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https://api.github.com/repos/pandas-dev/pandas/issues/12104 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12104/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12104/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12104/events | https://github.com/pandas-dev/pandas/issues/12104 | 127,802,321 | MDU6SXNzdWUxMjc4MDIzMjE= | 12,104 | Setting values on a DataFrame with tz aware DatetimeIndex fails | {
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... | closed | false | null | [] | null | 1 | 2016-01-20T22:47:28Z | 2016-01-20T23:05:03Z | 2016-01-20T23:04:48Z | NONE | null | ``` python
tdf = pd.DataFrame({'a': np.arange(10), 'b': np.arange(10) + 5},
index=pd.date_range('20160119 09:00', freq='5T',
periods=10, tz='US/Eastern'))
new_values = tdf.sample(2)['b'] + 100
tdf.loc[new_values.index, 'b'] = new_values.values
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12105 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12105/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12105/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12105/events | https://github.com/pandas-dev/pandas/issues/12105 | 127,807,559 | MDU6SXNzdWUxMjc4MDc1NTk= | 12,105 | pandas.merge indicator is ignoring suffixes | {
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The output will show an indicator column which will show 'left', 'right' or 'both' instead of 'leftDataFrameSuffix', 'rightdfSuffix' or 'both'.
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https://api.github.com/repos/pandas-dev/pandas/issues/12106 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12106/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12106/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12106/events | https://github.com/pandas-dev/pandas/issues/12106 | 127,813,592 | MDU6SXNzdWUxMjc4MTM1OTI= | 12,106 | pytables.open prints to stdout when attempting to open a read-only HDF5 file | {
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"descri... | closed | false | null | [] | null | 2 | 2016-01-20T23:55:42Z | 2016-01-21T00:51:38Z | 2016-01-21T00:01:32Z | NONE | null | ERROR: type should be string, got "https://github.com/pydata/pandas/blob/master/pandas/io/pytables.py#L539\n\n```\n try:\n self._handle = tables.open_file(self._path, self._mode, **kwargs)\n except (IOError) as e: # pragma: no cover\n if 'can not be written' in str(e):\n print('Opening %s in read-only mode' % self._path)\n self._handle = tables.open_file(self._path, 'r', **kwargs)\n else:\n raise\n```\n\nThe above code prints to stdout when attempting to read from a readonly file. This is a problem for programs which use stdout for their own output. It should either print to sys.stderr or whatever log the rest of the library uses.\n" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12107 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12107/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12107/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12107/events | https://github.com/pandas-dev/pandas/pull/12107 | 127,823,534 | MDExOlB1bGxSZXF1ZXN0NTY2OTI3NDU= | 12,107 | PEP: pandas/core final round (api, matrix, sparse, cleanup of common,… | {
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Besides groupby we're clean.
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https://api.github.com/repos/pandas-dev/pandas/issues/12108 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12108/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12108/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12108/events | https://github.com/pandas-dev/pandas/issues/12108 | 127,836,477 | MDU6SXNzdWUxMjc4MzY0Nzc= | 12,108 | assert_frame_equal fails when dtype is categorical | {
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assert_frame_equal(desired_df, df)
File "C:\Anaconda\lib\site-packages\pandas\util\testing.py", line 1049, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "C:\Anaconda\lib\site-packages\pandas\util\testing.py", line 927, in assert_series_equal
assert_attr_equal('dtype', left, right)
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https://api.github.com/repos/pandas-dev/pandas/issues/12110 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12110/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12110/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12110/events | https://github.com/pandas-dev/pandas/pull/12110 | 127,913,524 | MDExOlB1bGxSZXF1ZXN0NTY3NDIzMTg= | 12,110 | ERR: read_csv with dtype on empty data, #12048. | {
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closes #12048.
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> > > k = test[0] - test[1]
Works most of the time, but sometimes gives a column of zero (or near zeros) as output.
Does not seem to affect small dataframes.
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This replaces the implementation of `nanskew` and `nankurt` by an algorithm that does not produce round-off error due to subtracting very similar large values. The sample skew and kurtosis are computed directly from the moments, rather than expanding the moments, wh... | {
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``` py... | {
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The previous behaviour was to use a `__getitem__` lookup which caused incorrect behaviour with numeric columns. The adjusted code uses `iloc` numerical indexing to resolve the issue.
