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https://api.github.com/repos/pandas-dev/pandas/issues/11904 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11904/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11904/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11904/events | https://github.com/pandas-dev/pandas/pull/11904 | 123,912,567 | MDExOlB1bGxSZXF1ZXN0NTQ1OTgwMTY= | 11,904 | Issue 11833-2 | {
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} | 2 | 2015-12-26T06:36:46Z | 2015-12-26T11:16:37Z | 2015-12-26T11:16:03Z | CONTRIBUTOR | null | updated the to_string() function of DataFrameFormatter(TableFormatter) class ,in format.py file, of the core directory.This is another commit . I hope this works
#11833
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| null | 7 | 2015-12-26T09:28:23Z | 2019-08-27T09:43:35Z | 2015-12-26T13:36:14Z | NONE | null | Hello,
`pd.DataFrame.to_dict` accepts `orient` parameter in
`['dict', 'list', 'series', 'split', 'records', 'index']` (default being `'dict'`)
http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.to_dict.html
`from_dict` only accepts `orient` parameter in `['columns', 'index']` (default being `'columns'`)
I think that `from_dict` should accept same parameter `orient` for API consistency
http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.from_dict.html
Same for `Panel`
http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.Panel.from_dict.html
`to_dict`: missing method for Panel
Same for `Series`
http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.Series.to_dict.html
`from_dict`: missing method for Series
Kind regards
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| null | 4 | 2015-12-26T19:23:28Z | 2015-12-27T04:31:20Z | 2015-12-27T04:31:20Z | CONTRIBUTOR | null | Updated the format.py file in pandas core directory
Function updated : to_string()
Class : DataFrameFormatter
.travis.yml also changed to for fewer builds
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} | 2 | 2015-12-27T07:28:56Z | 2015-12-27T13:59:08Z | 2015-12-27T12:12:36Z | NONE | null | Hi all.
With the current version in order to achieve filtering that is reversed to 'pd.Series.isin()' we need to define a function or use a lambda, ie:
```
df = df[df['city'].apply(lambda x: 'TA' in x if x is not None else False)]
```
I tought it could be a bit easier if we could have a syntax like:
```
df = df['TA' is in df['city']]
```
I believe this will involve overriding `__contains__` in `pd.Series`.
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} | 10 | 2015-12-27T09:27:06Z | 2016-01-02T10:51:32Z | 2016-01-02T10:51:32Z | CONTRIBUTOR | null | closes #11833
Final Commits
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} | 14 | 2015-12-27T15:44:51Z | 2016-03-10T00:52:08Z | 2016-01-08T14:07:46Z | CONTRIBUTOR | null | closes #4327
copied from `numpy` nosetester.
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} | 12 | 2015-12-27T17:52:12Z | 2016-01-15T16:27:25Z | 2016-01-15T16:24:35Z | CONTRIBUTOR | null | closes #7061
I'm working on solving issue #7061: currently, with Python 2 it is not possible to use `.to_latex()` when the dataframe contains utf-8 strings. This PR adds an `encoding` parameter to make it possible.
The work is not completely done though, and I'm making this PR to start a discussion here. In its current state, the PR quickly fixes the issue and provides tests, so it is kind of a minimal PR.
@jreback in the discussion on issue #7061, you made the following suggestion:
> you _may_ want to add a `LatexFormatter` (as a sub-class of `DataFrameFormatter`) as this will allow some re-factoring to be internally done later on.
I would be glad to work in that direction, but I'm a bit confused by the current organization of the code. This `LatexFormatter` would seem to me to be similar to `HTMLFormatter`, which inherits from `TableFormatter` (exactly as `DataFrameFormatter`). In that case, `DataFrameFormatter` would use `LatexFormatter` for `.to_latex()` exactly as it uses `HTMLFormatter` for `.to_html()`. Is that what you had in mind?
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} | 30 | 2015-12-28T05:37:00Z | 2016-12-19T12:55:55Z | 2016-12-19T12:55:55Z | NONE | null | Pandas v0.17.1 won't import for Python 3.5 due to boto. Replacing with boto3 appears to fix the issue. I'd like to suggest replacing dependencies on boto with boto3 for at least Python 3.5. I didn't test with other Python 3x versions. Thank you
Update (2016-01-10T02:45:00Z): There are significant API changes between `boto` and `boto3` (http://boto3.readthedocs.org/en/latest/guide/migrations3.html). Doing `import boto3 as boto` as below will allow pandas to import for Python 3.5, but then AWS functionality is broken.
```
samuel_harrold@instance-20151227t225000z:~$ ipython
Python 3.5.1 |Anaconda 2.4.1 (64-bit)| (default, Dec 7 2015, 11:16:01)
Type "copyright", "credits" or "license" for more information.
IPython 4.0.1 -- An enhanced Interactive Python.
? -> 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)
<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
/home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/__init__.py in <module>()
40
41 # let init-time option registration happen
---> 42 import pandas.core.config_init
43
44 from pandas.core.api import *
/home/samuel_harrold/anaconda3/lib/python3.5/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
/home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/core/format.py in <module>()
8 from pandas.core.base import PandasObject
9 from pandas.core.common import adjoin, notnull
---> 10 from pandas.core.index import Index, MultiIndex, _ensure_index
11 from pandas import compat
12 from pandas.compat import(StringIO, lzip, range, map, zip, reduce, u,
/home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/core/index.py in <module>()
29 from pandas.core.strings import StringAccessorMixin
30 from pandas.core.config import get_option
---> 31 from pandas.io.common import PerformanceWarning
32
33
/home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/io/common.py in <module>()
66
67 try:
---> 68 from boto.s3 import key
69 class BotoFileLikeReader(key.Key):
70 """boto Key modified to be more file-like
/home/samuel_harrold/anaconda3/lib/python3.5/site-packages/boto/__init__.py in <module>()
1214 return storage_uri(uri_str)
1215
-> 1216 boto.plugin.load_plugins(config)
/home/samuel_harrold/anaconda3/lib/python3.5/site-packages/boto/plugin.py in load_plugins(config)
90 return
91 directory = config.get('Plugin', 'plugin_directory')
---> 92 for file in glob.glob(os.path.join(directory, '*.py')):
93 _import_module(file)
/home/samuel_harrold/anaconda3/lib/python3.5/posixpath.py in join(a, *p)
87 path += sep + b
88 except (TypeError, AttributeError, BytesWarning):
---> 89 genericpath._check_arg_types('join', a, *p)
90 raise
91 return path
/home/samuel_harrold/anaconda3/lib/python3.5/genericpath.py in _check_arg_types(funcname, *args)
141 else:
142 raise TypeError('%s() argument must be str or bytes, not %r' %
--> 143 (funcname, s.__class__.__name__)) from None
144 if hasstr and hasbytes:
145 raise TypeError("Can't mix strings and bytes in path components") from None
TypeError: join() argument must be str or bytes, not 'NoneType'
In [2]: exit()
samuel_harrold@instance-20151227t225000z:~$ cp /home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/io/common_boto3.py /home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/io/common.py
samuel_harrold@instance-20151227t225000z:~$ diff /home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/io/common.py /home/samuel_harrold/anaconda3/lib/python3.5/site-packages/pandas/io/common_orig.py
68c68
< from boto3.s3 import key
---
> from boto.s3 import key
272c272
< import boto3 as boto
---
> import boto
samuel_harrold@instance-20151227t225000z:~$ ipython
Python 3.5.1 |Anaconda 2.4.1 (64-bit)| (default, Dec 7 2015, 11:16:01)
Type "copyright", "credits" or "license" for more information.
IPython 4.0.1 -- An enhanced Interactive Python.
? -> 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
In [2]: # success
In [3]: exit()
samuel_harrold@instance-20151227t225000z:~$ ipython
Python 3.5.1 |Anaconda 2.4.1 (64-bit)| (default, Dec 7 2015, 11:16:01)
Type "copyright", "credits" or "license" for more information.
IPython 4.0.1 -- An enhanced Interactive Python.
? -> 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 as pd
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-0.bpo.4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
In [3]:
```
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} | 1 | 2015-12-28T17:41:23Z | 2015-12-28T17:52:03Z | 2015-12-28T17:51:54Z | CONTRIBUTOR | null | Small change, to include the pd alias and to remove an extra colon on the last example.
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| null | 40 | 2015-12-28T19:11:55Z | 2016-11-22T11:53:33Z | 2016-11-22T11:53:33Z | CONTRIBUTOR | null | Fix issue #11759
Two things:
1. `Series.rank` and `DataFrame.rank` have been moved to `generic.py` and get the same signature [done with PR #11924]
2. It can happen that aggregate functions are applied to grouped object in a recursive way. Instead of raising a plain `ValueError` when that fails, and catching it afterwards indistinctly, I propose to raise an `_ErrorContainer` exception containing the last meaningful error to have been raised. Thus, when everything else has failed, we can still recover a meaningful exception and throw it back at the user.
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| null | 14 | 2015-12-28T22:04:06Z | 2019-08-22T17:23:13Z | 2019-08-22T17:23:13Z | CONTRIBUTOR | null | I found the excellent and excellently-recorded pycon 2015 pandas tutorial among a forest of tutorials.
wouldn't it be nice for newcomer to find a link to it (or other excellent ones) in the website or the Wiki ?
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} | 21 | 2015-12-29T04:09:22Z | 2016-08-10T10:15:35Z | 2016-08-10T10:15:35Z | CONTRIBUTOR | null | - I get a cryptic error `AttributeError: 'str' object has no attribute 'cursor'` if sqlalchemy is not installed and I pass a database string URI to `pandas.read_sql`. I think the error should be made more explicit.
