modifying the query on the way in. This might also need to accept the `condvars` argument to pass ad-hoc variables to PyTables for use in queries.
Any thoughts on this? I'm curious what kinds of queries motivated the creation of this pandas-specific query syntax initially.
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} | 0 | 2015-10-11T11:41:01Z | 2015-10-11T15:40:28Z | 2015-10-11T15:40:28Z | CONTRIBUTOR | null | The error message shown when a BigQuery streaming insert fails could be improved by adding the location field.
Code to reproduce:
```
from datetime import datetime
from pandas import DataFrame
from pandas.io import gbq
import pytz
test_timestamp = datetime.now(pytz.timezone('US/Arizona'))
bad_df = DataFrame({'bools': [False, False], 'flts': [0.0, 1.0], 'ints': [0, '1'], 'strs': ['a', 1],
'times': [test_timestamp, test_timestamp]}, index=range(2))
gbq.to_gbq(bad_df, destination_table, project_id, verbose=True,if_exists='replace')
```
Output:
```
Streaming Insert is 100% Complete
Error at Row: 0, Reason: invalid, Message: Could not convert value to string
Error at Row: 1, Reason: invalid, Message: Could not convert value to string
```
Proposed output:
```
Streaming Insert is 100% Complete
Error at Row: 0, Reason: invalid, Location: Field:ints, Message: Could not convert value to string
Error at Row: 1, Reason: invalid, Location: Field:strs, Message: Could not convert value to string
```
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} | 4 | 2015-10-11T12:03:18Z | 2015-10-14T12:00:45Z | 2015-10-11T15:40:28Z | CONTRIBUTOR | null | closes #11285
This is a minor change to include `'Location'` in the error message reported when a streaming insert fails using the `df.to_gbq()`
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/40"
} | 1 | 2015-10-11T12:18:30Z | 2016-07-19T13:06:23Z | 2016-07-19T13:06:23Z | NONE | null | While packaging pandas 0.17 for Nix, I noticed this test to fail. I've tested with both Python 2.7 and 3.4.
```
test_invalid_index_types (pandas.tseries.tests.test_frequencies.TestFrequencyInference) ... ERROR
======================================================================
ERROR: test_invalid_index_types (pandas.tseries.tests.test_frequencies.TestFrequencyInference)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/tseries/tests/test_frequencies.py", line 565, in test_invalid_index_types
self.assertRaises(ValueError, lambda : frequencies.infer_freq(i))
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/util/testing.py", line 1879, in assertRaises
_callable(*args, **kwargs)
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/tseries/tests/test_frequencies.py", line 565, in <lambda>
self.assertRaises(ValueError, lambda : frequencies.infer_freq(i))
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/tseries/frequencies.py", line 855, in infer_freq
index = pd.DatetimeIndex(index)
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/tseries/index.py", line 315, in __new__
subarr = tools.to_datetime(data, box=False)
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/tseries/tools.py", line 276, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/tseries/tools.py", line 395, in _to_datetime
return _convert_listlike(arg, box, format)
File "/tmp/nix-build-python2.7-pandas-0.17.0.drv-0/pandas-0.17.0/pandas/tseries/tools.py", line 372, in _convert_listlike
require_iso8601=require_iso8601)
File "pandas/tslib.pyx", line 1828, in pandas.tslib.array_to_datetime (pandas/tslib.c:36848)
cpdef array_to_datetime(ndarray[object] values, errors='raise',
File "pandas/tslib.pyx", line 2017, in pandas.tslib.array_to_datetime (pandas/tslib.c:36443)
raise
File "pandas/tslib.pyx", line 2011, in pandas.tslib.array_to_datetime (pandas/tslib.c:36333)
oresult[i] = parse_datetime_string(val, dayfirst=dayfirst,
File "pandas/tslib.pyx", line 1602, in pandas.tslib.parse_datetime_string (pandas/tslib.c:29166)
dt = parse_date(date_string, default=_DEFAULT_DATETIME,
File "/nix/store/cc5vqjhmxwnm5f0jwixyi0g643y3iv4f-python2.7-dateutil-2.2/lib/python2.7/site-packages/dateutil/parser.py", line 748, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "/nix/store/cc5vqjhmxwnm5f0jwixyi0g643y3iv4f-python2.7-dateutil-2.2/lib/python2.7/site-packages/dateutil/parser.py", line 310, in parse
res, skipped_tokens = self._parse(timestr, **kwargs)
TypeError: 'NoneType' object is not iterable
```
Since Travis does not seem to be failing I could imagine this is a Nix issue. If so, do you have any suggestion regarding dependencies of software this specific piece of code that is tested needs?
