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} | 0 | 2015-03-22T22:35:50Z | 2015-03-23T09:13:07Z | 2015-03-23T09:13:06Z | MEMBER | null | Replaced the manual contents with the automatic (like now in 0.16.0), and a small rephrase (putting the bug fixes first)
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| null | 2 | 2015-03-23T00:51:44Z | 2015-03-23T01:26:44Z | 2015-03-23T01:26:31Z | CONTRIBUTOR | null | This behavior seems strange to me. Starting with a multiindex dataframe with unbalanced levels:
``` python
In [3]: pd.__version__
Out[3]: '0.15.2'
In [4]: df = pd.read_csv(StringIO('''
A,B,x
three,a,1
three,b,2
three,c,3
two,a,4
two,b,5
one,a,6'''), index_col=list('AB'))
In [5]: df
Out[5]:
x
A B
three a 1
b 2
c 3
two a 4
b 5
one a 6
```
Groupby aggregations on this dataframe seem to revert the index to the cross product of the levels, potentially leaving many NAs in the result:
``` python
In [6]: df.groupby(level=[0,1]).mean()
Out[6]:
x
A B
one a 6
b NaN
c NaN
three a 1
b 2
c 3
two a 4
b 5
c NaN
```
But with the series this doesn't occur (no NAs in the result):
``` python
In [7]: df.x.groupby(level=[0,1]).mean()
Out[7]:
A B
one a 6
three a 1
b 2
c 3
two a 4
b 5
Name: x, dtype: int64
```
I'm wondering if this is a bug or intended behavior? I haven't been able to find any mention of different behavior in the docs.
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} | 1 | 2015-03-23T04:20:16Z | 2020-06-28T04:37:46Z | null | MEMBER | null | Compare the output of these three groupby/apply calls:
```
In [66]: df = pd.DataFrame({'a': [1, 1, 2, 2], 'b': range(4)})
In [67]: df
Out[67]:
a b
0 1 0
1 1 1
2 2 2
3 2 3
In [68]: df.groupby('a')['b'].apply(lambda x: 2 * x)
Out[68]:
0 0
1 2
2 4
3 6
dtype: int64
In [69]: df.groupby('a').apply(lambda x: 2 * x)['b']
Out[69]:
0 0
1 2
2 4
3 6
Name: b, dtype: int64
In [70]: df.groupby('a').apply(lambda x: 2 * x['b'])
Out[70]:
a
1 0 0
1 2
2 2 4
3 6
Name: b, dtype: int64
```
The first two are what I expected; the last is not -- I expected the same result as the previous two.
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} | 1 | 2015-03-23T05:32:39Z | 2015-03-25T00:49:17Z | 2015-03-24T10:36:05Z | MEMBER | null | @TomAugspurger does this look like the right place to put this?
@jorisvandenbossche can we possibly merge this into the docs for v0.16?
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} | 1 | 2015-03-23T09:12:32Z | 2015-03-24T10:35:53Z | 2015-03-24T10:35:53Z | MEMBER | null | now this was only done in the plotting docs itself, with as a result that the actual style used is a mixture between the two
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} | 0 | 2015-03-23T10:48:33Z | 2018-07-14T16:57:55Z | null | MEMBER | null | Seeing #9648, we could maybe start adding memory benchmarks as well (this would be possible with asv?)
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} | 8 | 2015-03-23T12:11:47Z | 2020-03-11T13:58:00Z | 2015-03-25T22:56:58Z | CONTRIBUTOR | null | there’s no way to drop rows with duplicated index using `drop_duplicates`.
we’d have to add a copy of the index as column, or do this:
``` python
df[np.logical_not(df.index.duplicated(take_last=True).values)]
```
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| null | 1 | 2015-03-23T12:59:26Z | 2015-03-23T14:49:18Z | 2015-03-23T14:48:59Z | NONE | null | I have a dataframe with a column containing dates (I do not want to use this column as the index of my dataframe). When printing the dataframe, the timezone information is lost. Directly accessing the _.values_ shows the correct timezone.
This might be related to #6415
``` python
import pandas as pd
print "pandas version : ", pd.__version__
series = pd.to_datetime([1426374000000, 1426415697429], unit='ms').tz_localize('UTC').tz_convert('Europe/Berlin')
print '-- Printing series --'
print series
df = pd.DataFrame(series)
print '-- Printing df --'
print df
print '-- Printing df.values --'
print df.values
```
```
pandas version : 0.16.0
-- Printing series --
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-03-15 00:00:00+01:00, 2015-03-15 11:34:57.429000+01:00]
Length: 2, Freq: None, Timezone: Europe/Berlin
-- Printing df --
0
0 2015-03-14 23:00:00
1 2015-03-15 10:34:57.429000
-- Printing df.values --
[['2015-03-15T00:00:00.000000000+0100']
['2015-03-15T11:34:57.429000000+0100']]
```
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} | 4 | 2015-03-23T13:34:23Z | 2015-04-28T01:02:30Z | 2015-04-28T01:02:30Z | NONE | null | Hi,
I have something like the following csv file:
```
MyColumn
0
1
0
1
```
Note the initial space in each row.
Upgrading from 0.14.1 to 0.16 I recognized that read_csv started throwing away the 0 rows
```
In [28]: import pandas
In [29]: from StringIO import StringIO
In [30]: data = 'MyColumn\n 0\n 1\n 0\n 1'
In [31]: pandas.read_csv(StringIO(data))
Out[31]:
MyColumn
0 1
1 1
```
skipinitialspace=True did not help:
```
In [32]: pandas.read_csv(StringIO(data), skipinitialspace=True)
Out[32]:
MyColumn
0 1
1 1
```
however, skip_blank_lines=False would help:
```
In [34]: pandas.read_csv(StringIO(data), skip_blank_lines=False)
Out[34]:
MyColumn
0 0
1 1
2 0
3 1
```
Not sure if this is working as intended.
Cheers,
Alex
PS:
Having a second columns works as expected:
```
In [40]: data = 'MyColumn,SecondColumn\n 0, 2\n 1, 3\n 0, 0\n 1, 4'
In [41]: pandas.read_csv(StringIO(data))
Out[41]:
MyColumn SecondColumn
0 0 3
1 1 4
2 0 0
3 1 6
```
UPDATE:
Made code more reproducable.
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} | 31 | 2015-03-23T13:45:08Z | 2015-11-12T23:43:34Z | 2015-08-14T15:40:16Z | CONTRIBUTOR | null | Here is an initial attempt to port Jack Cushman's xport file reader into Pandas.
Partially addresses #4052 (but this only deals with xport files, not sas7bdat files)
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| null | 12 | 2015-03-23T18:21:21Z | 2020-06-29T15:36:49Z | null | CONTRIBUTOR | null | Is this desired behavior and something I need to work around or a bug? Notice the byte type marker is written to disk so you can't round-trip the data. This works fine in Python 2 with unicode AFAICT.
```
In [1]: pd.__version__
Out[1]: '0.15.2-252-g0d35dd4'
In [2]: pd.DataFrame.from_dict({'a': ['a', 'b', 'c']}).a.str.encode("utf-8").to_csv("tmp.csv")
In [3]: !cat tmp.csv
0,b'a'
1,b'b'
2,b'c'
```
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} | 10 | 2015-03-23T20:19:33Z | 2015-05-01T16:45:21Z | 2015-05-01T16:45:21Z | CONTRIBUTOR | null | After upgrading to 0.16.0 BigQuery functionality is not working. Upon invoking pd.read_gbq(), the _test_imports() function is running, which in turn runs _importers(). Everything is all well and good up to here, but per pdb, once _importers() returns, the imported modules are not accessible and read_gbq() throws `NameError: global name 'OAuth2WebServerFlow' is not defined`.
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} | 3 | 2015-03-24T00:39:48Z | 2016-01-26T14:32:58Z | 2016-01-26T14:32:58Z | NONE | null | When converting a string date column to datetime, if the string has a GMT timezone suffix (e.g. "-0800"), it takes 1000x longer to parse:
```
dates = pd.Series(pd.date_range('1/1/2000', periods=2000))
string_dates = dates.apply(lambda s: str(s))
tz_string_dates = string_dates.apply(lambda dt: dt + ' -0800')
%timeit pd.to_datetime(string_dates)
> 1000 loops, best of 3: 579 µs per loop
%timeit pd.to_datetime(tz_string_dates)
> 1 loops, best of 3: 562 ms per loop
```
Note microseconds vs milliseconds. 3 orders of magnitude... seems unnecessary. This can make loading CSVs into correctly-typed dataframes very, very, very slow for large datasets.
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} | 27 | 2015-03-24T06:13:49Z | 2015-08-19T00:44:21Z | 2015-08-19T00:32:16Z | CONTRIBUTOR | null | @jorisvandenbossche Here's the initial pass I was referencing in our discussion in #9660; also relevant closes #8361.
A few caveats:
- The `eval` suite will have to be converted by hand, as my `ast` transformer isn't parsing the string in `eval("df1 + df2")`
- Lots of test failures that I haven't even looked at
- `vbench` tended to swallow failing tests, so maybe this is okay?
- Uses `conda` instead of `virtualenv` as I'm on OSX and `pip install` fails on several packages.
- Takes a while - haven't had a chance to compare against `vbench`. It's possible `asv` is doing a lot more iterations by default.
- The conversion depends on a non-stdlib package called `astor`, which does `ast -> source` conversion. Not aware of a way to do this with the stdlib.
- The code size blows up as I'm not de-duplicating the setup in any way. Probably something that will just have to be fixed by hand after the switch is made.
Still a work-in-progress, but hopefully good enough that you (or someone else) can give this a shot.
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} | 17 | 2015-03-24T09:50:21Z | 2018-03-02T02:37:42Z | 2018-01-19T22:11:23Z | NONE | null | Hi,
Lately I have started to use pandas heavily with relatively big files.
When the RAM becomes full I delete (with del and gc) the big objects (Data
frames), though this doesn't clear the memory.
Can some onr please advise how to clear variables from RAM, similar to R with rm(some_object) ; gc()
Additionally did some experiments about this and here are the results :
Started with ~600MB RAM:
1. Ran two queries that jumped the memorey to ~ 3900MB (query -> to Data frame)
2. Removing these Data frames with one of the followings :
%xdel df
%reset_selective -f df
def malloc_trim():
import ctypes
ctypes.CDLL('libc.so.6').malloc_trim(0)
and then gc.collect(), reduced the RAM similarly (with all three methods) to ~3000MB
3. afterwards running %reset -f => most of the RAM was cleaned
4. The script do some data "Jiu Jitsu" a then store result to HD. Restarting IPython and loading this file barley uses RAM !
What would you recommend ?
Regards ,
Boris
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} | 1 | 2015-03-24T12:50:10Z | 2015-03-25T22:57:26Z | 2015-03-25T22:57:26Z | CONTRIBUTOR | null | fixes #9708
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| null | 3 | 2015-03-24T15:54:16Z | 2015-09-10T18:31:05Z | 2015-03-24T15:57:34Z | NONE | null | I just upgraded to 0.16.0 from 0.15.2 and the following command stopped working
```
dflabels.to_csv('labels.do', encoding='utf-8', index=False, sep='\n', quoting=False, quotechar=' ')
```
I get the following error
```
TypeError Traceback (most recent call last)
<ipython-input-20-332582a26117> in <module>()
----> 1 dflabels.to_csv('labels.do', encoding='utf-8', index=False, sep='\n', quoting=False, quotechar=' ')
TypeError: to_csv() got an unexpected keyword argument 'quoting'
```
Any one else having this issue?