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```
before after ratio
[1330b9fe] [404911c6]
17.25ms 1.21ms 0.07 categoricals.categorical_constructor_with_datetimes.time_datetimes_with_nat
```
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Adds line_terminator optional argument to to_clipboard
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-01-25T08:52:00Z | 2016-04-17T14:31:40Z | 2016-04-17T14:31:40Z | CONTRIBUTOR | null | Specifying `dropna=False` drops column level names:
```
import pandas as pd
df = pd.DataFrame({"Col1": [23], "Col2": [45]})
names1 = pd.crosstab(df["Col1"], df["Col2"]).columns.names
names2 = pd.crosstab(df["Col1"], df["Col2"], dropna=False).columns.names
print(names1, names2)
>>>['Col2'] [None]
```
P.S. At first i t... | {
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Add src->buffer = NULL; after garbage collecting src->buffer in the buffer_rd_bytes routine in io.c to fix the segfault
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``` python
In[3]: pd.to_timedelta(1000, unit='ms')
Out[3]: Timedelta('0 days 00:00:01')
In[4]: pd.to_timedelta(1000.0, unit='ms')
Out[4]: Timedelta('0 days 00:00:0... | {
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Doc builds are failing, testing if using the latest `ipython` version solves it.
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so something changed in what conda is pull in I think (as I cannot repro this, though I have a slightly older build env)
if anyone wants to take a look
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- [x] the repr: can we follow here PEP8 as well? :-) (I mean spaces after the comma's, I think this would make it a bit more readable, maybe also quotes around the strings)
- [x] `upsample` has been removed? (but is still in the documentation) Or is this now `asfreq`... | {
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This is due to the fact that the skiplist implementation only works with doubles. Would you be opposed if I added insta... | {
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"de... | closed | false | null | [] | null | 6 | 2016-01-26T11:40:51Z | 2016-01-26T14:31:38Z | 2016-01-26T13:50:02Z | NONE | null | map seems to remove the default behaviour - workaround is to use a lambda function
``` python
import collections
import pandas as pd
a=pd.Series([1,2,3])
print 'a = '
print a
c=collections.defaultdict(int)
c[1]=1
c[2]=4
b=a.map(c)
print 'b = '
print b
# b.iloc[2] set to Nan (ignoring defaultdict)
d=a.map(lambda x... | {
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... | 5 | 2016-01-26T12:52:12Z | 2021-09-28T18:13:10Z | null | CONTRIBUTOR | null | Is there a way to right-align numbers in the HTML repr of tables? Right now they are left aligned, and it is a bit of a pain.
I see there is a lot of work on conditional HTML formatting recently (e.g, #10250). Is it already possible to achieve this?
/cc @TomAugspurger
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... | 6 | 2016-01-26T15:08:40Z | 2019-11-03T17:24:25Z | 2019-11-03T12:59:56Z | NONE | null | Working with the new style API, I would like to turn several columns of a dataframe into something like a heatmap. I would like to use the same range for all columns in the subset, i.e., the colour map should take a minimum and maximum value I specify, regardless of what actual range each column has. Currently using th... | {
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... | closed | false | null | [] | null | 2 | 2016-01-26T15:46:11Z | 2016-01-26T18:04:22Z | 2016-01-26T18:04:22Z | NONE | null | I came across an intermittent issue that took some experimentation to track down. I was using a oneliner function for taring dataframes (or series, I've noticed the issue there as well) based on the last 50 rows:
``` python
def tare(df):
return df - df[-50:].mean()
```
On rare occasions, this gave me an obviously... | {
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Inserting an ` ` into that cell prevents this from happening. Would a PR be accepted whi... | {
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... | 5 | 2016-01-26T16:16:00Z | 2021-02-10T19:43:50Z | 2021-02-10T19:43:50Z | CONTRIBUTOR | null | [Right now it says](https://github.com/pydata/pandas/blob/564166529071d33844712ab6038f6a237ddcb8f1/pandas/core/style.py#L357):
``` python
def apply(self, func, axis=0, subset=None, **kwargs):
"""
Apply a function column-wise, row-wise, or table-wase,
updating the HTML representation with th... | {
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... | 3 | 2016-01-26T17:12:42Z | 2019-11-02T01:13:53Z | 2019-11-02T01:13:52Z | CONTRIBUTOR | null | xref #12141
currently we have backup routines for everywhere we use bottleneck.
If we made it a requirement (rather than a highly recommended dep) we could forgo this.
its easily installable via `conda` and `pip` (wheels); this used to be an issue.