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| null | 16 | 2015-10-11T16:12:52Z | 2020-05-04T18:34:26Z | 2018-01-09T00:39:09Z | CONTRIBUTOR | null | Currently there is no way to specify a rolling window for the exponentially weighted functions. From the [docs](http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions) the weighting function is giving by
However I am wondering if it is straightforward to add a _window_ parameter, similar to the rolling window statistics. This would change the formula to look like
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| null | 3 | 2015-10-11T16:38:59Z | 2015-10-13T11:56:45Z | 2015-10-12T15:59:52Z | NONE | null | Hi all,
I've enjoyed using pandas for a little while now. Thanks for developing such a useful and convenient tool!
A little nit has been nagging at me about pct_change function (and the .pct_change method of Series, DataFrame, etc.). If I run a sequence of numbers that increase by 100% (python 0.16.2):
``` python
import pandas as pd
series = pd.Series([0.5,1,2,4])
print series.pct_change()
```
the output is:
```
0 NaN
1 1
2 1
3 1
dtype: float64
```
Based on the name pct_change, I'd expect the result should the percentage change (i.e., scaled by 100):
```
0 NaN
1 100
2 100
3 100
dtype: float64
```
In general it looks to me like in general pct_change is really a _fractional_ change. Perhaps the name would suggest the behavior more closely if the function and method were called frac_change? I've become familiar with pandas enough now to know the expected behavior, but this could be a confusing turn-off for newer users.
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| null | 1 | 2015-10-12T00:44:53Z | 2015-10-12T01:32:01Z | 2015-10-12T01:32:01Z | NONE | null | It's not clear to me from the [docs](http://pandas.pydata.org/pandas-docs/stable/indexing.html#selection-by-position) what exactly is meant by "boolean array". It seems from all of the examples I've seen that `.loc` expects a one dimensional array that just selects rows or columns (e.g. https://github.com/pydata/pandas/issues/5257).
Should it be possible to select using a 2D array (the same shape as the dataframe)? This is possible in R, and returns a vector of the values selected, although it would be nice to also have the option of returning a dataframe with NaNs where the `False` values are (this is very useful for un-gap-filling data, if you have an index of what was gap filled).
e.g.:
``` python
In [2]: a = pd.DataFrame(dict(a=[1,2,3], b=[4,5,6]))
In [3]: a
Out[3]:
a b
0 1 4
1 2 5
2 3 6
In [4]: (a%2==0).as_matrix()
Out[4]:
array([[False, True],
[ True, False],
[False, True]], dtype=bool)
# This doesn't work, it gives ValueError: too many values to unpack (expected 1)
In [5]: a.loc[(a%2==0).as_matrix()]
# But this result would be nice:
Out[6]:
a b
0 NaN 4
1 2 NaN
2 NaN 6
```
Maybe with a `collapse=` option to `loc`, to return a series instead of a DF. Not sure how that would work with multiple data types though...
Or perhaps there is already a way to do something like this?
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} | 4 | 2015-10-12T03:13:05Z | 2015-10-13T11:49:29Z | 2015-10-13T11:49:26Z | NONE | null | Fixes #11283
First PR to this project. The fix is minuscule, but let me know if I did anything awry.
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| null | 14 | 2015-10-12T03:41:58Z | 2020-05-07T22:23:40Z | null | NONE | null | Hi All,
I'm just using 0.17 for testing exporting multiindex column dataframe. When i'm pivot table dataframe with X column as index (and without "columns" argument), **which will not create multiindex column**,
pivot table code (without columns argument):
```
df.pivot_table(index=["X"], columns=[], aggfunc={"Y": len,"Z":np.sum}, fill_value=0)
df.to_excel(FILE_NAME)
```
the output using to_excel() follow new format:
But, the question arise when i'm trying to pivot table X column as index (and with "columns" argument), **which will create multiindex columns**. Then, i'm trying to export it using to_excel() and still follow old format.
pivot table code (with columns argument):
```
df.pivot_table(index=["X"], columns=["A"], aggfunc={"Y": len,"Z":np.sum}, fill_value=0)
df.to_excel(FILE_NAME)
```
I don't know if this newest excel export format only applied to single column index, not multicolumn index. Was this case expected or there is something wrong?
