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} | 13 | "2013-05-01T13:54:42Z" | "2021-03-10T17:44:52Z" | "2013-09-20T23:43:00Z" | NONE | null | I am using pandas 0.11.in python 2.7.3
When I read csv data with a windows line terminator (\r\n), pandas creates extra rows in the dataframe ie it does not recognise the line terminator..i
test1.csv
Id,Description,Field1,Field2^M
1,"test data 1 ^M\n",2,3^M
2,"test data 2 ^M\n",3,4^M
In [80]: in_csv = 'test1.csv'
In [81]: cols = ['Id', 'Description', 'Field1', 'Field2']
In [82]: reader = pd.read_csv(in_csv, usecols=cols, dtype=object)
In [83]: print reader
Id Description Field1 Field2
0 \r NaN NaN NaN
1 1 test data 1 \r\n 2 3
2 \r NaN NaN NaN
3 2 test data 2 \r\n 3 4
4 \r NaN NaN NaN
In [86]: reader.to_csv(out_csv, index=False, encoding='utf-8')
Id,Description,Field1,Field2
^M,,,
1,test data 1 ^M\n,2,3
^M,,,
2,test data 2 ^M\n,3,4
^M,,,
I cannot use...
lineterminator : string (length 1), default None
reader = pd.read_csv(in_csv, usecols=cols, dtype=object, lineterminator='\r\n')
ValueError: Only length-1 line terminators supported
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https://api.github.com/repos/pandas-dev/pandas/issues/3502 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3502/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3502/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3502/events | https://github.com/pandas-dev/pandas/pull/3502 | 13,849,393 | MDExOlB1bGxSZXF1ZXN0NTQ3MDgwMQ== | 3,502 | BUG: GH3480 Fix regression in a DataFrame apply with axis=1 | {
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closes #3480
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} | 4 | "2013-05-01T14:09:44Z" | "2013-09-22T16:15:26Z" | "2013-09-22T16:15:26Z" | NONE | null | I am using pandas 0.11.in python 2.7.3
I am processing csv data with windows line terminators in some of the field data. The input data is properly quoted(with QUOTE_MINIMAL )
when writing the data using to_csv, pandas does not quote the fields with windows line terminators ie not using QUOTE_MINIMAL ?
test3.csv
Id,Description,Field1,Field2
1,"test data 1 ^M\n",2,3
2,"test data 2 ^M\n",3,4
```
In [93]: in_csv = 'test3.csv'
In [94]: cols = ['Id', 'Description', 'Field1', 'Field2']
In [95]: reader = pd.read_csv(in_csv, usecols=cols, dtype=object)
In [96]: print reader
Id Description Field1 Field2
0 1 test data 1 \r\n 2 3
1 2 test data 2 \r\n 3 4
In [97]: reader.to_csv(out_csv, index=False, encoding='utf-8')
Id,Description,Field1,Field2
1,test data 1 ^M\n,2,3
2,test data 2 ^M\n,3,4
```
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Series([1], Index(np.array([2**64-1],'u8'), 'u8')) + Series([1], Index(np.array([2**64-2],'u8'), 'u8'))
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} | 5 | "2013-05-02T01:14:01Z" | "2014-06-13T16:52:03Z" | "2013-05-02T14:51:57Z" | CONTRIBUTOR | null | - Non-unique index support clarified #3092
- Fix assigning a new index to a duplicate index in a DataFrame would fail #3468
- Fix construction of a DataFrame with a duplicate index
- ref_locs support to allow duplicative indices across dtypes,
allows iget support to always find the index (even across dtypes) #2194
- applymap on a DataFrame with a non-unique index now works
(removed warning) #2786, and fix #3230
- Fix to_csv to handle non-unique columns #3495
- Modification to cache_readonly to allow you to pass an argument (allow_setting), to 'set'
this value (useful in order to avoid a computation you know to be true, e.g. is_unique = True
for a default index
partially fixes #3468
This would previously raise (same dtype assignment to a non-multi dtype frame with dup indicies)
```
In [6]: df = DataFrame([[1,2]], columns=['a','a'])
In [7]: df.columns = ['a','a.1']
In [8]: df
Out[8]:
a a.1
0 1 2
```
construction of a multi-dtype frame with a dup index (#2194) is fixed
```
In [18]: DataFrame([[1,2,1.,2.,3.,'foo','bar']], columns=list('aaaaaaa'))
Out[18]:
a a a a a a a
0 1 2 1 2 3 foo bar
```
This was also previously would raise
```
In [3]: df_float = DataFrame(np.random.randn(10, 3),dtype='float64')
In [4]: df_int = DataFrame(np.random.randn(10, 3),dtype='int64')
In [5]: df_bool = DataFrame(True,index=df_float.index,columns=df_float.columns)
In [6]: df_object = DataFrame('foo',index=df_float.index,columns=df_float.columns)
In [7]: df_dt = DataFrame(Timestamp('20010101'),index=df_float.index,columns=df_float.columns)
In [9]: df = pan.concat([ df_float, df_int, df_bool, df_object, df_dt ], axis=1)
In [14]: cols = []
In [15]: for i in range(5):
....: cols.extend([0,1,2])
....:
In [16]: df.columns = cols
In [17]: df
Out[17]:
0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
0 0.586610 0.369944 1.341337 1 1 1 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
1 -1.944284 -0.813987 0.061306 0 0 1 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
2 -1.688694 1.644802 0.659083 0 0 0 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
3 1.422893 0.712382 0.749263 -1 0 -1 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
4 -0.453802 0.228886 -0.339753 2 0 -2 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
5 -0.189643 1.309407 -0.386121 0 0 0 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
6 0.455658 0.822050 -0.741014 0 0 0 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
7 -0.484678 -1.089146 0.774849 0 1 0 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
8 0.720365 1.696400 -0.604040 -1 0 0 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
9 -0.344480 0.886489 0.274428 1 0 0 True True True foo foo foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
```
For those of you interested.....here is the new ref_loc indexer for duplicate columns
its by necessity a block oriented indexer, returns the column map (by column number) to a tuple of the block and the index in the block, only created when needed (e.g. when trying to get a column via iget and the index is non-unique, and the results are cached), this is #3092
```
In [1]: df = pd.DataFrame(np.random.randn(8,4),columns=['a']*4)
In [2]: df._data.blocks
Out[2]: [FloatBlock: [a, a, a, a], 4 x 8, dtype float64]
In [3]: df._data.blocks[0]._ref_locs
In [4]: df._data._set_ref_locs()
Out[4]:
array([(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 0),
(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 1),
(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 2),
(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 3)], dtype=object)
```
Fixed the #2786, #3230 bug that caused applymap to not work (we temp worked around by raising a ValueError; removed that check)
```
n [3]: In [3]: df = pd.DataFrame(np.random.random((3,4)))
In [4]: In [4]: cols = pd.Index(['a','a','a','a'])
In [5]: In [5]: df.columns = cols
In [6]: In [6]: df.applymap(str)
Out[6]:
a a a a
0 0.494204195164 0.534601503195 0.471870025143 0.880092879641
1 0.860369768954 0.0472931994392 0.775532754792 0.822046777859
2 0.478775855962 0.623584943227 0.932012693593 0.739502590395
```
Finally, to_csv writing has been fixed to use a single column mapper (which is derived from the ref_locs if the index is non-unique or the column numbering if it is unique)
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} | 6 | "2013-05-02T02:45:24Z" | "2014-06-20T12:16:59Z" | "2013-05-03T15:52:21Z" | CONTRIBUTOR | null | Incorrectly accessed variable `edge` instead of pre-filled value `start`.