- Also I think there is a shadowing issue with`_SQLALCHEMY_INSTALLED` in `_engine_builder`.
```
/opt/conda/lib/python3.4/site-packages/pandas/io/sql.py in read_sql_query(sql, con, index_col, coerce_float, params, parse_dates, chunksize)
427 return pandas_sql.read_query(
428 sql, index_col=index_col, params=params, coerce_float=coerce_float,
--> 429 parse_dates=parse_dates, chunksize=chunksize)
430
431
/opt/conda/lib/python3.4/site-packages/pandas/io/sql.py in read_query(self, sql, index_col, coerce_float, params, parse_dates, chunksize)
1569
1570 args = _convert_params(sql, params)
-> 1571 cursor = self.execute(*args)
1572 columns = [col_desc[0] for col_desc in cursor.description]
1573
/opt/conda/lib/python3.4/site-packages/pandas/io/sql.py in execute(self, *args, **kwargs)
1532 cur = self.con
1533 else:
-> 1534 cur = self.con.cursor()
1535 try:
1536 if kwargs:
AttributeError: 'str' object has no attribute 'cursor'
```
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| null | 1 | 2015-12-29T06:58:56Z | 2015-12-29T19:22:23Z | 2015-12-29T19:22:23Z | NONE | null | As per this: http://stackoverflow.com/questions/34236870/pandas-read-html-to-return-raw-html-contents-for-certain-rows-cells-etc?noredirect=1#comment56359521_34236870 Pandas returns the HTML stripped text of tables it scrapes. It might be nice to be able to specify columns for which you would return the raw HTML for post-processing. This would let you maintain references to images, grab links, etc.
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| null | 3 | 2015-12-29T12:59:37Z | 2015-12-29T19:19:33Z | 2015-12-29T19:19:33Z | NONE | null | (I know that the following bug report will be dismissed, since what I refer to here as a "bug" is clearly intentional. I'm posting it only for-the-record.)
Currently, an exception is raised if DataFrame.groupby is called with an empty tuple as argument:
```
>>> df.groupby(by=())
...
ValueError: No group keys passed!
```
Indeed, the underlying code is pretty direct about it:
```
.../python2.7/site-packages/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort)
2277
2278 if len(groupings) == 0:
-> 2279 raise ValueError('No group keys passed!')
2280
```
This is not the correct behavior for this condition. It is therefore a "design bug". The correct behavior should be returning a trivial grouping, consisting of exactly 1 group (containing, of course, all the rows). This is consistent with the semantics "each group consists of the rows that agree on the grouping columns": when there are no grouping columns, all rows fall into one group.
In particular, the proposed behavior will render the expression
```
df.groupby(by=tuple(df.columns))
```
valid even when `df` has no columns. The expression above groups identical rows together, and it should work for all valid dataframes. In a dataframe with no columns, all the rows are identical.
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} | 3 | 2015-12-29T18:51:08Z | 2015-12-29T21:12:59Z | 2015-12-29T21:12:56Z | CONTRIBUTOR | null | closes #11885
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} | 13 | 2015-12-29T22:27:20Z | 2016-01-28T21:46:58Z | 2016-01-28T21:41:24Z | CONTRIBUTOR | null | I'm pulling this out from PR #11918. It is a first step toward solving issue #11759
The signature of `Series.rank` changes, and is now the same as `DataFrame.rank`. This may cause some problems...
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} | 0 | 2015-12-29T22:39:08Z | 2015-12-31T13:46:31Z | 2015-12-31T13:46:31Z | MEMBER | null | ```
In [12]: s2
Out[12]:
0 1 days
1 2 days
2 3 days
dtype: timedelta64[ns]
In [9]: pd.Timestamp('2012-01-01') + s2
Out[9]:
0 2012-01-02
1 2012-01-03
2 2012-01-04
dtype: datetime64[ns]
In [10]: pd.Timestamp('2012-01-01') - s2
TypeError: can only operate on a timedelta/DateOffset and a datetime for addition, but the operator [__rsub__] was passed
In [11]: pd.Timestamp('2012-01-01') + (-s2)
Out[11]:
0 2011-12-31
1 2011-12-30
2 2011-12-29
dtype: datetime64[ns]
```
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} | 3 | 2015-12-30T03:17:51Z | 2019-07-01T11:41:25Z | 2019-07-01T11:41:25Z | NONE | null | I'm encountering a bug when I query for a multiindex dataframe with a timezoned DatetimeIndex in one of the multiindex levels.
This only happens
1) for a multiindex with one of the levels as timestamps with timezones (As seen in [1]). If timestamps have no timezone set, there is no issue (As seen in [2])
2) if the query returns no rows
3) in pandas 0.17.\* This was working fine in pandas 0.16.*
``` python
In [1]: periods = 10
...: dts = pd.date_range('20151201', periods=periods, freq='D', tz='UTC') #WITH TIMEZONE
...: mi = pd.MultiIndex.from_arrays([dts, range(periods)], names = ['DATE', 'NO'])
...: df = pd.DataFrame({'MYCOL':0}, index=mi)
...: file_path = 'table.h5'
...: key = 'mykey'
...: with pd.HDFStore(file_path, 'w') as store:
...: store.append(key, df, format='table', append=True)
...: dfres = store.select(key, where="""DATE > '20151220'""")
...: print(dfres)
...:
...:
Traceback (most recent call last):
File "<ipython-input-1-e0b7db50fd4d>", line 9, in <module>
dfres = store.select(key, where="""DATE > '20151220'""")
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/io/pytables.py", line 669, in select
return it.get_result()
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/io/pytables.py", line 1352, in get_result
results = self.func(self.start, self.stop, where)
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/io/pytables.py", line 662, in func
columns=columns, **kwargs)
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/io/pytables.py", line 4170, in read
df = super(AppendableMultiFrameTable, self).read(**kwargs)
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/io/pytables.py", line 4029, in read
df = concat(frames, axis=1, verify_integrity=False).consolidate()
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/tools/merge.py", line 813, in concat
return op.get_result()
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/tools/merge.py", line 995, in get_result
mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy)
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4456, in concatenate_block_managers
for placement, join_units in concat_plan]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4456, in <listcomp>
for placement, join_units in concat_plan]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4553, in concatenate_join_units
for ju in join_units]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4553, in <listcomp>
for ju in join_units]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4801, in get_reindexed_values
missing_arr = np.empty(self.shape, dtype=empty_dtype)
TypeError: data type not understood
In [2]: periods = 10
...: dts = pd.date_range('20151201', periods=periods, freq='D') #WITHOUT TIMEZONE
...: mi = pd.MultiIndex.from_arrays([dts, range(periods)], names = ['DATE', 'NO'])
...: df = pd.DataFrame({'MYCOL':0}, index=mi)
...: file_path = 'table.h5'
...: key = 'mykey'
...: with pd.HDFStore(file_path, 'w') as store:
...: store.append(key, df, format='table', append=True)
...: dfres = store.select(key, where="""DATE > '20151220'""")
...: print(dfres)
...:
...:
Empty DataFrame
Columns: [MYCOL]
Index: []
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
Jinja2: None
```
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| null | 1 | 2015-12-30T09:41:15Z | 2015-12-30T15:22:19Z | 2015-12-30T15:22:19Z | NONE | null | In the following example, df column a are exported as text (string) in xlsx file, but imported back to df2 as int.
In [161]: df = pd.DataFrame({'a':['01','02','03','04'], 'b':[5,6,7,8]})
In [162]: df
Out[162]:
a b
0 01 5
1 02 6
2 03 7
3 04 8
In [163]: df.dtypes
Out[163]:
a object
b int64
dtype: object
In [164]: df.to_excel('tmp.xlsx')
In [165]: df2 = pd.read_excel('tmp.xlsx')
In [166]: df2
Out[166]:
a b
0 1 5
1 2 6
2 3 7
3 4 8
In [167]: df2.dtypes
Out[167]:
a int64
b int64
dtype: object
Users usually set columns (which contain numeric data) as "text" in excel for special purposes, pandas should keep these dtypes as str, please do not try to convert it back to numberic. At least, pandas should provide an option in pd.read_excel() to switch this dtype conversion.
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https://api.github.com/repos/pandas-dev/pandas/issues/11928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11928/events | https://github.com/pandas-dev/pandas/issues/11928 | 124,338,279 | MDU6SXNzdWUxMjQzMzgyNzk= | 11,928 | Linting and PEP8 compliance | {
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} | 35 | 2015-12-30T11:47:38Z | 2016-02-03T14:11:24Z | 2016-02-03T14:06:28Z | MEMBER | null | - [ ] update docs with tools to use & that Travis is checking
I have been away from the codebase for a while and ouch, my eyes. I have used pyflakes or flake8 assiduously for Python development for a long time now and it seems that conventional style isn't being followed in the codebase:
$ flake8 pandas/core | wc -l
2047
It's a big codebase so adding a pre-commit hook might be too onerous, but can we get flake8 back to zero (with some well-placed `# noqa` marks on unintentionally-unused-imports)?
Secondly, perhaps we should add a Readability or Style label for these kinds of issues?
