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} | 9 | 2015-07-16T16:48:05Z | 2020-08-21T22:42:51Z | 2020-08-21T22:42:51Z | CONTRIBUTOR | null | With some parameters, the last group yield by resample is empty. Example :
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.normal(size=(10000,4)))
df.index = pd.timedelta_range(start='0s', periods=10000, freq='3906250n')
df.loc['1s':,:].resample('3s',how=lambda x : len(x))
```
Depending of the 'how' function used, this can lead to surprising bugs.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.16.0-294-g45f69cd
nose: 1.3.6
Cython: 0.20.2
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 3.0.0-dev
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.8.2
pymysql: None
psycopg2: 2.5.3 (dt dec mx pq3 ext)
```
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} | 14 | 2015-07-16T19:33:45Z | 2015-07-21T12:59:42Z | 2015-07-21T12:59:38Z | CONTRIBUTOR | null | When calling `get_bucket`, boto will by default try to establish that the S3 bucket exists by listing all of the keys that exist in it. This behavior is controlled by the "validate" keyword, which defaults to True. If your access key doesn't have permission to read everything in a bucket (even if you do have permission to read the file you're trying to access), this generates an uninformative exception.
This PR sets "validate=False". This means that boto will trust you that the bucket exists, and not try to check immediately. If the bucket actually doesn't exist, the `get_contents_as_string` call a couple of lines later will generate the exception "S3ResponseError: S3ResponseError: 404 Not Found".
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} | 5 | 2015-07-16T20:38:02Z | 2015-07-21T11:40:06Z | 2015-07-21T11:40:02Z | CONTRIBUTOR | null | Fix for https://github.com/pydata/pandas/issues/9789.
How do you feel about direct URLs in the doctrings?
I saw them in a few other docstrings. Pandas maintainers, any preferences?
Still need to fix the Examples at the bottom of the page.
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} | 4 | 2015-07-17T02:21:58Z | 2021-04-18T07:23:45Z | null | NONE | null | If a series contains boolean data, `value_counts()` do not return the count in the descending order. However the counts are correct if accessing through `iloc`. I supposed that is because 0 is cast into False and 1 is cast into True?
``` py
import pandas as pd
str_series = pd.Series(['f', 'f', 't', 't', 't'])
bool_series = pd.Series([False, False, True, True, True])
counts = bool_series.value_counts()
assert counts.index[0] == True
assert counts.index[1] == False
assert counts[0] == 3 # counts[0] is actually 2
assert counts[1] == 2 # counts[1] is actually 3
counts = bool_series.value_counts()
assert counts.index[0] == True
assert counts.index[1] == False
assert counts.iloc[0] == 3 # this works
assert counts.iloc[1] == 2 # this works
counts = str_series.value_counts()
assert counts.index[0] == 't'
assert counts.index[1] == 'f'
assert counts[0] == 3
assert counts[1] == 2
```
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| null | 15 | 2015-07-17T15:12:53Z | 2017-11-13T14:16:36Z | 2017-11-12T11:59:45Z | NONE | null | I was using Pandas version 0.16.1 on a Red Hat Enterprise Linux Server release 6.6 system and it was working fine but I wanted to update Pandas to version 0.16.2 so I uninstalled it and reinstalled it using
```
pip uninstall pandas
pip install pandas
```
The installation seemed to work fine. However, when I attempt to import pandas
```
import pandas
```
I get the error:
```
Traceback (most recent call last):
File "path_to_python/python3.4/site-packages/pandas/__init__.py", line 7, in <module>
from pandas import hashtable, tslib, lib
ImportError: path_to_python/python3.4/site-packages/pandas/hashtable.cpython-34m.so: undefined symbol: pyobject_cmp
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "path_to_python/python3.4/site-packages/pandas/__init__.py", line 13, in <module>
"extensions first.".format(module))
ImportError: C extension: path_to_python/python3.4/site-packages/pandas/hashtable.cpython-34m.so:
undefined symbol: pyobject_cmp not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
```
However:
```
cd path_to_python/python3.4/site-packages/pandas/
python setup.py build_ext --inplace
```
doesn't work either as there is no setup.py to be found. The error is:
```
Fatal Python error: Py_Initialize: can't initialize sys standard streams
AttributeError: 'module' object has no attribute 'OpenWrapper'
Aborted (core dumped)
```
I also tried to install Pandas by downloading the source and then:
```
python setup.py install
python setup.py build_ext --inplace
```
This time the second command generated a lot of output and successfully completed. However, I still get the same error when I try to import pandas
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} | 1 | 2015-07-17T16:40:37Z | 2015-07-17T21:03:53Z | 2015-07-17T21:03:49Z | MEMBER | null | Closes #10591. The same fix as pydata/pandas-datareader#53.
Though it is not a bug, added it to the bug section because users likely to check it when they meet 404.
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} | 13 | 2015-07-17T18:03:18Z | 2015-09-01T11:49:40Z | 2015-09-01T11:49:40Z | CONTRIBUTOR | null | In lines 930-931 of pandas.core.index.py, the following code states
``` python
from pandas.core.indexing import _maybe_convert_indices
return _maybe_convert_indices(indexer, len(self))
```
In pandas.core.indexing.py, the function is called `maybe_convert_indices`. In the 3 other places where the function is called, it is called using the correct name. The leading underscore should be removed from the function name in index.py.
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} | 35 | 2015-07-17T19:52:51Z | 2019-08-18T14:39:19Z | 2018-07-04T12:59:41Z | CONTRIBUTOR | null | example: https://www.wakari.io/sharing/bundle/adamgreenhall/test-scatter
I think this happens specifically for pandas scatter plots with colorbars in ipython. The xticks are still working for:
- non-colorbar pandas scatter plots
- the same scatter plot using matplotlib
- standard python scripts using `plt.savefig`
related problem with `%matplotlib inline`?: https://github.com/ipython/ipython/issues/1443/
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| null | 1 | 2015-07-17T19:57:13Z | 2015-07-17T19:59:11Z | 2015-07-17T19:59:02Z | NONE | null | If you try to read a csv file into a dataframe, and the csv file has just the header and no rows, it throws 'object of type 'bool' has no len()'. This issue does not seem to exist in 0.16.1.
The sample csv file is blank:
id,strategyId,symbol,side,target,time,reason,epochTime
Code that causes the issue:
```
import pandas as pd
df = pd.read_table(path_to_csv, sep=',', index_col=False)
```
Code that does not have the issue:
```
import pandas as pd
df = pd.read_table(path_to_csv, sep=',')
```
## INSTALLED VERSIONS
commit: None
python: 2.7.7.final.0
python-bits: 64
INSTALLED VERSIONSOS: Linux
OS-release: 2.6.32-431.3.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
------------------LC_ALL: None
LANG: en_US.utf8
commit: None
pandas: 0.16.2
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nose: 1.3.4
python-bits: 64Cython: None
OS: Linuxnumpy: 1.9.2
OS-release: 2.6.32-431.3.1.el6.x86_64scipy: 0.15.1
machine: x86_64
statsmodels: None
processor: x86_64
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nose: 1.3.4bottleneck: 1.0.0
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numpy: 1.9.2numexpr: 2.4
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pytz: 2015.4bs4: None
bottleneck: 1.0.0html5lib: None
tables: Nonehttplib2: None
numexpr: 2.4apiclient: None
sqlalchemy: 0.9.8matplotlib: 1.3.1
pymysql: Noneopenpyxl: None
psycopg2: None
xlrd: None
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xlsxwriter: None
lxml: None
bs4: None
html5lib: None
Nonehttplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
None
## INSTALLED VERSIONS
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.3.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.16.2
nose: 1.3.4
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: None
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
None
## INSTALLED VERSIONS
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.3.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
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nose: 1.3.4
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numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: None
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
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} | 0 | 2015-07-17T23:51:25Z | 2015-07-18T15:00:58Z | 2015-07-18T15:00:58Z | CONTRIBUTOR | null | tests were not closing on windows properly
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} | 19 | 2015-07-18T04:40:51Z | 2015-07-22T10:52:57Z | 2015-07-22T10:52:51Z | CONTRIBUTOR | null | Added more explicit note about the fact numba does not yet recognize pandas objects so one must always use `.values`, and added example of `numba.vectorize`.
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} | 14 | 2015-07-18T17:34:33Z | 2015-10-06T03:19:45Z | 2015-07-20T23:24:49Z | CONTRIBUTOR | null | Closes #10178
Closes #8154
Using the example data from the issue:
```
In [2]: df = DataFrame({'date_text':["2015-05-18" for i in range(10**6)]})
```
Before:
```
In [3]: %timeit pd.to_datetime(df['date_text'],format="%Y-%m-%d", box=False).values.view('i8')/10**9
1 loops, best of 3: 3.14 s per loop
In [4]: %timeit pd.to_datetime(df['date_text'],infer_datetime_format=True, box=False).values.view('i8')/10**9
1 loops, best of 3: 253 ms per lo
```
After:
```
In [6]: %timeit pd.to_datetime(df['date_text'],format="%Y-%m-%d", box=False).values.view('i8')/10**9
1 loops, best of 3: 217 ms per loop
In [7]: %timeit pd.to_datetime(df['date_text'],infer_datetime_format=True, box=False).values.view('i8')/10**9
1 loops, best of 3: 243 ms per loop
```
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| null | 23 | 2015-07-18T17:59:46Z | 2020-04-05T01:24:42Z | null | NONE | null | I am trying to read a fixed width file using read_fwf function and coerce the column data types by using the 'converters' parameter. I created a dictionary that specifies a function that converts to the type that I desire for each of the columns in the data file.
The problem is that not all columns are getting set to the type I specify and it appears that any column for which the first few rows of the data set are blank will be inferred or defaulted and not coerced to the data type I explicitly specified in the dictionary passed via the converters parameter.