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https://api.github.com/repos/pandas-dev/pandas/issues/9719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9719/events | https://github.com/pandas-dev/pandas/issues/9719 | 64,039,775 | MDU6SXNzdWU2NDAzOTc3NQ== | 9,719 | Cannot get Yahoo option data using Remote Data Access of Pandas | {
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} | 4 | 2015-03-24T16:17:33Z | 2015-03-25T08:46:06Z | 2015-03-25T08:46:06Z | NONE | null | I am trying to download Yahoo option data using the remote data access function of Pandas, I installed Anaconda on my laptop and launch ipython-qtconsole. I ran the following commands and got error "AttributeError: 'str' object has no attribute 'month', do I miss a module? If so, how can I find it?
from pandas.io.data import Options
aapl = Options('aapl', 'yahoo')
data = aapl.get_all_data()
AttributeError Traceback (most recent call last) in () ----> 1 data = aapl.get_all_data()
C:\Personal\Anaconda\lib\site-packages\pandas\io\data.pyc in get_all_data(self, call, put) 1109 1110 for month in months: -> 1111 m2 = month.month 1112 y2 = month.year 1113
AttributeError: 'str' object has no attribute 'month'
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} | 4 | 2015-03-24T16:57:07Z | 2015-03-30T07:58:26Z | 2015-03-30T07:58:26Z | NONE | null | Came a across this very odd problem while trying to get difference between two columns in a DataFrame of type datetime64. The following simple example illustrates the problem:
```
df = pd.DataFrame({'A': pd.date_range('20150301', '20150304'),
'B': pd.date_range('20150303', '20150306')},
index = [1, 2, 3, 3])
df
A B
1 2015-03-01 2015-03-03
2 2015-03-02 2015-03-04
3 2015-03-03 2015-03-05
3 2015-03-04 2015-03-06
df.B - df.A
1 2 days
2 2 days
3 2 days
3 1 days
3 3 days
3 2 days
```
dtype: timedelta64[ns]
What happens generally is that the output contains an entry for the difference in every pair in the Cartesian product of the rows containing the duplicate index. I learned this the hard way on a df with 30K+ rows and only 50 distinct indices ending up trying to compute a series with 385 million+ entries, quickly freezing my computer.
On a related note, using the eval method to compute the difference throws the following error:
```
df.eval('B - A')
ValueError: unkown type timedelta64[ns]
```
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} | 1 | 2015-03-24T20:46:41Z | 2015-05-11T12:47:19Z | 2015-05-11T12:28:04Z | MEMBER | null | Closes #9599
With this I updated the docs (assign docstring included in API, and the cat/str/dt accessors now documented correctly).
The Categorical page without methods and attributes is not yet working.
Will complete this for the v0.16.1 docs
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} | 16 | 2015-03-25T04:38:20Z | 2015-05-01T16:45:03Z | 2015-05-01T16:45:03Z | CONTRIBUTOR | null | closes #9713
closes #8327
Changes to be committed:
modified: pandas/io/gbq.py
Moved google api imports into functions that call them, removed unused imports. This fixes #9713
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https://api.github.com/repos/pandas-dev/pandas/issues/9723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9723/events | https://github.com/pandas-dev/pandas/pull/9723 | 64,245,180 | MDExOlB1bGxSZXF1ZXN0MzE5MTI2NjA= | 9,723 | BUG: _retry_read_url retries connection even if successful | {
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| null | 1 | 2015-03-25T11:17:27Z | 2015-03-25T11:22:46Z | 2015-03-25T11:18:52Z | CONTRIBUTOR | null | {
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| null | 2 | 2015-03-25T11:26:11Z | 2021-04-18T05:57:13Z | null | CONTRIBUTOR | null | Based on the Stack Overflow question [here](http://stackoverflow.com/q/29253027/3005188) I believe I've found a bug for when a scatter plot only has three points. The full details are in the question but I'll sketch out the details here.
The code (below) will create a scatter plot where each of the points has a different colour, rather than a single consistent colour (as is expected).
It happens particularly when `seaborn` is used because, by default, `seaborn` will modify the `rcParams['patch.facecolor']` to be an RGB tuple. If you don't use `seaborn` then this value defaults to a string `'b'` which indicates the colour blue. Of course, this bug would arise whenever a user may use a custom `patch.facecolor` that happens to be an RGB tuple, so it isn't specifically restricted to `seaborn`.
The issue arises because when a sequence of numbers is passed for the `c` argument with matplotlib, matplotlib will try to map it to the current colormap. When there are only three scatter points the sequence lengths match (i.e. `len(data) == len(colors)`) and so the numbers are mapped and the resulting colours used in the plot.
### Reproducing code
``` python
from pandas import DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
df = DataFrame({'x': np.random.uniform(0, 1, 3), 'y': np.random.uniform(0, 1, 3)})
df.plot(kind = 'scatter', x = 'x', y = 'y')
plt.show()
```

### Versions
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.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: None
pandas: 0.15.0
nose: 1.3.3
Cython: None
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.4.1
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.7
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: 2.0.5
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 2 | 2015-03-25T11:44:34Z | 2015-03-25T22:48:08Z | 2015-03-25T22:06:21Z | CONTRIBUTOR | null | This allows the test to pass when using dateutil version 2.4.0
```
In [2]: '2.4.0' <= LooseVersion('2.4')
Out[2]: False
In [3]: '2.4.0' <= LooseVersion('2.4.0')
Out[3]: True
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9726/events | https://github.com/pandas-dev/pandas/issues/9726 | 64,252,679 | MDU6SXNzdWU2NDI1MjY3OQ== | 9,726 | "c"-engine read_table sigsegv when chunksize close to multiple of file length? | {
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| null | 2 | 2015-03-25T11:58:31Z | 2015-12-11T15:21:16Z | 2015-12-11T15:21:10Z | NONE | null | I have the following script:
```
table_generator = pd.io.parsers.read_table(chromosome_file, sep="\t", engine="c",
chunksize=50000, names=["Start", "End", "Strand"],
usecols=[1, 2, 5])
for chunk in table_generator:
print chunk
print chromosome_file
```
I get an error when the file length is close to a multiple of the chunksize:
```
fish: 'python exorcised.py' terminated by signal SIGSEGV (Address boundary error)
```
Changing the chunksize or the lengths of the files allows me to avoid the error.
The error seems to happen before or when the tiny leftover chunk is read (since the previous `print` was displayed.)
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.14.1
statsmodels: 0.6.1
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
```
The files read look like the following:
```
chrY 59052460 59052659 Keratinocyte_H3K27me3_03 1 +
chrY 246219 246418 Melanocyte_H3K27me3_02 0 -
chrY 9978094 9978293 Melanocyte_H3K27me3_03 1 +
chrY 2472778 2472977 Fibroblast_H3K27me3_01 0 +
chrY 13266277 13266476 Keratinocyte_H3K27me3_03 1 -
chrY 699049 699248 Fibroblast_H3K27me3_02 0 -
chrY 23986624 23986823 Melanocyte_H3K27me3_01 0 -
chrY 562143 562342 Fibroblast_H3K27me3_03 1 +
chrY 23026706 23026905 Melanocyte_H3K27me3_01 0 -
chrY 17509636 17509835 Melanocyte_H3K27me3_03 1 +
```
With special symbols showing:
```
chrY^I10500^I10699^IMelanocyte_H3K27me3_01^I0^I+$
```
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| null | 5 | 2015-03-25T14:53:38Z | 2015-09-16T11:25:53Z | 2015-03-25T15:19:47Z | CONTRIBUTOR | null | ``` python
>>> import pandas as pd
>>> pd.version.version
'0.15.2'
>>> df = pd.DataFrame({u'A': {123.0: 2.0}, u'B': {123.0: 1}})
>>> df
A B
123 2 1
>>> df['B'].dtype
dtype('int64')
>>> type(df.iterrows().next()[1]['B'])
<type 'numpy.float64'>
```
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} | 9 | 2015-03-25T17:18:20Z | 2021-04-18T05:58:08Z | null | NONE | null | As per http://pandas.pydata.org/pandas-docs/stable/io.html#handling-bad-lines, records with too many fields cause (depending on error_bad_lines) an exception to be thrown, or stderr to be written to.
Would it be possible to add an option, defaulting to False, that gave similar warnings/errors if there are _too few_ fields in a record (compared to all the other records. The current behaviour is just to insert NaN's - but there are cases where data integrity is important so knowing that some records are missing fields is important.
Cheers ^
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| null | 2 | 2015-03-25T18:45:57Z | 2015-03-25T21:26:50Z | 2015-03-25T21:26:50Z | NONE | null | I have an odd issue where if I specify the axis in concat it throws the following error:
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
Pandas version: 0.16.0
OS: Ubuntu 14.04 LTS x64
Here's the pickle of the data structure causing this (it's rather large, ~60 megs, but it's my real use case which caused this error)
https://www.dropbox.com/s/02dn99dbi6p14um/pandas_error.pickle?dl=0
```
import pickle
import pandas as pd
l = pickle.load(open('pandas_error.pickle'))
scan_query = '(index >= 624.306193 & index <= 624.326171) | (index >= 624.807981 & index <= 624.826795) | (index >= 625.309633 & index <= 625.32635) | (index >= 628.315655 & index <= 628.331508) | (index >= 628.815043 & index <= 628.83164)'
# works
pd.concat([i.dropna().to_frame().query(scan_query) for i in l])
# breaks
#pd.concat([i.dropna().to_frame().query(scan_query) for i in l], axis=1)
```
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} | 0 | 2015-03-25T20:34:47Z | 2015-04-02T21:25:39Z | 2015-04-02T21:25:39Z | NONE | null | See repro below:
``` python
import pandas as pd
import numpy as np
temp = pd.Series(np.random.randn(10))
temp[3:6] = np.nan
temp[8] = np.nan
nan_index = np.isnan(temp)
# this works
temp1 = temp.copy()
temp1[nan_index] = [99, 99, 99, 99]
temp1[nan_index]
3 99
4 99
5 99
8 99
dtype: float64
# this doesn't - values look like they're being assigned in a different order?
temp2 = temp.copy()
temp2[nan_index] = [99, 99, 99, np.nan]
3 NaN
4 99
5 99
8 99
dtype: float64
# ... but it works properly when using .loc
temp2 = temp.copy()
temp2.loc[nan_index] = [99, 99, 99, np.nan]
3 99
4 99
5 99
8 NaN
dtype: float64
```
output of show_versions():
```
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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.0
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.2.1
dateutil: 2.4.1
pytz: 2015.2
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.6
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 2 | 2015-03-25T23:19:53Z | 2021-06-26T22:40:16Z | null | NONE | null | I recently ran into an issue where pandas is not handling the "<=" logical operator when comparing dates in an interesting way.
```
import pandas as pd
rng = pd.date_range('1/1/2011', periods=72, freq='H')
df = pd.DataFrame(rng)
df.columns=['date']
crit = (df['date'] >= '2011-01-01') & (df['date'] <= '2011-01-02')
df.loc[crit, 'New_Col'] = 'First two days?'
```
Anything after 2011-01-02 00:00:00 is not included in the less than or equal to 2011-01-02. One would expect that less than or equal to mean that entire day. Is this by design, or is this a bug?
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} | 5 | 2015-03-26T14:18:03Z | 2018-07-06T22:49:29Z | 2018-07-06T22:49:17Z | NONE | null | Series.sum returns a numpy type, except when it's empty, in which case it returns a python int of value "0":
```
In [2]: type(pd.Series([0]).sum())
Out[2]: numpy.int64
In [3]: type(pd.Series().sum())
Out[3]: int
```
This poses a problem when I do `1 / myserie.sum()` because I expect to obtain `np.inf` rather than a divison by 0 exception.
I think the return type of `Series.sum()` for empty series should be inferred from the Series's dtype This way, `Series([], dtype='str').sum()` would return an empty string, and `Series([]).sum()` would return `np.float64(0)` since an empty series' default type seems to be float64.