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| null | 2 | 2015-12-30T15:44:55Z | 2015-12-30T15:48:07Z | 2015-12-30T15:47:08Z | NONE | null | ```
>>> import pandas as pd
>>> print pd.to_datetime('22620411')
2262-04-11 00:00:00
>>> print pd.to_datetime('22620412')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Anaconda3\envs\py27\lib\site-packages\pandas\util\decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "C:\Anaconda3\envs\py27\lib\site-packages\pandas\tseries\tools.py", line 276, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "C:\Anaconda3\envs\py27\lib\site-packages\pandas\tseries\tools.py", line 397, in _to_datetime
return _convert_listlike(np.array([ arg ]), box, format)[0]
File "C:\Anaconda3\envs\py27\lib\site-packages\pandas\tseries\tools.py", line 383, in _convert_listlike
raise e
pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-04-12 00:00:00
>>>
```
Please someone confirm the to_datetime() error above. I checked on both python 2.7.11 and python 3.5.1. The exact day could be different with 22620411. You can reproduce easily with 99991231 which is the important day for many ERP related SQL database to represent permanent valid date.
I think the above is very similar with https://github.com/pydata/pandas/issues/4255 which is the closed one.
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https://api.github.com/repos/pandas-dev/pandas/issues/11930 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11930/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11930/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11930/events | https://github.com/pandas-dev/pandas/issues/11930 | 124,377,333 | MDU6SXNzdWUxMjQzNzczMzM= | 11,930 | DataFrame.head(0) doesn't return an empty frame | {
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} | 7 | 2015-12-30T17:32:06Z | 2016-01-02T06:39:59Z | 2016-01-02T06:39:59Z | NONE | null | ``` python
pandas.DataFrame({"stuff":[1,2,3,4,5]}).head(0)
```
returns
``` python
stuff
0 1
1 2
2 3
3 4
4 5
```
I would expect an empty data frame, in the same way that
``` python
[1,2,3,4,5][0:0]
```
returns an empty list.
This is with pandas version 0.15.0.
The documentation (http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.head.html) says `head(n)` returns 'first n rows', so I'd expect 0 rows if n is 0.
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https://api.github.com/repos/pandas-dev/pandas/issues/11931 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11931/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11931/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11931/events | https://github.com/pandas-dev/pandas/issues/11931 | 124,384,359 | MDU6SXNzdWUxMjQzODQzNTk= | 11,931 | Test failure on OpenBSD: Couldn't find a tree builder with the features you requested: html5lib | {
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| null | 3 | 2015-12-30T18:37:53Z | 2015-12-30T21:23:39Z | 2015-12-30T21:23:39Z | NONE | null | Hi,
I'm trying to port pandas-0.17.1 to OpenBSD. There are some failing tests. The first class of failure looks like this:
```
FAIL: test_invalid_url (pandas.io.tests.test_html.TestReadHtml)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1790, in wrapper
return t(*args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/io/tests/test_html.py", line 290, in test_invalid_url
tm.assert_equal(str(e), 'No tables found')
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 600, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: : "Couldn't find a tree builder with the features you requested: html5lib. Do you need to install a parser library?" != 'No tables found'
```
I have installed:
```
py-beautifulsoup-3.2.1
py-beautifulsoup4-4.1.3
py-html5lib-0.11.1
```
Any ideas? Thanks.
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https://api.github.com/repos/pandas-dev/pandas/issues/11932 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11932/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11932/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11932/events | https://github.com/pandas-dev/pandas/issues/11932 | 124,385,151 | MDU6SXNzdWUxMjQzODUxNTE= | 11,932 | Test failure on OpenBSD: unexpected keyword argument 'encoding' | {
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| null | 1 | 2015-12-30T18:43:45Z | 2015-12-31T13:51:28Z | 2015-12-31T13:51:17Z | NONE | null | On pandas-0.17.1 on OpenBSD, many tests are failing like this:
```
ERROR: test_infer_s3_compression (pandas.io.tests.test_parsers.TestS3)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1790, in wrapper
return t(*args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/io/tests/test_parsers.py", line 4368, in test_infer_s3_compression
engine='python', compression='infer')
File "/usr/local/lib/python2.7/site-packages/pandas/io/parsers.py", line 498, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python2.7/site-packages/pandas/io/parsers.py", line 262, in _read
compression=kwds.get('compression', None))
File "/usr/local/lib/python2.7/site-packages/pandas/io/common.py", line 290, in get_filepath_or_buffer
encoding=encoding))
TypeError: get_contents_as_string() got an unexpected keyword argument 'encoding'
```
This is under Python-2.7.11.
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} | 8 | 2015-12-30T19:40:43Z | 2015-12-30T20:45:27Z | 2015-12-30T20:38:38Z | NONE | null | I was trying to use the round method on a dataframe and was getting some columns not rounded. This example replicates it:
```
0 1
0 163.339996338 55.178787875
1 166.788757324 47.98198625
2 199.475143433 64.40626575
3 257.137786865 88.855215375
4 248.984573364 86.0543125
df.dtypes:
0 float32
1 float64
dtype: object
df.round(3)
0 163.339996338 55.179
1 166.789001465 47.982
2 199.475006104 64.406
3 257.138000488 88.855
4 248.98500061 86.054
```
I'm not sure if this is being caused only by the different types for each column, but if I convert column 0 to a `float64` the rounding is applied to both columns.
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} | 1 | 2015-12-30T19:46:48Z | 2016-02-20T18:27:34Z | 2016-02-20T18:27:34Z | NONE | null | In the case below, setting `utc=True` in pandas.to_datetime actually removes the UTC attribute from the input:
```
import pandas as pd
import pytz
start = pd.Timestamp('2014-01-01', tz=pytz.UTC)
end = pd.Timestamp('2014-01-03', tz=pytz.UTC)
date_range = pd.bdate_range(start, end)
df = pd.DataFrame({'x':1}, index=date_range)
df.index
# DatetimeIndex([...], dtype='datetime64[ns, UTC]', freq='B')
pd.to_datetime(df.index, utc=True)
# DatetimeIndex([...], dtype='datetime64[ns]', freq='B')
```
the last function removes the UTC attribute from the input, even though `utc` is set to `True`.
This only happens in pandas 0.17.1. pandas 0.16.x have a different implementation and does not have this issue.
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https://api.github.com/repos/pandas-dev/pandas/issues/11935 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11935/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11935/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11935/events | https://github.com/pandas-dev/pandas/pull/11935 | 124,404,815 | MDExOlB1bGxSZXF1ZXN0NTQ4MzI4NDU= | 11,935 | [WIP] adding overby to core api | {
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| null | 4 | 2015-12-30T21:57:57Z | 2015-12-31T16:51:33Z | 2015-12-31T13:44:21Z | NONE | null | **I'd love to get some feedback on this before I proceed further.** @jreback and @TomAugspurger available?
### What is this?
This adds a new function to core DataFrames called `overby`. The main use case for this is to group data, sort within groups and apply a function to the resulting iterator.
It's very similar to the `OVER` clauses in some [SQL engines](https://my.vertica.com/docs/4.1/HTML/Master/10955.htm).
### Examples:
#### Cumulative Sum within group
Suppose I want to do groupby + cumulative sum within groups. The current best answer is to do [groupby twice](http://stackoverflow.com/questions/22650833/pandas-groupby-cumulative-sum):
```
df.groupby(by=['name','day']).sum().groupby(level=[0]).cumsum()
```
With overby, this becomes:
``` python
def cumulative_sum(iterrows):
cumsum = 0
for _, row in iterrows:
cumsum += row['no']
yield cumsum
result = df.overby('name', 'day', cumulative_sum)
```
This is more efficient than two `groupby`s.