Thanks
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} | 8 | 2015-10-12T07:40:01Z | 2015-10-15T14:45:02Z | 2015-10-15T14:20:58Z | CONTRIBUTOR | null | It's already resolved. I added hex string color test.
closes #10299
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} | 5 | 2015-10-12T08:26:46Z | 2015-10-14T10:56:38Z | 2015-10-14T10:56:26Z | CONTRIBUTOR | null | closes #11080
Worked for this during PyCon JP 2015 pandas sprint /w @sinhrks
I found `test_frame.py:TestDataFrame.test_sort_index` cover this change.
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} | 2 | 2015-10-12T11:30:23Z | 2015-10-13T12:25:28Z | 2015-10-13T12:25:28Z | NONE | null | Second tz_convert fails with AmbiguousTimeError ('UTC' in the example, but other timezones fail too).
EDIT: Actually it's not only second tz_convert that fails, it looks like most (if not all) of the .dt accessed properties are affected
``` python
t = pd.Series(pd.date_range('2015-01-01', '2016-01-01', freq='T'))
t = t.dt.tz_localize('UTC').dt.tz_convert('America/Chicago')
t.head(10)
```
```
0 2014-12-31 18:00:00-06:00
1 2014-12-31 18:01:00-06:00
2 2014-12-31 18:02:00-06:00
3 2014-12-31 18:03:00-06:00
4 2014-12-31 18:04:00-06:00
5 2014-12-31 18:05:00-06:00
6 2014-12-31 18:06:00-06:00
7 2014-12-31 18:07:00-06:00
8 2014-12-31 18:08:00-06:00
9 2014-12-31 18:09:00-06:00
dtype: datetime64[ns, America/Chicago]
```
``` python
t.dt.tz_convert('UTC')
```
```
---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
/usr/lib/python3.5/site-packages/pandas/core/series.py in _make_dt_accessor(self)
2639 try:
-> 2640 return maybe_to_datetimelike(self)
2641 except Exception:
/usr/lib/python3.5/site-packages/pandas/tseries/common.py in maybe_to_datetimelike(data, copy)
48 if is_datetime64_dtype(data.dtype) or is_datetime64tz_dtype(data.dtype):
---> 49 return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index, name=data.name)
50 elif is_timedelta64_dtype(data.dtype):
/usr/lib/python3.5/site-packages/pandas/util/decorators.py in wrapper(*args, **kwargs)
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
/usr/lib/python3.5/site-packages/pandas/tseries/index.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
343 subarr = tslib.tz_localize_to_utc(ints, tz,
--> 344 ambiguous=ambiguous)
345
pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:64516)()
AmbiguousTimeError: Cannot infer dst time from Timestamp('2015-11-01 01:00:00'), try using the 'ambiguous' argument
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-2-eb75730cd6fc> in <module>()
----> 1 t.dt.tz_convert('UTC')
/usr/lib/python3.5/site-packages/pandas/core/generic.py in __getattr__(self, name)
2239 or name in self._metadata
2240 or name in self._accessors):
-> 2241 return object.__getattribute__(self, name)
2242 else:
2243 if name in self._info_axis:
/usr/lib/python3.5/site-packages/pandas/core/base.py in __get__(self, instance, owner)
186 # this ensures that Series.str.<method> is well defined
187 return self.accessor_cls
--> 188 return self.construct_accessor(instance)
189
190 def __set__(self, instance, value):
/usr/lib/python3.5/site-packages/pandas/core/series.py in _make_dt_accessor(self)
2640 return maybe_to_datetimelike(self)
2641 except Exception:
-> 2642 raise AttributeError("Can only use .dt accessor with datetimelike "
2643 "values")
2644
AttributeError: Can only use .dt accessor with datetimelike values
```
``` python
```
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} | 9 | 2015-10-12T11:50:20Z | 2018-02-25T16:05:27Z | 2018-02-25T16:05:27Z | NONE | null | Hello,
I work on a dataframe with a multi index (Date, InputTime) and this dataframe may contain some NaN values in the columns (Value, Id). I want to fill forward value but by Date only and I don't find anyway to do this a in a very efficient way. I'm using Pandas 0.16.2 and numpy 1.9.2.
Here is the type of dataframe I have :
Dataframe example
And here is the result I want :
So to properly fillback by date I can use groupby(level=0) function. The groupby call is fast but the fill forward function applied on the "group by dataframe" is really too slow.
Here is the code I use to compare simple fill forward (which doesn't give the expected result but is run very quickly) and expected fill forward by date (which give expected result but is really too slow).