Signed-off-by: Kevin Stone [email protected]
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} | 4 | "2013-05-02T05:34:41Z" | "2014-06-24T13:24:56Z" | "2013-05-05T09:14:08Z" | NONE | null | #3468
as discussed, an added option to disable th mangling behavior
of dupe columns taken by pd.read_csv going back to olden days.
Not sure what the default should be, currently the default is compatible
with existing scripts.
Tested with both c and python parser engines.
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} | 8 | "2013-05-02T11:04:11Z" | "2014-01-24T14:24:00Z" | "2014-01-24T14:18:57Z" | NONE | null | RELEASE.rst for 0.11.1 gave fair warning to users.
Update: there was no 0.11.1, it became 0.12.
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} | 8 | "2013-05-02T14:48:45Z" | "2017-07-06T23:12:48Z" | "2017-07-06T23:12:48Z" | CONTRIBUTOR | null | See the mailing list "[pydata] Covariance matrix not positive semi-definite."
Currently, a covariance matrix is computed using pairwise available observations ie., if there is missing data at an index but not in the two pairs it still uses those pairs in the pairwise covariance matrix. The result of this computation is not a covariance matrix and can be non positive semi-definite.
What to do in this case? 1) Warn? 2) Raise an error? 3) Only use observations for which all variables are available?
3 is tempting, the resultant covariance matrix will be a true covariance matrix, but it's an inconsistent estimator of the covariance.
My vote is for 2, so that the user is forced to think what they actually want to compute. Ideally, the error message will point to estimators that are appropriate for this situation, but these are not online yet (from statsmodels).
https://github.com/statsmodels/statsmodels/pull/631
https://github.com/statsmodels/statsmodels/issues/303
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} | 1 | "2013-05-02T18:08:26Z" | "2015-10-14T11:45:14Z" | "2015-10-14T11:45:14Z" | CONTRIBUTOR | null | Had a preview issue where the summary of returned results don't match the google analytics web portal results. This is probably why.
Need to look at the top level element for "containsSampledData".
https://developers.google.com/analytics/devguides/reporting/core/v3/reference#sampling
in pandas.io.ga.GDataReader.get_data, there’s the `_read` nested function which contains a call to execute the query object from google’s library. Pandas is looking for the results in the ‘rows’ attribute, I bet containsSampledData is also in there.
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Calling series.values on a datetime64 series will mess up the values, truncating them to 1970.
import pandas as pd
y = pd.DataFrame(pd.date_range('12/15/2012', '1/6/2013', freq='B'), columns=['dt'])
# Look at y, note the dates are all in 2012 and 2013. So far so good.
z = y.values
# Look at z -- uh oh, all the values are now in 1970.
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} | 1 | "2013-05-02T18:46:25Z" | "2014-06-16T01:29:27Z" | "2013-05-08T21:51:09Z" | CONTRIBUTOR | null | Various bugs related to datetime64s
- Properly convert np.datetime64 objects in a Series, #3416
This would convert to object dtype previously
```
In [1]: dates = [
...: np.datetime64(datetime.date(2013, 1, 1)),
...: np.datetime64(datetime.date(2013, 1, 2)),
...: np.datetime64(datetime.date(2013, 1, 3)),
...: ]
In [2]: s = pd.Series(dates)
In [3]: s
Out[3]:
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
dtype: datetime64[ns]
```
- Do not allow astypes on `datetime64[ns]` except to `object`, and
`timedelta64[ns]` to `object/int`, #3425
- Do not allow datetimelike/timedeltalike creation except with valid types
(e.g. cannot pass `datetime64[ms]`, #3423
Any creation/astype of a datetimelike to a non accepted dtype will raise
```
In [6]: Series([Timestamp('20130101'),Timestamp('20130102')],dtype='datetime64[s]')
TypeError: cannot convert datetimelike to dtype [datetime64[s]]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3517 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3517/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3517/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3517/events | https://github.com/pandas-dev/pandas/pull/3517 | 13,908,335 | MDExOlB1bGxSZXF1ZXN0NTUwMjE1Mg== | 3,517 | TST: skip tests needing xlrd | {
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} | 3 | "2013-05-03T01:43:08Z" | "2014-01-17T13:37:10Z" | "2013-05-20T18:54:07Z" | CONTRIBUTOR | null | The link to the pdf version here:
http://pandas.pydata.org/pandas-docs/stable/
point the user here:
http://pandas.pydata.org/pandas-docs/stable/pandas.pdf
Which is the 0.10.1 version of the docs, not the 0.11.0 version of the docs.
It should be updated so that the 0.11.0 pdf versions of the docs are available for Pandas users.
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https://api.github.com/repos/pandas-dev/pandas/issues/3519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3519/events | https://github.com/pandas-dev/pandas/pull/3519 | 13,920,299 | MDExOlB1bGxSZXF1ZXN0NTUwODc5MA== | 3,519 | Parse raised exceptions and their error messages sans interpolation/format spec | {
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} | [] | closed | false | null | [] | null | 7 | "2013-05-03T03:14:34Z" | "2014-06-15T06:07:10Z" | "2013-05-31T16:42:54Z" | MEMBER | null | This PR partially addresses #3024. It also provides a shiny new script useful looking at the current state of the messages in raised exceptions. Here's an example of some output:
# Example
You can reproduce this with
``` sh
scripts/parse_except.py --type empty --module pandas --kind assert
```
from the top level pandas directory.
# Description
The above line searches for all empty raises in the module `pandas` whose constructor matches the regular expression `'.*assert.*'`--not case sensitive.