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... | 1 | 2016-01-26T18:01:22Z | 2020-05-22T03:25:11Z | null | CONTRIBUTOR | null | xref #12130
we should clear() the _cache variable (set by `cache_readonly`) in between unit tests. same with the DataFrame tests.
this will prevent any tests from mutating these 'fixtures' and causing issues in later tests
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https://api.github.com/repos/pandas-dev/pandas/issues/12151 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12151/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12151/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12151/events | https://github.com/pandas-dev/pandas/pull/12151 | 128,988,813 | MDExOlB1bGxSZXF1ZXN0NTcyOTcyNDU= | 12,151 | BUG: getitem and a series with a non-ndarray values | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12152 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12152/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12152/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12152/events | https://github.com/pandas-dev/pandas/issues/12152 | 129,027,226 | MDU6SXNzdWUxMjkwMjcyMjY= | 12,152 | better docs for read_csv() argument infer_datetime_format | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-01-27T04:36:59Z | 2016-03-14T13:02:22Z | 2016-03-14T13:02:22Z | CONTRIBUTOR | null | This gives pretty decent explanation of the infer_datetime_format parameter:
http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#id55
The api docs are fairly opaque.
Improve api docs to make it clearer:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
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https://api.github.com/repos/pandas-dev/pandas/issues/12153 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12153/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12153/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12153/events | https://github.com/pandas-dev/pandas/issues/12153 | 129,107,313 | MDU6SXNzdWUxMjkxMDczMTM= | 12,153 | TypeError: 'TextFileReader' object is not an iterator | {
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csv = pd.read_csv(file_path, chunksize=10)
chunk = next(csv)
```
yields
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-21-ec4c9826eb96> in <module>()
1 csv = pd.read_csv(file_path, chu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12154 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12154/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12154/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12154/events | https://github.com/pandas-dev/pandas/issues/12154 | 129,120,992 | MDU6SXNzdWUxMjkxMjA5OTI= | 12,154 | "min_itemsize" doesn't work for MultiIndex columns in table format | {
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"description": "read_hdf, HDFStore",
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... | 8 | 2016-01-27T11:52:29Z | 2021-04-23T00:39:57Z | null | MEMBER | null | ```
df = pd.DataFrame([[1,2,3],[4,5,6]],
columns=pd.MultiIndex.from_tuples([(1,'a'), (1,'b'), (2,'c')])).astype(str)
store = pd.HDFStore('/tmp/store.hdf')
store.append('test', df, min_itemsize={1 : 20})
```
yields
```
---------------------------------------------------------------------------
ValueError... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12155 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12155/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12155/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12155/events | https://github.com/pandas-dev/pandas/issues/12155 | 129,167,608 | MDU6SXNzdWUxMjkxNjc2MDg= | 12,155 | BUG: GroupBy.apply iterates over first group twice | {
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Error reproduction
``` python
>>> import pandas as pd
>>> df = pd.DataFrame({'one':[1,2,3,1,2,3],'two':[4,5,6,7... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12156 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12156/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12156/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12156/events | https://github.com/pandas-dev/pandas/issues/12156 | 129,176,679 | MDU6SXNzdWUxMjkxNzY2Nzk= | 12,156 | Adding DateOffset(days=1) produces NonExistentTimeError | {
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... | 12 | 2016-01-27T15:27:03Z | 2021-01-25T18:54:07Z | 2021-01-25T18:54:06Z | NONE | null | ```
pd.date_range(pd.Timestamp('2015-1-1', tz='US/Eastern'), pd.Timestamp('2016-1-1', tz='US/Eastern'), freq='H') + pd.DateOffset(days=1)
Traceback (most recent call last):
File "/home/jeff/.virtualenvs/omnipotent/local/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12157 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12157/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12157/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12157/events | https://github.com/pandas-dev/pandas/issues/12157 | 129,178,697 | MDU6SXNzdWUxMjkxNzg2OTc= | 12,157 | read_excel(..., index_col=0, squeeze=True) raises AttributeError | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-01-27T15:33:44Z | 2016-01-30T19:22:40Z | 2016-01-30T19:22:40Z | NONE | null | `pd.read_excel` with keyword argument `squeeze=True` raises an AttributeError. This is unexpected, in that:
- I would expect it to work analogously to `pd.read_csv`, which does not exhibit the same problem
- it worked fine in a previous version (0.15 I think?)