As a test, I tried to coerce all column types to a string by passing a dictionary that looked something like this:
type_dict = {0: conv_str, 1: conv_str, 2: conv_str ... (n-1): conv_str), where n = number of columns
and conv_str is a function that is defined as follows:
def conv_str(x):
return str(x)
As previously explained, in the resulting data frame, all columns get converted to type string as desired, with the exception of the columns that had blank values for the first few rows of the data set. Those columns that had blank values get defaulted to the 'float' data type.
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} | 3 | 2015-07-18T20:59:43Z | 2015-07-20T05:37:35Z | 2015-07-20T03:21:57Z | CONTRIBUTOR | null | this allows the `con` argument of `pd.read_sql`/`pd.read_sql_query` to be a sqlalchemy [`Session`](http://docs.sqlalchemy.org/en/rel_1_0/orm/session.html) object.
this allows for the use of temporary tables.
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} | 2 | 2015-07-18T22:45:29Z | 2015-08-26T01:30:47Z | 2015-08-26T01:30:47Z | CONTRIBUTOR | null | The low level Cython routine for outer_join_indexer uses the wrong size to handle the special case that the right array is empty (see: https://github.com/pydata/pandas/blob/master/pandas/src/generate_code.py#L2134). The fix is simple: if the right array is empty (`nright == 0`), it should of course loop and copy the left array (`for i in range(nleft)`).
Due to this bug, the merged index and both indexers are never written when the right array is empty:
```
In [91]: from pandas.algos import outer_join_indexer_float64
In [92]: outer_join_indexer_float64(array([1.,2.,3.]), array([]))
Out[92]:
(array([ 6.92981993e-310, 2.56210349e-316, 4.27255699e+180]),
array([140261116804248, 140261116804248, 140260374492336]),
array([140261116804232, 140261116804232, 50534992]))
```
`Index.union` already preempts this special case separately, so this bug will only affect a handful of people that interact directly with the Cython algos. If desired, I could prepare a PR tomorrow, though it might be overkill for such a small change.
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} | 21 | 2015-07-19T01:46:06Z | 2015-07-28T14:47:17Z | 2015-07-28T10:05:34Z | CONTRIBUTOR | null | Current behavior:
``` python
In [32]:
period_index=pd.PeriodIndex(start='2015-01-01',end='2015-01-07',freq='B')
period_index
Out[32]:
PeriodIndex(['2015-01-01', '2015-01-02', '2015-01-05', '2015-01-06',
'2015-01-07'],
dtype='int64', freq='B')
In [33]:
period_index.drop(period_index[:2])
Out[33]:
Int64Index([11742, 11743, 11744], dtype='int64')
```
New behavior:
``` python
In [32]: period_index.drop(period_index[:2])
Out[32]: PeriodIndex(['2015-01-05', '2015-01-06', '2015-01-07'], dtype='int64', freq='B')
```
I could combine with #10599, but am having a couple of issues with this (will post below) that I need help with - I'll take direction on whether to combine or not.
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} | 4 | 2015-07-19T02:52:02Z | 2015-07-19T14:31:02Z | 2015-07-19T05:32:18Z | NONE | null | I posted a slightly-ranting [question about this on StackOverflow](//stackoverflow.com/questions/31497504/join-dataframes-on-a-partially-matching-index).
Basically, I want to use pandas to join two dataframes wherein the index levels of one are a subset of the index levels of the other. For example, the two DFs below have `index.names` of `('fruit', 'citrus')` and `('citrus',)` respectively:
```
fruit_max_by_date =
0
fruit citrus
apples False 6
bananas False 5
oranges True 8
lemons True 9
[4 rows x 1 columns]
citrus_max_by_date =
1
citrus
False 6
True 9
[2 rows x 1 columns]
```
The intuitive approach to joining them fails, because the indexes do not **exactly** match:
```
fruit_max_by_date.join(citrus_max_by_date) =>
0 1
fruit citrus
apples False 6 NaN
bananas False 5 NaN
oranges True 8 NaN
lemons True 9 NaN
[4 rows x 2 columns]
```
I found a variety of workarounds, none of which I consider very satisfactory. Eventually I found one that doesn't involve giving up on using an index altogether. It involves rebroadcasting the "smaller" index to match the "larger" index:
```
fruit_max_by_date.join(
citrus_max_by_date.reindex(fruit_max_by_date.index, level='citrus') ) =>
0 1
fruit citrus
apples False 6 6
bananas False 5 6
oranges True 8 9
lemons True 9 9
[4 rows x 2 columns]
```
Is there any reason not to make this behavior **automatic**?
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} | 3 | 2015-07-19T03:10:37Z | 2015-07-28T17:42:19Z | 2015-07-28T11:03:50Z | CONTRIBUTOR | null | Just some miscellaneous improvements on things that tripped me up a bit.
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} | 2 | 2015-07-19T04:45:58Z | 2016-02-16T14:50:50Z | 2016-02-16T14:50:50Z | CONTRIBUTOR | null | At some point deprecate `items` in `msgpack.encode()` for blocks. xref #10527
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} | 1 | 2015-07-19T05:43:14Z | 2015-07-19T14:06:40Z | 2015-07-19T10:33:03Z | NONE | null | The behavior of these methods differs in confusing ways:
DF:
- [`DataFrame.sort_index`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) takes a `by` parameter, in which case the index is resorted by a specific column's values
- [`DataFrame.sort`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort.html) defaults to `inplace=False`
- `DataFrame.order` does not exist
Series:
- [`Series.sort`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort.html) defaults to `inplace=True`
- [`Series.order`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.order.html) defaults to `inplace=False`, but is otherwise identical to `Series.sort`
- [`Series.sort_index`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) does not take a `by` parameter
This makes it very hard to remember which one to use, especially after an aggregating function. For example:
```
df.max(axis=1).order(ascending=False) # max returns a Series in this case, use Series methods
df.max(level='Foo').sort_index(by='Bar') # max returns a DF in this case, use DF methods
```
Any reason not to make the `inplace` behavior identical for `DataFrame.sort` and `Series.sort`? Also, `Series.sort_index` could take `by=0` to replicate the behavior of `DataFrame`.
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} | 25 | 2015-07-19T06:51:30Z | 2016-02-14T22:15:10Z | 2015-09-03T13:09:57Z | CONTRIBUTOR | null | closes #10590
For groupby the time stamps gets converted to integervalue `tslib.iNaT`
which is -9223372036854775808. The aggregation is then done using this
value with incorrect result as a consequence. The solution proposed here
is to replace its value by np.nan in case it is a `datetime64[ns]`
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} | 13 | 2015-07-19T06:54:19Z | 2016-07-21T15:45:17Z | 2015-11-10T01:27:48Z | CONTRIBUTOR | null | To address #10536, but it's clearly not enough. What should be done for `SparseSeries` of different `kind`s and different `fill` values?
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| null | 9 | 2015-07-19T07:04:19Z | 2019-09-18T14:02:16Z | 2019-09-18T14:02:16Z | CONTRIBUTOR | null | These other issues will be fixed by this
- [x] #667 other dtypes PR #13849
- [x] #10818 `from_coo` issues (same as #10560)
- [ ] #10079 empty construction
- [x] #10560 `__repr__` broken, PR #12779
- [x] #10536 concat `SparseSeries`, PR #12844
- [x] #12174 concat `SparseDataFrame`? PR #12966
- [x] #4400 more testing of indexing (#12848)
- [x] #12794 more/better sparse docs PR https://github.com/pandas-dev/pandas/commit/b13ddd57f6f88f75d987dd84d208a2329f1d94bb
- [ ] #12855 return types
- [x] #13110 non float repr PR #13849
- [x] #13001 signatures PR #13849
- [x] #13144 repr of large arrays (#13163)
- [x] `SparseSeries` with `MultiIndex` indexing (#13163)
- [ ] `SparseDataFrame/Series` with `MultiIndex` indexing (#15447)
- [ ] `SparseSeries` with non-unique index indexing
- [ ] `SparseDataFrame` with non-unique index indexing
- [ ] slicing / reindexing `SparseDataFrame` to return subclass (#13787)
- [ ] concat (#16874)
The fundamental issue is that slicing is broken in sparse.
```
In [11]: s = Series([1]+[np.nan]*5).to_sparse()
In [12]: s
Out[12]:
0 1
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
In [13]: s.iloc[0:3]
Out[13]:
[1.0, nan, nan]
Fill: nan
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
In [14]: type(s.iloc[0:3])
Out[14]: pandas.sparse.array.SparseArray
In [15]: type(s)
Out[15]: pandas.sparse.series.SparseSeries
[14] should be a SparseSeries (not SparseArray which is the underlying object the SparseSeries holds). its not getting wrapped when sliced somewhere.
```
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} | 10 | 2015-07-19T10:16:42Z | 2015-09-01T12:08:42Z | 2015-09-01T12:07:44Z | CONTRIBUTOR | null | Partial fix for https://github.com/pydata/pandas/issues/5023
flake8 doesn't do a very good job at checking code in the docstrings for PEP8 compliance, so I'm just eyeballing it at the moment and my PEP8 knowledge leaves something to be desired. Apologies if I missed something really obvious.