```
import numpy as np
import pandas as pd
import datetime as dt
# Show pandas & numpy versions
print('pandas '+pd.__version__)
print('numpy '+np.__version__)
# Build a big list of (Date,InputTime,Value,Id)
listdata = []
d = dt.datetime(2001,10,6,5)
for i in range(0,100000):
listdata.append((d.date(), d, 2*i if i%3==1 else np.NaN, i if i%3==1 else np.NaN))
d = d + dt.timedelta(hours=8)
# Create the dataframe with Date and InputTime as index
df = pd.DataFrame.from_records(listdata, index=['Date','InputTime'], columns=['Date', 'InputTime', 'Value', 'Id'])
# Simple Fill forward on index
start = dt.datetime.now()
for col in df.columns:
df[col] = df[col].ffill()
end = dt.datetime.now()
print "Time to fill forward on index = " + str((end-start).total_seconds()) + " s"
# Fill forward on Date (first level of index)
start = dt.datetime.now()
for col in df.columns:
df[col] = df[col].groupby(level=0).ffill()
end = dt.datetime.now()
print "Time to fill forward on Date only = " + str((end-start).total_seconds()) + " s"
```
Here are the time results I have:
So, the fill foward on the group by dataframe is 10000 times slower than the simple fill forward. I cannot understand why pandas is running so slowly. I need to have comparable perfs with the simple fill forward so just a couple of milliseconds.
Could somebody address the performance issue? Or give me a solution to do this kind of action in a very efficient way?
Thanks
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} | 7 | 2015-10-12T13:39:40Z | 2019-07-30T03:29:08Z | 2019-07-30T03:29:02Z | CONTRIBUTOR | null | ``` python
======================================================================
FAIL: test_expand_user (pandas.io.tests.test_common.TestCommonIOCapabilities)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_4/lib/pandas/io/tests/test_common.py", line 20, in test_expand_user
self.assertTrue(isabs(expanded_name))
AssertionError: False is not true
```
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.8-lh
machine: x86_64
processor: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
pandas: 0.17.0
nose: 1.3.7
Cython: 0.23.3
numpy: 1.9.3
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.4
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.8
pymysql: 0.6.6.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
happens the same way with python3.3. Py2 untested yet.
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| null | 3 | 2015-10-12T13:40:38Z | 2015-11-26T14:05:41Z | 2015-11-26T14:05:41Z | CONTRIBUTOR | null | ``` python
======================================================================
FAIL: test_get_filepath_or_buffer_with_path (pandas.io.tests.test_common.TestCommonIOCapabilities)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.0/work/pandas-0.17.0-python3_3/lib/pandas/io/tests/test_common.py", line 34, in test_get_filepath_or_buffer_with_path
self.assertTrue(isabs(filepath_or_buffer))
AssertionError: False is not true
```
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.8-lh
machine: x86_64
processor: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
pandas: 0.17.0
nose: 1.3.7
Cython: 0.23.3
numpy: 1.9.3
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.4
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.8
pymysql: 0.6.6.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
happens the same way with python3.3. Py2 untested yet.
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} | 6 | 2015-10-12T14:11:42Z | 2016-12-03T12:42:31Z | 2016-04-26T18:54:47Z | CONTRIBUTOR | null | ```
testOdArray (pandas.io.tests.test_json.test_ujson.NumpyJSONTests) ... Segmentation fault (core dumped)
```
``` python
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.8-lh
machine: x86_64
processor: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.3
numpy: 1.9.3
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
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bs4: 4.4.1
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apiclient: 1.3.1
sqlalchemy: 1.0.8
pymysql: 0.6.6.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
Abrt report: https://gist.github.com/jlec/6282f0dae0586d6749af
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| null | 7 | 2015-10-12T14:40:57Z | 2019-02-14T05:23:36Z | 2015-11-05T12:57:18Z | NONE | null | closes #10355
Previously grouping columns were square rooted when as_index=False
New method closely follows the format of var() method.
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} | 0 | 2015-10-12T15:45:45Z | 2015-10-13T12:25:28Z | 2015-10-13T12:25:28Z | CONTRIBUTOR | null | closes #11295
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} | 18 | 2015-10-12T17:36:27Z | 2015-10-13T13:53:26Z | 2015-10-13T13:53:26Z | NONE | null | With pandas 0.16.2:
> > > import pandas as pd
> > > pd.DataFrame([1.12345678901234567890]).astype(str)
> > > 0
> > > 0 1.12345678901
With pandas 0.17:
> > > import pandas as pd
> > > pd.DataFrame([1.12345678901234567890]).astype(str)
> > > 0
> > > 0 1.1234567890123457
I read the 0.17 release log but couldn't figure out why that is. Is it a bug or a new feature, and if it's a new feature how can I re-activate the old behavior?
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