Steps to reproduce:
``` python
In [1]: import pandas as p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12158 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12158/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12158/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12158/events | https://github.com/pandas-dev/pandas/pull/12158 | 129,204,958 | MDExOlB1bGxSZXF1ZXN0NTczODg2MjA= | 12,158 | BUG in MultiIndex.drop for not-lexsorted multi-indexes, #12078 | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-01-27T16:54:27Z | 2016-01-28T21:46:26Z | 2016-01-28T21:28:09Z | CONTRIBUTOR | null | Closes #12078
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https://api.github.com/repos/pandas-dev/pandas/issues/12159 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12159/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12159/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12159/events | https://github.com/pandas-dev/pandas/pull/12159 | 129,263,363 | MDExOlB1bGxSZXF1ZXN0NTc0MTk2OTk= | 12,159 | CI: fixup windows builds | {
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closes #12159
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https://api.github.com/repos/pandas-dev/pandas/issues/12160 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12160/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12160/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12160/events | https://github.com/pandas-dev/pandas/issues/12160 | 129,290,439 | MDU6SXNzdWUxMjkyOTA0Mzk= | 12,160 | DEPR: rename Timestamp.offset internally .freq / deprecate passing offset in favor of freq | {
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This is a bit non-trivial so pulling out to a separate issue from #6581
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https://api.github.com/repos/pandas-dev/pandas/issues/12161 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12161/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12161/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12161/events | https://github.com/pandas-dev/pandas/issues/12161 | 129,303,659 | MDU6SXNzdWUxMjkzMDM2NTk= | 12,161 | Accessor api docs not building with sphinx 1.3.x | {
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See eg the doc build on travis for the errors, and the api overview page on the dev docs.
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Closes https://github.com/pydata/pandas/issues/12134
Closes https://github.com/pydata/pandas/issues/12125
This adds a `.format` method to Styler for formatting the display value
(the actual text) of each scalar value.
In the processes of cleaning up the template, I... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12163 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12163/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12163/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12163/events | https://github.com/pandas-dev/pandas/issues/12163 | 129,389,481 | MDU6SXNzdWUxMjkzODk0ODE= | 12,163 | Dead code breaks unpickling of timezone objects | {
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"followers_url": "https://api.github.com/users/j... | 4 | 2016-01-28T09:05:35Z | 2020-01-21T01:06:10Z | 2020-01-21T01:06:10Z | CONTRIBUTOR | null | There is a problem when writing pickles with Pandas 0.14.1 and reading them with Pandas 0.17.1. The problem occurs if the pickles contain `isodate.UTC` or other timezone objects.
The problem can be reduced to this test case:
**my_tz.py**:
```
from datetime import tzinfo
class MyTz(tzinfo):
def __init__(self):
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-01-28T12:55:13Z | 2016-02-13T15:18:24Z | 2016-02-13T15:18:24Z | CONTRIBUTOR | null | While working on issue #12031 (adding a `decimal` parameter to `.to_latex()` and `.to_html()`), I realized a solution would be pretty straight-forward if only `FloatArrayFormatter` was a bit cleaned.
Currently, there are two methods that do a similar work:
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``` python
chunk.to_hdf('all_data.h5', 'df',mode='a',format='table',append=True)
```
Discussion on stackoverflow:
http://stackoverflow.com/questions/31419768/pandas-appending-suing-to-hdf-now-crashes-after-updating-to-pyta... | {
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With this code I get a similar error:
``` python
import pandas as pd
import numpy as np
from pdb import set_trace as pdb
for _ in xrange(1... | {
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"id": 241332... | closed | false | null | [] | null | 9 | 2016-01-28T16:44:17Z | 2016-07-01T16:55:07Z | 2016-03-23T12:56:52Z | NONE | null | Passing the `y` argument of a plot to `DataFrame.eval` allows plotting a numerical expression.
Makes numerical data exploration much easier. E.g. graphically finding the norm of two curves.
See example here:
[example notebook](https://gist.github.com/TsvikiHirsh/46fd47bc35dc1874234d)
No support for multicolumn plottin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12169 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12169/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12169/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12169/events | https://github.com/pandas-dev/pandas/issues/12169 | 129,526,690 | MDU6SXNzdWUxMjk1MjY2OTA= | 12,169 | Resample category data with timedelta index | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-01-28T17:58:27Z | 2016-02-10T17:29:34Z | 2016-02-10T17:29:34Z | NONE | null | Hi,
I get a very strange behavior when i try to resample categorical data with and timedelta index, as compared to a datetime index.