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} | 2 | 2015-07-19T15:38:19Z | 2015-08-13T13:37:26Z | 2015-08-13T13:37:16Z | MEMBER | null | From the mailing list: https://groups.google.com/forum/?fromgroups#!topic/pydata/fpYfwkZvdxI
```
In [37]: tips = pd.read_csv('https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv')
In [38]: pd.crosstab(tips.day, tips.size)
Out[38]:
col_0 1708
day
Fri 19
Sat 87
Sun 76
Thur 62
In [39]: pd.__version__
Out[39]: '0.16.2'
```
while it should be:
```
In [3]: pd.crosstab(tips.day, tips.size)
Out[3]:
size 1 2 3 4 5 6
day
Fri 1 16 1 1 0 0
Sat 2 53 18 13 1 0
Sun 0 39 15 18 3 1
Thur 1 48 4 5 1 3
In [4]: pd.__version__
Out[4]: '0.14.1'
```
But strange thing is, I can't reproduce it with simple example:
```
In [43]: df = pd.DataFrame({'a':list('abcabcabcab'), 'b':[1,1,1,1,2,3,2,2,3,3,1]
})
In [44]: pd.crosstab(df['a'], df['b'])
Out[44]:
b 1 2 3
a
a 2 1 1
b 2 2 0
c 1 0 2
```
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} | 1 | 2015-07-19T20:26:50Z | 2015-07-21T12:39:44Z | 2015-07-21T12:39:44Z | CONTRIBUTOR | null | - skipping a couple of parsing routines that cause odd test failures on windows
- msgpack compat for windows (not tested before, prob not working correctly). Slight internal API change where writing the `dtype.name` rather than `dtype.num`, but back-compat is ok on this. (and forward compat is better).
- skipping a buggy test for ujson on windows on 2.7 only
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} | 5 | 2015-07-19T20:46:27Z | 2019-10-07T01:13:10Z | 2019-10-07T01:13:10Z | CONTRIBUTOR | null | xref #10630
- [x] csv parsing then turning into a recarray causing segfaults, only when run with the entire parsing tests (e.g. `test_parsing.py` (2 tests) (#13373)
- [ ] in `test_libsparse`, the test_intersect is segfaulting, not really sure why.
- [ ] in `test_ujson` with small exponentials, only on win-64 with 2.7.
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} | 9 | 2015-07-19T21:29:41Z | 2015-07-26T01:54:28Z | 2015-07-26T01:54:21Z | CONTRIBUTOR | null | Probably work in progress... Up to now only tests locally with 'not slow' (whoever wrote `nosetests --with-id --failed` should be praised! :-) )
See here: https://github.com/pydata/pandas/issues/10482
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} | 10 | 2015-07-19T22:50:48Z | 2018-07-19T11:28:03Z | 2015-08-21T20:27:52Z | NONE | null | Although in docs it says `method='spline'` is a choice, but it raises a `ValueError`.
```
import numpy as np
import pandas as pd
np.random.seed(1)
s = pd.Series(np.arange(10)**2)
s[np.random.randint(0,9,3)] = np.nan
s.interpolate(method='spline')
# raise ValueError
ValueError: invalid method 'spline' to interpolate.
```
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| null | 1 | 2015-07-20T05:57:29Z | 2015-07-20T06:02:42Z | 2015-07-20T06:02:42Z | NONE | null | http://pandas-docs.github.io/pandas-docs-travis/
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| null | 2 | 2015-07-20T09:32:16Z | 2015-07-20T13:39:04Z | 2015-07-20T13:39:04Z | CONTRIBUTOR | null | Using `combine_first` on an empty frame with a single-column index fails if the other frame has a multi-index and is not empty.
This used to work for pandas version 0.14.1
``` python
import pandas as pd
midx = pd.MultiIndex.from_arrays([[-1], [1]])
a = pd.DataFrame({'a': [1]}, index=midx)
print a
b = pd.DataFrame({'a': []})
print b
print b.combine_first(a)
```
```
$ python x.py
a
-1 1 1
Empty DataFrame
Columns: [a]
Index: []
Traceback (most recent call last):
File "x.py", line 11, in <module>
print b.combine_first(a)
File "/users/is/ahlpypi/egg_cache/p/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 3388, in combine_first
return self.combine(other, combiner, overwrite=False)
File "/users/is/ahlpypi/egg_cache/p/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/frame.py", line 3283, in combine
this, other = self.align(other, copy=False)
File "/users/is/ahlpypi/egg_cache/p/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 3374, in align
fill_axis=fill_axis)
File "/users/is/ahlpypi/egg_cache/p/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 3395, in _align_frame
return_indexers=True)
File "/users/is/ahlpypi/egg_cache/p/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/index.py", line 2005, in join
return self._join_multi(other, how=how, return_indexers=return_indexers)
File "/users/is/ahlpypi/egg_cache/p/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/index.py", line 2098, in _join_multi
raise ValueError("cannot join with no level specified and no overlapping names")
ValueError: cannot join with no level specified and no overlapping names
```
## INSTALLED VERSIONS
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-400.1.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.16.2
nose: 1.3.0
Cython: 0.22
numpy: 1.9.2
scipy: 0.9.0
statsmodels: None
IPython: 3.2.0-1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 0.7.4
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: $Rev$
apiclient: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 7 | 2015-07-20T12:44:57Z | 2015-07-31T22:35:22Z | 2015-07-31T22:35:22Z | CONTRIBUTOR | null | xref #8894
xref #10615
xref #10154
The idea is that if you have completely unparseable dates with or w/o a format specified, then the default will be to raise. Further, since `coerce` is by default `False`. You will get an immediate exception rather than have strings returned.
I think some tests will need to be adjusted and a simple example for the user in the whatsnew.
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} | 6 | 2015-07-20T13:09:31Z | 2015-07-24T13:46:23Z | 2015-07-24T13:44:14Z | CONTRIBUTOR | null | … improved unit tests
this is a follow-up to https://github.com/pydata/pandas/pull/9947
@jreback
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} | 22 | 2015-07-20T13:16:06Z | 2015-09-06T17:31:30Z | 2015-09-06T16:31:07Z | MEMBER | null | closes #9966, #9974 (PR)
I understand these are never used because `Index` is no longer the subclass of `np.array`. Correct?
- [x] Add tests for ~~all~~ almost ufuncs
- http://docs.scipy.org/doc/numpy/reference/ufuncs.html
- ~~CategoricalIndex~~: Needs to be done separately to fix `Categorical`, because number of categories can be changed.
```
np.sin(pd.Categorical([1, 2, 3]))
array([ 0.84147098, 0.90929743, 0.14112001])
```
- [x] MultiIndex: Raise `TypeError` or `AttributeError`, as ufuncs are performed to array of tuples.
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} | 3 | 2015-07-20T13:20:42Z | 2015-07-28T10:08:42Z | 2015-07-28T10:08:42Z | CONTRIBUTOR | null | Currently merge columns are not automatically inferred when there are duplicate column names. Instead a `MergeError` is raised. This pull request enables merging with duplicate column names as long as they are not merged upon.
```
df = DataFrame({'key': [1, 2, 3],
'v1': [4, 5, 6],
'v2': [7, 8, 9]})
df.columns = ['key', 'foo', 'foo']
df2 = DataFrame({'key': [1, 2, 3],
'v1': [4, 5, 6],
'v2': [7, 8, 9]})
df2.columns = ['key', 'bar', 'bar']
# worked before
merge(df, df2, on=['key'])
# now works as well since 'foo' and 'bar' don't interfere
merge(df, df2)
```
If the common cols are not unique a `MergeError` is still raised as in #2649
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} | 2 | 2015-07-20T15:41:51Z | 2021-04-18T22:30:19Z | null | NONE | null | As outlined in my [stackoverflow question](http://stackoverflow.com/questions/31520033/how-to-stop-pandas-adding-time-to-column-title-after-transposing-a-datetime-inde), pandas `to_html()` doesn't behave consistently with a datetime index after being transposed.
A simple example:
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5, 5), columns = ['a', 'b', 'c', 'd', 'e'], index = ["2015-01-05","2015-01-04","2015-01-03","2015-01-02","2015-01-01"])
df.index = pd.to_datetime(df.index)
```
Then `df.to_html()` outputs:
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>a</th>
<th>b</th>
<th>c</th>
<th>d</th>
<th>e</th>
</tr>
</thead>
<tbody>
<tr>
<th>2015-01-05</th>
<td>-0.025414</td>
<td>0.001241</td>
<td>-0.110182</td>
<td>0.101301</td>
<td>0.904187</td>
</tr>
<tr>
<th>2015-01-04</th>
<td>-0.113769</td>
<td>1.490484</td>
<td>0.386378</td>
<td>0.152447</td>
<td>0.440914</td>
</tr>
<tr>
<th>2015-01-03</th>
<td>-0.807287</td>
<td>0.161482</td>
<td>-0.255942</td>
<td>0.041502</td>
<td>-0.314685</td>
</tr>
<tr>
<th>2015-01-02</th>
<td>-0.849947</td>
<td>0.600126</td>
<td>-1.516112</td>
<td>-0.571000</td>
<td>0.013668</td>
</tr>
<tr>
<th>2015-01-01</th>
<td>-0.421461</td>
<td>-0.287318</td>
<td>-1.411263</td>
<td>-0.088717</td>
<td>-0.823639</td>
</tr>
</tbody>
</table>
```
Where as `df.T.to_html()` adds time (00:00:00) to the column titles:
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>2015-01-05 00:00:00</th>
<th>2015-01-04 00:00:00</th>
<th>2015-01-03 00:00:00</th>
<th>2015-01-02 00:00:00</th>
<th>2015-01-01 00:00:00</th>
</tr>
</thead>
<tbody>
<tr>
<th>a</th>
<td>-0.025414</td>
<td>-0.113769</td>
<td>-0.807287</td>
<td>-0.849947</td>
<td>-0.421461</td>
</tr>
<tr>
<th>b</th>
<td>0.001241</td>
<td>1.490484</td>
<td>0.161482</td>
<td>0.600126</td>
<td>-0.287318</td>
</tr>
<tr>
<th>c</th>
<td>-0.110182</td>
<td>0.386378</td>
<td>-0.255942</td>
<td>-1.516112</td>
<td>-1.411263</td>
</tr>
<tr>
<th>d</th>
<td>0.101301</td>
<td>0.152447</td>
<td>0.041502</td>
<td>-0.571000</td>
<td>-0.088717</td>
</tr>
<tr>
<th>e</th>
<td>0.904187</td>
<td>0.440914</td>
<td>-0.314685</td>
<td>0.013668</td>
<td>-0.823639</td>
</tr>
</tbody>
</table>
```
This behaviour is inconsistent.