Tested with Pandas 0.16.0
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| null | 7 | 2015-03-26T14:37:00Z | 2015-03-27T13:57:41Z | 2015-03-27T13:57:41Z | NONE | null | I have this method in a class:
```
def save(self, filename, key='/'):
"""Saves to an HDF5 store."""
with pd.HDFStore(filename) as store:
for g in self.GENDERS:
df = getattr(self, g)
store.put(key+g, df,
data_columns=df.columns,
format='table',
encoding='utf-8',
dropna=False)
```
and in my Unittest:
```
def test_save_to_hdf(self):
td = TrackData()
with tempfile.NamedTemporaryFile(suffix='.h5') as temp:
td.save(temp.name)
```
When I run nosetests I get this:
```
ERROR: test_save_to_hdf (courtana.tracker.tests.test_trackdata.TestTrackData)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/hugo/projects/courtana/courtana/tracker/tests/test_trackdata.py", line 34, in test_save_to_hdf
td.save(temp.name)
File "/home/hugo/projects/courtana/courtana/tracker/trackdata.py", line 54, in save
with pd.HDFStore(filename) as store:
AttributeError: __exit__
```
Why did this suddenly stop working?
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} | 8 | 2015-03-26T14:59:21Z | 2015-09-17T23:48:15Z | 2015-09-17T23:48:15Z | NONE | null | I am trying to read a file 57MB with `pandas.csv_read`. The file contains a header (5 rows), afterwads integer values and at the end float values:
```
info
2681087 53329 1287215 1287215 53328
RSA 53328 53328 1287215 0
(I14) (I14) (d25.15) (d25.15)
F 1 5332
1
33
61
92
128
...
165
205
239
272
0.112474585277959E+09
0.126110931411177E+09
0.515995872032845E+09
0.126110931411175E+09
-0.194634413074014E+09
0.112474585277950E+09
...
```
When I read the txt file:
import pandas as pd
pd.read_csv(file, skiprows=5+n_int_values, header=None, engine='c',
dtype=np.float, low_memory=False)
The result is an error:
```
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-118-699921ac7a12> in <module>()
----> 1 a=pd.read_csv(loc, skiprows=5+n_coloums+n_rows, header=None, engine='c',
low_memory=False, error_bad_lines=False)
C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect,
compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator,
header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues,
true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace,
as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines,
error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col,
dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose,
encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
468 skip_blank_lines=skip_blank_lines)
469
--> 470 return _read(filepath_or_buffer, kwds)
471
472 parser_f.__name__ = name
C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds)
254 return parser
255
--> 256 return parser.read()
257
258 _parser_defaults = {
C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
713 raise ValueError('skip_footer not supported for iteration')
714
--> 715 ret = self._engine.read(nrows)
716
717 if self.options.get('as_recarray'):
C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
1162
1163 try:
-> 1164 data = self._reader.read(nrows)
1165 except StopIteration:
1166 if nrows is None:
pandas\parser.pyx in pandas.parser.TextReader.read (pandas\parser.c:7426)()
pandas\parser.pyx in pandas.parser.TextReader._read_rows (pandas\parser.c:8377)()
pandas\parser.pyx in pandas.parser.raise_parser_error (pandas\parser.c:20728)()
CParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input
file.
```
This happens on pandas 0.16.0, on anaconda python 2.7.8. On an older version - 0.14.1. it works correctly.
Note: When I use `engine='python'`, the txt file is loaded normaly.
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} | 1 | 2015-03-26T15:06:50Z | 2015-08-18T11:20:29Z | 2015-08-18T11:20:29Z | CONTRIBUTOR | null | ```
In [3]: df = pd.DataFrame(np.random.randn(5, 5))
In [4]: s = pd.Series(range(5))
In [5]: df.where(df.isnull(), s, axis=0)
Out[5]:
0 1 2 3 4
0 0 1 2 3 4
1 0 1 2 3 4
2 0 1 2 3 4
3 0 1 2 3 4
4 0 1 2 3 4
In [6]: df.where(df.isnull(), s, axis=1)
Out[6]:
0 1 2 3 4
0 0 1 2 3 4
1 0 1 2 3 4
2 0 1 2 3 4
3 0 1 2 3 4
4 0 1 2 3 4
```
The right axis _is_ used when aligning indices, but it's not used when broadcasting the aligned Series. You also get the right result if the DataFrame is not square, because core.internals.where will transpose if necessary to the get the dimensions to fit.
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} | 6 | 2015-03-26T17:52:47Z | 2015-09-01T14:14:18Z | 2015-04-02T14:30:11Z | CONTRIBUTOR | null | The following code will show the x labels on the right side, but actually it should show the labels at the bottom (or better: should show all!)
```
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.gridspec as gridspec
import pandas as pd
gs = mpl.gridspec.GridSpec(2, 2)
ax_tl = plt.subplot(gs[0,0])
ax_ll = plt.subplot(gs[1,0])
ax_tr = plt.subplot(gs[0,1])
ax_lr = plt.subplot(gs[1,1])
axes = [ax_tl, ax_ll, ax_tr, ax_lr]
df = pd.DataFrame({"a":[1,2,3,4,5,6], "b":[1,2,3,4,5,6]})
for ax in axes:
df.plot(x="a", y="b", title="title", ax=ax)
gs.tight_layout(plt.gcf())
```
IMO: in `MPLPlot.__init__()`
```
def __init__(..., sharex=None,...):
[...]
if sharex is None:
if not ax is None:
# if we get a ax, the users should know what he does...
sharex=False
else:
sharex = True
[...]
```
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} | 12 | 2015-03-27T02:25:52Z | 2021-04-18T06:05:37Z | 2021-04-18T06:05:37Z | CONTRIBUTOR | null | A frequent bug I end up fixing in my own code when working with integer data results from my data auto converting to float, usually on a merge, reindex, unstack, or pivot.
```
In [6]: df = pd.DataFrame(np.random.randn(5, 5), columns=list('abcde'), dtype=int)
In [7]: df.dtypes
Out[7]:
a int64
b int64
c int64
d int64
e int64
dtype: object
In [8]: df.reindex(range(3)).dtypes
Out[8]:
a int64
b int64
c int64
d int64
e int64
dtype: object
In [9]: df.reindex(range(6)).dtypes
Out[9]:
a float64
b float64
c float64
d float64
e float64
dtype: object
```
As far as I know, there is no way to know whether this has happened without doing additional checks after an operation.... or fixing my code, but that would be much easier if I could catch a warning, since i am often performing a long series of operations and figuring out which one converted everything to float is time consuming.
I would also like to suggest converting to object over converting to float if nans are being produced. At least types of existing data would be preserved.
(I realize reindex is not the ideal choice since it has fill_value as an argument)
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| null | 2 | 2015-03-27T09:04:37Z | 2015-04-07T09:06:10Z | 2015-03-27T14:53:02Z | NONE | null | See stackoverflow description:
http://stackoverflow.com/questions/29296045/pandas-yahoo-datareader-remotedataerror-when-end-date-added?noredirect=1#comment46788752_29296045
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} | 8 | 2015-03-27T13:50:33Z | 2015-03-31T11:54:12Z | 2015-03-31T11:54:03Z | CONTRIBUTOR | null | This does two things:
- fix for changing the wrong xlabels/xticklabels if a gridspec is used and the ax is passed in (not sure about using subplot without gridspec)
- changes the default behaviour for sharex if an axis is passed in: before the visibility changes were applied per default, now they are only applied if explicitly requested via `sharex=True`
Closes: #9737
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} | 58 | 2015-03-27T22:01:57Z | 2015-04-20T14:35:36Z | 2015-04-20T11:19:57Z | CONTRIBUTOR | null | closes #7629
xref #8613
xref #8074
- [x] docs / whatsnew
- [x] ~~auto-create a `CategoricalIndex` when grouping by a `Categorical` (this doesn't ATM)~~
- [x] adding a value not in the Index, e.g. `df2.loc['d'] = 5` should do what? (currently will coerce to an `Index`)
- [x] `pd.concat([df2,df])` should STILL have a `CategoricalIndex` (yep)?
- [x] implement `min/max`
- [x] fix groupby on cat column
- [x] add `Categorical` wrapper methods
- [x] make repr evalable / fix
- [x] contains should be on values not categories
A `CategoricalIndex` is essentially a drop-in replacement for `Index`, that works nicely for non-unique values. It uses a `Categorical` to represent itself. The behavior is very similar to using a duplicated Index (for say indexing).
Groupby works naturally (and returns another `CategoricalIndex`). The only real departure is that `.sort_index()` works like you would expected (which is a good thing:). Clearly this will provide idempotency for `set/reset` index w.r.t. Categoricals, and thus memory savings by its representation.
This doesn't change the API at all. IOW, this is not turned on by default, you have to either use `set/reset`, assign an index, or pass a `Categorical` to `Index`.
```
In [1]: df = DataFrame({'A' : np.arange(6,dtype='int64'),
...: 'B' : Series(list('aabbca')).astype('category',categories=list('cab')) })
In [2]: df
Out[2]:
A B
0 0 a
1 1 a
2 2 b
3 3 b
4 4 c
5 5 a
In [3]: df.dtypes
Out[3]:
A int64
B category
dtype: object
In [5]: df.B.cat.categories
Out[5]: Index([u'c', u'a', u'b'], dtype='object')
In [6]: df2 = df.set_index('B')
In [7]: df2
Out[7]:
A
B
a 0
a 1
b 2
b 3
c 4
a 5
In [8]: df2.index
Out[8]: CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'], dtype='category')
In [9]: df2.index.categories
Out[9]: Index([u'c', u'a', u'b'], dtype='object')
In [10]: df2.index.codes
Out[10]: array([1, 1, 2, 2, 0, 1], dtype=int8)
In [11]: df2.loc['a']
Out[11]:
A
B
a 0
a 1
a 5
In [12]: df2.loc['a'].index
Out[12]: CategoricalIndex([u'a', u'a', u'a'], dtype='category')
In [13]: df2.loc['a'].index.categories
Out[13]: Index([u'c', u'a', u'b'], dtype='object')
In [14]: df2.sort_index()
Out[14]:
A
B
c 4
a 0
a 1
a 5
b 2
b 3
In [15]: df2.groupby(level=0).sum()
Out[15]:
A
B
a 6
b 5
c 4
In [16]: df2.groupby(level=0).sum().index
Out[16]: CategoricalIndex([u'a', u'b', u'c'], dtype='category')
```
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} | 2 | 2015-03-28T03:30:41Z | 2015-04-07T09:05:49Z | 2015-03-29T17:10:43Z | CONTRIBUTOR | null | Two bug fixes from pandas-datareader.
pydata/pandas-datareader#25
pydata/pandas-datareader#24
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} | 3 | 2015-03-28T14:08:51Z | 2015-06-10T13:41:24Z | 2015-04-02T21:25:39Z | CONTRIBUTOR | null | closes #9731
The main issue is when the destination and source arrays have different lengths, `np.putmask` doesn't behave like `arr[mask] = values`:
"Sets `a.flat[n] = values[n]` for each n where `mask.flat[n]==True`"
We have to use `np.place` instead. A secondary issue is that `np.place` doesn't automatically convert an integer to a `datetime64` like `np.putmask` does (I created a numpy issue for this), so we need an additional check for that case. The rest of the commit is just cleaning up `_maybe_upcast_putmask`, which had some parameters that were never used, and a confusing docstring.
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} | 6 | 2015-03-28T17:42:50Z | 2015-04-09T15:13:42Z | 2015-04-09T15:13:42Z | CONTRIBUTOR | null | Line 334: raw_locales = [] deletes the data received from locale_getter, so the return from this function is always a blank list.