Note: **This assumes you have installed pandas with `python setup.py develop`**. This will not work without that (well, that's not strictly true, but it will search for pandas code that's installed wherever you installed it `/usr/*/site-packages/*/pandas` or `$VIRTUAL_ENV/*/pandas`, for example, which is probably not what you want).
To see all of the options and a short description you can do the usual:
``` sh
scripts/parse_except.py --help
```
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} | 4 | "2013-05-03T14:00:13Z" | "2013-08-05T01:45:54Z" | "2013-08-05T01:45:54Z" | MEMBER | null | This would be nice. The idea is simple: be able to modify the elements of vectorized string list or tuples, e.g., after splitting them, a la
``` python
s = Series(['a/b/c', 'd/e/f'])
sp = s.split('/')
sp.str[0] = 'x'
assert np.all(sp.str[0] == 'x')
# as well as
sp.str.set(0, 'y')
assert np.all(sp.str[0] == 'y')
```
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import pandas as pd, pylab
#int index
col = pd.MultiIndex.from_tuples([('c1',0),('c1',1),('c2',0)],names=['f','s'])
dat = pylab.randn(2,len(col))
df1 = pd.DataFrame(dat, columns=col)
#Text index
col = pd.MultiIndex.from_tuples([('c3','x'),('c3','y')])
dat = pylab.randn(2,len(col))
df2 = pd.DataFrame(dat, columns=col)
#This does not merge column indexes
df_a = pd.concat([df1,df2])
#But this does
df_b = pd.concat([df1,df2], axis=1)
col = pd.MultiIndex.from_tuples([('c1','0'),('c1','1'),('c2','0')],names=['f','s'])
dat = pylab.randn(2,len(col))
df4 = pd.DataFrame(dat, columns=col)
df_d = pd.concat([df4,df2], axis=0)
```
```
In [31]: df_a
Out[31]:
c1 c2 c3
0 1 0 x y
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
0 NaN NaN NaN -0.694275 -1.357936
1 NaN NaN NaN -1.450523 -1.453957
```
This is unexpected behavior, especially since axis=1 does not care
```
In [32]: df_b
Out[32]:
f c1 c2 c3
s 0 1 0 x y
0 0.381601 -0.730360 0.157936 -0.694275 -1.357936
1 0.344333 -1.308118 1.503335 -1.450523 -1.453957
```
This now works, because the sub-index is same type (str)
```
In [33]: df_d
Out[33]:
c1 c2 c3
0 1 0 x y
0 0.162019 -0.325463 -0.200149 NaN NaN
1 -0.142477 -0.089191 -0.439161 NaN NaN
0 NaN NaN NaN -0.694275 -1.357936
1 NaN NaN NaN -1.450523 -1.453957
```
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} | 1 | "2013-05-03T16:47:52Z" | "2013-05-04T08:45:13Z" | "2013-05-04T08:45:13Z" | MEMBER | null | HTML is repr'd, not the symbols `<` and `>`:
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https://api.github.com/repos/pandas-dev/pandas/issues/3523 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3523/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3523/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3523/events | https://github.com/pandas-dev/pandas/issues/3523 | 13,947,560 | MDU6SXNzdWUxMzk0NzU2MA== | 3,523 | TypeError: object of type 'NoneType' has no len() with MySQL empty columns | {
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} | [] | closed | false | null | [] | null | 13 | "2013-05-03T18:50:58Z" | "2013-06-04T15:03:52Z" | "2013-06-04T15:03:52Z" | NONE | null | pandas '0.11.0'
mysql.connector '1.0.9'
# Import statements and options
import mysql.connector
from mysql.connector import errorcode
import pandas.io.sql as sql
import pandas as pd
from pandas import DataFrame
## Error
Running this code against MySQL (view includes several completely blank columns):
``` python
#Queries for CCI since Nov 2012
queries = sql.read_frame("select * from vw_queries_lj where query_account_id='34' and from_unixtime(query_created, '%Y-%m-%d') >= '2012-11-01' limit 100;", con)
```
Causes this error:
``` python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-17-78e88f1ac26a> in <module>()
1 #Queries for CCI since Nov 2012
----> 2 queries = sql.read_frame("select * from vw_queries_lj where query_account_id='34' and from_unixtime(query_created, '%Y-%m-%d') >= '2012-11-01' limit 100;", con)
3
/Users/randyzwitch/anaconda/lib/python2.7/site-packages/pandas/io/sql.pyc in read_frame(sql, con, index_col, coerce_float, params)
160
161 result = DataFrame.from_records(rows, columns=columns,
--> 162 coerce_float=coerce_float)
163
164 if index_col is not None:
/Users/randyzwitch/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in from_records(cls, data, index, exclude, columns, coerce_float, nrows)
1070 else:
1071 arrays, arr_columns = _to_arrays(data, columns,
-> 1072 coerce_float=coerce_float)
1073
1074 arr_columns = _ensure_index(arr_columns)
/Users/randyzwitch/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _to_arrays(data, columns, coerce_float, dtype)
5449 return arrays, columns
5450
-> 5451 if len(data) == 0:
5452 return [], [] # columns if columns is not None else []
5453 if isinstance(data[0], (list, tuple)):
TypeError: object of type 'NoneType' has no len()
```
## No error
Running query on primary key (i.e. no blanks) returns a proper data frrame
``` python
#Queries for CCI since Nov 2012
queries = sql.read_frame("select token from vw_queries_lj where query_account_id='34' and from_unixtime(query_created, '%Y-%m-%d') >= '2012-11-01' limit 100;", con)
```
## Expectation
My expected behavior would be that pandas would be able to handle blank columns as character or float, not as NoneType
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} | 4 | "2013-05-03T19:30:30Z" | "2013-09-22T00:55:07Z" | "2013-09-22T00:55:07Z" | MEMBER | null | This seems like a bug:
``` python
import pandas as pd
import numpy as np
def create_ns_index(start, fs, nsamples, name='time'):
ns = int(1e9 / fs)
dtstart = np.datetime64(start)
dt = dtstart + np.arange(nsamples) * np.timedelta64(ns, 'ns')
freq = ns * pd.datetools.Nano()
return pd.DatetimeIndex(dt, freq=freq, name=name)
index = create_ns_index('2012-09-20T00:00:00', 24414, 400)
assert index.freq == '40960N'
assert index.inferred_freq == '40960N'
# try to create the same index using the period string
index = pd.DatetimeIndex(start=index[0], end=index[-1], freq=index.freq)
# throws an AttributeError
# now try to create the same index using date_range
index = pd.date_range(start=index[0], end=index[-1], freq=index.freq)
# throws the same AttributeError
# maybe it works if I use pd.datetools.Nano()
index = pd.DatetimeIndex(start=index[0], end=index[-1], freq=index.freq.n * pd.datetools.Nano())
# no such luck
# try to use periods...also fails with an ambiguous ValueError
index = pd.date_range(start=index[0], end=index[-1], periods=index.size)
```
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} | 42 | "2013-05-03T19:43:54Z" | "2014-06-12T15:57:14Z" | "2013-06-10T14:11:28Z" | CONTRIBUTOR | null | warning: prototype!