``` pandas
>> d1 = pd.DataFrame({'Group_obj': 'A'}, index=pd.date_range('2000-1-1', periods=20, freq='s'))
>> d1['Group'] = d1['Group_obj'].astype('category')
>> d1
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12170 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12170/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12170/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12170/events | https://github.com/pandas-dev/pandas/issues/12170 | 129,549,647 | MDU6SXNzdWUxMjk1NDk2NDc= | 12,170 | DataFrame.to_msgpack unexpectedly defaults to latin-1 encoding | {
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I tried saving a DataFrame with Unicode labels using the `.to_msgpack` method. I didn't specify an encoding, because I assumed it would use UTF-8, which is the default encoding for Python in my locale (en_US.UTF-8) as well as just a sensible encoding to use in general.
Instead, it tried to encode... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12171 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12171/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12171/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12171/events | https://github.com/pandas-dev/pandas/issues/12171 | 129,595,537 | MDU6SXNzdWUxMjk1OTU1Mzc= | 12,171 | CLN: change getargspec -> signature (using from pandas.compat) | {
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```
pandas/compat/__init__.py:
113 : return inspect.getargspec(f).args
# change here
pandas/tests/series/test_analytics.py:
4 : from inspect import getargspec
468 : if 'numeric_only' in getargspec(f).args:
pandas/util/decorators.py:
278 : fr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12172 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12172/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12172/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12172/events | https://github.com/pandas-dev/pandas/issues/12172 | 129,639,770 | MDU6SXNzdWUxMjk2Mzk3NzA= | 12,172 | Why does Series.plot() not have a kind='scatter' option? | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2016-01-29T01:55:18Z | 2016-07-11T08:22:19Z | 2016-07-11T08:22:19Z | NONE | null | I run into use-cases for this feature often. It seems so simple that it's hard to believe this is purely an oversight.
So, why does `Series.plot()` not have a `kind='scatter'` option?
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https://api.github.com/repos/pandas-dev/pandas/issues/12173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12173/events | https://github.com/pandas-dev/pandas/pull/12173 | 129,731,598 | MDExOlB1bGxSZXF1ZXN0NTc2MzI1MTc= | 12,173 | Iterableiterator | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2016-01-29T10:52:53Z | 2016-02-06T21:02:48Z | 2016-02-06T20:04:13Z | MEMBER | null | If the approach is not overkill, it can be used also in `UTF8Recoder` and `UnicodeReader` from `pandas/common/io.py`. Closes #12153.
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https://api.github.com/repos/pandas-dev/pandas/issues/12174 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12174/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12174/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12174/events | https://github.com/pandas-dev/pandas/issues/12174 | 129,762,596 | MDU6SXNzdWUxMjk3NjI1OTY= | 12,174 | concat() on Sparse dataframe returns strange results | {
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"de... | closed | false | null | [] | null | 10 | 2016-01-29T13:13:11Z | 2017-03-19T04:43:40Z | 2016-01-29T13:46:24Z | NONE | null | I open a stackoverflow question here :
http://stackoverflow.com/questions/35083277/pandas-concat-on-sparse-dataframes-a-mystery
And someone ask me to open an issue on Github.
To summarise, I don't really understand what's going on after a concat of 2 sparse data frame...
After such concat, df.density or df.memory_usag... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12175 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12175/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12175/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12175/events | https://github.com/pandas-dev/pandas/pull/12175 | 129,792,843 | MDExOlB1bGxSZXF1ZXN0NTc2NTk0MDk= | 12,175 | Add ZIP file decompression and TestCompression. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12176 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12176/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12176/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12176/events | https://github.com/pandas-dev/pandas/issues/12176 | 129,792,935 | MDU6SXNzdWUxMjk3OTI5MzU= | 12,176 | confusing ImportError message in `__init__.py` | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-01-29T15:15:58Z | 2016-02-27T14:28:05Z | 2016-02-27T14:28:05Z | NONE | null | In some cases the error message could be more clear, as discussed here:
https://github.com/pydata/pandas/commit/69cab57c9889391a587656ff8c6a7f9b38187bb1#commitcomment-15762471
my example:
```
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/marcin/install/lib/py... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12177 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12177/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12177/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12177/events | https://github.com/pandas-dev/pandas/issues/12177 | 129,801,164 | MDU6SXNzdWUxMjk4MDExNjQ= | 12,177 | TST: catch some more warnings | {
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like to `assert_produces_warning` on these to remove from the test output; these are legit.