``` python
try:
raw_locales = locale_getter()
except:
return None
try:
# raw_locales is "\n" seperated list of locales
# it may contain non-decodable parts, so split
# extract what we can and then rejoin.
raw_locales = []
for x in raw_locales:
try:
raw_locales.append(str(x, encoding=pd.options.display.encoding))
except:
pass
except TypeError:
pass
if prefix is None:
return _valid_locales(raw_locales, normalize)
found = re.compile('%s.*' % prefix).findall('\n'.join(raw_locales))
return _valid_locales(found, normalize)
```
https://github.com/pydata/pandas/blob/8d2818e32d0bbb50e183ccb5724c391e4f604670/pandas/util/testing.py#L334
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} | 13 | 2015-03-28T22:43:43Z | 2015-04-05T23:10:10Z | 2015-04-05T23:10:10Z | CONTRIBUTOR | null | closes #9330 and another version of the same problem [here on SO](http://stackoverflow.com/questions/29311659/how-can-pandas-dataframes-appear-identical-but-fail-equals) by canonicalizing the block order during an `equals` comparison.
Tested at the block manager level and above it at the frame level.
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} | 3 | 2015-03-29T04:41:40Z | 2016-01-30T19:27:56Z | 2016-01-30T19:27:56Z | CONTRIBUTOR | null | Currently:
``` python
In [2]: df = pd.DataFrame({'x':['a', 'a', 'b'], 'y':['j', 'k', 'j'], 'z':[0, 1, 2]})
In [3]: df.set_index(['x', 'y']).unstack()
Out[3]:
z
y j k
x
a 0 1
b 2 NaN
```
If I want to fill with -1, i need to `fillna` and then `astype` back to `int`. Ideally:
``` python
In [3]: df.set_index(['x', 'y']).unstack(fill_value=-1)
Out[3]:
z
y j k
x
a 0 1
b 2 -1
```
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} | 1 | 2015-03-29T21:49:59Z | 2015-09-21T15:35:33Z | 2015-09-11T14:52:55Z | CONTRIBUTOR | null | Starting with openpyxl 2.2.0, various tests in `pandas.io.tests.test_excel` fail with tracebacks similar to this one.
```
======================================================================
ERROR: test_basics_with_nan (pandas.io.tests.test_excel.Openpyxl2Tests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/pandas/io/tests/test_excel.py", line 1229, in wrapped
orig_method(self, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/io/tests/test_excel.py", line 641, in test_basics_with_nan
self.frame.to_excel(path, 'test1')
File "/usr/local/lib/python2.7/site-packages/pandas/core/frame.py", line 1267, in to_excel
startrow=startrow, startcol=startcol)
File "/usr/local/lib/python2.7/site-packages/pandas/io/excel.py", line 778, in write_cells
xcell.style = xcell.style.copy(**style_kwargs)
File "/usr/local/lib/python2.7/site-packages/openpyxl/styles/styleable.py", line 107, in style
protection=self.protection
File "/usr/local/lib/python2.7/site-packages/openpyxl/styles/__init__.py", line 42, in __init__
self._font = font
File "/usr/local/lib/python2.7/site-packages/openpyxl/descriptors/base.py", line 35, in __set__
raise TypeError('expected ' + str(self.expected_type))
TypeError: expected <class 'openpyxl.styles.fonts.Font'>
```
`pandas.io.excel._Openpyxl2Writer.write_cells` uses `openpyxl.styles.Style.copy` to merge newly specified style data with style data on existing cells. Sadly, the obvious workaround—iterate over the fields of the `Style` object and use them to build an updated `Style` object—won't work either without additional coddling, because the fields don't reliably return the native object that the `Style` constructor demands.
``` python
>>> from openpyxl.styles import Style
>>> s0 = Style()
>>> s1 = Style(fill=s0.fill)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/obrienjw/venv/py27-openpyxl220/lib/python2.7/site-packages/openpyxl-2.2.0-py2.7.egg/openpyxl/styles/__init__.py", line 43, in __init__
self._fill = fill
File "/Users/obrienjw/venv/py27-openpyxl220/lib/python2.7/site-packages/openpyxl-2.2.0-py2.7.egg/openpyxl/descriptors/base.py", line 35, in __set__
raise TypeError('expected ' + str(self.expected_type))
TypeError: expected <class 'openpyxl.styles.fills.Fill'>
```
(See upstream issue: 444 [copy() method on Style object raises TypeError](https://bitbucket.org/openpyxl/openpyxl/issue/444/copy-method-on-style-object-raises))
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} | 3 | 2015-03-29T23:34:21Z | 2019-10-21T20:06:20Z | null | CONTRIBUTOR | null | followup to #9741
These are all commented in `pandas/core/test_frame/test_categorical_indexing_index`
- [ ] support slice semantics
- [x] fix index name issue on selection for non-existant label that IS in the categories
- [x] fix index name issue on assignment for non-existan label that IS in the categories
- [ ] duplicated returns Boolean array? / test
- [ ] in repr, use `max_categories/max_seq_len` to display a truncated view
These last 2 were 'solved' by putting the following in `core/internals/reindex_indexer`
but this caused some issues as the `MultiIndex._shallow_copy` is really a view semantic (so that needs to be fixed as well)
```
# make sure that we propogate the correct meta data for this axis
# we need to do this because we are not doing a take / reindex
# which would do this automatically
new_axis = self.axes[axis]._shallow_copy(new_axis)
```
```
In [1]: In [4]: df = DataFrame({'A' : np.arange(6,dtype='int64'),
...: ...: 'B' : Series(list('aabbca')).astype('category',categories=list('cabe')) }).set_index('B')
In [2]: df
Out[2]:
A
B
a 0
a 1
b 2
b 3
c 4
a 5
In [3]: df.loc['e'] = 20
In [4]: df
Out[4]:
A
a 0
a 1
b 2
b 3
c 4
a 5
e 20
```
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} | 1 | 2015-03-30T00:57:25Z | 2016-07-04T11:21:41Z | 2016-07-04T11:21:38Z | NONE | null | I was trying to fix a stock data feed error in data.py for python 2.7
I used line 124 to look into the problem.
```
# for codes see: http://www.gummy-stuff.org/[redacted]-data.htm
```
Is another reliable source available?
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| null | 9 | 2015-03-30T11:49:16Z | 2018-10-21T00:57:42Z | 2018-10-21T00:57:42Z | CONTRIBUTOR | null | I made an toy dataframe:
df = pandas.DataFrame([[1, 1, 1],[2, 1, 1],[2, 1, 1],[numpy.nan, numpy.nan, numpy.nan]], columns=["a","b","c"])
I try different methods to fill missing values. These work as expected:
df.fillna(df.mean())
df.fillna(df.median())
But this doesnt work:
df.fillna(df.mode())
Inspecting the output from df.mode() I see it has different format than df.mean() and df.median(). As I user I would expect the same behavior for these functions, and be able to fill missing values as described.
Using Pandas 0.15.2
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} | 3 | 2015-03-31T00:30:34Z | 2021-04-18T06:10:33Z | null | NONE | null | I have a dictionary of Series that I'm converting to a DataFrame via `DataFrame.from_dict`, and the Series have index values that are tuples, as in these examples:
``` python
In [39]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x',), ('y',), ('z',)])}, orient='index')
Out[39]:
x y z
a 1 2 3
In [40]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x', 1), ('y', 2), ('z', 3)])}, orient='index')
Out[40]:
x y z
1 2 3
a 1 2 3
```
Note that in the first example the columns are a plain `Index`, but in the second example it's converted to `MultiIndex`. This causes a problem for me later when I index into the DataFrame columns and expect to get tuples out (with `MultiIndex` the values are represented as tuples).
When `orient='columns'` the tuples are not touched:
``` python
In [49]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x',), ('y',), ('z',)])}, orient='columns')
Out[49]:
a
(x,) 1
(y,) 2
(z,) 3
In [50]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x', 1), ('y', 2), ('z', 3)])}, orient='columns')
Out[50]:
a
(x, 1) 1
(y, 2) 2
(z, 3) 3
```
I can get what I'm after by using `orient='columns'` and then doing the transpose:
``` python
In [51]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x',), ('y',), ('z',)])}, orient='columns').transpose()
Out[51]:
(x,) (y,) (z,)
a 1 2 3
In [52]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x', 1), ('y', 2), ('z', 3)])}, orient='columns').transpose()
Out[52]:
(x, 1) (y, 2) (z, 3)
a 1 2 3
```
But I was caught unawares by single-item tuples being treated differently than multi-item tuples, and the difference in behavior between index/columns orientation.
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} | 4 | 2015-03-31T00:35:32Z | 2019-12-12T21:05:08Z | 2019-12-12T21:05:08Z | CONTRIBUTOR | null | I came across some inconsistencies in how `first_valid_index()` works on empty `Series` and `DataFrames` (and `last_valid_index()`).
``` python
>>> print(pd.DataFrame().first_valid_index())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/frame.py", line 3332, in first_valid_index
return self.index[self.count(1) > 0][0]
File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/index.py", line 915, in __getitem__
return getitem(key)
IndexError: index 0 is out of bounds for axis 0 with size 0
>>> print(pd.Series().first_valid_index())
None
>>> print(pd.SparseDataFrame([]).first_valid_index())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/frame.py", line 3332, in first_valid_index
return self.index[self.count(1) > 0][0]
File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/ops.py", line 919, in f
res = self._combine_const(other, func, raise_on_error=False)
TypeError: _combine_const() got an unexpected keyword argument 'raise_on_error'
>>> print(pd.SparseSeries([]).first_valid_index())
None
```
Is there a reason for this different behavior? Note also that the `DataFrame`/`SparseDataFrame` exceptions are different. Here's the [`DataFrame`](https://github.com/pydata/pandas/blob/671c4b3c52a02ee3736752b2645d08762c0fedaf/pandas/core/frame.py#L3421) and [`Series`](https://github.com/pydata/pandas/blob/a4772028fef7f660cf6d94129a1a9a6f3852e441/pandas/core/series.py#L2375) methods. The `Series` method has a `if len(self) == 0` check. I can make a PR if you think one of these should be changed and tell me which way you want to go.
For what it's worth, I came across this issue trying to use this function in a list comprehension to get a list of minimum times from a handful of `Series, DataFrames, and SparseDataFrames` that may or may not be empty. (Although it just occurred to me that I could, of course, put an if empty check in my list comprehension.)
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| null | 1 | 2015-03-31T01:43:02Z | 2019-12-13T20:38:40Z | 2019-12-13T20:38:40Z | NONE | null | Hey folks --
I'm writing a large CSV (100M rows) to a sqlite database through Pandas in 10k chunks.
```
disk_engine = create_engine('sqlite:///100M.db')
chunksize = 10000
index_start = 0
for df in pd.read_csv('100M_rows.csv', chunksize=chunksize, iterator=True, encoding='utf-8'):
df.index += index_start
df.to_sql('data', disk_engine, if_exists='append')
index_start = df.index[-1] + 1
```
I'm seeing non-linear writing speeds after writing 1M rows:

Is there a more efficient way I should be writing these appends? Many thanks!
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} | 13 | 2015-03-31T02:23:20Z | 2015-05-05T18:03:08Z | 2015-04-03T00:41:09Z | CONTRIBUTOR | null | Closes #6232
In working with the various "Contributing" documents recently it was clear that we needed to combine them. This is a stab at doing so. I took the various components from pydata.org, CONTRIBUTING.md, various wiki pages, and the already included Documentation documents and created this file. I think it's a good start.
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} | 2 | 2015-03-31T08:01:57Z | 2016-04-06T19:17:26Z | 2016-04-06T19:17:26Z | NONE | null | xref #12203
The arrays passed to the date_parser function is different when `names` and `use_cols` are specified to limit the number of parsed columns.
When running the example code below, the date_parser function receives two arguments, one array with '20140101' strings, and one array with integers. The default `date_parser` fails to process this input.
When assigning an empty list to `DROPPED_COLUMNS` (so that all columns are parsed), the second array contains strings instead of integers, and the datetimes are parsed correctly.
The problem doesn't occur with `engine='python'`. I haven't tested the influence of the `header` and `index_cols` options.