msgpack serialization/deseriiization
- support all pandas objects: Timestamp,Period,all index types,Series,DataFrame,Panel,Sparse suite
- docs included (in io.rst)
- iterator support
- top-level api support
Here are 2 features which I think msgpack supports, but have to look further
- no support for compression directly, but can compress the file (e.g. gzip)
- access is sequential
- versioning, is not that hard because its pretty easy to deal with a
change in the schema (which is not directly stored), and this is MUCH more
transparent than pickle
usage is exactly like pickle (aside from that its in a different namespace), allowing
arbitrary combinations of storage, e.g. this supports the added storage of pandas objects,
but obviously can store `{ 'frame1' : df1, 'frame2' : df2 }` etc
storage example `DataFrame(np.random.rand(1000,10))` on my machine stores in 128k file size.
and scales pretty well, e.g. 10k rows is 1.26mb
Not completely happy with the `packers` name, any suggestions?
closes #686
```
In [1]: df = DataFrame(randn(10,2),
...: columns=list('AB'),
...: index=date_range('20130101',periods=10))
In [2]: pd.to_msgpack('foo.msg',df)
In [3]: pd.read_msgpack('foo.msg')
Out[3]:
A B
2013-01-01 0.676700 -1.702599
2013-01-02 -0.070164 -1.368716
2013-01-03 -0.877145 -1.427964
2013-01-04 -0.295715 -0.176954
2013-01-05 0.566986 0.588918
2013-01-06 -0.307070 1.541773
2013-01-07 1.302388 0.689701
2013-01-08 0.165292 0.273496
2013-01-09 -3.492113 -1.178075
2013-01-10 -1.069521 0.848614
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3526 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3526/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3526/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3526/events | https://github.com/pandas-dev/pandas/issues/3526 | 13,949,853 | MDU6SXNzdWUxMzk0OTg1Mw== | 3,526 | fillna/fill_value fails when filling with a list | {
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} | [] | closed | false | null | [] | null | 1 | "2013-05-03T19:50:01Z" | "2013-05-06T18:45:27Z" | "2013-05-06T18:45:27Z" | CONTRIBUTOR | null | ``` python
In [48]: s = pd.Series([np.NaN, 'hello world'])
In [49]: s
Out[49]:
0 NaN
1 hello world
dtype: object
In [50]: s.fillna([])
Out[50]:
0 NaN
1 hello world
dtype: object
In [51]: s.fillna(['not empty'])
Out[51]:
0 not empty
1 hello world
dtype: object
```
Had a quick peak in the code and it looks like a numpy issue (1.6.2)
``` python
In [52]: a = np.array([np.NaN, 'hello world'])
In [53]: m = np.array([True, False])
In [54]: a[m] = []
In [55]: a
Out[55]:
array(['nan', 'hello world'],
dtype='|S11')
In [56]: a[m] = ['not empty']
In [57]: a
Out[57]:
array(['not empty', 'hello world'],
dtype='|S11')
```
similar issue when using operators supporting `fill_value`
``` python
In [58]: s1 = pd.Series([np.NaN, [1,2], [10, 20]])
In [59]: s2 = pd.Series([[3,4], np.NaN, [30, 40]])
In [60]: s1.add(s2, fill_value=[])
...
TypeError: unsupported operand type(s) for +: 'float' and 'list'
```
Did not try for DataFrame, but i suppose this will be the same.
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https://api.github.com/repos/pandas-dev/pandas/issues/3527 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3527/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3527/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3527/events | https://github.com/pandas-dev/pandas/pull/3527 | 13,957,385 | MDExOlB1bGxSZXF1ZXN0NTUyODMxNw== | 3,527 | fix qt repr bug | {
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} | 1 | "2013-05-03T23:59:41Z" | "2014-07-03T08:22:45Z" | "2013-05-04T00:22:02Z" | MEMBER | null | addresses #3522.
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} | 2 | "2013-05-04T23:27:26Z" | "2013-11-19T17:06:29Z" | "2013-11-19T17:06:29Z" | NONE | null | I have some depth data from an echo-sounder in the following format
Date Time Depth
2013-00-28 00:00:38.652 2798.09
2013-00-28 00:00:48.632 2795.90
2013-00-28 00:00:58.388 2783.43
2013-00-28 00:01:08.160 2793.54
2013-00-28 00:01:17.517 2784.77
2013-00-28 00:01:27.289 2784.89
2013-00-28 00:01:37.860 2786.27
2013-00-28 00:01:47.631 2775.23
2013-00-28 00:01:57.388 2783.54
2013-00-28 00:02:07.367 2786.70
2013-00-28 00:02:17.123 2780.25
...
It is irregularly sampled in time, with time intervals varying between about 8 and 15 s. I would like to resample it to 20s intervals.Can I do this with pandas.DataFrame.resample?
I've tried reading it in with:
dtz = pandas.read_csv(infile,sep=' ',parse_dates=[[0,1]])
And resampling using:
dtz['Depth'].resample('20S',fill_method='pad',limit=6)
But I get the following error:
raise TypeError('Only valid with DatetimeIndex or PeriodIndex')
TypeError: Only valid with DatetimeIndex or PeriodIndex
Any comments on what I'm doing wrong?
I also tried to get the date time info into an index:
dtz = pd.read_csv(infile,sep=' ',index_col=0,parse_dates=[[0,1]])
To no avail
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} | 1 | "2013-05-05T22:29:45Z" | "2017-03-16T12:27:47Z" | "2017-03-16T12:27:47Z" | MEMBER | null | e.g. http://stackoverflow.com/questions/16319106/whats-the-equivalent-of-cut-qcut-for-pandas-date-fields
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https://api.github.com/repos/pandas-dev/pandas/issues/3530 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3530/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3530/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3530/events | https://github.com/pandas-dev/pandas/pull/3530 | 13,985,743 | MDExOlB1bGxSZXF1ZXN0NTUzODI3NQ== | 3,530 | DOC: Fix header | {
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} | [] | closed | false | null | [] | null | 0 | "2013-05-06T01:05:28Z" | "2014-07-16T08:07:14Z" | "2013-05-06T04:50:56Z" | CONTRIBUTOR | null | I went searching for how to make a MultiIndex on the docs page and couldn't figure out where to look anymore.