```
./home/travis/build/pydata/pandas/pandas/indexes/base.py:2385: RuntimeWarning: unorderable types: str() < int(), sort order is undefined fo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12178 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12178/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12178/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12178/events | https://github.com/pandas-dev/pandas/issues/12178 | 129,802,708 | MDU6SXNzdWUxMjk4MDI3MDg= | 12,178 | DEPR: remove some Exception.message usage | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-01-29T15:42:09Z | 2016-04-18T17:46:27Z | 2016-04-18T17:46:27Z | CONTRIBUTOR | null | this is pretty old code, I think you you simply eliminate the try:excepts: here entirey: https://github.com/pydata/pandas/blob/master/pandas/computation/tests/test_eval.py#L52
```
In [4]: pd.test(raise_warnings='release')
Running unit tests for pandas
pandas version 0.17.1+289.g0977cb6
numpy version 1.10.2
pandas is i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12179 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12179/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12179/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12179/events | https://github.com/pandas-dev/pandas/pull/12179 | 129,813,128 | MDExOlB1bGxSZXF1ZXN0NTc2NjgwNjQ= | 12,179 | DOC: reformat install.rst testing section | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-01-29T16:20:38Z | 2016-01-29T16:39:09Z | 2016-01-29T16:39:09Z | CONTRIBUTOR | null | TST: `run.test()` now defaults warnings based on release version
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https://api.github.com/repos/pandas-dev/pandas/issues/12180 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12180/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12180/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12180/events | https://github.com/pandas-dev/pandas/issues/12180 | 129,843,114 | MDU6SXNzdWUxMjk4NDMxMTQ= | 12,180 | Series.str.get_dummies fails if one of the categorical variables is called 'name' | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-01-29T18:16:15Z | 2016-02-01T20:09:16Z | 2016-02-01T20:09:16Z | CONTRIBUTOR | null | This works as expected:
``` python
>>> s = pd.Series(['Name', 'email|Name|address', 'address|email'])
>>> s.str.get_dummies(sep='|')
Name address email
0 1 0 0
1 1 1 1
2 0 1 1
```
However, if any of the categorical variables is named exactly `'name'`, then there's ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12181 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12181/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12181/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12181/events | https://github.com/pandas-dev/pandas/issues/12181 | 129,867,309 | MDU6SXNzdWUxMjk4NjczMDk= | 12,181 | Reindexing doesn't work as expected for MultiIndexs | {
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In the following example, I'm setting up some schedule data for individuals who belong to households. Three tables will be... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12182/events | https://github.com/pandas-dev/pandas/issues/12182 | 129,929,298 | MDU6SXNzdWUxMjk5MjkyOTg= | 12,182 | BUG: _repr_latex is called in jupyter qtconsole | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12183 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12183/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12183/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12183/events | https://github.com/pandas-dev/pandas/issues/12183 | 129,929,860 | MDU6SXNzdWUxMjk5Mjk4NjA= | 12,183 | Please I have serious issue with the CSV, Pandas, Dataframe and JSON, I need someone to assist. | {
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"followers_url": "https://api.github.com/users/jorisva... | 6 | 2016-01-30T02:23:37Z | 2016-01-30T14:06:04Z | 2016-01-30T02:28:43Z | NONE | null | `import pandas as pd
import datetime
from pandas_datareader import data, wb
import csv
import json
# declaring libraries for the analysis
out= open("testfile.csv", "rt") # read the .csv file
data = csv.reader(out)
data = [[row[0],row[1] + "_" + row[2],row[3] +"_" + row[4], row[5],row[6]] for row in data] #this part i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12184 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12184/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12184/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12184/events | https://github.com/pandas-dev/pandas/pull/12184 | 130,001,145 | MDExOlB1bGxSZXF1ZXN0NTc3NDI1NDY= | 12,184 | BUG: read_excel with squeeze=True | {
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... | closed | false | null | [] | null | 3 | 2016-01-30T15:47:27Z | 2016-02-01T00:25:12Z | 2016-01-30T19:22:40Z | CONTRIBUTOR | null | closes #12157