A remedy was found by [EdChum on stackoverflow](http://stackoverflow.com/a/31520529/1510846).
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| null | 7 | 2015-07-20T19:41:55Z | 2018-09-05T16:27:45Z | 2015-07-20T21:54:30Z | CONTRIBUTOR | null | Hello, this error happens when loading a large (~3.3GB) stata 13 file:
``` python
/Users/makmana/colombia/colombia/datasets.py in <lambda>()
111
112 industry4digit_department = {
--> 113 "read_function": lambda: pd.read_stata("/Users/makmana/ciddata/Subnationals/Atlas/Colombia/beta/output2008_2013.dta"),
114 "field_mapping": pila_to_atlas,
115 "classification_fields": {
/Users/makmana/colombia/env/lib/python3.4/site-packages/pandas/io/stata.py in read_stata(filepath_or_buffer, convert_dates, convert_categoricals, encoding, index, convert_missing, preserve_dtypes, columns, order_categoricals, chunksize, iterator)
160 return reader
161
--> 162 return reader.read()
163
164 _date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
/Users/makmana/colombia/env/lib/python3.4/site-packages/pandas/io/stata.py in read(self, nrows, convert_dates, convert_categoricals, index, convert_missing, preserve_dtypes, columns, order_categoricals)
1349 self.path_or_buf.seek(self.data_location + offset)
1350 read_lines = min(nrows, self.nobs - self._lines_read)
-> 1351 data = np.frombuffer(self.path_or_buf.read(read_len), dtype=dtype,
1352 count=read_lines)
1353 self._lines_read += read_lines
OSError: [Errno 22] Invalid argument
> /Users/makmana/colombia/env/lib/python3.4/site-packages/pandas/io/stata.py(1351)read()
1350 read_lines = min(nrows, self.nobs - self._lines_read)
-> 1351 data = np.frombuffer(self.path_or_buf.read(read_len), dtype=dtype,
1352 count=read_lines)
```
where `read_len` is `3525947880` and `self.path_or_buf` is `<_io.BufferedReader name='/Users/makmana/ciddata/Subnationals/Atlas/Colombia/beta/output2008_2013.dta'>`.
At that point, the question in my head was "well, what /is/ a reasonable read_len"? So I binary-searched until I converged to a value that was around 721000000, but then when I quit a bunch of other applications and somehow it started working again! This makes me think this has to do with available memory, maybe.
Another funny thing is that this happens on `Python 3.4.2 (default, Oct 19 2014, 17:52:17) [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.51)] on darwin`, but when I do the same read_stata on `Python 2.7.9 (default, Jan 7 2015, 11:50:42) [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.56)] on darwin`, it doesn't choke. Both are using numpy 1.9.2 and pandas 0.16.2.
A final insight is that it fails very quickly (quicker than it could have possibly loaded the file) with that read_len. With smaller read_len values, say dividing by 2, it waits for a long time and then fails.
[This issue](https://github.com/numpy/numpy/issues/3858) and [this one](http://stackoverflow.com/questions/11662960/ioerror-errno-22-invalid-argument-when-reading-writing-large-bytestring) perhaps might be related.
I can't really share the file because of data confidentiality but I'd be happy to dig through to figure out what might be going on wrong if someone has ideas and pointers.
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} | 2 | 2015-07-21T06:13:46Z | 2015-07-21T17:33:46Z | 2015-07-21T10:53:01Z | CONTRIBUTOR | null | Resubmitting pull request #10576 to resolve issue #9266 .
@jreback — This should be good to merge.
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} | 1 | 2015-07-21T06:17:08Z | 2015-07-21T11:37:54Z | 2015-07-21T11:37:51Z | CONTRIBUTOR | null | #10566 removed one of the implementations of `bind_method` and fixed a typo
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} | 2 | 2015-07-21T07:26:53Z | 2015-07-24T13:50:22Z | 2015-07-24T13:50:18Z | CONTRIBUTOR | null | closes #10408
closes #10412
Fix setting values with python datetime.date and numpy datetime64.
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} | 1 | 2015-07-21T13:43:35Z | 2015-09-10T22:11:55Z | 2015-09-10T22:11:55Z | NONE | null | # For a small multiindex the in operator works
mi = pd.MultiIndex.from_arrays([range(100),range(100)])
(1000001,0) in mi
# For a large multiindex the in operator throws an IndexError
mi = pd.MultiIndex.from_arrays([range(1000000),range(1000000)])
(1000001,0) in mi
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} | 6 | 2015-07-21T15:42:48Z | 2015-07-25T19:01:44Z | 2015-07-21T17:20:08Z | NONE | null | Pandas is not able to export a matrix as csv file and reimport it and keeping the data consistent. Using default arguments for `to_csv`, it will add an additional column for the indeces. Importing with default parameters will treat this column as data, not as index....
```
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10,10))
df.to_csv('np.csv')
print pd.read_csv('np.csv')
Unnamed: 0 0 1 2 3 4 5 \
0 0 0.055663 0.492976 0.936424 0.931585 0.043748 0.931660
1 1 0.946510 0.481707 0.935273 0.987895 0.982537 0.735273
2 2 0.429818 0.090192 0.923747 0.973678 0.432166 0.318196
3 3 0.579657 0.599554 0.794318 0.631867 0.700044 0.834421
4 4 0.438074 0.747774 0.034653 0.113885 0.982059 0.736432
5 5 0.379523 0.094214 0.435573 0.729742 0.778312 0.341792
6 6 0.542644 0.175657 0.913459 0.532352 0.607791 0.369434
7 7 0.132935 0.052179 0.145688 0.549158 0.127237 0.475737
8 8 0.454960 0.872086 0.006616 0.444334 0.435469 0.435362
9 9 0.141345 0.512531 0.900547 0.570482 0.366632 0.992289
6 7 8 9
0 0.385482 0.432543 0.927187 0.408233
1 0.385019 0.905481 0.852093 0.368507
2 0.641478 0.966683 0.706884 0.229032
3 0.592390 0.091528 0.969585 0.177480
4 0.805170 0.585675 0.024259 0.961815
5 0.818240 0.688166 0.175099 0.583955
6 0.697869 0.202709 0.458018 0.546078
7 0.597875 0.625422 0.055143 0.720858
8 0.866318 0.348642 0.855215 0.689258
9 0.723096 0.194654 0.681293 0.941478
```
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} | 14 | 2015-07-21T17:56:27Z | 2017-11-29T08:01:48Z | 2017-03-13T14:36:26Z | NONE | null | I was surprised to learn that default behavior for reading in a CSV like this:
```
%more foo.txt
x,y
"NA",NA
"foo",foo
```
is the following:
```
In [56]: pd.read_csv('foo.txt')
Out[56]:
x y
0 NaN NaN
1 foo foo
```
I realize this can be changed by using `pd.read_csv('foo.txt', keep_default_na=False)`, but this doesn't seem like ideal default behavior for the following reasons.
- NA can be a valid value in numerous cases
- Abbreviation for North America, sodium, lots of other things
- Possible value for any code with two alphabetic characters
- NA might very well be a valid response (distinct from true missing) in survey data even if it literally means something like "not applicable".
- This behavior is likely unexpected in many cases and dangerous for that reason. [Here is a recent example from stack overflow.](http://stackoverflow.com/questions/31527138/slicing-a-pandas-dataframe-using-two-strings) As a counter-example, I checked the behavior of Excel and Stata and both will read in NA as a text value, not as missing.
- It just seems like better practice would be to handle interpretation of NA and NaN after reading in the data as text rather than doing this on the fly. When this behavior is unwanted, it may be very hard to detect after the fact.
Edit to add: At risk of belaboring the point, note that the following round trips with either `read_csv` or `from_csv` (using all default values) will convert 'NA' to NaN:
```
df=pd.DataFrame({ 'x':['NA','foo'] })
df.to_csv('test.csv')
df1 = pd.read_csv('test.csv')
df2 = pd.DataFrame.from_csv('test.csv')
```
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} | 3 | 2015-07-21T18:02:20Z | 2016-04-03T14:24:21Z | 2016-04-03T14:24:21Z | NONE | null | This isn't a huge deal, but it seems a little odd:
``` python
In [1]: import pandas as pd
In [2]: a = pd.SparseArray([True, False, False, False, True], fill_value=False, dtype=bool)
In [3]: a
Out[3]:
[True, False, False, False, True]
Fill: False
IntIndex
Indices: array([0, 4], dtype=int32)
In [4]: a.dtype
Out[4]: dtype('bool')
In [5]: d = a.to_dense()
In [6]: d
Out[6]: array([ 1., 0., 0., 0., 1.])
In [7]: d.dtype
Out[7]: dtype('float64')
```
I would have expected `d` to retain the dtype of `bool`. I can cast down, but I am still wasting 7 bytes per element in the process.
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} | 15 | 2015-07-21T22:01:53Z | 2015-07-24T19:27:54Z | 2015-07-24T18:43:40Z | NONE | null | Address #8685 . The file in the test `test_url_gz` currently points to my fork to demonstrate that the tests pass, but should be changed to point to the real pydata master after merging.
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| null | 2 | 2015-07-22T03:07:10Z | 2015-07-22T11:30:37Z | 2015-07-22T10:36:31Z | CONTRIBUTOR | null | This crashes:
```
import pandas as pd
index = pd.timedelta_range(start="06:00:01", end="12:00:00", freq='S')
pd.Series(range(index.size), index=index).plot()
```
Traceback at https://gist.github.com/andreabedini/074d529ff63356312ce6
So far I have figured out that `_get_default_locs` gets called with completely bogus `vmin` an `vmax`.