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} | 8 | "2013-05-06T15:35:58Z" | "2014-06-17T13:08:16Z" | "2013-05-08T20:26:10Z" | CONTRIBUTOR | null | - will retain index attributes (freq,tz,name) on recreation #3499
- support datelike columns with a timezone as data_columns #2852
- will raise if trying to append with a different timezone than existing
will warn if the existing frequency of an index is different than an appended one
(thought raising was too strict)
```
In [5]: df = DataFrame(dict(A = Series(xrange(3), index=date_range('2000-1-1',periods=3,freq='H'))))
In [6]: df2 = DataFrame(dict(A = Series(xrange(3), index=date_range('2002-1-1',periods=3,freq='D'))))
In [9]: df.index
Out[9]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 00:00:00, ..., 2000-01-01 02:00:00]
Length: 3, Freq: H, Timezone: None
In [10]: df2.index
Out[10]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2002-01-01 00:00:00, ..., 2002-01-03 00:00:00]
Length: 3, Freq: D, Timezone: None
In [12]: df.to_hdf('test.h5','data',mode='w',append=True)
In [13]: df2.to_hdf('test.h5','data',append=True)
pandas/io/pytables.py:1148: FrequencyWarning:
the frequency of the existing index is [<1 Hour>] which conflicts with the new freq [<1 Day>],
resetting the frequency to None
warnings.warn(ws, FrequencyWarning)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3532 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3532/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3532/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3532/events | https://github.com/pandas-dev/pandas/issues/3532 | 14,008,059 | MDU6SXNzdWUxNDAwODA1OQ== | 3,532 | "can't compare datetime.date to str" in slicing/indexing (version 0.11.0) | {
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} | [] | closed | false | null | [] | null | 5 | "2013-05-06T16:11:06Z" | "2013-05-07T17:53:31Z" | "2013-05-07T17:53:31Z" | CONTRIBUTOR | null | Hello all
I don't know if this has been already fixed in developing 0.12, but just in case:
it seemed that indexing with datetime.date does not work properly.
E.g. the code
```
IDX=[datetime.strptime(yy,'%Y-%m-%d') for yy in ["2005-01-01","2006-01-01","2006-01-01","2007-01-01"]]
IDX
df = pd.DataFrame([1,2,3,4], index=IDX)
print type(df.index[0])
print df.ix['2005-01-01']
df[:'2006-01-02']
```
works correctly. And the code
```
IDX=[datetime.date(datetime.strptime(yy,'%Y-%m-%d')) for yy in ["2005-01-01","2006-01-01","2006-01-01","2007-01-01"]]
IDX
df = pd.DataFrame([1,2,3,4], index=IDX)
print type(df.index[0])
print df.ix['2005-01-01']
df[:'2006-01-02']
```
failed with the issue "TypeError: can't compare datetime.date to str"
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https://api.github.com/repos/pandas-dev/pandas/issues/3533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3533/events | https://github.com/pandas-dev/pandas/pull/3533 | 14,015,884 | MDExOlB1bGxSZXF1ZXN0NTU1MjQwOQ== | 3,533 | BUG: Fixed bug in mixed frame assignment with aligned series (GH3492) | {
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} | 2 | "2013-05-06T20:11:21Z" | "2013-05-10T10:14:01Z" | "2013-05-10T10:14:01Z" | CONTRIBUTOR | null | When I go to
http://pandas.pydata.org/pandas-docs/dev/whatsnew.html
I would like to see the next release' changes included
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} | 1 | "2013-05-06T20:12:44Z" | "2013-05-10T10:03:19Z" | "2013-05-10T10:03:19Z" | CONTRIBUTOR | null | add a link to
http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.html.read_html.html#pandas.io.html.read_html
on
http://pandas.pydata.org/pandas-docs/dev/io.html
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} | 0 | "2013-05-06T22:38:17Z" | "2014-07-16T08:07:27Z" | "2013-05-08T21:44:20Z" | CONTRIBUTOR | null | PERF: hdf performance by doing recarray translation in cython
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
write_store_table_dc | 155.4217 | 269.1494 | 0.5775 |
write_store_table_panel | 93.9604 | 135.8333 | 0.6917 |
write_store_table_mixed | 116.6536 | 151.5377 | 0.7698 |
write_store_table | 66.0146 | 80.5717 | 0.8193 |
write_store_table_wide | 100.0113 | 121.9817 | 0.8199 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
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] | closed | false | null | [] | null | 1 | "2013-05-07T13:49:23Z" | "2013-06-06T12:05:21Z" | "2013-06-06T12:05:21Z" | NONE | null | Hi, as heading states. Column of Nones could be dropped, but fillna(0) should change this. Using aggfunc=np.sum does not help. (all version 0.10.1)
In [160]: f = pd.DataFrame({'A' : ['one', 'two'] \* 3,
......: 'B' : ['x', 'y', 'z'] \* 2,
......: 'C' : ['foo', 'bar', 'foobar', 'bar', 'foobar', 'foo'],
......: 'D' : np.random.randn(6),
......: 'E' : None})
In [161]: pd.pivot_table(f, values=['D','E'], rows=['A','B'], cols=['C'])
Out[161]:
D
C bar foo foobar
A B
one x NaN -2.271229 NaN
y NaN NaN -1.996931
z NaN NaN 1.565822
two x -1.644891 NaN NaN
y -0.915024 NaN NaN
z NaN -0.681877 NaN
In [162]: f.E = f.E.fillna(0)
In [163]: f
Out[163]:
A B C D E
0 one x foo -2.271229 0
1 two y bar -0.915024 0
2 one z foobar 1.565822 0
3 two x bar -1.644891 0
4 one y foobar -1.996931 0
5 two z foo -0.681877 0
In [164]: type(f.E[0])
Out[164]: int
In [165]: pd.pivot_table(f, values=['D','E'], rows=['A','B'], cols=['C'])
Out[165]:
D
C bar foo foobar
A B
one x NaN -2.271229 NaN
y NaN NaN -1.996931
z NaN NaN 1.565822
two x -1.644891 NaN NaN
y -0.915024 NaN NaN
z NaN -0.681877 NaN
In [166]: pd.pivot_table(f, values=['D','E'], rows=['A','B'], cols=['C'], aggfunc=np.sum)
Out[166]:
D
C bar foo foobar
A B
one x NaN -2.271229 NaN
y NaN NaN -1.996931
z NaN NaN 1.565822
two x -1.644891 NaN NaN
y -0.915024 NaN NaN
z NaN -0.681877 NaN
In [167]: f.E = 1
In [168]: type(f.E[0])
Out[168]: numpy.int64
In [169]: pd.pivot_table(f, values=['D','E'], rows=['A','B'], cols=['C'])
Out[169]:
D E
C bar foo foobar bar foo foobar
A B
one x NaN -2.271229 NaN NaN 1 NaN
y NaN NaN -1.996931 NaN NaN 1
z NaN NaN 1.565822 NaN NaN 1
two x -1.644891 NaN NaN 1 NaN NaN
y -0.915024 NaN NaN 1 NaN NaN
z NaN -0.681877 NaN NaN 1 NaN
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} | 3 | "2013-05-07T16:48:48Z" | "2021-04-11T02:00:17Z" | "2021-04-11T02:00:17Z" | CONTRIBUTOR | null | Along the lines of wes's answer to this question:
http://stackoverflow.com/questions/16384332/how-to-speed-up-pandas-row-filtering-by-string-matching
I think it is possible to have a dictionary recording certain parameters for a series (or a column in a frame), something like
`conditions = dict(sorted=False, nulls=False, unique=True)`
that would alllow certain operations to be speeded up, of course these conditions
would have to be updated in various scenarios, e.g. when sorting by a certain
column, then you could set the sorted condition = `True` (and invalidate when
sorting by other columns). however, and this might be a bit complicated to determine
(in which case you could just set `sorted = None`, meaning I don't know).