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https://api.github.com/repos/pandas-dev/pandas/issues/12185 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12185/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12185/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12185/events | https://github.com/pandas-dev/pandas/issues/12185 | 130,009,580 | MDU6SXNzdWUxMzAwMDk1ODA= | 12,185 | read_csv() restarts index (if not loaded) at every chunk | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2016-01-30T16:45:53Z | 2016-07-29T00:15:15Z | 2016-07-29T00:15:15Z | MEMBER | null | See [this comment](https://github.com/pydata/pandas/pull/12173#discussion_r51347252). Basically we now have
```
In [8]: read_csv(StringIO(u'A,B\n1,2\n3,4'))
Out[8]:
A B
0 1 2
1 3 4
```
but
```
In [9]: concat(read_csv(StringIO(u'A,B\n1,2\n3,4'), chunksize=1))
Out[9]:
A B
0 1 2
0 3 4
```
We want the... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12186 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12186/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12186/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12186/events | https://github.com/pandas-dev/pandas/issues/12186 | 130,024,848 | MDU6SXNzdWUxMzAwMjQ4NDg= | 12,186 | pd.read_excel parses rows as int if containing only numbers, even when marked as text or indicated string with e.g. '02 | {
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"color": "009... | closed | false | null | [] | null | 1 | 2016-01-30T19:00:24Z | 2016-01-30T19:19:08Z | 2016-01-30T19:19:08Z | CONTRIBUTOR | null | When loading an excel file with pandas that contains a row with integers that have preceding zeros such as 01, the number is automatically parsed as integer and results as 1. This even happens when the row is formatted as 'text' or the number is entered as '02 (second row). I believe it would be correct if the row was ... | {
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... | 10 | 2016-01-31T02:01:50Z | 2021-07-28T11:07:56Z | null | NONE | null | Currently, Pandas interpolation interpolates all gaps, regardless of there size and the limit parameter is used to limit the number of replacement : if there is a gap of 3 values and limit=2, pandas replaces the first 2 values.
I have difficulty understanding why someone would want to do interpolation on only a few ... | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2016-01-31T02:25:51Z | 2016-05-26T12:54:22Z | 2016-05-26T12:54:22Z | CONTRIBUTOR | null | From the docs I see that the difference between .merge and .join is that .join operates using indexes by default, but it also lets you use columns, so I tried to use it for that since it sounds natural when coming from the SQL world.
From the [docs](http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12189 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12189/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12189/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12189/events | https://github.com/pandas-dev/pandas/issues/12189 | 130,078,458 | MDU6SXNzdWUxMzAwNzg0NTg= | 12,189 | BUG: Automatic upcast to object fails when unstacking datetime/timedelta with incompatible fill_value | {
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... | 2 | 2016-01-31T04:39:13Z | 2020-03-31T05:35:28Z | null | CONTRIBUTOR | null | Converting to object before then unstack works, whereas automatic upcast does not happen. Missing values are replaced with NaT instead of automatically upcasting to object and filling with the requested value.
```
In [44]: td = [Timedelta(days=i) for i in range(4)]
In [45]: data = Series(td)
In [46]: data.index = Mul... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12190 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12190/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12190/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12190/events | https://github.com/pandas-dev/pandas/pull/12190 | 130,168,467 | MDExOlB1bGxSZXF1ZXN0NTc3NzY3MzE= | 12,190 | DEPR: deprecate options.display.mpl_style | {
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Deprecates pd.options.display.mpl_style in favor of matplotlib's style sheets.
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WIP, but wanted to put it up as is, since it probably interacts with #12129
cc: @kawochen
this is how I modified the schema - open to suggestions
``` python
# normal block
{'dtype': dtype,
'data': ExtType(0, <bytes>)}
# Categorical block
{'dtype': 'categorical',
'data': {
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I modified the description in DataFrame.join to make clear the difference with DataFrame.merge, also added examples.
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This is a work in progress.