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| null | 8 | 2015-07-22T08:35:40Z | 2015-07-27T10:10:39Z | 2015-07-22T12:30:18Z | NONE | null | I find the docs around MultiIndex slicers to be quite confusing. It implies the MultiIndex needs to be lexsorted, and introduces the sortlevel() function but then has a caveat that this doesn't actually ensure sortedness.
There's some more details of my explorations and questions on StackOverflow:
http://stackoverflow.com/questions/31427466/ensuring-lexicographical-sort-in-pandas-multiindex
I'd like either a simple one-liner to ensure lexsortedness, more reassurance that the usual ways to create a MultiIndex will lexsort the labels in each level, or some more elaboration in the docs about exactly what the issues will be with indexing and slicing if the labels are not lexsorted.
Does my example show a bug in is_lexsorted too? I would expect sorted2.is_lexsorted() to be false here, as 'col1' is not lexsorted.
``` python
In [8]:
sorted2 = df3.sortlevel()
sorted2
Out[8]:
data
col1 col2
b 1 three
3 one
d 1 two
a 2 four
In [9]: sorted2.index.is_lexsorted()
Out[9]: True
In [10]: sorted2.index
Out[10]:
MultiIndex(levels=[[u'b', u'd', u'a'], [1, 2, 3]],
labels=[[0, 0, 1, 2], [0, 2, 0, 1]],
names=[u'col1', u'col2'])
```
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| null | 1 | 2015-07-22T11:50:00Z | 2015-09-13T20:20:52Z | 2015-09-13T20:20:52Z | NONE | null | Hi All, I cannot use io.gbq due to an issue with the version check of the installed google library.
```
>>> pkg_resources.get_distribution('google-api-python-client').version
'1.2'
```
The [offending line](https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L29) executes the version check as follows:
```
>>> pkg_resources.get_distribution('google-api-python-client').version < '1.2.0'
True
```
but
```
>>> pkg_resources.get_distribution('google-api-python-client').version < '1.2'
False
```
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} | 11 | 2015-07-22T14:49:38Z | 2015-08-26T01:55:25Z | 2015-08-26T01:55:25Z | MEMBER | null | From #10636
When parsing an invalid date, there are some possible different actions to take:
- ignore -> just returning the original value unparsed (`errors='ignore'`)
- raise an error (`errors='raise'`)
- coerce / convert to NaN (`coerce=True`)
AFAIK, these three options are just three different options and mutually exclusive (or is there somewhere in the codebase an interaction between both?), so they could be controlled by a single keyword.
Having two keyword can make it even confusing what you should expect when combining those two, eg the example of coerce=True and error=raise.
Proposal: add another `'coerce'` option to the `errors` keyword that is equivalent to `coerce=True`
-> this gives the `errors` keyword three options: `'ignore'|'raise'|'coerce'`
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} | 10 | 2015-07-22T16:14:34Z | 2015-07-27T08:32:22Z | 2015-07-27T08:32:22Z | CONTRIBUTOR | null | Hello,
it will be nice if `read_sql` (and so `read_sql_query` and `read_sql_table` could directly accept database URI as `con` parameter
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql.html
So we could do
```
import pandas as pd
uri = 'dialect+driver://username:password@host:port/database'
query = 'SELECT * FROM table'
df = pd.read_sql(query, uri)
```
instead of :
```
import pandas as pd
from sqlalchemy import create_engine
uri = 'dialect+driver://username:password@host:port/database'
engine = create_engine(uri)
query = 'SELECT * FROM table'
df = pd.read_sql(query, engine)
```
Kind regards
PS: this kind of idea is also used by Blaze / odo
http://blaze.pydata.org/
http://odo.readthedocs.org/
PS2: A similar approach could maybe also apply to `to_sql(...)`
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} | 5 | 2015-07-22T17:23:09Z | 2015-11-10T01:21:15Z | 2015-11-10T01:21:15Z | NONE | null | Also fixes values and get_values.
fixes #10648
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} | 7 | 2015-07-22T19:20:42Z | 2015-09-13T16:16:13Z | 2015-09-13T16:16:13Z | NONE | null | Fixes [bug](https://github.com/pydata/pandas/issues/10652) with version check for google-api-python-client
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} | 9 | 2015-07-22T21:46:10Z | 2015-09-01T12:06:24Z | 2015-09-01T12:06:24Z | NONE | null | When the plot method of a dataframe is passed an `ax` keyword to render to a matplotlib subplot, the minor ticks and their labels are rendered regardless of whether `sharex|y=True`. The major ticks/labels are correctly removed.
Here's a [short notebook](https://gist.github.com/williamsmj/12a0abb2fb98aec67c17) that reproduces the issue. Note that the minor ticks are included in the upper axes of the final figure.
I am 90% certain this is a regression, but I wasn't able to pin down which version of pandas or matplotlib prompted this bug. Nor was I able to figure out whether this was due to pandas or matplotlib.
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} | 10 | 2015-07-22T22:13:01Z | 2015-07-27T20:45:02Z | 2015-07-25T14:52:12Z | CONTRIBUTOR | null | Addresses [GH9428](https://github.com/pydata/pandas/issues/9428).
When constructing an empty DataFrame with a string dtype (e.g. `str`, `np.unicode_`, `U5`), the dtype is now promoted to the `object` dtype. This is now consistent with Series and avoids the confusing behaviour described in the original issue.
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} | 4 | 2015-07-23T00:21:58Z | 2015-08-17T23:42:53Z | 2015-07-23T11:16:39Z | CONTRIBUTOR | null | Addresses #10565
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} | 11 | 2015-07-23T08:55:09Z | 2015-10-09T13:03:57Z | 2015-10-09T13:03:57Z | NONE | null | Hey folks,
I posted this on [SO](https://stackoverflow.com/questions/31582114/groupby-to-latex-output-broken) and was asked to file a report here as well.
I'm trying to export `pandas.DataFrame.describe()` to `LaTex` using the `to_latex()`-method. This works all fine as long as I don't apply the `groupby()`-method beforehand. With a grouped DataFrame, the first row has no values, even though its label is `count`. Note that the first row of a grouped dataframe is used to mark down the variable used for grouping in iPython notebook.
I'm using pandas 0.16.2, python 3.
Is this a bug or am I doing something wrong?
Cheers,
Jakob
Here some examples:
Without `groupby`:
```
\begin{tabular}{lr}
\toprule
{} & IS\_FEMALE \\
\midrule
count & 2267.000000 \\
mean & 0.384649 \\
...
...
75\% & 1.000000 \\
max & 1.000000 \\
\bottomrule
\end{tabular}
```
[](http://i.stack.imgur.com/gK10V.png)
With `groupby`:
```
\begin{tabular}{llr}
\toprule
& & IS\_FEMALE \\
\midrule
0 & count & \\ % <-- note missing value here
& mean & 1134.000000 \\
& std & 0.554674 \\
...
...
& 75\% & 0.000000 \\
& max & 0.000000 \\
\bottomrule
\end{tabular}
```
[](http://i.stack.imgur.com/lt9z9.png)
Output in the notebook:
[](http://i.stack.imgur.com/wT8Tp.png)
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} | 18 | 2015-07-23T12:10:44Z | 2017-01-23T19:56:31Z | 2015-11-18T11:46:48Z | MEMBER | null | Accessors should be enabled depending on `categories`. Should care `CategoricalIndex` also.
```
import pandas as pd
s = pd.Series(['A', 'B'], dtype='category')
s.str
# AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas
s = pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-01')], dtype='category')
s.dt
# AttributeError: Can only use .dt accessor with datetimelike values
```
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} | 5 | 2015-07-23T16:13:01Z | 2015-07-24T17:22:08Z | 2015-07-24T17:21:46Z | NONE | null | ### Code and Error description
If I try to normalize a date on daylight saving date day, the normalize_date() can put a Timestamp object into inconsistent state.
``` python
>>> import pandas as pd
>>> original_midnight = pd.Timestamp('20121104', tz='US/Eastern')
>>> original_midday = pd.Timestamp('20121104T120000', tz='US/Eastern')
>>> str(pd.tslib.normalize_date(original_midday))
'2012-11-04 00:00:00-05:00'
>>> str(original_midnight)
'2012-11-04 00:00:00-04:00'
>>> pd.tslib.normalize_date(original_midday) == original_midnight
False
>>> pd.tslib.normalize_date(original_midday).tzinfo
<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>
>>> original_midnight.tzinfo
<DstTzInfo 'US/Eastern' EDT-1 day, 20:00:00 DST>
```
according to the [implementation](https://github.com/pydata/pandas/blob/master/pandas/tslib.pyx#L4303-L4317) It only replaces the with zeroes the time part.