In a different example, to have a better understanding of what's going on:
``` python
print df
"""
id t x
0 0 1 2
1 2 1 0
2 1 0 2
3 1 0 1
4 0 0 1
5 2 1 1
6 2 1 1
7 0 0 1
"""
def cumulative_sum(iterrows):
sum_ = 0
for _, row in iterrows:
sum_ += row['x']
yield {'cumsum':sum_, 'value_added': row['x']}
print df.overby('id', 't', cumulative_sum)
"""
cumsum value_added
id
0 0 1 1
1 2 1
2 4 2
2 0 0 0
1 1 1
2 2 1
1 0 1 1
1 3 2
"""
```
#### `nlargest` simplified
`nlargest` is a Pandas function that operates on grouped objects to find the largest values. With overby, an alternative implementation is clear:
``` python
df = pd.DataFrame([
{'id': 1,'x': 0},
{'id': 1,'x': 2},
{'id': 1,'x': 1},
{'id': 2,'x': 1},
{'id': 2,'x': 3},
{'id': 2,'x': 10},
])
## more verbose
def first_n(iterrows, n=2):
counter = 0
for _, row in iterrows:
yield row['x']
counter += 1
if counter >= n:
break
top_2 = df.overby('id', 'x', first_n, ascending=False)
## less verbose and a one liner
top_2 = df.overby('id', 'x', lambda iter: (row['x'] for _, row in islice(iter,2)), ascending=False)
```
This implementation is obviously much slower than the builtin version, _but_ it offers much more flexibility (example: one can put conditionals before the yield to filter some things out)
### Accumulating fact tables
This is an example from my production environment at work. Using `overby`, it's possible to build very powerful accumulating fact tables for use in data warehousing. Consider the following:
``` python
previous_input = pd.DataFrame([
{'user': 1, 'number_of_purchases`: 9, 'first_purchase_at': Jan 01, 2015, '10th_purchase_at`: None}
])
new_purchases = pd.DataFrame([
{'user': 1, 'first_purchase_at': Jan 05, 2015, 'number_of_purchases': 1, '10th_purchase_at`: None},
{'user': 2, 'first_purchase_at': Jan 05, 2015, 'number_of_purchases': 1, '10th_purchase_at`: None},
])
all_data = pd.concat([previous_input, new_purchases])
def reduce_metrics(iterrows):
return reduce(iterrows)
def reduce(current_row, next_row):
return
{
'user': current_row['user'],
'first_purchase_at': min(current_row['first_purchase_at'], next_row['first_purchase_at']),
'10th_purchase_at': something_more_complicated_than_min(current_row, next_row),
'number_of_purchases': current_row['number_of_purchases'] + next_row[''number_of_purchases']
}
all_data.overby('user', 'first_purchase_at', reduce_metrics)
"""
results look like:
{'user': 1, 'first_purchase_at': Jan 01, 2015, 'number_of_purchases': 10, '10th_purchase_at`: Jan 05, 2015},
{'user': 2, 'first_purchase_at': Jan 05, 2015, 'number_of_purchases': 1, '10th_purchase_at`: None},
"""
```
### To Do
- [ ] tests
- [ ] docs
- [ ] reviews
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| null | 1 | 2015-12-31T11:06:46Z | 2018-10-21T01:40:56Z | 2018-10-21T01:40:56Z | CONTRIBUTOR | null | By default, interpolate only works in forward direction
``` python
In [1]: import numpy as np
...: import pandas as pd
...: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, 13, np.nan, np.nan])
In [2]: ser.interpolate()
Out[2]:
0 NaN
1 NaN
2 5.000000
3 7.666667
4 10.333333
5 13.000000
6 13.000000
7 13.000000
dtype: float64
```
In order to interpolate backwards at the start of the series, the only option one currently has is to provide a limit value:
``` python
In [3]: ser.interpolate(limit_direction='both', limit=1000)
Out[3]:
0 5.000000
1 5.000000
2 5.000000
3 7.666667
4 10.333333
5 13.000000
6 13.000000
7 13.000000
dtype: float64
```
Actually, once the limit is set, the argument limit_direction does not only apply to the limit, but also to the direction of the interpolation itself at start and end of the series:
``` python
In [4]: ser.interpolate(limit_direction='backward', limit=1000)
Out[4]:
0 5.000000
1 5.000000
2 5.000000
3 7.666667
4 10.333333
5 13.000000
6 NaN
7 NaN
dtype: float64
```
Thus, a logic API change seems to replace the argument 'limit_direction' by 'direction', and extending its use to the cases where limit is None (which here corresponds to infinity). Then one would be able to do:
``` python
In [5]: ser.interpolate(direction='both')
Out[5]:
0 5.000000
1 5.000000
2 5.000000
3 7.666667
4 10.333333
5 13.000000
6 13.000000
7 13.000000
dtype: float64
```
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frame.head(0) and frame.tail(0) now return empty DataFrames.
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| null | 13 | 2016-01-01T13:51:36Z | 2021-04-21T05:31:09Z | null | NONE | null | for example:
``` python
Second().rollback(pd.Timestamp('2015-02-02 08:35:22.555'))
```
does not change the timestamp and returns:
``` python
Timestamp('2015-02-02 08:35:22.555000')
```
This will probably also affect `DatetimeIndex.snap()` method.
(using pandas 0.17.1)
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| null | 5 | 2016-01-01T19:35:58Z | 2016-01-02T02:16:16Z | 2016-01-02T01:25:50Z | NONE | null | One thing that is important for me, since convert_objects has been deprecated (#11221), is that removing it from my code yields a bug that I've reproduced in simplified form here:
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'a': [1, 2]})
print df.dtypes #The dtype of a is an int64
df.a = df.a.astype(np.object)
print df.dtypes #Now, the dtype of a is an object
df.a.str.decode('utf-8', errors='ignore').str.encode('utf-8')
## AttributeError: Can only use .str accessor with string values,
## which use np.object_ dtype in pandas
```
In my code, I use convert_objects to convert everything that can be converted away from an object into something else, then I use the column's dtype to check if it can be handled by the unicode decode-encode step.
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} | 2 | 2016-01-02T09:42:35Z | 2016-01-03T16:36:16Z | 2016-01-03T16:36:16Z | CONTRIBUTOR | null | closes #11833
I had to create a new PR , due to unresolved conflicts
Just look at the third commit , it is all squashed , ignore the rest
I don't get it why the travis build is not being executed for the third commit, it fails for its parent(the first commit) , but would pass for itself , as it is an updated version of its parent and the bug in the parent in tackled
I even checked for the merge-ability of the third commit , it merges with the branch. Still the travis build is not being executed
@jreback
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| null | 14 | 2016-01-02T22:48:53Z | 2016-09-09T22:45:16Z | 2016-09-09T22:45:16Z | CONTRIBUTOR | null | See #10797
The desired result is to allow slices of the column index to be used in set_index. Like this:
```
>>> import pandas as pd
>>> data1 = 'x'*5 + 'y'*5
>>> data2 = ['a','b','c','d','e']*2
>>> data3 = range(10)
>>> data_dict = {'Cat': list(data1), 'SubCat': list(data2), 'Vals':data3}
>>> df = pd.DataFrame(data_dict)
>>> ordered_df = df[['Cat', 'SubCat', 'Vals']]
>>> ordered_df.set_index(ordered_df.columns[:2])
Vals
Cat SubCat
x a 0
b 1
c 2
d 3
e 4
y a 5
b 6
c 7
d 8
e 9
```
However, passing an Index of values to set_index currently has similar behavior to passing an array of values; the index will be used as the values of the new index. As to not break this behavior, this PR only treats the index like a list of column labels when the length of the index is not the same as the length of the DataFrame.
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} | 13 | 2016-01-03T13:21:24Z | 2016-01-04T02:53:58Z | 2016-01-04T02:53:54Z | CONTRIBUTOR | null | xref #11928
Putting this out there as a start. Will add to this over the next couple of days.
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} | 2 | 2016-01-03T15:25:15Z | 2016-01-06T17:07:02Z | 2016-01-06T17:07:02Z | CONTRIBUTOR | null | Hello,
Series doesn't have is a `is_unique` attribute
Index have a `is_unique` attribute but not (values of a) Series
``` python
>>> df.index.is_unique
True
>>> df['Column1'].is_unique
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-417-c450d4c4ceda> in <module>()
----> 1 df['Date'].is_unique
//anaconda/envs/py27/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
2358 return self[name]
2359 raise AttributeError("'%s' object has no attribute '%s'" %
-> 2360 (type(self).__name__, name))
2361
2362 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'is_unique'
```
I'm doing this
``` python
def is_unique(ser):
return len(np.unique(ser.values)) == len(ser.values)
```
but this kind of attribute could be useful when you want to set a column as index and ensure that there is no duplicate keys.
PS: I know there is
``` python
df.set_index('Column1', verify_integrity=True)
```
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} | 2 | 2016-01-03T16:40:57Z | 2016-01-06T17:06:45Z | 2016-01-06T17:06:45Z | CONTRIBUTOR | null | Try to fix https://github.com/pydata/pandas/issues/11946
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| null | 7 | 2016-01-03T18:08:48Z | 2020-04-22T14:46:00Z | 2016-01-04T01:07:33Z | NONE | null | I want to read in Excel-Data (.xlsx) and have a problem with a wrong encoding of special characters. In the Excel-File, portuguese special characters, for example on "A", look like this:
**Á**
However, after reading in the data with Pandas Excel IO, they look like this:
**Ã**
I have no idea why this happens and how to solve this. Any help would be great.
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} | 1 | 2016-01-03T19:15:08Z | 2016-01-06T16:19:41Z | 2016-01-06T16:19:41Z | CONTRIBUTOR | null | xref #10000
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} | 37 | 2016-01-03T21:47:08Z | 2016-01-16T17:12:07Z | 2016-01-16T15:13:58Z | CONTRIBUTOR | null | Fix layout and pep8 compliance for categorical, common, and frame. Used YAPF with default settings and then manual modifications including:
-fixing line length of comments/docstrings for pep8 compliance
-converting lambdas to functions for pep8 compliance
-change YAPF default formatting of error strings
-change some YAPF line break choices to make them more natural in my eyes. For example converting
```
if (function1(argument) and function2(
argument)):
```
to
```
if (function1(argument) and
function2(argument)):
```
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} | 7 | 2016-01-04T02:04:06Z | 2016-01-04T13:15:20Z | 2016-01-04T02:07:23Z | NONE | null | The parameter for categories is documented as being `index-like`.
Does that include dictionaries?
If so, some unexpected results occurred when working with integers and dictionaries in version 0.17.1
The following sets out what I found
``` python
sex = [1,2,0,1]
categories = {1:'Male', 2:'Female', 0:'Unknown'}
pd.Categorical.from_codes(sex, categories=categories)
```
returns the the array with keys instead of the values (though i suspect that was luck given outcomes below)
```
[1, 2, 0, 1]
Categories (3, int64): [0, 1, 2]
```
Swapping the dictionary key/values around shows doesn't match values to keys though:
```
sex = [1,2,0,1]
categories = {'Male':1, 'Female':2, 'Unknown':0}
pd.Categorical.from_codes(sex, categories=categories)
```
returns an array that has incorrectly mapped codes and categories
```
[Unknown, Male, Female, Unknown]
Categories (3, object): [Female, Unknown, Male]
```
Using a non-sequential numerical ordering for the codes fails with a dictionary
``` python
sex = [1,2,9,1]
categories = {'Male':1, 'Female':2, 'Unknown':9}
pd.Categorical.from_codes(sex, categories=categories)
```
Fails with `ValueError: codes need to be between -1 and len(categories)-1`
at line 386 in categorical.py at `if len(codes) and (codes.max() >= len(categories) or codes.min() < -1)`
presumably because the second logical element.