Python script:
```
from __future__ import print_function, division
import pandas as pd
CSV = '2014.csv'
DROPPED_COLUMNS = ['NCDC', 'I', 'QCP']
# DROPPED_COLUMNS = []
with open(CSV) as csv:
csv.readline()
column_names = csv.readline().split()
used_columns = [i for i, column_name in enumerate(column_names)
if column_name not in DROPPED_COLUMNS]
used_col_names = [column_name for i, column_name in enumerate(column_names)
if i in used_columns]
parse_dates = [[i for i, column_name in enumerate(used_col_names)
if column_name in ('Date', 'HrMn')]]
print(parse_dates)
data = pd.read_csv(csv, header=None, names=used_col_names, index_col=False, engine='python',
parse_dates=parse_dates, usecols=used_columns)
print(data)
```
Contents of 2014.csv:
```
Identification TEMP
USAF NCDC Date HrMn I Type QCP Temp Q
062693,99999,20140101,0025,4,FM-15, , 7.0,1,
062693,99999,20140101,0055,4,FM-15, , 6.0,1,
062693,99999,20140101,0125,4,FM-15, , 6.0,1,
062693,99999,20140101,0155,4,FM-15, , 6.0,1,
062693,99999,20140101,0225,4,FM-15, , 6.0,1,
062693,99999,20140101,0255,4,FM-15, , 6.0,1,
062693,99999,20140101,0325,4,FM-15, , 6.0,1,
062693,99999,20140101,0355,4,FM-15, , 6.0,1,
```
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} | 6 | 2015-03-31T08:15:01Z | 2015-05-04T19:09:49Z | 2015-05-04T19:09:49Z | CONTRIBUTOR | null | ```
In [1]: index = pd.DatetimeIndex(start='20150101', end='20150331', freq='B')
In [2]: df = pd.DataFrame(index=index, data=len(index)*[0])
In [3]: df.resample('BM', how='last')
Out[3]:
0
2015-01-30 0
2015-02-27 0
2015-03-31 0
2015-04-30 NaN
```
This is both in 0.14.1 and 0.15.2
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} | 8 | 2015-03-31T10:09:05Z | 2015-04-11T13:05:32Z | 2015-04-11T13:05:32Z | NONE | null | I find this surprising:
```
import pandas as pd
pd.Series([pd.Timestamp('2010-01-04 00:00:00')]).astype(str)
TypeError: cannot astype a datetimelike from [datetime64[ns]] to [|S0]
```
str(pd.Timestamp) works though, so why astype shouldn't?
Also the type representation "|S0" is not entirely clear to me.
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} | 7 | 2015-03-31T11:47:51Z | 2015-09-19T00:38:25Z | 2015-04-10T06:20:57Z | CONTRIBUTOR | null | Fixes GH #9757
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| null | 7 | 2015-03-31T11:55:39Z | 2015-04-08T12:22:22Z | 2015-04-04T18:34:13Z | CONTRIBUTOR | null | Get `FloatingPointError` ([stack trace](http://pastebin.com/m1fZH4ie)) if call `to_html` method of a DataFrame that contains some NaN values. To reproduce:
``` python
import pandas as pd
import numpy as np
pd.DataFrame(data=[np.nan, 3, 4]).to_html()
```
The problem seems to be that `FloatArrayFormatter._format_strings` assumes that the array it is trying to format as a string contains no `np.nan` values when checking to see if the array contains any very large or very small values.
Version info:
```
commit: None
python: 3.4.3.final.0
OS: Linux
machine: x86_64
pandas: 0.16.0
Cython: 0.22
numpy: 1.9.2
```
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} | 3 | 2015-03-31T13:27:19Z | 2015-08-18T11:09:30Z | 2015-08-18T11:09:30Z | NONE | null | Memorial day is incorrectly defined.
```
>>> from pandas.tseries.holiday import AbstractHolidayCalendar, USMemorialDay
>>> class MemorialDayCalendar(AbstractHolidayCalendar): rules=[USMemorialDay]
>>> MemorialDayCalendar().holidays('2021','2022')
<class 'pandas.tseries.index.DatetimeIndex'>
[2021-05-24]
Length: 1, Freq: None, Timezone: None
```
The [actual date](http://www.calendar-12.com/holidays/memorial_day/2021) of Memorial day in 2021 is 5/31.
Memorial day should be defined as:
`Holiday('Memorial Day' , month=5 , day=25, offset=DateOffset(weekday=MO(1)))`
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| null | 2 | 2015-03-31T13:38:50Z | 2015-04-04T18:34:32Z | 2015-04-04T18:34:20Z | CONTRIBUTOR | null | {
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} | 1 | 2015-03-31T13:58:19Z | 2015-04-18T23:18:33Z | 2015-04-18T23:18:33Z | MEMBER | null | `Series.to_frame` uses `pandas.DataFrame` statically as frame constructor. It is nice if `Series` has overridable property to specify frame-like constructor, like `DataFrame` has `_constructor_sliced`.
How about `Series._constructor_frame`?
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} | 7 | 2015-03-31T16:45:07Z | 2015-08-12T15:14:02Z | 2015-08-12T15:14:02Z | NONE | null | closes #9760
Memorial Day was incorrectly defined.
It is the last Monday in May, so the earliest it could be is the 25th (not the 24th).
This change fixes the problem.
```
>>> from pandas.tseries.holiday import AbstractHolidayCalendar, USMemorialDay
>>> class MemorialDayCalendar(AbstractHolidayCalendar):rules=[USMemorialDay]
>>> MemorialDayCalendar().holidays('2021','2022')
```
Will now return Timestamp('2021-05-31 00:00:00') instead of Timestamp('2021-05-24 00:00:00')
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} | 2 | 2015-03-31T16:53:40Z | 2015-04-06T00:18:30Z | 2015-04-06T00:18:30Z | CONTRIBUTOR | null | Hi,
For illustration purpose, I'll assume the following dataset:
``` python
In [1]: d=pd.DataFrame({'col1':[9.999e-8, 1e-7, 1.0001e-7, 2e-7, 4.999e-7, 5e-7, 5.0001e-7, 6e-7]})
In [2]: d
Out[2]:
col1
0 9.999000e-08
1 1.000000e-07
2 1.000100e-07
3 2.000000e-07
4 4.999000e-07
5 5.000000e-07
6 5.000100e-07
7 6.000000e-07
```
I've noticed the following behavior (in pandas 0.16 and 0.15.2):
1) When values from range [1e-7, 5e-7] are displayed along with values that are less than 1e-7, the output is OK:
``` python
In [3]: d[0:6]
Out[3]:
col1
0 9.999000e-08
1 1.000000e-07
2 1.000100e-07
3 2.000000e-07
4 4.999000e-07
5 5.000000e-07
```
2) When values exclusively from that range are displayed, the output is 0:
``` python
In [4]: d[1:6]
Out[4]:
col1
1 0
2 0
3 0
4 0
5 0
```
3) When values from that range are displayed along with values greater than 5e-7, the output for them is 0.000000:
``` python
In [5]: d[1:8]
Out[5]:
col1
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.000000
6 0.000001
7 0.000001
```
I'm assuming it's a rounding / output formatting error.
Also:
``` python
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: None
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
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 | 2015-03-31T19:51:46Z | 2016-04-25T13:42:20Z | 2016-04-25T13:42:20Z | NONE | null | # problem
`concat` fails when:
1. Using a SparseDataFrame
2. AND that SparseDataFrame has a column of all `0.0` and a `fill_value` of `0.0`
If that SparseDataFrame doesn't have an all `0.0` column, it works beautifully.
``` python
import unittest
import pandas as pd
from pandas.util.testing import assert_frame_equal
import sys
class TestSparseConcat(unittest.TestCase):
def setUp(self):
self.orig = {"A": [1.0, 0.0, 1.0, 0.0]}
self.to_add = {
"B": [0.0, 0.0, 0.0, 0.0],
"C": [0.0, 0.0, 1.0, 0.0]
}
merged = self.orig.copy()
merged.update(self.to_add)
self.expected = pd.DataFrame(data=merged)
def test_concat_df_to_df(self):
A = pd.DataFrame(data=self.orig)
B = pd.DataFrame(data=self.to_add)
C = pd.concat([A, B], axis=1)
assert_frame_equal(C, self.expected) # this works
def test_concat_sparse_to_df(self):
A = pd.DataFrame(data=self.orig)
B = pd.DataFrame(data=self.to_add).to_sparse(fill_value=0.0)
C = pd.concat([A, B], axis=1)
sys.stderr.write("\nExpected:\n{}\nGot:\n{}\n".format(self.expected, C))
assert_frame_equal(C, self.expected) # this DOESN'T work
if __name__ == "__main__":
unittest.main()
```
# output
```
.
Expected:
A B C
0 1 0 0
1 0 0 0
2 1 0 1
3 0 0 0
Got:
A B C
0 1 NaN 0
1 0 NaN 0
2 1 NaN 1
3 0 NaN 0
F
======================================================================
FAIL: test_concat_sparse_to_df (__main__.TestSparseConcat)
----------------------------------------------------------------------
Traceback (most recent call last):
File "example.py", line 28, in test_concat_sparse_to_df
assert_frame_equal(C, self.expected)
File "/home/vagrant/.virtualenvs/ai-modeling/lib/python2.7/site-packages/pandas/util/testing.py", line 748, in assert_frame_equal
check_exact=check_exact)
File "/home/vagrant/.virtualenvs/ai-modeling/lib/python2.7/site-packages/pandas/util/testing.py", line 692, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "das/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2758)
File "das/src/testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1843)
File "das/src/testing.pyx", line 102, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2010)
AssertionError: First object is null, second isn't: nan != 0.0
----------------------------------------------------------------------
Ran 2 tests in 0.024s
FAILED (failures=1)
```
# metadata
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-300.3.1.el6uek.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.3
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.0.0
sphinx: 1.2
patsy: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.2.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 1 | 2015-03-31T20:51:31Z | 2015-04-02T13:58:00Z | 2015-04-01T19:17:09Z | MEMBER | null | Derived from #9111.
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} | 5 | 2015-03-31T22:56:10Z | 2020-10-23T22:07:24Z | null | CONTRIBUTOR | null | This is pandas 0.15.2
```
In [1]: from pandas import *
In [2]: df = DataFrame({"A" : [1,2,3,4,5], "B": [3.1, 4, 5, 6, 7]})
In [3]: df[["A", "B"]]["A"] = 5 # no warning - bug?!
In [4]: df
Out[4]:
A B
0 1 3.1
1 2 4.0
2 3 5.0
3 4 6.0
4 5 7.0
In [5]: df[["A"]]["A"] = 5
C:\Anaconda\Scripts\ipython-script.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
if __name__ == '__main__':
```
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| null | 9 | 2015-04-01T08:59:55Z | 2015-04-12T13:30:23Z | 2015-04-12T13:30:23Z | NONE | null | In pandas 0.15.2:
```
from pandas import Series
a = Series(['A', None])
b = Series(['A', None])
a == b
```
gives
```
0 True
1 False
dtype: bool
```
while `a[1] == b[1]` gives `True`. This is a) inconsistent and b) the behaviour of `a == b` is wrong (None is equal to None, so `a==b` should return `[True, True]`.
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https://api.github.com/repos/pandas-dev/pandas/issues/9769 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9769/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9769/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9769/events | https://github.com/pandas-dev/pandas/pull/9769 | 65,662,237 | MDExOlB1bGxSZXF1ZXN0MzI0MDk2Mzg= | 9,769 | Added documentation for mode() | {
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| null | 3 | 2015-04-01T11:20:03Z | 2015-04-02T18:25:54Z | 2015-04-02T18:25:44Z | CONTRIBUTOR | null | This relates to issue #9750
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} | 10 | 2015-04-01T12:11:53Z | 2015-04-18T03:41:12Z | 2015-04-18T02:53:14Z | CONTRIBUTOR | null | Ideally, I would love for this to be the default, but that wouldn't be backwards-compatible in the case where the filename ends in '.gz' or '.bz2' and you want to treat it as uncompressed. That seems like it would be very rare, though.