I believe either the replace should be timezone aware or the function should look like this:
``` python
if PyDateTime_Check(dt):
return pd.Timestamp(dt.date(), tz=dt.tz)
elif PyDate_Check(dt):
return datetime(dt.year, dt.month, dt.day)
else:
raise TypeError('Unrecognized type: %s' % type(dt))
```
#### INSTALLED VERSIONS
---
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-44-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.0
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 0.8.0
tables: 3.0.0
numexpr: 2.3
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 1 | 2015-07-24T09:05:20Z | 2015-07-24T09:25:11Z | 2015-07-24T09:25:11Z | CONTRIBUTOR | null | Hello,
This enhancement is quite similar to https://github.com/pydata/pandas/issues/10654
`to_sql` method should accept database URI as con parameter
So we could do
```
import pandas as pd
db_uri = 'dialect+driver://username:password@host:port/database'
query = 'SELECT * FROM table'
df.to_sql("table", db_uri)
```
instead of :
```
import pandas as pd
from sqlalchemy import create_engine
db_uri = 'dialect+driver://username:password@host:port/database'
engine = create_engine(db_uri)
df.to_sql("table", engine)
```
Might apply to :
- Series http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.to_sql.html
- Panel http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.to_sql.html
- and DataFrame http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html
Blaze uses table URI like `'dialect+driver://username:password@host:port/database::table'`
so passing table as parameter is not necessary, as it is contained inside (table) URI
but It may be difficult to mimic this API without breaking eggs (`con` should be first parameter and `table` should be second with a default `None` value)
Kind regards
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} | 3 | 2015-07-24T09:21:46Z | 2015-08-18T10:51:57Z | 2015-08-18T10:51:57Z | CONTRIBUTOR | null | join='left' does a right join and 'right' does a left join
See the notebook for details
https://nbviewer.jupyter.org/gist/gdementen/fa4d388993ee981b5698
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} | 10 | 2015-07-24T11:58:54Z | 2015-07-27T08:32:23Z | 2015-07-27T08:32:22Z | CONTRIBUTOR | null | It should fix https://github.com/pydata/pandas/issues/10654
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} | 2 | 2015-07-24T14:04:36Z | 2019-10-13T23:57:44Z | 2019-10-13T23:57:44Z | MEMBER | null | Simple case of concatting two frames with equal (or at least partially overlapping indices) index seems to be quicker when using merge instead of concat:
```
In [98]: df1 = pd.DataFrame(np.random.randn(10000,2), columns=['a', 'b'])
In [99]: df2 = pd.DataFrame(np.random.randn(10000,2), columns=['c', 'd'])
In [100]: %timeit pd.concat([df1, df2], axis=1)
1000 loops, best of 3: 1.46 ms per loop
In [101]: %timeit pd.merge(df1, df2, left_index=True, right_index=True)
The slowest run took 5.32 times longer than the fastest. This could mean that an intermediate result is being cached
1000 loops, best of 3: 519 µs per loop
```
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} | 8 | 2015-07-24T20:48:54Z | 2017-02-16T16:16:49Z | 2017-02-16T16:16:48Z | CONTRIBUTOR | null | xref #12898 (same fix)
(c.f. http://stackoverflow.com/questions/31617084/how-to-have-groupby-first-not-remove-timezone-info-from-datetime-columns)
Take a dataframe with a column of tz-aware datetime.datetime objects, and group it by a different column, then return the first row from each group. There are some ways to do this that leave the datetime as it is; and then at least two ways that convert it to a tz-naive pandas Timestamp object.
```
In [1]: import pandas as pd
In [2]: import datetime
In [3]: import pytz
In [4]: dates = [datetime.datetime(2015,1,i,tzinfo=pytz.timezone('US/Pacific')) for i in range(1,5)]
In [5]: df = pd.DataFrame({'A': ['a','b']*2,'B': dates})
In [6]: df
Out[6]:
A B
0 a 2015-01-01 00:00:00-08:00
1 b 2015-01-02 00:00:00-08:00
2 a 2015-01-03 00:00:00-08:00
3 b 2015-01-04 00:00:00-08:00
In [7]: grouped = df.groupby('A')
In [8]: grouped.nth(0) #B stays a datetime.datetime with timezone info
Out[8]:
B
A
a 2015-01-01 00:00:00-08:00
b 2015-01-02 00:00:00-08:00
In [9]: grouped.head(1) #B stays a datetime.datetime with timezone
Out[9]:
B
0 2015-01-01 00:00:00-08:00
1 2015-01-02 00:00:00-08:00
In [10]: grouped.first() #B is naive pd.TimeStamp in UTC
Out[10]:
B
A
a 2015-01-01 08:00:00
b 2015-01-02 08:00:00
```
And apparently `grouped.apply(lambda x: x.iloc[0])` does the same as `.first()`.
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| null | 8 | 2015-07-24T21:00:16Z | 2015-07-27T15:05:27Z | 2015-07-25T13:50:13Z | NONE | null | I have been trying for a while to save a pandas dataframe to an HDF5 file. I tried various different phrasings eg. `df.to_hdf` etc. but to no avail. I am running this in a python virtual environment see [here](http://docs.python-guide.org/en/latest/dev/virtualenvs/). Even without the use of the VE it has the same error. The following script comes up with the error below:
```
''' This script reads in a pickles dictionary converts it to panda
dataframe and then saves it to an hdf file. The arguments are the
file names of the pickle files.
'''
import numpy as np
import pandas as pd
import pickle
import sys
# read in filename arguments
for fn in sys.argv[1:]:
print 'converting file %s to hdf format...' % fn
fl = open(fn, 'r')
data = pickle.load(fl)
fl.close()
frame = pd.DataFrame(data)
fnn = fn.split('.')[0]+'.h5'
store = pd.HDFStore(fnn)
store.put([fn.split('.')[0]], frame)
store.close()
frame = 0
data = 0
```
Error is:
```
$ ./p_to_hdf.py LUT_*.p
converting file LUT_0.p to hdf format...
Traceback (most recent call last):
File "./p_to_hdf.py", line 22, in <module>
store = pd.HDFStore(fnn)
File "/usr/lib/python2.7/site-packages/pandas/io/pytables.py", line 270, in __init__
raise Exception('HDFStore requires PyTables')
Exception: HDFStore requires PyTables
```
`pip list` shows both pandas and tables are installed and the latest versions.
```
pandas (0.16.2)
tables (3.2.0)
```
I can `import tables` without errors in Ipython.
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| null | 1 | 2015-07-24T22:38:48Z | 2015-07-24T22:41:00Z | 2015-07-24T22:40:32Z | NONE | null | It seems strange to me that `pandas.read_csv` is not a direct reciprocal function to `df.to_csv`. In this illustration, notice how when using all the default settings the original and final DataFrames differ by the "Unnamed" column.
```
In [1]: import pandas as pd
In [2]: orig_df = pd.DataFrame({'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); orig_df
Out[2]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
[4 rows x 3 columns]
In [3]: orig_df.to_csv('test.csv')
In [4]: final_df = pd.read_csv('test.csv'); final_df
Out[4]:
Unnamed: 0 AAA BBB CCC
0 0 4 10 100
1 1 5 20 50
2 2 6 30 -30
3 3 7 40 -50
[4 rows x 4 columns]
```
It seems the default `read_csv` should instead be
```
In [6]: final2_df = pd.read_csv('test.csv', index_col=0); final2_df
Out[7]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
[4 rows x 3 columns]
```
or the default `to_csv` should instead be
```
In [8]: df.to_csv('test2.csv', index=False)
```
which when read gives
```
In [9]: pd.read_csv('test2.csv')
Out[9]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
```
[4 rows x 3 columns]
This inconsistency make using these functions unintuitive. I do not really care which way it goes but I think the default behavior should be consistent with options for differences rather than different with options for consistency.
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} | 5 | 2015-07-25T05:23:51Z | 2018-01-31T15:19:42Z | 2018-01-31T15:19:42Z | NONE | null | This code:
``` python
import pandas as pd
from numpy.random import randn
df = pd.DataFrame(randn(8, 4), columns=['A', 'B', 'C', 'D'])
def function(group):
group.sort(columns = 'C', inplace=True)
group = group[['A', 'B', 'C', 'D']]
return group
df2 = df.groupby(['A']).apply(function)
```
produces this error:
``` python
ValueError: cannot reindex from a duplicate axis
```
It's the combination of the sort and the column selecting inside of a grouped apply that causes the problem. I'm happy to give more detail on why I want to do this, but this is the simplest most striped down code that produces the error.
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} | 10 | 2015-07-25T16:25:03Z | 2015-08-26T01:30:07Z | 2015-08-26T01:30:07Z | CONTRIBUTOR | null | ```
import pandas as pd
# import dask.dataframe as dd
N = 5000
df = pd.DataFrame({'A': [1, 2] * N, 'B': [3, 4] * N, 'C': ['a', 'b'] * N})
df.to_hdf('foo.h5', '/data', format='table', mode='a')
for _ in range(3):
df.to_hdf('foo.h5', '/data', format='table', mode='a', append=True)
```
This actually works when `N` is smaller, perhaps something with a pytables chunksize
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} | 0 | 2015-07-25T19:04:16Z | 2015-11-14T15:08:07Z | 2015-11-14T15:08:07Z | CONTRIBUTOR | null | ```
In[10]: a = pandas.Series(pandas.Categorical(list("abc")))
In[11]: a.cat.labels = [1,2]
In[12]: a.cat.labels
Traceback (most recent call last):
File "C:\portabel\miniconda\envs\pandas_dev\lib\site-packages\IPython\core\interactiveshell.py", line 2883, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-a68ee763e4e8>", line 1, in <module>
a.cat.labels
AttributeError: 'CategoricalAccessor' object has no attribute 'labels'
```
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} | 7 | 2015-07-26T01:31:52Z | 2015-08-01T00:23:50Z | 2015-07-31T22:35:22Z | CONTRIBUTOR | null | closes #10636
changes `to_timedelta` API as well to similar effect.
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| null | 12 | 2015-07-26T14:26:58Z | 2015-10-12T02:45:53Z | 2015-09-10T11:59:39Z | CONTRIBUTOR | null | This PR fix a BUG #10645
@sinhrks would you review my PR?
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} | 13 | 2015-07-26T16:29:27Z | 2015-09-04T09:33:57Z | 2015-09-04T09:33:57Z | MEMBER | null | Closes #9584
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| null | 1 | 2015-07-26T17:29:08Z | 2015-07-26T17:32:09Z | 2015-07-26T17:32:09Z | NONE | null | http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sample.html
Many functions indicate (e.g.) "New in version 0.13.0"
The addition of DataFrame.sample() in [AFAIK] 0.16.2, which bit me hard today as my dataframes all have a column "sample" so you can see how this totally hosed up all my df.sample accesses/queries ...
It would be nice if the "New in version" string coudl be added to this function as a heads up to others with a column labeled 'sample' (which in my view seems like it would be a pretty common column name?) are affected by this as well.
Thanks in advance
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} | 10 | 2015-07-26T17:34:25Z | 2021-10-16T14:03:04Z | 2018-07-04T12:59:41Z | NONE | null | I'm currently using python 2.7, pandas 0.16.2.dev, and ipython version 3.1.0
In brief, it seems that there are cases that the sharex option of pandas.DataFrame.plot(kind='hexbin') defaults to True instead of False. This causes the x label and xtick labels to be invisible.