I'm guessing it's meant to be a cheque on the count of the number of unique elements in `codes` being greater than or equal to the number of items in `categories`.
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| null | 6 | 2016-01-04T14:38:12Z | 2021-10-26T08:51:02Z | null | CONTRIBUTOR | null | pandas generally tries to coerce values to fit the column dtype, or upcasts the dtype to fit.
For a setting operation this is convenient & I think expected as a user
```
In [35]: df = DataFrame({'A' : Series(dtype='M8[ns]'), 'B' : Series([np.nan],dtype='object'), 'C' : ['foo'], 'D' : [1]})
In [36]: df
Out[36]:
A B C D
0 NaT NaN foo 1
In [37]: df.dtypes
Out[37]:
A datetime64[ns]
B object
C object
D int64
dtype: object
In [38]: df.loc[0,'D'] = 1.0
In [39]: df.dtypes
Out[39]:
A datetime64[ns]
B object
C object
D float64
dtype: object
```
However for a `.fillna` (or `.replace`) operation this might be a bit unexpected. So `A` was coerced to `object` dtype, even though it was `datetime64[ns]`.
```
In [40]: df.fillna('')
Out[40]:
A B C D
0 foo 1
In [41]: df.fillna('').dtypes
Out[41]:
A object
B object
C object
D float64
dtype: object
```
So a possibility is to add a keyword `errors='raise'|'coerce'|'ignore'`. This last behavior would be equiv of `errors='coerce'`. While skipping this column would be done with `errors='coerce'`. (and of course `raise` would raise.
Ideally would have a default of `coerce` I think (to skip for non-compat values). Any thoughts on this?
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} | 7 | 2016-01-04T16:34:21Z | 2019-10-16T22:33:22Z | 2019-10-16T22:33:15Z | MEMBER | null | I've found that using parentheses (which allow for automatic continuation) instead of `\` results in a lot more readable code. The lone exception is using `\` with long strings and avoiding a line break at the start of the string.
Worse:
```
return GOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOD and LET and THE and HATE and FLOW\
and THROUGH and YOU
```
Better:
```
return (GOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOD and LET and THE and HATE and FLOW
and THROUGH and YOU)
```
OK:
```
return """\
GOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOD and LET and THE and HATE and FLOW
and THROUGH and YOU"""
```
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| null | 3 | 2016-01-04T16:38:55Z | 2019-12-12T14:47:28Z | 2019-12-12T14:47:28Z | MEMBER | null | Per things like #11954 it would be good to make a list of style preferences that are not officially a part of PEP8 or linting tools
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| null | 13 | 2016-01-04T17:02:48Z | 2016-01-07T09:50:15Z | 2016-01-07T01:25:09Z | MEMBER | null | Just as an example (using the default configuration). The same files are adjusted as in https://github.com/pydata/pandas/pull/11951
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} | 1 | 2016-01-04T23:09:14Z | 2016-01-05T00:39:06Z | 2016-01-05T00:39:03Z | CONTRIBUTOR | null | closes #11835
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} | 20 | 2016-01-05T00:54:47Z | 2016-01-08T13:51:46Z | 2016-01-08T13:51:46Z | CONTRIBUTOR | null | Hi,
When trying to concat() multiple big fortran order arrays, there is a big performance hit, as most of the work goes into calling ravel().
See:
https://github.com/pydata/pandas/blob/master/pandas/core/internals.py#L4772
You can see the `is_null(self)` is using just a few values from the data after calling `.ravel()`
An easy fix is to change that line to `values_flat = values.ravel(order='K')`
Here is a link to numpy.ravel docs: http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.ravel.html
`‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.`
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} | 8 | 2016-01-05T02:04:15Z | 2017-06-12T21:56:38Z | 2017-06-07T21:53:06Z | CONTRIBUTOR | null | The [DataFrame.boxplot](http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.boxplot.html) reference documentation says I can change the size of a plot though the `figsize` keyword, but this appears not to work.
Using `df.plot(kind='box')` does respect `figsize`.
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
print("pandas version = {}".format(pandas.__version__))
print("python version = {}".format(sys.version))
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot(kind='box', figsize=(12,8))
plt.show()
df.boxplot(return_type='axes', figsize=(12,8))
plt.show()
```
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} | 18 | 2016-01-05T06:54:27Z | 2016-09-15T20:47:43Z | 2016-01-06T22:08:00Z | MEMBER | null | Summary of implementation approach
- Base `pandas::Array` class
- Implement dtype-specific Array subclasses and use dynamic dispatch to invoke generic array methods
- Hold NumPy arrays internally
- For data types that don't have a well-defined NA values, use a bitmask to store null/not-null
I adapted several bits of code from github.com/libdynd/dynd-python to save me time sorting out issues with NumPy's PyArray_API global variable and other fun stuff. We'll need to add a copyright notice to the LICENSES directoy
Demo:
``` python
In [1]: import pandas.native as nt
In [2]: arr = np.arange(10)
In [3]: parr = nt.to_array(arr)
In [4]: parr
Out[4]: <pandas.native.Array at 0x7f51dfce3e40>
In [5]: parr.dtype
Out[5]: PandasType(int64)
In [6]: parr[3]
Out[6]: 3
In [7]: from pandas.native import NA
In [8]: parr[3] = NA
In [10]: parr[3]
Out[10]: NA
In [11]: parr[3] = 12
In [13]: parr[3]
Out[13]: 12
```
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} | 2 | 2016-01-05T10:45:40Z | 2016-01-05T12:43:24Z | 2016-01-05T11:14:45Z | CONTRIBUTOR | null | I would like to save a _df_ with the following _index_ to an Excel file:
```dtype='datetime64[ns, Atlantic/Reykjavik]', name=u'Dates', length=4360, freq='H')
```
it shows the following error:
```
TypeError: Timestamp subtraction must have the same timezones or no timezones
```
```
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} | 1 | 2016-01-05T12:16:26Z | 2018-03-07T09:27:24Z | 2018-03-07T09:27:24Z | CONTRIBUTOR | null | - add to API.rst
- [ ] `Int64Index`
- [ ] `Float64Index`
- [ ] `PeriodIndex`
- [x] `MultiIndex` (#11893)
- [ ] `RangeIndex` (#11892)
- add to main docs (`CategoricalIndex` / `Float64Index` described in MultiIndex / Advanced Indexing sections). Expand this section to include.
- [ ] `Index`
- [ ] `DatetimeIndex/TimedeltaIndex/PeriodIndex` (already in timeseries.rst/timedelta.rst, need to cross-link to new section)
- [x] `Int64Index`
- [x] `RangeIndex`
cross-link from internals.rst about the Index internals.
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} | 4 | 2016-01-05T12:20:57Z | 2016-01-12T20:24:46Z | 2016-01-12T20:24:46Z | CONTRIBUTOR | null | Hi,
I was looking at the recent ENH #4314 and there is a wrong implementation of `round` method for datetime.
``` cython
def round(self, freq):
"""
return a new Timestamp rounded to this resolution
Parameters
----------
freq : a freq string indicating the rouding resolution
"""
cdef int64_t unit
cdef object result, value
from pandas.tseries.frequencies import to_offset
unit = to_offset(freq).nanos
if self.tz is not None:
value = self.tz_localize(None).value
else:
value = self.value
result = Timestamp(unit*np.floor(value/unit),unit='ns')
if self.tz is not None:
result = result.tz_localize(self.tz)
return result
```
You are flooring the nano timestamp instead of rounding. You should replace `np.floor` by `np.round`.
Also I propose to add `floor` and `ceil` method to Timestamp in order to have the same behavior that `float`. (It is very usefull to get the upper/lower bound for a specific freq, likely more usefull than round)
Do you agree @jreback ?
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} | 5 | 2016-01-05T14:33:23Z | 2016-01-12T20:25:09Z | 2016-01-12T20:25:09Z | CONTRIBUTOR | null | closes #11963
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} | 5 | 2016-01-05T15:27:00Z | 2016-02-11T02:42:04Z | 2016-02-11T02:42:04Z | CONTRIBUTOR | null | It would be nice to have a method to change the `.name` attribute on the columns/index, to permit it in a method chain. (This is a suggestion based on [this stackoverflow question](http://stackoverflow.com/questions/34598733/dataframe-method-for-renaming-axis).)
Instead of
```
df.columns.name = 'A'
df.index.name = 'B'
df
A 0 1
B
0 9 4
1 15 0
2 17 16
```
it would allow something like
```
df.rename_columns('A').rename_index('B')
```
or perhaps be a keyword argument to the existing `.rename()`. Does this seem like something that would fit in with pandas?
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| null | 2 | 2016-01-05T16:39:34Z | 2016-01-07T13:02:59Z | 2016-01-07T13:02:51Z | CONTRIBUTOR | null | Exception handler didn't use the list of columnar NaN types if they were generated.
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} | 5 | 2016-01-05T23:25:10Z | 2016-01-08T16:55:56Z | 2016-01-08T13:51:34Z | CONTRIBUTOR | null | fixes #11958
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| null | 2 | 2016-01-06T00:02:50Z | 2020-04-27T03:08:10Z | 2016-01-13T02:53:38Z | NONE | null | When I use
test_labels.to_csv("./data/test_label.csv", headers=True, index = False)
to write dataframe into csv file, it always convert inteders into float number,(I check the test_labels.dtype, it shows <type "int">. How can I let it not convert. Thanks
python 2.7.11, pandas 0.17.1, windows 10
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} | 9 | 2016-01-06T13:00:53Z | 2016-01-06T16:42:44Z | 2016-01-06T14:15:09Z | NONE | null | **[in]**
```
for cl in train.columns:
print cl, type(train.loc[0, cl])
print cl, type(train[cl][0])
break
```
**[out]**
```
record_ID <type 'numpy.float64'>
record_ID <type 'numpy.int64'>
```
Though I can solve it in many cases with `train[cl].dtype`, which gives the correct output:
**[out]**
```
record_ID int64
```
But if we work with string data types the output will be `object` instead of `str` as data type.