But many operations could preserve these conditions (e.g. a reindexing with a monotonic index will preserver the sort, but will invalidate the nulls, if its not identical to the current index)
not-trivial but might be worth it
e.g. using the fact that I already computed nulls, I can go directly to numpy land if I already know I don't need to do the null check
or if its already sorted, then can use `searchsorted`
```
In [6]: %timeit values=='A0003'
10 loops, best of 3: 164 ms per loop
In [7]: %timeit pd.lib.scalar_compare(values,'A0003',operator.eq)
1 loops, best of 3: 255 ms per loop
In [8]: %timeit values.sort()
1 loops, best of 3: 2.29 s per loop
In [9]: %timeit pd.isnull(values)
10 loops, best of 3: 105 ms per loop
```
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} | 3 | "2013-05-07T21:02:27Z" | "2013-06-13T19:12:08Z" | "2013-06-13T19:12:08Z" | MEMBER | null | Was surprised at how difficult it was to convert a unix timestamp to a pandas Timestamp recently
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} | 18 | "2013-05-07T21:54:16Z" | "2013-09-20T03:01:36Z" | "2013-05-25T23:10:58Z" | CONTRIBUTOR | null | Max_rows seems to have a maximum value of 60 (in 0.11 and dev, this works as expected in 0.10.1):
```
In [1]: %paste
from pandas import *
n = 100
foo = DataFrame(index=range(n))
foo['floats'] = np.random.randn(n)
set_option('display.max_rows', 101)
## -- End pasted text --
In [2]: foo.head(100)
Out[2]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 0 to 99
Columns: 1 entries, floats to floats
dtypes: float64(1)
In [3]: foo.head(61)
Out[3]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 61 entries, 0 to 60
Data columns (total 1 columns):
floats 61 non-null values
dtypes: float64(1)
In [4]: foo.head(60)
Out[4]:
floats
0 -0.631717
1 0.231468
2 -0.465971
3 -1.427025
...
```
```
In [5]: %paste
set_option('display.max_rows', 10)
## -- End pasted text --
In [6]: foo.head(10)
Out[6]:
floats
0 -0.194574
1 -0.130100
2 0.547474
3 0.669904
4 0.776946
5 -2.384084
6 -0.011147
7 -0.272660
8 -0.051326
9 1.757020
In [7]: foo.head(11)
Out[7]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 11 entries, 0 to 10
Data columns (total 1 columns):
floats 11 non-null values
dtypes: float64(1)
```
```
In [8]: version.version # same true for 0.11
Out[8]: '0.12.0.dev-d925966'
```
Essentially from http://stackoverflow.com/questions/16424493/pandas-setting-no-of-max-rows.
Related: #3395, #3426.
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```
b = pd.Series({"playerid": 544911367940993}, dtype='float64')
b['playerid'].astype('string')
```
This should output 544911367940993 or 544911367940993.0, but instead it prints '5.44911367941e+14'.
It seems this is because Pandas uses str(b['playerid']) instead of b['playerid'].**repr**()
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} | [] | closed | false | null | [] | null | 0 | "2013-05-08T00:49:15Z" | "2013-05-15T23:13:42Z" | "2013-05-08T00:50:59Z" | CONTRIBUTOR | null | read_csv's usecols argument is currently undocumented.
This patch fixes this:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
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} | 3 | "2013-05-08T01:00:50Z" | "2014-07-16T08:07:34Z" | "2013-05-15T23:31:24Z" | CONTRIBUTOR | null | read_csv's usecols is currently not documented here:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
This patch fixes that.
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https://api.github.com/repos/pandas-dev/pandas/issues/3545 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3545/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3545/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3545/events | https://github.com/pandas-dev/pandas/issues/3545 | 14,103,982 | MDU6SXNzdWUxNDEwMzk4Mg== | 3,545 | Floating point precision in DataFrame.read_csv | {
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} | [] | closed | false | null | [] | null | 1 | "2013-05-08T14:38:02Z" | "2013-05-10T10:15:23Z" | "2013-05-10T10:15:23Z" | NONE | null | Floating point round errors appear just after you read float figures from a CSV file.
How to reproduce:
```
Date, Close
2013-01-01, 25.56
```
It will read 25.26 as 25.559999999999999
Possibly duplicate of https://github.com/pydata/pandas/issues/2069
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} | 1 | "2013-05-08T16:01:40Z" | "2013-06-14T15:36:07Z" | "2013-05-08T18:00:28Z" | CONTRIBUTOR | null | ```
>>> import pandas as pd
>>> pd.__version__
'0.12.0.dev'
>>> index = pd.date_range(start='2013-05-31 00:00', end='2013-05-31 23:00', freq='H')
>>> series = pd.Series(range(len(index)), index=index)
>>> print series.index.summary()
DatetimeIndex: 24 entries, 2013-05-31 00:00:00 to 2013-05-31 23:00:00
Freq: H
>>> print series['2013-05'].index.summary()
DatetimeIndex: 1 entries, 2013-05-31 00:00:00 to 2013-05-31 00:00:00
Freq: H
```
I would expect the slice to be the same as the original series.