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| null | 2 | 2015-04-01T13:41:02Z | 2015-05-27T13:08:30Z | 2015-05-27T13:07:49Z | NONE | null | http://pandas.pydata.org/pandas-docs/dev/index.html PDF linnk returns a 0.14.x version of the document, other PDF links return 404. It all depends on how you get to the link. I suggest checking all the documentation links on the website to verify they are linking to the appropriate version of the documentation.
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} | 7 | 2015-04-01T13:50:32Z | 2020-11-13T04:43:10Z | 2020-11-13T04:43:10Z | CONTRIBUTOR | null | ``` python
import numpy as np
import pandas as pd
df = pd.DataFrame({i:pd.Series(np.random.normal(size=10),
index=range(10)) for i in range(11)})
df_g = df.groupby(['a']*6+['b']*5, axis=1)
```
This, if I well understood, should build a groupby object grouping columns, and so give the possibility to later aggregate them. And indeed :
``` python
df_g.sum()
```
works well. But
``` python
df_g.head()
```
Throws an error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 986, in head
in_head = self._cumcount_array() < n
File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 1044, in _cumcount_array
cumcounts[indices] = values
IndexError: index 10 is out of bounds for axis 1 with size 10
```
and
``` python
df_g.apply(lambda x : x.sum())
```
from which I expected the same result as the first example, gives this table :
```
a b
0 -0.381070 NaN
1 -1.214075 NaN
2 -1.496252 NaN
3 3.392565 NaN
4 -0.782376 NaN
5 1.306043 NaN
6 NaN -1.772334
7 NaN 4.125280
8 NaN 1.992329
9 NaN 4.283854
10 NaN -4.791092
```
I didn't really get what's happening, I don't exclude a misunderstanding or an error from myself.
```
pd.show_versions()
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: 0.16.0-28-gcb8c130
nose: 1.3.4
Cython: 0.20.2
numpy: 1.9.2
scipy: 0.14.0
statsmodels: None
IPython: 3.0.0-dev
sphinx: 1.2.2
patsy: None
dateutil: 2.4.1
pytz: 2015.2
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: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: 2.5.3 (dt dec mx pq3 ext)
```
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} | 4 | 2015-04-01T14:08:43Z | 2016-07-11T13:28:23Z | 2016-07-11T13:28:20Z | CONTRIBUTOR | null | I have two multi-index dataframes `a_df` and `b_df`. When I try to concat them, the operation fails, but it works if `head` them first to a small size.
More specifically, note the following:
```
> pd.concat([a, b],
join='outer', axis=1, verify_integrity=True).dropna()
Empty DataFrame
Columns: [budget, default_bid]
Index: []
```
versus the following:
```
> pd.concat([a.head(100), b.head(100)],
join='outer', axis=1, verify_integrity=True).dropna()
budget default_bid
flight_uid created_at
0F0092Ntoi 2015-03-28 306115.26 6.00
0F0099v8iI 2015-03-28 10984.27 41.00
0F01MfxxSI 2015-03-28 3000.00 0.90
0F01SZnTfs 2015-03-28 3000.00 1.60
0F01ddgkRa 2015-03-28 1414.71 2.52
0F01ee81fL 2015-03-28 0.00 6.00
0F01f2HpD3 2015-03-28 425.00 40.00
0F01f4aW8n 2015-03-28 1575.76 1.25
0F01o6T23a 2015-03-28 0.00 9.00
0F02BZeTr6 2015-03-28 50893.42 1.50
```
Here are the first few entries for `a_df` and `b_df`:
```
> a_df.head(10)
flight_uid created_at
0F0092Ntoi 2015-03-28 306115.26
0F0099v8iI 2015-03-28 10984.27
0F01MfxxSI 2015-03-28 3000.00
0F01SZnTfs 2015-03-28 3000.00
0F01ddgkRa 2015-03-28 1414.71
0F01ee81fL 2015-03-28 0.00
0F01f2HpD3 2015-03-28 425.00
0F01f4aW8n 2015-03-28 1575.76
0F01o6T23a 2015-03-28 0.00
0F02BZeTr6 2015-03-28 50893.42
Name: budget, dtype: float64
```
```
> b_df.head(10)
flight_uid created_at
0F0092Ntoi 2015-03-28 6.00
0F0099v8iI 2015-03-28 41.00
0F01MfxxSI 2015-03-28 0.90
0F01SZnTfs 2015-03-28 1.60
0F01ddgkRa 2015-03-28 2.52
0F01ee81fL 2015-03-28 6.00
0F01f2HpD3 2015-03-28 40.00
0F01f4aW8n 2015-03-28 1.25
0F01o6T23a 2015-03-28 9.00
0F02BZeTr6 2015-03-28 1.50
Name: default_bid, dtype: float64
```
Here are the types for both:
```
> a_df.reset_index().dtypes
flight_uid object
created_at object
budget float64
dtype: object
```
More specifically:
```
> a_df.reset_index()['created_at'][0]
datetime.date(2015, 3, 28)
```
This is my configuration:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.4
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: 2.4.1
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext lo64)
```
Happy to post the data anywhere if that helps.
In case it helps, it seems to work if I pre-convert the field `'created_at'` (in the index) to `pd.DatetimeIndex()`, i.e., doing the following with both `a_df` and `b_df`:
``` python
a_df = a_df.reset_index()
a_df['created_at'] = pd.DatetimeIndex(a_df['created_at'])
a_df.set_index(['flight_uid', 'created_at'])
```
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} | 2 | 2015-04-01T14:22:32Z | 2015-04-01T15:22:04Z | 2015-04-01T15:20:56Z | NONE | null | I got a KeyError when running my program with pandas 0.15.2 on python 3.4.
I have a TimeSeries created from a dp.read_sql_query using a sqlalchemy engine for a sqlite database.
I use this TimeSeries as a storage for data to work on. In each iteration I work on some data and errase it from the series with drop inplace.
It has been working without hassle for some time now, but somehow today it threw a KeyError. By postmortem debugging I verified that the key was indeed present, so I do not know how I could avoid this crash from happening again.
Here is the traceback:
File "<ipython-input-35-6945f8f1d7ab>", line 1, in <module>
runfile('C:/Users/u5en/Programação/Projeto Extração/Extração.py', wdir='C:/Users/u5en/Programação/Projeto Extração')
File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 580, in runfile
execfile(filename, namespace)
File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 48, in execfile
exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace)
File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 448, in <module>
main()
File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 438, in main
executor(sap, tarefa, banco )
File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 357, in executor
extrai_frac(sap, tabela, preencher_sap, linha_a_iterar, leiaute, produto)
File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 284, in extrai_frac
registra_log(produto, tabela, leiaute, seq, dados[ind_entrada[0]],
File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1780, in **getitem**
return self._getitem_column(key)
File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1787, in _getitem_column
return self._get_item_cache(key)
File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1068, in _get_item_cache
values = self._data.get(item)
File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py", line 2849, in get
loc = self.items.get_loc(item)
File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\index.py", line 1402, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas\index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas\index.c:3807)
File "pandas\index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas\index.c:3687)
File "pandas\hashtable.pyx", line 696, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12310)
File "pandas\hashtable.pyx", line 704, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12261)
KeyError: 6
In [36]: debug
> c:\users\u5en\appdata\local\continuum\anaconda3\lib\site-packages\pandas\core\frame.py(1780)**getitem**()
> 1779 else:
> -> 1780 return self._getitem_column(key)
> 1781
ipdb> key
6
ipdb> u
> c:\users\u5en\programação\projeto extração\extração.py(284)extrai_frac()
> 283 if tam_bloco==1: #Desiste da entrada única
> --> 284 registra_log(produto, tabela, leiaute, seq, dados[ind_entrada[0]],
> 285 tam_bloco, inicio_dados, fim_dados, "x")
ipdb> ind_entrada[0]
6
ipdb> dados
GUID
6 4E1DCC24F58173FAE10000000A19FF81
7 B07D0F53BB47EF79E10000000A1D00F6
8 5081D6378BE20D30E10000000A1D001D
9 4D4A2852F17736BEE10000000A19FF83
10 A6BF5F5365BB183EE10000000A1D2028
11 9175355128FCEC72E10000000A1D2028
12 DDA3DE53533C041DE10000000A1D2028
13 142207533F63D632E10000000A1D2028
14 50741D7B4FE61F18E10000000A1D0022
15 08485E5381EB4B3EE10000000A1D00F8
16 F4657B5249D9ED18E10000000A1D00F8
17 D0A95451844A9D19E10000000A1D00F8
18 4FD60BFC2B7A1159E10000000A1D0026
19 50920B577EF70BE2E10000000A1D0026
20 765E59539656A375E10000000A1D00F6
21 16FA7B538A6A3A7DE10000000A1D2028
22 E5330753B6589922E10000000A1D2028
23 1748E852010AA076E10000000A1D2028
24 4DD1A2D327FB51F3E10000000A19FF83
25 5085A682BF56192BE10000000A1D0022
26 4E1D0227DB213A10E10000000A19FF82
27 1805E953691ECA44E10000000A1D00F8
28 101E6E52598EE224E10000000A1D00F8
29 DE221954306A8713E10000000A1D00F8
30 4E6EAC19A5F6699EE10000000A19FF81
31 51317752E9E2956EE10000000A1D00F6
32 EEB3D551DEA7923EE10000000A1D00F8
33 20D366516BFE074FE10000000A1D00F8
34 CFFB63534A6A3F12E10000000A1D2028
35 99AC1A50E6C0A128E20030C60D1E02BC
... ...
1212 48A25A511DA62E47E10000000A1D00F8
1213 97B754526E0B863CE10000000A1D00F6
1214 E20EFD543AF42D4BE10000000A1D211B
1215 CA250554AEF2857FE10000000A1D00F6
1216 6249EC54DF70A2CBE10000000A1D211B
1217 BD5A73541E886A70E10000000A1D00F6
1218 CB3173544288AC4EE10000000A1D00F8
1219 C96FAC5406FADA26E10000000A1D00F8
1220 7F749754BB617815E10000000A1D211B
1221 E70FEB54E2CBCC70E10000000A1D00F8
1222 EA460854938F9350E10000000A1D2028
1223 EA6E4F54EF75025AE10000000A1D00F8
1224 E3BB98543622BF62E10000000A1D2028
1225 E3DA035508382825E10000000A1D00F6
1226 96BE97545F8ADB4EE10000000A1D00F6
1227 15407352C1CD7A33E10000000A1D2028
1228 EE55BF51D50E2E23E10000000A1D00F6
1229 ECBFAD53CB8DC10DE10000000A1D00F6
1230 F120BC5464E73829E10000000A1D2028
1231 EF2E0E556FDDB927E10000000A1D211B
1232 C4AEAA50BF24A371E2000D3560A76C25
1233 34A84A54A762386DE10000000A1D00F6
1234 F2DF19544FC51832E10000000A1D00F6
1235 DAE89750A2E7A12CE2009DD40A5B4CB2
1236 722CAD53CE9B961FE10000000A1D00F6
1237 EA0EBC5069E94D74E2005420EF347364
1238 F922745456997A40E10000000A1D00F8
1239 E4124A510D49371DE10000000A1D00F8
1240 BFB753526DF6F81BE10000000A1D2028
1241 A0CFDA53E983EA44E10000000A1D2028
[1236 rows x 1 columns]
ipdb> dados[ind_entrada[0]]
**\* KeyError: 6
ipdb> dados[6]
**\* KeyError: 6
ipdb> dados[ind_entrada]
**\* IndexError: indices are out-of-bounds
ipdb> entrada.index
Int64Index([6], dtype='int64')
ipdb> dados[entrada.index.values]
**\* IndexError: indices are out-of-bounds
ipdb> dados[dados.index.values[0]]
**\* KeyError: 6
ipdb>
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} | 2 | 2015-04-01T14:31:30Z | 2015-04-04T18:32:40Z | 2015-04-04T18:32:40Z | MEMBER | null | ```
import pandas as pd
class TestDataFrame(pd.DataFrame):
_metadata = ['testattr']
@property
def _constructor(self):
return TestDataFrame
df = TestDataFrame({0: [1, 2, 3], 1: [1, 2, 3]})
df.testattr = 'XXX'
df.testattr
# XXX
# Using list is OK
df.iloc[[0, 1], :].testattr
# XXX
# Using slice is NG
df.iloc[0:1, :].testattr
# AttributeError: 'TestDataFrame' object has no attribute 'testattr'
```
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} | 7 | 2015-04-01T14:53:48Z | 2016-09-09T21:22:15Z | 2015-04-07T22:36:59Z | CONTRIBUTOR | null | The order of columns coming out of `.assign` is hard to predict
``` Python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a': [1], 'b': [2]})
In [3]: df
Out[3]:
a b
0 1 2
In [4]: df.assign(d=df.a*10, a=df.b*10, e=df.a*20)
Out[4]:
a b e d
0 20 2 20 10
```
Would it be reasonable to keep all existing keys in their current location and then add the remaining keys in sorted order at the end?