Something shortstop by me or what happens?
Thank you.
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} | 7 | 2016-01-06T14:01:57Z | 2020-01-01T16:02:31Z | 2020-01-01T15:54:11Z | CONTRIBUTOR | null | xref #11960
Create `libpandas` to support current user-facing API and container API (currently only numpy).
- `ImmutableArray`, single immutable dtyped container with a `pandas.DataType` dtype mirroring numpy/python scalars as well as current pandas extension types (`Categorical`, `datetime w/tz`), integer/boolean NA support, future types (`string` w/encoding, var-len), maybe ragged array etc.
- cached-properties:
- `isnull/notnull`
- `is_monotonic_increasing/is_monotonic_decreasing`
- methods:
- indexing getitem ops (positional, slicing, fancy)
- `Array` - mutable sub-class of `ImmutableArray`
- cached-properties above are now just properties
- indexing supports setitem as well
- `Table` - ordered-dict-like container of `Array`
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| null | 2 | 2016-01-06T16:10:11Z | 2016-01-06T16:16:05Z | 2016-01-06T16:14:12Z | CONTRIBUTOR | null | Sorry, I know this is more of a question (and maybe implementation/platform-specific) than anything else, so apologies if this is not the right venue.
Consider a large dataframe. Does transposing it trigger a full re-allocation of its data in memory? And if so, could that be improved?
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} | 16 | 2016-01-06T16:19:35Z | 2016-02-10T22:25:27Z | 2016-02-10T17:48:52Z | CONTRIBUTOR | null | supersedes #11950
xref #10000
using `xarray` >= 07.0
```
In [10]: Series([1,2,3]).to_xarray()
Out[10]:
<xarray.DataArray (index: 3)>
array([1, 2, 3])
Coordinates:
* index (index) int64 0 1 2
In [11]: DataFrame({'A' : [1,2,3], 'B' : ['foo','bar','baz']}).to_xarray()
Out[11]:
<xarray.Dataset>
Dimensions: (index: 3)
Coordinates:
* index (index) int64 0 1 2
Data variables:
A (index) int64 1 2 3
B (index) object 'foo' 'bar' 'baz'
In [14]: Panel(np.random.randn(2, 3, 4)).to_xarray()
Out[14]:
<xarray.DataArray (items: 2, major_axis: 3, minor_axis: 4)>
array([[[ 0.23726039, 0.44636322, 0.04425575, 1.06178388],
[-0.58236405, 0.62602167, 0.36156612, -0.12687913],
[-0.67854107, 0.72270844, -1.15402631, -1.89758909]],
[[ 0.42948622, 0.93227075, -0.13943692, 0.83043343],
[ 1.0355157 , -0.30532004, -0.16337369, -0.29283026],
[ 0.65199775, -0.11131818, 0.37853134, 1.56620844]]])
Coordinates:
* items (items) int64 0 1
* major_axis (major_axis) int64 0 1 2
* minor_axis (minor_axis) int64 0 1 2 3
```
TODO:
- [ ] examples in doc-string
- [ ] how-to-convert/use xarray
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| null | 3 | 2016-01-06T18:45:50Z | 2016-01-06T21:17:19Z | 2016-01-06T21:17:03Z | NONE | null | I have a dataframe of tweets, where the "created_at" column is stored as a datetime object. I just upgraded to the latest version of pandas, and now get this error:
```
df['created_at'].map(lambda d: d.strftime("%Y%m%d"))
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got DatetimeIndex)
```
strangely enough, everything works correctly if I do this:
```
df2 = df.set_index('created_at')
df2.index.map(lambda x: x.strftime("%Y%m%d"))
```
and this:
```
def f(d):
return d['created_at'].strftime("%Y%m%d")
df.apply(lambda row: f(row), axis=1)
```
What is going on here?
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} | 5 | 2016-01-06T19:04:40Z | 2016-01-25T15:40:47Z | 2016-01-25T15:40:47Z | NONE | null | I'm not sure if this is a bug, but I'v noticed a strange behavior of skew() and kurt() on constant series:
``` python
#skew:
pd.Series(3075.3, np.arange(300)).skew() #returns -105683727.49921818
pd.Series(3075.2, np.arange(300)).skew() #returns nan
pd.Series(3075.5, np.arange(300)).skew() #returns 0
#kurt:
pd.Series(3075.3, np.arange(300)).kurt() #returns 9305221026420888.0
pd.Series(3075.2, np.arange(300)).kurt() #returns -7459707710545691.0
pd.Series(3075.5, np.arange(300)).kurt() #returns 0
```
(using pandas 0.17.1 (from anacondas) on a 64bit intel windows 7 pc)
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} | 1 | 2016-01-07T01:27:59Z | 2016-01-07T01:32:01Z | 2016-01-07T01:32:01Z | CONTRIBUTOR | null | The problem was I only had 7 digits in the date string.
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} | 3 | 2016-01-07T01:33:27Z | 2016-11-18T11:56:19Z | 2016-11-18T11:56:19Z | CONTRIBUTOR | null | ```
In [2]: pd.Timestamp('2010011')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-ccfd37d6ef5d> in <module>()
----> 1 pd.Timestamp('2010011')
/Users/jreback/pandas/pandas/tslib.pyx in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:8958)()
296 cdef _Timestamp ts_base
297
--> 298 ts = convert_to_tsobject(ts_input, tz, unit)
299
300 if ts.value == NPY_NAT:
/Users/jreback/pandas/pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas/tslib.c:24122)()
1232
1233 if util.is_string_object(ts):
-> 1234 return convert_str_to_tsobject(ts, tz, unit)
1235
1236 if ts is None or ts is NaT:
/Users/jreback/pandas/pandas/tslib.pyx in pandas.tslib.convert_str_to_tsobject (pandas/tslib.c:26110)()
1361 ts = parse_datetime_string(ts, dayfirst=dayfirst, yearfirst=yearfirst)
1362 except Exception:
-> 1363 raise ValueError
1364
1365 return convert_to_tsobject(ts, tz, unit)
ValueError:
```
we are should have a nice error message here. This is normally caught for example in `to_datetime`, but if its only a single `Timestamp` no message.
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| null | 16 | 2016-01-07T01:57:26Z | 2021-04-21T05:40:37Z | 2021-04-21T05:40:37Z | NONE | null | - [ ] `scatter_matrix` (deprecate, redirect to `seaborn`)
- [ ] `andrews_curve`
- [ ] `parallel_coordinates`
- [ ] `lag_plot` (maybe rename to `lag`)
- [ ] `autocorrelation_plot` (maybe rename to `autocorrelation`?)
- [ ] `bootstrap_plot` (maybe rename to `bootstrap`?)
- [ ] `radviz`
I think it would be nice to allow [scatter_matrix](http://pandas.pydata.org/pandas-docs/version/0.15.0/visualization.html#scatter-matrix-plot) to be called directly on a DataFrame. Currently, `scatter_matrix` is a separate function that takes a DataFrame as a parameter, but it seems like it would be easy enough to rework to allow it to be called directly on a DataFrame as well. Effectively, the convenience function would look something like:
``` python
def scatter_matrix(self, ...):
pandas.tools.plotting.scatter_matrix(self.data, ...)
```
Is this feature feasible?
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} | 7 | 2016-01-07T04:48:14Z | 2021-04-21T05:41:26Z | null | CONTRIBUTOR | null | Seems surprising, I thought `.name` would come along.
``` python
In [31]: idx = pd.MultiIndex.from_product([['A'], ['a', 'b', 'c']])
In [32]: idx.name = 'foo'
In [33]: idx2 = idx.set_names(['l1', 'l2'])
In [34]: idx2.name
```
Bug, or user error since MultiIndex.name isn't all that useful?
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} | 25 | 2016-01-07T04:57:47Z | 2016-02-11T03:03:32Z | 2016-02-11T02:42:04Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9494
closes https://github.com/pydata/pandas/issues/11965
Okay refactored to overload `NDFrame.rename` and `NDFrame.rename_axis`
New rules:
- Series.rename(scalar) or Series.rename(list-like) will alter `Series.name`
- NDFrame.rename(mapping) and NDFrame.rename(function) operate as before (alter labels)
- NDFrame.rename_axis(scaler) or NDFrame.rename_axis(list-like) will change the Index.name or MutliIndex.names
- NDFrame.rename_axis(mapping) and NDFrame.rename_axis(function) operate as before.
I don't like overloading the method like this, but `rename` and `rename_axis` are the right method names. It's _mostly_ backwards compatible, aside from people doing things like `try: series.rename(x): except TypeError: ...` and I don't know what our guarantees are around things like that.
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} | 7 | 2016-01-07T10:04:04Z | 2016-05-11T22:18:27Z | 2016-05-11T22:18:27Z | NONE | null | There appears to be an interaction between the option for float display length and pivot_table methods on integers. This issue arose when working on a dataset with both floats and integers - but this arose on operations on integers part of the dataset.
The following example throws an error from the `format.py` module.
Swap the comments on the formatting options (undoing the setting for floats) and it runs fine.
In my dataset - there were no missing values following the pivot table operation and the error still occurs. In the sample code below, NaNs may be causing the formatting issue, but that didn't appear to be the case for me.