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} | 5 | "2013-05-08T17:19:50Z" | "2020-11-11T10:18:21Z" | "2013-05-10T09:25:32Z" | CONTRIBUTOR | null | Notice in the html below the second index column's 'a' field is given as `<th rowspan="2" valign="top">a</th>`, expanding over what should be an empty cell.
I actually found this using a pivot table, `margins=True`, as this creates a row keyed like `('All', '', '')`.
```
print df = pd.DataFrame({'c1': ['a', 'b'], 'c2': ['a', ''], 'data': [1, 2]}).set_index(['c1', 'c2'])
data
c1 c2
a a 1
b 2
print df.to_html()
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>data</th>
</tr>
<tr>
<th>c1</th>
<th>c2</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>a</th>
<th rowspan="2" valign="top">a</th>
<td> 1</td>
</tr>
<tr>
<th>b</th>
<td> 2</td>
</tr>
</tbody>
</table>
```
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} | 2 | "2013-05-09T15:11:36Z" | "2014-02-15T21:15:44Z" | "2014-02-15T21:15:44Z" | CONTRIBUTOR | null | related is #1148 (the groupby)
sse also #3551
useful in a case where the returned arrity is > 1 (right now not implemented)
essentially
```
In [3]: df = DataFrame(randn(20,2),index=date_range('20130101',periods=20,freq='min'),
columns=list('AB'))
df.resample('15Min',how='ohlc')
NotImplementedError:
```
Easy enough to create manually
```
In [17]: pd.Panel(dict([ (i,df[i].resample('15Min',how='ohlc')) for i in df.columns ]))
Out[17]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 2 (major_axis) x 4 (minor_axis)
Items axis: A to B
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-01 00:15:00
Minor_axis axis: open to close
```
From this question
https://groups.google.com/forum/?fromgroups=#!topic/pystatsmodels/yHSC84koetU
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https://api.github.com/repos/pandas-dev/pandas/issues/3552 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3552/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3552/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3552/events | https://github.com/pandas-dev/pandas/issues/3552 | 14,152,654 | MDU6SXNzdWUxNDE1MjY1NA== | 3,552 | combine_first converts dtype from bool to object | {
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} | 3 | "2013-05-09T16:04:20Z" | "2014-06-19T15:13:37Z" | "2013-05-09T19:18:31Z" | NONE | null | possibly related to #3041 but still present in 0.11 and current HEAD (23f6058bd6249c21b5db95530a703fa4bc4c2510)
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df1 = pd.DataFrame([[np.nan, 3.,True], [-4.6, np.nan, True], [np.nan, 7., False]])
In [4]: df2 = pd.DataFrame([[-42.6, np.nan, True], [-5., 1.6, False]], index=[1, 2])
In [5]: df1[2]
Out[5]:
0 True
1 True
2 False
Name: 2, dtype: bool
In [6]: df2[2]
Out[6]:
1 True
2 False
Name: 2, dtype: bool
In [7]: df1.combine_first(df2)[2]
Out[7]:
0 True
1 True
2 False
Name: 2, dtype: object
```
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} | 0 | "2013-05-09T16:39:42Z" | "2013-05-10T09:07:38Z" | "2013-05-10T09:07:15Z" | MEMBER | null | Piggybacking off of #3547.
``` python
import pandas as pd
from nose.tools import assert_raises
pd.set_option('display.multi_sparse', False)
df = pd.DataFrame({'c1': ['a', 'b'], 'c2': ['a', ''], 'data': [1, 2]}).set_index(['c1', 'c2'])
assert_raises(TypeError, df.to_html) # True
```
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ENH: `convert_objects` now accepts a `copy` parameter (defaults to `True`)
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} | 6 | "2013-05-09T20:42:22Z" | "2013-05-10T20:09:51Z" | "2013-05-10T20:09:51Z" | NONE | null | This code used to work some time around version 0.10, but now generates a ValueError:
In [14]: y
Out[14]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: z1 to z2
Major_axis axis: 0 to 2
Minor_axis axis: a to b
In [15]: y.transpose('minor', 'major', 'items')
Out[15]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: a to b
Major_axis axis: 0 to 2
Minor_axis axis: z1 to z2
## In [16]: y.transpose( items='minor', major='major', minor='items')
ValueError Traceback (most recent call last)
<ipython-input-16-00ebdc16128e> in <module>()
----> 1 y.transpose( items='minor', major='major', minor='items')
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0.dev_be25266-py2.7-linux-x86_64.egg/pandas/core/panel.pyc in transpose(self, _args, *_kwargs)
1176 except (IndexError):
1177 raise ValueError(
-> 1178 "not enough arguments specified to transpose!")
1179
1180 axes = [self._get_axis_number(kwargs[a]) for a in self._AXIS_ORDERS]
ValueError: not enough arguments specified to transpose!
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} | 1 | "2013-05-09T22:25:23Z" | "2014-07-16T08:08:02Z" | "2013-05-10T09:05:54Z" | MEMBER | null | fixes #3553.
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} | 7 | "2013-05-09T23:57:03Z" | "2014-06-12T15:13:45Z" | "2013-05-10T20:08:55Z" | MEMBER | null | fixes #3556.
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https://api.github.com/repos/pandas-dev/pandas/issues/3559 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3559/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3559/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3559/events | https://github.com/pandas-dev/pandas/pull/3559 | 14,173,885 | MDExOlB1bGxSZXF1ZXN0NTYzMzg5Nw== | 3,559 | ENH: raise useful error message on invalid concat arguments | {
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} | 0 | "2013-05-10T00:38:57Z" | "2014-06-26T17:46:41Z" | "2013-05-10T10:43:07Z" | MEMBER | null | fixes #3481.
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} | 1 | "2013-05-10T01:22:14Z" | "2014-06-23T18:20:54Z" | "2013-05-10T09:29:37Z" | MEMBER | null | addresses #3460.
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} | 5 | "2013-05-10T07:18:40Z" | "2013-05-14T21:44:57Z" | "2013-05-14T21:44:57Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
ind = ['A', 'A', 'B', 'C']i
df = pd.DataFrame({'test':range(len(ind))}, index=ind)
rows = ['C', 'B']
res = df.ix[rows]
assert rows == list(res.index) # fails
```
The problem is that the resulting DataFrame keeps the ordering of the `df.index` and not the `rows` key. You'll notice that the `rows` key doesn't reference a duplicate value.
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} | 3 | "2013-05-10T14:32:01Z" | "2013-05-10T16:29:24Z" | "2013-05-10T16:04:38Z" | NONE | null | carried over from #3487
``` python
DataFrame.from_records([], columns=['a','b','c'])
Empty DataFrame
Columns: [a, b, c]
Index: []
```
vs.