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} | 4 | 2015-04-01T15:11:18Z | 2015-07-23T11:01:52Z | 2015-04-16T06:15:12Z | NONE | null | If I try to run this code:
``` python
pd.DataFrame.from_dict({
('c1', 0): pd.Series({x: x for x in range(4)}),
('c1', 1): pd.Series({x: x+4 for x in range(4)}),
('c2', 0): pd.Series({x: x for x in range(4)}),
('c2', 1): pd.Series({x: x+4 for x in range(4)}),
('c3', 0): pd.Series({x: x for x in range(4)}),
}).T.to_latex()
```
The result is 2 rows instead of 5 in the latex output. If I use a csv it works.
I'm running pandas 0.16.0 under python 2.7 on Mac OS X 10.10
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} | 11 | 2015-04-01T15:43:04Z | 2015-04-09T15:21:34Z | 2015-04-05T13:52:22Z | NONE | null | Hi, don't know if this is my Python/some module version.. but when I try to do the following, I end up with two figures popping up, instead of the expected one:
```
df[[0, 1]].plot(kind='bar', stacked=True, ylim=(0, 100))
df[[2]].plot(secondary_y=True, color='orange', linewidth=2.0)
```
The data in both plots looks right, the left/right y axes are correct etc.. but they're not on the same figure :(
Also if this is of any help, if I do `fig = plt.figure()` before running the commands above, I end up with another completely empty figure shown to me first..
Sorry if this is something stupid and/or not caused by pandas..
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} | 15 | 2015-04-01T16:36:12Z | 2018-03-19T21:45:09Z | 2017-12-10T15:41:37Z | NONE | null | When merging an int dtype with a str dtype the join does not work:
``` python
>>> import pandas as pd
>>> df1 = pd.DataFrame({"A":[0]})
>>> df2 = pd.DataFrame({"A":["0"]})
>>> pd.merge(df1, df2, on=["A"])
Empty DataFrame
Columns: [A]
Index: []
```
I think it would be better to get a warning that the join is performed on incompatible column dtypes.
This is my pandas version:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2014.9
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: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.5.6
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
Thanks for all your work on pandas!
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} | 6 | 2015-04-01T20:06:37Z | 2015-04-03T22:42:19Z | 2015-04-03T22:42:19Z | NONE | null | calling to_json on an apparently empty dataframe may segfault.
using the same dataframe with 0.15.2 works.
A git bisect says that commit a67bef49e0b8e89e785c08011e8331e9eb7e54b8 is when the problem first appeared.
segfault:
Program terminated with signal SIGSEGV, Segmentation fault.
#0 0x00007fd42aae8735 in PdBlock_iterBegin (_obj=0x7fd423d22410, tc=0x7fff1bf57310) at pandas/src/ujson/python/objToJSON.c:1057
(gdb) list
1052 // init a dedicated context for this column
1053 NpyArr_iterBegin(obj, tc);
1054 npyarr = GET_TC(tc)->npyarr;
1055
1056 // set the dataptr to our desired column and initialise
1057 npyarr->dataptr += npyarr->stride \* idx;
1058 NpyArr_iterNext(obj, tc);
1059 GET_TC(tc)->itemValue = NULL;
1060 ((PyObjectEncoder*) tc->encoder)->npyCtxtPassthru = NULL;
1061
Following is the test code and ascii-pickle file that reproduces the behavior:
```
import pickle
p = pickle.load(open("p.pkl", "rb"))
p.to_json("/tmp/foo.json", date_format='iso', orient='records')
```
below is the pickled dataframe that causes the problem.
(I've been unable to construct another dataframe that exhibits this behavior)
```
ccopy_reg
_reconstructor
p0
(cpandas.core.frame
DataFrame
p1
c__builtin__
object
p2
Ntp3
Rp4
g0
(cpandas.core.internals
BlockManager
p5
g2
Ntp6
Rp7
((lp8
cpandas.core.index
_new_Index
p9
(cpandas.core.index
Index
p10
(dp11
S'data'
p12
cnumpy.core.multiarray
_reconstruct
p13
(cnumpy
ndarray
p14
(I0
tp15
S'b'
p16
tp17
Rp18
(I1
(I5
tp19
cnumpy
dtype
p20
(S'O8'
p21
I0
I1
tp22
Rp23
(I3
S'|'
p24
NNNI-1
I-1
I63
tp25
bI00
(lp26
S'symbol'
p27
aS'side'
p28
aS'shares'
p29
aS'entry_price'
p30
aS'entry_time'
p31
atp32
bsS'name'
p33
Nstp34
Rp35
ag9
(cpandas.core.index
Int64Index
p36
(dp37
g12
g13
(g14
(I0
tp38
g16
tp39
Rp40
(I1
(I0
tp41
g20
(S'i8'
p42
I0
I1
tp43
Rp44
(I3
S'<'
p45
NNNI-1
I-1
I0
tp46
bI00
S''
p47
tp48
bsg33
Nstp49
Rp50
a(lp51
g13
(g14
(I0
tp52
g16
tp53
Rp54
(I1
(I3
I0
tp55
g44
I00
g47
tp56
bag13
(g14
(I0
tp57
g16
tp58
Rp59
(I1
(I2
I0
tp60
g23
I00
(lp61
tp62
ba(lp63
g9
(g10
(dp64
g12
g13
(g14
(I0
tp65
g16
tp66
Rp67
(I1
(I3
tp68
g23
I00
(lp69
g28
ag29
ag31
atp70
bsg33
Nstp71
Rp72
ag9
(g10
(dp73
g12
g13
(g14
(I0
tp74
g16
tp75
Rp76
(I1
(I2
tp77
g23
I00
(lp78
g27
ag30
atp79
bsg33
Nstp80
Rp81
a(dp82
S'0.14.1'
p83
(dp84
S'axes'
p85
g8
sS'blocks'
p86
(lp87
(dp88
S'mgr_locs'
p89
g13
(g14
(I0
tp90
g16
tp91
Rp92
(I1
(I3
tp93
g44
I00
S'\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00'
p94
tp95
bsS'values'
p96
g54
sa(dp97
g89
c__builtin__
slice
p98
(I0
I6
I3
tp99
Rp100
sg96
g59
sasstp101
bb.
```
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| null | 4 | 2015-04-01T21:28:00Z | 2015-04-01T21:57:19Z | 2015-04-01T21:57:19Z | NONE | null | When a Series or DataFrame has boolean elements, but the dtype is not bool, the elementwise negation operator (~) performs bitwise complement instead of negation. I got bit by this surprising behavior and it took awhile to track down and understand:
```
In [1]: import pandas as pd
In [2]: s = pd.Series([None,True])
In [3]: s.iloc[0] = False
In [4]: s
Out[4]:
0 False
1 True
dtype: object
In [5]: ~s
Out[5]:
0 -1
1 -2
dtype: object
```
whereas one would normally expect this sort of behavior:
```
In [6]: t = pd.Series([False,True])
In [7]: t
Out[7]:
0 False
1 True
dtype: bool
In [8]: ~t
Out[8]:
0 True
1 False
dtype: bool
```
Here's my version information:
```
% ipython
Python 2.7.9 |Anaconda 2.2.0 (x86_64)| (default, Dec 15 2014, 10:37:34)
Type "copyright", "credits" or "license" for more information.
IPython 3.0.0 -- An enhanced Interactive Python.
In [12]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
```
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} | 21 | 2015-04-02T00:52:27Z | 2015-04-13T16:01:48Z | 2015-04-13T16:01:48Z | CONTRIBUTOR | null | 6717aa06dcaa1950ffb46fef454f5df9404209bd removed zlib and blosc from the global namespace.
```
from pandas import read_csv
table = read_csv('aadhaar_data.csv')
table.to_msgpack('d.msg')
# NameError: global name 'blosc' is not defined
table.to_msgpack('d-blosc.msg', compress='blosc')
# NameError: global name 'zlib' is not defined
table.to_msgpack('d-zlib.msg', compress='zlib')
```
This pull request restores zlib and blosc compression in to_msgpack via local imports.
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} | 23 | 2015-04-02T02:12:17Z | 2019-10-21T08:25:45Z | 2019-09-16T02:33:40Z | CONTRIBUTOR | null | I am calling to_string() without any parameters and it beautifully fixed-formatted my dataframe apart from my very wide filename column, that is being truncated with "...". How can I avoid that?
``` text
FILENAME OBS_ID XUV
0 'mvn_iuv_l1a_IPH3-cycle00007-mode040-muvdark_2... 40 MUV
1 'mvn_iuv_l1a_IPH2-cycle00047-mode050-muvdark_2... 50 MUV
2 'mvn_iuv_l1a_apoapse-orbit00127-mode2001-muvda... 2001 MUV
3 'mvn_iuv_l1a_APP1-orbit00087-mode1031-fuvdark_... 1031 FUV
4 'mvn_iuv_l1a_IPH2-cycle00005-mode060-fuvdark_2... 60 FUV
```
I tried calling it like this, but to no avail (same output):
``` python
with open('test_summary_out.txt','w') as f:
f.write(summarydf.head().to_string(formatters={'filename':lambda x: "{:100}".format(x)}))
```
Version: 0.16 with Python 3.4
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} | 2 | 2015-04-02T07:15:43Z | 2015-04-14T13:41:01Z | 2015-04-14T13:41:01Z | CONTRIBUTOR | null | In 0.16.0 comparing MultiIndices using `==` raises an exception.
``` python
from StringIO import StringIO
import pandas as pd
# create a series using a multiindex
df = pd.read_csv(StringIO('a,b,c\n1,2,3'), index_col=[0, 1])
series = df['c']
series.index == series.index
# File "/Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/index.py", line 54, in wrapper
# func = getattr(self._data.view(np.ndarray), opname)
# AttributeError: 'NoneType' object has no attribute 'view'
print pd.__version__
#0.16.0
```
Another one I've come across is that there is an `AttributeError` when comparing indexes for which the underlying numpy arrays cannot be broadcast together. The error message itself is very obscure. I gather that `.equals()` can be used for what I'm trying to do in these examples, but still the errors when using `==` are surprising.