``` python
import pandas as pd
import numpy as np
#pd.set_option('display.float_format', None)
pd.set_eng_float_format(accuracy=1)
df = pd.DataFrame(data = np.random.randint(25, size=(10, 3)), columns = list('abc'))
df.pivot_table(values='a', index='b', columns='c')
```
cheers
Chris
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| null | 5 | 2016-01-07T10:39:41Z | 2016-01-07T23:09:28Z | 2016-01-07T12:53:18Z | NONE | null | I am trying to concatenating two Panels with duplicate `Items` (but different `major_axes`) together, without actually having to duplicate items... Did some searching but couldn't find much on SO.
Unfortunately I cannot upload pics to github at the moment.. **But screens are attached [here](http://stackoverflow.com/questions/34652835/pandas-how-to-concatenate-two-panels-without-duplicating-items-axis-i-e-colum).**
To illustrate what I mean:
```
# create data
import pandas as pd
df1 = pd.DataFrame({'A' : [1, 2, 3], 'B' : [2, 5, 6]}, index={0, 1, 2})
df2 = pd.DataFrame({'A' : [1, 2, 3], 'B' : [2, 5, 6]}, index={3, 4, 5})
df3 = pd.DataFrame({'A' : [5, 2, 3], 'B' : [2, 5, 6]}, index={6, 7, 8})
df4 = pd.DataFrame({'A' : [6, 2, 3], 'B' : [2, 5, 6]}, index={9, 10, 11})
pan1 = pd.Panel({'X' : df1, 'Y': df2})
pan2 = pd.Panel({'X' : df3, 'Y': df4})
# concat these two panels above
pan3 = pd.concat([pan1, pan2])
pan3
Output:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 12 (major_axis) x 2 (minor_axis)
Items axis: X to Y
Major_axis axis: 0 to 11
Minor_axis axis: A to B
```
**Note that the `Items` axis for `pan3` is of length 4, while `pan1` and `pan2` both have `Items` axis of length 2.**
Looking at `pan3`'s `minor_axis`, I get duplicated columns in the frame below. **This is my problem.**:
```
pan3.loc[:, :, 'B']
```
Screenshot: See this [link on SO](http://stackoverflow.com/questions/34652835/pandas-how-to-concatenate-two-panels-without-duplicating-items-axis-i-e-colum)
**What I actually want to see** can be replicated as follows in a more complicated way... (Not more duplicated columns in the screenshot):
```
df5 = pd.concat([df1, df3])
df6 = pd.concat([df2, df4])
pan4 = pd.Panel({'X':df5, 'Y':df6})
pan4.loc[:, :, 'B']
```
Screenshot: See this [link on SO](http://stackoverflow.com/questions/34652835/pandas-how-to-concatenate-two-panels-without-duplicating-items-axis-i-e-colum)
**Is this something that can be done with `pd.concat`?** Thanks guys.
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} | 3 | 2016-01-07T10:48:52Z | 2018-07-06T20:21:09Z | 2017-07-11T09:54:41Z | CONTRIBUTOR | null | This code works in 0.15.2 but breaks in 0.17.1
``` python
import pandas as pd
import numpy as np
pd.show_versions()
items = ['foo']
major_axis = pd.date_range('2013-04-01', '2013-06-30')
minor_axis = [0]
p = pd.Panel(
np.ones(major_axis.size).reshape(1, -1, 1),
items=items,
major_axis=major_axis
)
p.resample('Q', how='mean', axis=1, kind='period')
```
### 0.15.2:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.15.2
nose: 1.3.7
Cython: 0.22
numpy: 1.9.3
scipy: 0.16.0
statsmodels: None
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.7
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Out[1]:
<class 'pandas.core.panel.Panel'>
Dimensions: 1 (items) x 1 (major_axis) x 1 (minor_axis)
Items axis: foo to foo
Major_axis axis: 2013Q2 to 2013Q2
Minor_axis axis: 0 to 0
```
### 0.17.1:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.1
sphinx: 1.3
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.7
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
Jinja2: None
In [11]: p.resample('Q', how='mean', axis=1, kind='period')
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-11-37b0fd9cdc67> in <module>()
----> 1 p.resample('Q', how='mean', axis=1, kind='period')
c:\python\envs\pandas-0.17\lib\site-packages\pandas\core\generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind,
loffset, limit, base)
3641 fill_method=fill_method, convention=convention,
3642 limit=limit, base=base)
-> 3643 return sampler.resample(self).__finalize__(self)
3644
3645 def first(self, offset):
c:\python\envs\pandas-0.17\lib\site-packages\pandas\tseries\resample.pyc in resample(self, obj)
80
81 if isinstance(ax, DatetimeIndex):
---> 82 rs = self._resample_timestamps()
83 elif isinstance(ax, PeriodIndex):
84 offset = to_offset(self.freq)
c:\python\envs\pandas-0.17\lib\site-packages\pandas\tseries\resample.pyc in _resample_timestamps(self, kind)
285 # downsample
286 grouped = obj.groupby(grouper, axis=self.axis)
--> 287 result = grouped.aggregate(self._agg_method)
288 # GH2073
289 if self.fill_method is not None:
c:\python\envs\pandas-0.17\lib\site-packages\pandas\core\groupby.pyc in aggregate(self, arg, *args, **kwargs)
3661 """
3662 if isinstance(arg, compat.string_types):
-> 3663 return getattr(self, arg)(*args, **kwargs)
3664
3665 return self._aggregate_generic(arg, *args, **kwargs)
c:\python\envs\pandas-0.17\lib\site-packages\pandas\core\groupby.pyc in mean(self)
764 self._set_selection_from_grouper()
765 f = lambda x: x.mean(axis=self.axis)
--> 766 return self._python_agg_general(f)
767
768 def median(self):
c:\python\envs\pandas-0.17\lib\site-packages\pandas\core\groupby.pyc in _python_agg_general(self, func, *args, **kwargs)
1223 # iterate through "columns" ex exclusions to populate output dict
1224 output = {}
-> 1225 for name, obj in self._iterate_slices():
1226 try:
1227 result, counts = self.grouper.agg_series(obj, f)
c:\python\envs\pandas-0.17\lib\site-packages\pandas\core\groupby.pyc in _iterate_slices(self)
3637 slicer = lambda x: self._selected_obj[x]
3638 else:
-> 3639 raise NotImplementedError("axis other than 0 is not supported")
3640
3641 for val in slice_axis:
NotImplementedError: axis other than 0 is not supported
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11984 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11984/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11984/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11984/events | https://github.com/pandas-dev/pandas/issues/11984 | 125,374,742 | MDU6SXNzdWUxMjUzNzQ3NDI= | 11,984 | Pandas replacing values on specific columns. view vs. copy | {
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} | 10 | 2016-01-07T11:18:16Z | 2019-10-01T08:25:38Z | 2017-06-28T09:36:52Z | CONTRIBUTOR | null | Hello,
I asked a question on StackExchange which is explaining my bug/feature:
http://stackoverflow.com/questions/34653215/pandas-replacing-values-on-specific-columns
pd.show_versions() gives:
## INSTALLED VERSIONS
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
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| null | 2 | 2016-01-07T15:52:34Z | 2019-01-21T04:44:15Z | 2019-01-21T04:44:15Z | NONE | null | When using pd.ExcelWriter, it appears that calling the save method prevents you from subsequently creating new sheets. E.g. see the following test script
``` python
import pandas as pd
import numpy as np
import os
if __name__ == "__main__" :
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
currentpath = os.path.realpath(__file__)
outputpath = os.path.join(currentpath, "..\\test.xlsx")
writer = pd.ExcelWriter(outputpath)
df.to_excel(writer, "thing1")
writer.save() #if you remove this line it works as expected
new_sheet = writer.book.add_worksheet("thing2")
new_sheet.write(1,1,"test")
print writer.book.sheetnames
writer.save()
writer.close()
```
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} | 2 | 2016-01-07T16:44:19Z | 2016-01-08T14:04:28Z | 2016-01-08T14:04:28Z | NONE | null | - DataFrame.round() drops the index name in the column
- This is also seen when using np.round() on the dataframe.
- Further this is not seen when using other elementwise numpy operations such as np.floor()
- This is only observed in pandas 0.17.\* This was working fine in pandas 0.16.*
``` python
import pandas as pd
import numpy as np
rows=5
cols=2
index = pd.Index(range(rows), name='MYINDEX')
columns = pd.Index(range(cols), name='MYCOLUMNS')
df = pd.DataFrame(np.random.rand(rows,cols), index=index, columns=columns)
df
Out[2]:
MYCOLUMNS 0 1
MYINDEX
0 0.163862 0.828853
1 0.560261 0.980514
2 0.736242 0.055051
3 0.825197 0.783094
4 0.153369 0.069713
df.round(2)
Out[3]:
0 1
MYINDEX
0 0.16 0.83
1 0.56 0.98
2 0.74 0.06
3 0.83 0.78
4 0.15 0.07
np.round(df, 2)
Out[4]:
0 1
MYINDEX
0 0.16 0.83
1 0.56 0.98
2 0.74 0.06
3 0.83 0.78
4 0.15 0.07
np.floor(df)
Out[5]:
MYCOLUMNS 0 1
MYINDEX
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
Jinja2: None
```
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| null | 7 | 2016-01-07T16:44:29Z | 2021-09-09T13:45:38Z | null | NONE | null | hi
i would welcome if option `sparsify` could be set separately for header and body
as i want body without `sparsify=False` and head with `sparsify=True`
what do you think about it?