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https://api.github.com/repos/pandas-dev/pandas/issues/12195 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12195/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12195/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12195/events | https://github.com/pandas-dev/pandas/pull/12195 | 130,450,986 | MDExOlB1bGxSZXF1ZXN0NTc4ODMzNjI= | 12,195 | BUG: concat of tz series with NaT | {
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xref #11736
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https://api.github.com/repos/pandas-dev/pandas/issues/12196 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12196/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12196/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12196/events | https://github.com/pandas-dev/pandas/issues/12196 | 130,466,600 | MDU6SXNzdWUxMzA0NjY2MDA= | 12,196 | BUG: numpy master quantile interpolation method | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12197/events | https://github.com/pandas-dev/pandas/pull/12197 | 130,478,783 | MDExOlB1bGxSZXF1ZXN0NTc4OTc1ODE= | 12,197 | TST: work around numpy https://github.com/numpy/numpy/issues/7163 | {
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xref https://github.com/numpy/numpy/issues/7163
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https://api.github.com/repos/pandas-dev/pandas/issues/12198 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12198/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12198/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12198/events | https://github.com/pandas-dev/pandas/pull/12198 | 130,478,927 | MDExOlB1bGxSZXF1ZXN0NTc4OTc2NjM= | 12,198 | Add ability to specify AWS s3 host through AWS_S3_HOST environment variable | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-02-01T20:49:46Z | 2016-02-02T15:53:18Z | 2016-02-02T15:52:52Z | CONTRIBUTOR | null | The AWS s3 host used by read_csv currently defaults to s3.amazonaws.com and there is currently no way to change this. This pull requests adds functionality where the code checks the AWS_S3_HOST environment variable for a user-specified host and defaults back to the current s3.amazonaws.com host.
This is a simple code... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12199/events | https://github.com/pandas-dev/pandas/issues/12199 | 130,492,289 | MDU6SXNzdWUxMzA0OTIyODk= | 12,199 | fftpack.rfft overwrites data when input is a pandas series | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2016-02-01T21:39:18Z | 2016-02-01T23:58:45Z | 2016-02-01T23:58:45Z | NONE | null | Reproduce:
``` python
import pandas as pd
import numpy as np
from scipy import fftpack
x = np.array([1.,2.,3.,4.,5.])
xs = pd.Series(x)
print x
tmp = fftpack.rfft(x)
print x
print x
tmp = fftpack.rfft(xs)
print x
"""
output:
[ 1. 2. 3. 4. 5.]
[ 1. 2. 3. 4. 5.]
[ 1. 2. 3. 4. 5.]
[ 15. -2.5 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12200/events | https://github.com/pandas-dev/pandas/issues/12200 | 130,536,906 | MDU6SXNzdWUxMzA1MzY5MDY= | 12,200 | read_sql ignore type | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2016-02-02T01:09:41Z | 2016-06-24T14:32:55Z | 2016-06-24T14:31:23Z | NONE | null | Is it possible to ignore or prevent automatic typing when executing a sql command and populating a data frame? I have NA's in certain columns and those columns are converted to float64 (when they should be int64 with some NAs) and when written out (using to_csv) leave decimal points which does not make sense for that c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12201 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12201/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12201/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12201/events | https://github.com/pandas-dev/pandas/issues/12201 | 130,587,453 | MDU6SXNzdWUxMzA1ODc0NTM= | 12,201 | Reiindex_axis with copy=False return new object when expecting to change it inplace | {
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"descript... | closed | false | null | [] | null | 1 | 2016-02-02T06:19:57Z | 2016-02-02T17:18:14Z | 2016-02-02T17:18:04Z | NONE | null | I thought that `reindex_axis` with argument `copy=False` should change it inplace. From [docs](http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.reindex_axis.html):
> **copy** : _boolean_, _default True_
> Return a new object, even if the passed indexes are the same
However:
```
df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12202/events | https://github.com/pandas-dev/pandas/issues/12202 | 130,633,756 | MDU6SXNzdWUxMzA2MzM3NTY= | 12,202 | Resampling converts int to float, but only in group by | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-02-02T10:02:05Z | 2016-02-12T20:40:12Z | 2016-02-12T20:40:12Z | NONE | null | ``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'date': pd.date_range(start='2016-01-01', periods=4, freq='W'),
'group': [1, 1, 2, 2],
'val': [5, 6, 7, 8]})
df['val'] = df['val'].astype(np.int32)
df.set_index('date', inplace=True)
df['val'].dtype
#[out] dtype('int32'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12203 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12203/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12203/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12203/events | https://github.com/pandas-dev/pandas/issues/12203 | 130,682,653 | MDU6SXNzdWUxMzA2ODI2NTM= | 12,203 | io.read_csv: ValueError due to long lines | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2016-02-02T13:24:59Z | 2016-03-20T15:10:03Z | 2016-03-20T15:10:03Z | CONTRIBUTOR | null | I try to read a csv file were some lines are longer than the rest.
Pandas `C engine` throws an error with these lines.
But I do not was to skip these as discussed in [ a comment on a similar issue](https://github.com/pydata/pandas/issues/2886#issuecomment-30425534).
I prefer to "cut" the "bad columns" off using usec... | {
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