Preceding stack overflow and ipython github threads that led to here:
http://stackoverflow.com/questions/31220329/pandas-dataframe-hexbin-plot-has-no-xlabel-or-axis-values
https://github.com/ipython/ipython/issues/8653
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} | 13 | 2015-07-26T18:00:31Z | 2015-09-04T19:14:29Z | 2015-09-04T12:15:22Z | CONTRIBUTOR | null | closes #10242
This PR replaces the sum-of-squares algorithm used to compute the variance by a more stable algorithm. The algorithm here is essentially the same as used in numpy 1.8 and up, and I've added a TODO to replace the implementation with a direct call to numpy when that version is the default.
Somewhat counter to the discussion in #10242, I chose not to go with the Welford algorithm, for two reasons: numpy, and the fact that `_nanvar` needs to be able to deal with arrays of different shape, which is tricky to get right in Cython.
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} | 3 | 2015-07-26T23:14:34Z | 2015-08-02T21:46:24Z | 2015-08-02T21:46:24Z | MEMBER | null | As this comes up once in a while, I tried to improve/clarify the docs somewhat.
Further suggestions to add or make it clearer are welcome!
xref #10334
closes #7194
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} | 1 | 2015-07-27T02:19:49Z | 2015-07-27T13:11:20Z | 2015-07-27T13:10:49Z | CONTRIBUTOR | null | Use pandas internal [stdint.h](https://github.com/pydata/pandas/blob/master/pandas/src/headers/stdint.h).
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} | 6 | 2015-07-27T02:31:40Z | 2015-07-27T15:13:48Z | 2015-07-27T15:13:47Z | CONTRIBUTOR | null | Checking some older verions, and found that the versions that are not tested cannot be imported using `from_stata` or `StataReader`. For example,
http://fmwww.bc.edu/ec-p/data/wooldridge2k/401ksubs.dta
produces an error when trying to parse the header. This file is version 105. As far as I know, there are on online docs for older versions of dta, so it probably isn't worth trying to support them.
The simplest fix is to just modify the versions that are supported to only include 113+, which still covers a long history.
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} | 1 | 2015-07-27T02:39:31Z | 2015-07-27T13:10:34Z | 2015-07-27T13:10:34Z | CONTRIBUTOR | null | Together with #10681 this allows building Pandas for Python 3.5b4 using Visual Studio 2015.
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| null | 9 | 2015-07-27T08:20:52Z | 2015-07-28T16:37:16Z | 2015-07-28T16:37:16Z | NONE | null | I am using read_html to read an html table which contains timedelta information in various formats, e.g. "20s" or "20 seconds" for 20 seconds, "5 minutes", "12 hours", "4 days". The default parser incorrectly parses these in 1) converting the values to datetimes, with the time added as a timedelta to the start of the current day and 2) setting the days values to NaT
Previously, I could set the infer_types to None and wonderfully, no types would be inferred. Now, the data is simply lost and I have no option using read_html to preserve it.
The following approaches would solve the issue for me:
1) re-enable infer_types=None. I fail to see the reason why this was ever disabled. Coupling of behaviors is rarely desirable in libraries. The only behavior I actually want is to read an html table into a dataframe of strings. From there, I can convert data as desired or not.
2) implement date_parser as in read_csv. While this would solve the issue for me, this would still result in unavoidably undesirable behaviors for the other data types, e.g. the parser auto-converting money amount to float when decimal is required.
I'd love for pandas to be my go-to library for scraping web page tables, but with current the behavior, it's pretty much useless to me.
see also:
https://github.com/pydata/pandas/issues/7037
https://github.com/pydata/pandas/pull/4770
Code example:
# pandas 0.16.2
# python 3.4.3
```
import pandas as pd
import requests
import re
url = 'http://clashofclans.wikia.com/wiki/Barbarian'
overview_regex = re.compile('Preferred|Radius')
overview_table = pd.read_html(url, match=overview_regex, header=0 )
print overview_table #expect attack speed = 20s or 20 seconds, actual = 2015-07-27 00:00:01
print '----'
levels_regex = re.compile('Hitpoints|Total')
levels_table = pd.read_html(url, match=levels_regex, header=0 )
print levels_table #expect research time in hours or days, actual = NaT or 2015-07-27 06:00:00
```
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| null | 4 | 2015-07-27T12:19:48Z | 2015-07-27T20:53:47Z | 2015-07-27T12:25:36Z | NONE | null | When I try to initialize a new column with None, NaN is filled in instead. For example:
```
import pandas as pd
df=pd.DataFrame(1, index=['A'], columns=['x'])
df.ix[:,"Grouping"]=None
df
```
results in
```
x Grouping
A 1 NaN
```
Alternatively, if I assign a random number instead of the constant 1, I get None in the Grouping column
```
import pandas as pd
df=pd.DataFrame(np.random.randn(1,1), index=['A'], columns=['x'])
df.ix[:,"Grouping"]=None
df
```
```
x Grouping
A 0.254445 None
```
It should result in None in both cases
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} | 18 | 2015-07-28T07:11:55Z | 2015-08-18T11:09:58Z | 2015-08-18T11:09:49Z | CONTRIBUTOR | null | Close #10581 by upgrading to `msgpack` 0.4.6
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| null | 3 | 2015-07-28T15:13:49Z | 2015-07-28T22:15:32Z | 2015-07-28T21:44:23Z | CONTRIBUTOR | null | Resolves #10596
Prior was in #10599 FYI
I think this has a PyTables error which I need to resolve
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| null | 3 | 2015-07-28T15:46:33Z | 2015-07-29T02:06:32Z | 2015-07-28T21:38:40Z | NONE | null | Summing with missing data appear to be broken in 0.16.2. According to http://pandas.pydata.org/pandas-docs/version/0.16.2/missing_data.html#calculations-with-missing-data
When summing data, NA (missing) values will be treated as zero
=>>> If the data are all NA, the result will be NA <== This returns zero in 0.16.2 (it returns NA in 0.15.2).
Old behavior as specified in documentation:
New behavior which is different from documenation:
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| null | 2 | 2015-07-28T16:19:14Z | 2015-07-28T16:44:57Z | 2015-07-28T16:40:26Z | CONTRIBUTOR | null | This came up in #10679.
Numpy 1.8 implements `numpy.nanvar`, which can replace `pandas.core.nanops._nanvar` when the minimum requirement for Numpy is bumped to 1.8 . The implementation of `_nanvar` in #10679 is in fact based on the corresponding implementation in Numpy.
I haven't checked whether other nanops can be replaced by a suitable Numpy equivalent.
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} | 2 | 2015-07-28T18:40:45Z | 2016-07-08T15:16:46Z | 2016-07-08T15:16:46Z | NONE | null | I am using a to_html() to convert a dataframe into a table. i have various datatypes in Dataframe including float, int, datetime and timedelta.
I am using formatters to control outputs, It seems to be working for int and float but not for datetime.
``` python
datetime_formatter = lambda ts: "%s" % ts.strftime("%I:%M %p")
currency_formatter = lambda cash: "%.2f" % cash
```
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} | 42 | 2015-07-28T19:26:37Z | 2015-09-15T16:39:18Z | 2015-09-01T12:04:25Z | CONTRIBUTOR | null | closes #9218
closes #10420
**Edit: changed approach, see the rest of the thread**
Currently, interpolate() has a "limit" kwarg that, when set to n,
prevents interpolated values from propagating more than n rows forward.
This change adds a "backward_limit" kwarg that, when set to n, prevents
interpolated values from propagating more than n rows backward.
The behavior prior to this change is as though "backward_limit" existed
but was always set to 0. That is, interpolated values never filled in
NaNs immediately before a non-NaN value (unless those same NaNs happened
to follow a different, non-NaN value within "limit" rows).
In this change the "backward_limit" kwarg has no effect if "limit" is
not specified (i.e., None).
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} | 7 | 2015-07-28T20:16:54Z | 2016-06-19T06:14:46Z | 2015-09-10T22:11:55Z | NONE | null | Setting with enlargement seems to fail for DataFrames longer than `10**6 - 1`
`10**6` seems to be the exact treshold for me. That and anything bigger fails. Anything smaller works.
Example:
```
import pandas as pd
#works
X = pd.DataFrame(dict(x=range(10**6-1)))
X.loc[len(X)] = 42
#doesn't work
Y = pd.DataFrame(dict(y=range(10**6)))
Y.loc[len(Y)] = 42
>>> IndexError: index out of bounds
```
pd.show_versions() returns:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fi_FI
pandas: 0.16.2
nose: 1.3.6
Cython: 0.22
numpy: 1.9.2
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: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
```
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} | 2 | 2015-07-28T22:06:54Z | 2019-10-11T11:16:22Z | null | MEMBER | null | As this typically does not give you much memory usage improvement (which is a bit unexpected from the keyword explanation), this is worth a note in the docs.
From some discussion on gitter: https://gitter.im/pydata/pandas?at=55b61bf952d85d450f404be1 (with @litaotao) and https://gitter.im/pydata/pandas?at=554609295edd84254582fb39 (with @twiecki)
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| null | 5 | 2015-07-29T10:30:15Z | 2020-07-13T20:12:52Z | null | MEMBER | null | From SO: http://stackoverflow.com/questions/31690493/idxmax-doesnt-work-on-seriesgroupby-that-contains-nan
`idxmax` works with a normal dataframe:
```
In [60]: df = pd.DataFrame({'a':list('abab'), 'b':[0,1,2,3]})
In [61]: df.groupby('a').idxmax()
Out[61]:
b
a
a 2
b 3
```
Also when 'a' is a categorical there are no problems:
```
In [62]: df['a'] = df['a'].astype('category')
In [63]: df.groupby('a').idxmax()
Out[63]:
b
a
a 2
b 3
```
But when it is a categorical with an unused category, `idxmax` and `apply` don't work, only `agg` does:
```
In [67]: df['a'] = df['a'].cat.add_categories('c')
In [68]: df['a']
Out[68]:
0 a
1 b
2 a
3 b
Name: a, dtype: category
Categories (3, object): [a, b, c]
In [70]: df.groupby('a').idxmax()
Out[70]:
Empty DataFrame
Columns: []
Index: []
In [71]: df.groupby('a').apply(lambda x: x.idxmax())
...