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} | 13 | 2016-01-07T16:49:44Z | 2016-01-08T14:07:20Z | 2016-01-08T14:07:14Z | CONTRIBUTOR | null | closes #8760
closes #7718
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} | 2 | 2016-01-07T17:31:09Z | 2016-01-30T15:34:26Z | 2016-01-30T15:34:26Z | NONE | null | When using a regular expression in the `sep` argument of `read_csv`, the Python parser disregards quotes in the input file.
In the following example, `example.csv` is parsed correctly by `read_csv` without regexes in `sep`, while the version with regexes (which should evaluate to exactly the same as the previous version) fails because it parses the commas inside the quotes.
`example2.csv`, which doesn't contain quotes, is parsed correctly using the same code.
``` python
In [1]: import pandas
In [2]: !cat example.csv
a,b,c,d
q,w,e,r
a,s,d,f
"z,x,c,v",i,o,p
In [3]: pandas.read_csv('example.csv', engine = 'python', quotechar = '"', sep = ',')
Out[3]:
a b c d
0 q w e r
1 a s d f
2 z,x,c,v i o p
In [4]: pandas.read_csv('example.csv', engine = 'python', quotechar = '"', sep = '[,]')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-5f9ad4fdcd46> in <module>()
----> 1 pandas.read_csv('example.csv', engine = 'python', quotechar = '"', sep = '[,]')
/usr/local/lib/python2.7/dist-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, 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)
489 skip_blank_lines=skip_blank_lines)
490
--> 491 return _read(filepath_or_buffer, kwds)
492
493 parser_f.__name__ = name
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
276 return parser
277
--> 278 return parser.read()
279
280 _parser_defaults = {
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in read(self, nrows)
738 raise ValueError('skip_footer not supported for iteration')
739
--> 740 ret = self._engine.read(nrows)
741
742 if self.options.get('as_recarray'):
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in read(self, rows)
1593 content = content[1:]
1594
-> 1595 alldata = self._rows_to_cols(content)
1596 data = self._exclude_implicit_index(alldata)
1597
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _rows_to_cols(self, content)
1968 msg = ('Expected %d fields in line %d, saw %d' %
1969 (col_len, row_num + 1, zip_len))
-> 1970 raise ValueError(msg)
1971
1972 if self.usecols:
ValueError: Expected 4 fields in line 4, saw 7
In [5]: !cat example2.csv
a,b,c,d
q,w,e,r
a,s,d,f
z,x,c,v
In [6]: pandas.read_csv('example2.csv', engine = 'python', quotechar = '"', sep = '[,]')
Out[6]:
a b c d
0 q w e r
1 a s d f
2 z x c v
In [7]: pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-74-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.1
pip: 1.5.4
setuptools: 1.1.4
Cython: None
numpy: 1.10.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 3.1.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 5 | 2016-01-07T18:14:05Z | 2016-01-12T23:47:54Z | 2016-01-12T23:47:54Z | CONTRIBUTOR | null | I'll see if it's an easy fix.
```
import pandas as pd
import numpy as np
FLOAT_TYPES = list(np.typecodes['AllFloat'])
df = pd.util.testing.makeDataFrame()
df.select_dtypes(FLOAT_TYPES)
FLOAT_TYPES.remove('e')
df.select_dtypes(FLOAT_TYPES)
```
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} | 10 | 2016-01-07T18:35:32Z | 2016-01-08T14:04:55Z | 2016-01-08T14:04:55Z | CONTRIBUTOR | null | Fixes #11986. Used the same reconstruction method as `DataFrame.isin()` to ensure that column names are kept through rounding. Works with `np.round` as well.
Pretty minor fix -- does this need a release note or anything else?
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| null | 3 | 2016-01-07T21:33:16Z | 2018-10-26T05:57:34Z | 2018-10-26T05:57:34Z | CONTRIBUTOR | null | xref #7718
xref #11988
since we recently removed 2.6 (and 3.3) support, we are now free to use some features of later releases.
Some of these may be useful. We should investigate and create individual issues.
see [here](https://docs.python.org/2/whatsnew/2.7.html)
- The syntax for set literals (`{1,2,3}` is a mutable set).
- Dictionary and set comprehensions (`{i: i*2 for i in range(3)}`).
- `io` library (#10470)
- `argparse` (rather then `optparse`)
- maybe dictviews
- memoryviews
- `sysconfig`
- automatic number in `.format`
```
>>> '{}:{}:{day}'.format(2009, 4, day='Sunday')
'2009:4:Sunday'
```
- using `assertRaises` as a context manager
```
with self.assertRaises(KeyError):
{}['foo']
```
- lots of other improved testing goodies!
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| null | 1 | 2016-01-07T21:52:03Z | 2016-01-07T22:00:06Z | 2016-01-07T21:59:53Z | NONE | null | Hi, new to python and pandas, but am trying to work with information off the web that has serial numbers. Many of the numbers have leading zeros which that are important to carry over, but pandas interprets the values as integers and drops the zero. I was trying to change the dtype to str but read_html doesn't have a dtype argument, and it seems like infer_types = False is depricated (or at least I get an exception for unexpected keyword argument). What is the best way to get these zeros back?
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} | 1 | 2016-01-08T00:08:08Z | 2016-01-08T18:20:15Z | 2016-01-08T18:20:15Z | MEMBER | null | This is on master:
``````
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.17.1+179.g44e4c96'
In [3]: pd.Timedelta('1H')
Out[3]: Timedelta('0 days 01:00:00')
In [4]: pd.Timedelta(u'1H')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-9b0d9f7ba675> in <module>()
----> 1 pd.Timedelta(u'1H')
/Users/shoyer/dev/pandas/pandas/tslib.pyx in pandas.tslib.Timedelta.__new__ (pandas/tslib.c:39786)()
2329 value = value.value
2330 elif util.is_string_object(value):
-> 2331 value = np.timedelta64(parse_timedelta_string(value, False))
2332 elif isinstance(value, timedelta):
2333 value = convert_to_timedelta64(value,'ns',False)
/Users/shoyer/dev/pandas/pandas/tslib.pyx in pandas.tslib.parse_timedelta_string (pandas/tslib.c:46542)()
2784 return NPY_NAT
2785
-> 2786 for c in ts:
2787
2788 # skip whitespace / commas
TypeError: Expected str, got unicode```
``````
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| null | 2 | 2016-01-08T11:06:50Z | 2016-01-08T13:44:18Z | 2016-01-08T13:44:18Z | CONTRIBUTOR | null | Pandas version 0.17.1:
``` python
In [1]: import pandas as pd
...: data = pd.DataFrame({'d':[1,2,3],'e':[1.5,2,3],'f':[2,2,3]},index=list('abc'))
...: data.diff(axis=1)
Out[1]:
d e f
a NaN NaN 1
b NaN NaN 0
c NaN NaN 0
```
Hint: the following gives expected result
``` python
In [2]: data.astype(float).diff(axis=1)
Out[2]:
d e f
a NaN 0.5 0.5
b NaN 0.0 0.0
c NaN 0.0 0.0
```
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} | 2 | 2016-01-08T14:32:32Z | 2016-01-08T18:28:57Z | 2016-01-08T18:20:15Z | CONTRIBUTOR | null | closes #11995
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| null | 3 | 2016-01-08T16:57:58Z | 2016-01-08T21:04:48Z | 2016-01-08T18:26:10Z | NONE | null | One feature about DataFrame that I found uncomfortable is the inconsistency of results:
```
x = pd.DataFrame({'A': [1,2,3,4,5]}, index=['a','b','c','d','e'])
x.loc[['a', 'f']] # fine, value of 'f' is numpy.nan
x.loc[['f']] # throws
```
When developing data applications, I spend lot of time fixing this issue. I think we should make it more consistent. Either always returns (preferred) or always throws.
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| null | 2 | 2016-01-08T18:47:40Z | 2019-12-20T14:02:09Z | 2019-12-20T14:02:08Z | CONTRIBUTOR | null | ```
import pandas as pd
import numpy as np
df = pd.Panel(np.arange(8).reshape((2,2,2)))
print(df.values.flags)
print('-----')
with pd.HDFStore('temp.h5', 'w') as s:
s.put('df', df)
new_df = s.get('df')
print(new_df.values.flags)
```
outputs:
```
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
-----
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
```
Relevant link:
https://github.com/pydata/pandas/blob/f2b083beaee7302eb739d3847ea5271330ff5886/pandas/io/pytables.py#l2495
GenericFixed.write_array seems to transpose the data before writing, but unsure why that is the case
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| null | 7 | 2016-01-08T19:42:42Z | 2016-02-11T21:22:45Z | 2016-02-11T21:22:45Z | CONTRIBUTOR | null | closes #11435
closes #11334.
Added some doc examples to make the behavior with `nan` more clear, and catch an error condition when a `sep` is passed instead of an `others` (array) as an argument and print a more intuitive error message.
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} | 9 | 2016-01-08T23:51:02Z | 2016-01-25T20:12:42Z | 2016-01-24T23:04:08Z | MEMBER | null | Continuation of #11069 (It seems I cannot reopen that PR because I already force pushed the branch ..)
Closes #3800
TODO:
- [x] remove printing of doc build log file in after_script
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| null | 9 | 2016-01-09T00:04:24Z | 2018-07-13T04:55:53Z | 2018-07-13T04:55:46Z | CONTRIBUTOR | null | now RTD support conda, see [here](https://github.com/rtfd/readthedocs.org/issues/857).
for pure-python packages this is quite easy (e.g. @shoyer just added `xray` support).
We would have to build a conda package on travis. and push to our channel. Which is then pulled from RTD.
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