```
>>> pd.Index(['a', 'b']) == pd.Index(['a', 'b', 'c'])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-44325051df92> in <module>()
----> 1 pd.Index(['a', 'b']) == pd.Index(['a', 'b', 'c'])
/Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/index.pyc in wrapper(self, other)
57 # technically we could support bool dtyped Index
58 # for now just return the indexing array directly
---> 59 if is_bool_dtype(result):
60 return result
61 try:
/Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/common.py in is_bool_dtype(arr_or_dtype)
2510
2511 def is_bool_dtype(arr_or_dtype):
-> 2512 tipo = _get_dtype_type(arr_or_dtype)
2513 return issubclass(tipo, np.bool_)
2514
/Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/common.py in _get_dtype_type(arr_or_dtype)
2439 elif isinstance(arr_or_dtype, CategoricalDtype):
2440 return CategoricalDtypeType
-> 2441 return arr_or_dtype.dtype.type
2442
2443
AttributeError: 'NotImplementedType' object has no attribute 'dtype'
```
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| null | 3 | 2015-04-02T09:35:10Z | 2015-04-02T12:37:45Z | 2015-04-02T12:01:46Z | CONTRIBUTOR | null | ``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.normal(size=(10,15)))
df.columns = pd.timedelta_range(start='0s',periods=15,freq='1s')
df.groupby(['a']*5+['b']*5)
```
fails (throwing ValueError), whereas
``` python
df.transpose().groupby(['a']*5+['b']*5,axis=1)
```
doesn't.
```
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: 0.16.0-28-gcb8c130
nose: 1.3.4
Cython: 0.20.2
numpy: 1.9.2
scipy: 0.14.0
statsmodels: None
IPython: 3.0.0-dev
sphinx: 1.2.2
patsy: None
dateutil: 2.4.1
pytz: 2015.2
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: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: 2.5.3 (dt dec mx pq3 ext)
```
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} | 2 | 2015-04-02T11:02:27Z | 2015-04-14T13:28:04Z | 2015-04-14T13:28:04Z | NONE | null | This worked fine with pandas 0.15:
``` python
In [24]: import pandas as pd
In [25]: from decimal import Decimal
In [26]: s = pd.Series([Decimal(10)] * 10)
In [27]: s / Decimal(5)
Out[27]:
0 2
1 2
2 2
3 2
4 2
5 2
6 2
7 2
8 2
9 2
dtype: object
```
When I use 0.16.0, it raises:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-29-f6e1a63d5a28> in <module>()
----> 1 s / Decimal(5)
/home/jck/anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in wrapper(left, right, name)
530 if hasattr(lvalues, 'values'):
531 lvalues = lvalues.values
--> 532 return left._constructor(wrap_results(na_op(lvalues, rvalues)),
533 index=left.index, name=left.name,
534 dtype=dtype)
/home/jck/anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in na_op(x, y)
483 result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan)
484
--> 485 result = com._fill_zeros(result, x, y, name, fill_zeros)
486 return result
487
/home/jck/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in _fill_zeros(result, x, y, name, fill)
1407 y = np.array(y)
1408
-> 1409 if is_integer_dtype(y):
1410
1411 if (y == 0).any():
/home/jck/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in is_integer_dtype(arr_or_dtype)
2448
2449 def is_integer_dtype(arr_or_dtype):
-> 2450 tipo = _get_dtype_type(arr_or_dtype)
2451 return (issubclass(tipo, np.integer) and
2452 not issubclass(tipo, (np.datetime64, np.timedelta64)))
/home/jck/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in _get_dtype_type(arr_or_dtype)
2439 elif isinstance(arr_or_dtype, CategoricalDtype):
2440 return CategoricalDtypeType
-> 2441 return arr_or_dtype.dtype.type
2442
2443
AttributeError: 'Decimal' object has no attribute 'dtype'
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.4
Cython: 0.21
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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| null | 1 | 2015-04-02T11:49:45Z | 2015-04-02T12:00:07Z | 2015-04-02T11:59:47Z | NONE | null | ```
import datetime as dt; import pandas as pd
d2 = dt.datetime(2015, 4, 2, 12, 23, 12, 567000)
df = pd.DataFrame([['Bar', d2]], columns=['name', 'date'])
df2 = df.sort('date').groupby('name').last().reset_index()
```
`df2` is
name date
0 Bar 2015-04-02 12:23:12.567000064
i.e. 64 nanoseconds were added to the timestamp. The amounts added seem to be always the powers of 2, perhaps it is caused by some internal conversion to double?
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} | 6 | 2015-04-02T15:44:39Z | 2015-08-20T20:21:45Z | 2015-08-20T13:24:26Z | NONE | null | The documentation of http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Grouper.html is quite thin, when it comes to _additional kwargs to control time-like groupers (when freq is passed)_:
- convention is not described at all
- default values are not described for any of the additional kwargs
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} | 3 | 2015-04-02T15:53:03Z | 2020-01-03T23:23:44Z | 2020-01-03T23:23:44Z | NONE | null | `groupy(level=[0])` doesn't work on a unique index. I guess it is semantics whether one allows not only level=0, but also level=[0], but if not, the error message should be different:
```
df = pd.DataFrame(np.arange(0,9).reshape(3,3))
df.groupby(level=[0])
ValueError: level > 0 only valid with MultiIndex
```
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| null | 4 | 2015-04-02T18:00:12Z | 2015-04-02T20:33:05Z | 2015-04-02T20:33:05Z | NONE | null | Hi,
I tried installing pandas .16 using conda in windows but am getting the following issue. I tried reinstalling using an admin console but it doesn't work. I've downgraded to 0.15.2 which works again but could use help.
This is my first time posting so if this is not the appropriate channel please let me know.
Thanks in advance
C:\Users\kumarra>ipython
Python 3.4.1 |Continuum Analytics, Inc.| (default, Jun 11 2014, 17:27:11) [MSC v
.1600 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
```
IPython 3.0.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import pandas
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas\tslib.pyx in pandas.tslib.Timedelta.__new__ (pandas\tslib.c:29661)()
TypeError: 'nanoseconds' is an invalid keyword argument for this function
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
C:\Miniconda3\lib\site-packages\pandas\__init__.py in <module>()
45
46 # let init-time option registration happen
---> 47 import pandas.core.config_init
48
49 from pandas.core.api import *
C:\Miniconda3\lib\site-packages\pandas\core\config_init.py in <module>()
15 is_instance_factory, is_one_of_factory,
16 get_default_val)
---> 17 from pandas.core.format import detect_console_encoding
18
19
C:\Miniconda3\lib\site-packages\pandas\core\format.py in <module>()
22 import csv
23
---> 24 from pandas.tseries.period import PeriodIndex, DatetimeIndex
25
26 docstring_to_string = """
C:\Miniconda3\lib\site-packages\pandas\tseries\period.py in <module>()
6 from pandas.core.base import PandasObject
7
----> 8 import pandas.tseries.frequencies as frequencies
9 from pandas.tseries.frequencies import get_freq_code as _gfc
10 from pandas.tseries.index import DatetimeIndex, Int64Index, Index
C:\Miniconda3\lib\site-packages\pandas\tseries\frequencies.py in <module>()
7
8 from pandas.core.algorithms import unique
----> 9 from pandas.tseries.offsets import DateOffset
10 from pandas.util.decorators import cache_readonly
11 import pandas.tseries.offsets as offsets
C:\Miniconda3\lib\site-packages\pandas\tseries\offsets.py in <module>()
2150
2151
-> 2152 class Nano(Tick):
2153 _inc = Timedelta(nanoseconds=1)
2154 _prefix = 'N'
C:\Miniconda3\lib\site-packages\pandas\tseries\offsets.py in Nano()
2151
2152 class Nano(Tick):
-> 2153 _inc = Timedelta(nanoseconds=1)
2154 _prefix = 'N'
2155
pandas\tslib.pyx in pandas.tslib.Timedelta.__new__ (pandas\tslib.c:29707)()
ValueError: cannot construct a TimeDelta from the passed arguments, allowed keyw
ords are [days, seconds, microseconds, milliseconds, minutes, hours, weeks]
```
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} | 5 | 2015-04-02T18:44:22Z | 2015-06-02T19:25:19Z | 2015-05-27T02:48:47Z | NONE | null | When using df.plot() to creat a simple line chart from a dataframe, the expected behavior is that, if no 'grid' keyword is passed, the plot will default to the matplotlib 'axes.grid' rcParam setting to determine whether to show a grid or not. However (in 0.16), the grid is always being shown, regardless of the matplotlib setting, unless I explicitly pass grid=False.
In general, there are several plot types where grid=True is forced, which basically breaks the consistent styling that you get from matplotlib rcparams or another package like Seaborn. I can understand forcing a default grid=False for the type of plots where a grid doesn't make sense, but forcing grid=True, overriding the configured defaults, for things like boxplots seems a bit counter intuitive.
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} | 4 | 2015-04-02T21:04:00Z | 2015-04-04T21:28:12Z | 2015-04-04T18:32:40Z | MEMBER | null | Closes #9776.
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} | 11 | 2015-04-02T21:56:56Z | 2016-02-11T19:10:53Z | 2015-04-17T12:35:41Z | NONE | null | Try to the following code, and you will find column labels mismatched with the data in the Excel file :
`cols = pd.MultiIndex.from_tuples([('site',''),('2014','height'),('2014','weight')])`
`df = pd.DataFrame(np.random.randn(10,3), columns=cols)`
`df.to_excel('test.xlsx', index=False)`
I am using pandas v0.16.0.
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} | 1 | 2015-04-02T23:39:12Z | 2015-04-07T10:28:15Z | 2015-04-07T10:28:15Z | NONE | null | After executing `df.to_stata()` with `write_index=False`, all `NaNs` in `df` were automatically replaced by `8.988466e+307`. Please see the following code:
`df = pd.DataFrame(np.random.randn(5,4), columns=list('abcd'))`
`df.ix[2, 'a':'c'] = np.nan`
`print df`
`df.to_stata('test.dta', write_index=False)`
`print df`
I am using `pandas v0.16.0`. Thanks.
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} | 2 | 2015-04-03T02:04:49Z | 2015-04-03T11:07:53Z | 2015-04-03T11:07:53Z | CONTRIBUTOR | null | xref #9754
I just did
`pandoc doc/source/contributing.rst -t markdown > CONTRIBUTING.md`
to generate the new CONTRIBUTING file.
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} | 11 | 2015-04-03T04:54:57Z | 2015-04-03T19:58:47Z | 2015-04-03T17:18:52Z | MEMBER | null | This is an alternative to #9796.
I simply did a couple of changes to the restructuredtext source so it generates cleaner markdown. The main thing was making the categories not quite so nested. (Was that [heavy nesting](http://pandas-docs.github.io/pandas-docs-travis/contributing.html#committing-your-code) intentional?) I think pandoc was running out of header styles or something like that.
cc @rockg @jreback @jorisvandenbossche
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} | 2 | 2015-04-03T07:38:16Z | 2015-04-11T00:52:18Z | 2015-04-09T15:03:48Z | NONE | null | epi.txt
```
OBS ID PERIOD TMT Y0 AGE COUNT
1 1 1 0 11 31 5
2 1 2 0 11 31 3
3 1 3 0 11 31 3
4 1 4 0 11 31 3
5 2 1 0 11 30 3
6 2 2 0 11 30 5
7 2 3 0 11 30 3
8 2 4 0 11 30 3
9 3 1 0 6 25 2
10 3 2 0 6 25 4
```
```
epi = pandas.read_csv("./epi.datn.txt", sep=" ", index_col=True)
print(epi)
```
```
OBS PERIOD TMT Y0 AGE COUNT
ID
1 1 1 0 11 31 5
1 2 2 0 11 31 3
1 3 3 0 11 31 3
1 4 4 0 11 31 3
2 5 1 0 11 30 3
2 6 2 0 11 30 5
2 7 3 0 11 30 3
2 8 4 0 11 30 3
3 9 1 0 6 25 2
3 10 2 0 6 25 4
```
I had some code that used `False` to disable reading indexes from the data, and was expecting `True` to be the opposite. Instead it read the **second** column, because True == 1 in python.
I think that `True` should be explicitly handled, because this behaviour is counterintuitive given that `False` is meaningful. `False` is not the same as `index_col=0`, after all.
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} | 3 | 2015-04-03T12:44:36Z | 2015-11-12T23:44:34Z | 2015-04-07T10:28:47Z | CONTRIBUTOR | null | closes #9795
Just needed to move the data frame copy earlier in the execution path.
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