ValueError: could not convert string to float: a
In [72]: df.groupby('a').agg(lambda x: x.idxmax())
Out[72]:
b
a
a 2
b 3
c NaN
```
Others like `first`, `last`, `max`, `mean` do work correctly (`idxmin` also fails).
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| null | 28 | 2015-07-29T11:25:49Z | 2021-06-07T20:37:55Z | 2018-01-18T11:35:49Z | NONE | null | I just upgraded to latest Pandas 0.16, and the new error has hit me (with Pandas 0.15) when slicing with multiple values. (df.ix[[list_of_values]]). I actually think it is more valid to return an empty DataFrame, than to throw an error.
The best I've been able to come up with to reproduce the previous behaviour (fail silently, return empty DataFrame) is:
``` python
# I want to filter a DataFrame by the first level of the index
df.ix[df.reset_index().IndexValue.isin(list_of_values).values]
```
Not saying I'm right on the error/empty argument; but is the above the most elegant solution?
Perhaps we should consider three distinct slicing operations:
1. Insists on everything being there (and throws errors)
2. Creates spaces where there is nothing there (reindex)
3. Just returns what's present, leaving the rest out (silent error)
I would think anyone indexing would be vary aware of what they are expecting from the above?
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} | 8 | 2015-07-29T12:07:01Z | 2017-12-13T14:35:18Z | 2017-12-13T14:35:18Z | MEMBER | null | `s.astype('category', categories=['a', 'b', 'c'])` fails when the series is already of Categorical dtype:
> TypeError: _astype() got an unexpected keyword argument 'categories'
I am not sure if this _should_ work (it would then be equivalent to `set_categories`?), but in any case the current error message is not informative:
```
In [49]: s = pd.Series(['a', 'b', 'a'])
In [50]: s
Out[50]:
0 a
1 b
2 a
dtype: object
In [51]: s.astype('category')
Out[51]:
0 a
1 b
2 a
dtype: category
Categories (2, object): [a, b]
In [52]: s.astype('category', categories=['a', 'b', 'c'])
Out[52]:
0 a
1 b
2 a
dtype: category
Categories (3, object): [a, b, c]
In [53]: scat = s.astype('category')
In [54]: scat.astype('category', categories=['a', 'b', 'c'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-54-f955e6286a85> in <module>()
----> 1 scat.astype('category', categories=['a', 'b', 'c'])
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\generic.pyc in astype(self, dty
pe, copy, raise_on_error, **kwargs)
2415
2416 mgr = self._data.astype(
-> 2417 dtype=dtype, copy=copy, raise_on_error=raise_on_error, **kwa
rgs)
2418 return self._constructor(mgr).__finalize__(self)
2419
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in astype(self, d
type, **kwargs)
2516
2517 def astype(self, dtype, **kwargs):
-> 2518 return self.apply('astype', dtype=dtype, **kwargs)
2519
2520 def convert(self, **kwargs):
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in apply(self, f,
axes, filter, do_integrity_check, **kwargs)
2471 copy=align_copy)
2472
-> 2473 applied = getattr(b, f)(**kwargs)
2474
2475 if isinstance(applied, list):
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in astype(self, d
type, copy, raise_on_error, values, **kwargs)
371 def astype(self, dtype, copy=False, raise_on_error=True, values=None
, **kwargs):
372 return self._astype(dtype, copy=copy, raise_on_error=raise_on_er
ror,
--> 373 values=values, **kwargs)
374
375 def _astype(self, dtype, copy=False, raise_on_error=True, values=Non
e,
TypeError: _astype() got an unexpected keyword argument 'categories'
```
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} | 7 | 2015-07-29T12:37:26Z | 2015-07-30T14:36:19Z | 2015-07-30T14:36:19Z | NONE | null | Hello,
I'm using python 2.7.9 and pandas 0.15.2.
I'm trying to concatenate two DataFrames (to_concat is a list of 2 DataFrames) and I have the following error.
```
File "/Users/alexisbenoist/Documents/CoTH/AFDS/app/core.py", line 67, in train_dataflow
df = pd.concat(to_concat)
File "/Users/alexisbenoist/.virtualenvs/afds/lib/python2.7/site-packages/pandas/tools/merge.py", line 725, in concat
return op.get_result()
File "/Users/alexisbenoist/.virtualenvs/afds/lib/python2.7/site-packages/pandas/tools/merge.py", line 896, in get_result
mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy)
File "/Users/alexisbenoist/.virtualenvs/afds/lib/python2.7/site-packages/pandas/core/internals.py", line 4046, in concatenate_block_managers
for placement, join_units in concat_plan]
File "/Users/alexisbenoist/.virtualenvs/afds/lib/python2.7/site-packages/pandas/core/internals.py", line 4135, in concatenate_join_units
empty_dtype, upcasted_na = get_empty_dtype_and_na(join_units)
File "/Users/alexisbenoist/.virtualenvs/afds/lib/python2.7/site-packages/pandas/core/internals.py", line 4124, in get_empty_dtype_and_na
raise AssertionError("invalid dtype determination in get_concat_dtype")
AssertionError: invalid dtype determination in get_concat_dtype
```
I put the pickle of the DataFrames in a [gist](https://gist.github.com/Alexis-benoist/ba7f38deccc70d46c32f) using (I can upload it somewhere else if it's more convenient).
```
import pickle
with open(path, 'w+') as f:
pickle.dump(to_concat, f)
```
I feel that I'm doing something wrong or it's a bug from pandas because if do:
```
pd.DataFrame(np.concatenate(to_concat), columns=to_concat[0].columns.tolist())
```
It gives a result which seems logical.
Any input is welcome.
Cheers,
Alexis.
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} | 4 | 2015-07-29T17:34:42Z | 2015-09-02T11:54:14Z | 2015-09-02T11:54:14Z | CONTRIBUTOR | null | `pd.concat` nicely preserves name attributes when given a list of series. However, this only works if all series have the name attribute set, otherwise all names are lost:
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.16.2'
In [3]: foo = pd.Series([1,2], name='foo')
In [4]: bar = pd.Series([1,2])
In [6]: pd.concat([foo, bar], 1)
Out[6]:
0 1
0 1 1
1 2 2
```
Not sure this qualifies as a bug.
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} | 2 | 2015-07-29T19:57:58Z | 2015-08-13T13:08:11Z | 2015-08-13T13:08:11Z | MEMBER | null | Adding a DateOffset works for single Timestamps and for DatetimeIndex, but fails on a Series:
```
In [132]: s = pd.Series(pd.date_range('2012-01-01', periods=3))
In [133]: s
Out[133]:
0 2012-01-01
1 2012-01-02
2 2012-01-03
dtype: datetime64[ns]
In [134]: s[0] + pd.DateOffset(months=2)
Out[134]: Timestamp('2012-03-01 00:00:00')
In [135]: pd.DatetimeIndex(s) + pd.DateOffset(months=2)
Out[135]: DatetimeIndex(['2012-03-01', '2012-03-02', '2012-03-03'], dtype='datetime64[ns]', freq='D', tz=None)
In [136]: s + pd.DateOffset(months=2)
---------------------------------------------------------------------------
TypeError: cannot use a non-absolute DateOffset in datetime/timedelta operations
[<DateOffset: kwds={'months': 2}>]
```
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} | 2 | 2015-07-30T07:45:49Z | 2015-09-02T08:52:55Z | 2015-09-02T08:52:40Z | NONE | null | Recreating my original post from here: http://stackoverflow.com/questions/26659941/what-are-the-default-na-values-when-pandas-loads-data/31705571#31705571
This documentation http://pandas.pydata.org/pandas-docs/stable/io.html#na-values states:
> The default NaN recognized values are ['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A','N/A', 'NA', '#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan'].
However, this list is not complete.
If it was, these two pieces of code should produce the same result
The actual default values:
```
import pandas as pd
from StringIO import StringIO
sio = StringIO()
sio.write('"foo","bar"\n"1",""\n"NA","4"')
sio.seek(0)
pd.read_csv(sio, sep=",", quotechar='"')
foo bar
0 1 NaN
1 NaN 4
```
The default values copied and given:
```
sio = StringIO()
sio.write('"foo","bar"\n"1",""\n"NA","4"')
sio.seek(0)
pd.read_csv(sio, sep=",", quotechar='"',
keep_default_na=False,
na_values=['-1.#IND', '1.#QNAN', '1.#IND',
'-1.#QNAN', '#N/A','N/A', '#NA', 'NA'
'NULL', 'NaN', '-NaN', 'nan', '-nan'])
foo bar
0 1
1 NaN 4
```
Pandas version:
```
pd.__version__
'0.15.2'
```
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} | 1 | 2015-07-30T09:21:07Z | 2015-07-31T07:23:04Z | 2015-07-31T07:23:04Z | MEMBER | null | @jreback I just notices some tests were not using `assert_produces_warning` as a context manager, therefore these would never fail (you could put whatever in there).
I changed it to using `with`, as I don't think there is way to use that function not as a context?
@bashtage I also changed some of your tests. I am not familiar with this code, but I think this should still be correct
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} | 30 | 2015-07-30T10:47:07Z | 2015-11-15T16:29:49Z | 2015-11-15T16:29:49Z | NONE | null | This makes the usage of those methods more object oriented
Also, delete some trailing whitespaces.
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