``` python
DataFrame.from_records([], columns=['a','b','b'])
AssertionError: Number of manager items must equal union of block items
```
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} | 1 | "2013-05-10T14:42:27Z" | "2014-07-10T09:20:30Z" | "2013-05-14T21:44:58Z" | CONTRIBUTOR | null | close #3561
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} | 0 | "2013-05-10T15:00:17Z" | "2014-06-29T09:51:40Z" | "2013-05-10T16:01:33Z" | CONTRIBUTOR | null | closes #3562
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} | 10 | "2013-05-10T17:33:05Z" | "2013-05-13T14:46:38Z" | "2013-05-13T12:36:28Z" | NONE | null | ```
In [105]: df = pd.DataFrame([[1, 1.0]], columns=['x', 'y'])
In [112]: row = next(df.iterrows())[1]
In [113]: row
Out[113]:
x 1
y 1
Name: 0, dtype: float64
In [114]: row['x'].dtype
Out[114]: dtype('float64')
In [115]: df['x'].dtype
Out[115]: dtype('int64')
```
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} | 1 | "2013-05-10T20:09:49Z" | "2013-06-24T15:59:04Z" | "2013-06-24T15:59:04Z" | MEMBER | null | This is so minor I almost didn't want to open this issue, but here it is. The qt repr seems to output the repr of a frame larger than if you actually call `print(repr(frame))`. Screen shot:
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} | 2 | "2013-05-10T23:23:05Z" | "2018-07-06T20:20:59Z" | "2017-07-11T09:54:17Z" | NONE | null | ```
Panel.fillna()
```
should accept
```
(axis= [0, 1, 2] or ['items', 'major_axis', 'minor_axis'])
```
as an argument.
Right now it essentially defaults to
```
(axis='major_axis')
```
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} | 8 | "2013-05-10T23:53:17Z" | "2013-05-19T14:41:57Z" | "2013-05-19T14:41:57Z" | NONE | null | link to #1651 (to_csv) and #3141 (read_csv)
Currently (0.11) when the read_csv and to_csv methods handle multi-index row labels fine, but don't do as well with multi-index column labels. For the column index the to_csv method writes them out as a tuple into the 1st row of the CSV file. This reads back in as a tuple. It would be better if it actually wrote out each element of the multi-index as a row of the CSV file and you could then specify a range of rows for the header on read_csv to reconstruct the multi-index column header. I'm thinking of something like "header=[0,1]" to read in the first two rows of the CSV file as a 2 element multi-index column header. What's not clear to me is where you read/write the names of the indices.
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} | 29 | "2013-05-11T00:06:40Z" | "2014-06-13T08:15:50Z" | "2013-05-21T21:01:11Z" | MEMBER | null | Raise a `TypeError` alerting the user to the fact that they are trying to plot nonnumeric data, or if there are any numeric data plot those. closes #1818.
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} | 8 | "2013-05-11T00:16:57Z" | "2013-09-20T03:01:36Z" | "2013-05-25T23:25:04Z" | MEMBER | null | In the IPython notebook, HTML output it the default and whether summary view is displayed should not be governed by hypothetical line width. I ran into this problem in a demo recently and it took me a minute to figure out what was wrong, definitely a bad change in 0.11.
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} | 32 | "2013-05-11T04:08:31Z" | "2014-06-12T12:04:57Z" | "2013-05-19T14:22:04Z" | CONTRIBUTOR | null | In theory should close:
#3571, #1651, #3141
Works, but a couple of issues/caveats:
- ~~index_col needs to be specified as an integer list (can be fixed)~~
- header is a list of rows to read that contain the multi-index,
a row that is skipped (e.g. [0,1,3,5], will just be skipped, like a comment)
- the writing format might be a bit odd: the col names go in the first column,
other index_cols are blanks (they are separated, just == '')
The names of an multi-index on the index are after the columns and before the data,
and are a full row (but blank after the row names).
- ~~I am not sure if we should allow `df.to_csv('path',index=False)` when have a multi-index columns, could just ban it I guess (mainly as it screws up the write format, and then where do you put the names?)~~
- ~~The `cols` argument needs testing and prob is broken when using multi-index on the columns (it really should be specified as a tuple I think, but that is work, so maybe just ban it when using multi-index columns)~~
- needs more testing
```
In [14]: df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
In [15]: df.to_csv('test.csv')
In [16]: !cat 'test.csv'
C0,,C_l0_g0,C_l0_g1,C_l0_g2
C1,,C_l1_g0,C_l1_g1,C_l1_g2
C2,,C_l2_g0,C_l2_g1,C_l2_g2
C3,,C_l3_g0,C_l3_g1,C_l3_g2
R0,R1,,,
R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2
R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2
R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2
R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2
R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2
In [17]: res = read_csv('test.csv',header=[0,1,2,3],index_col=[0,1])
In [18]: res.index
Out[18]:
MultiIndex
[(u'R_l0_g0', u'R_l1_g0'), (u'R_l0_g1', u'R_l1_g1'), (u'R_l0_g2', u'R_l1_g2'), (u'R_l0_g3', u'R_l1_g3'), (u'R_l0_g4', u'R_l1_g4')]
In [19]: res.columns
Out[19]:
MultiIndex
[(u'C_l0_g0', u'C_l1_g0', u'C_l2_g0', u'C_l3_g0'), (u'C_l0_g1', u'C_l1_g1', u'C_l2_g1', u'C_l3_g1'), (u'C_l0_g2', u'C_l1_g2', u'C_l2_g2', u'C_l3_g2')]
In [20]: res
Out[20]:
C0 C_l0_g0 C_l0_g1 C_l0_g2
C1 C_l1_g0 C_l1_g1 C_l1_g2
C2 C_l2_g0 C_l2_g1 C_l2_g2
C3 C_l3_g0 C_l3_g1 C_l3_g2
R0 R1
R_l0_g0 R_l1_g0 R0C0 R0C1 R0C2
R_l0_g1 R_l1_g1 R1C0 R1C1 R1C2
R_l0_g2 R_l1_g2 R2C0 R2C1 R2C2
R_l0_g3 R_l1_g3 R3C0 R3C1 R3C2
R_l0_g4 R_l1_g4 R4C0 R4C1 R4C2
In [21]: res.index.names
Out[21]: ['R0', 'R1']
In [22]: res.columns.names
Out[22]: ['C0', 'C1', 'C2', 'C3']
```
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A DataFrame with MultiIndex:
df = df.reorder_levels() drops the dataframe name attribute.
thus, df.name after the reorder_levels() has no attribute 'name'
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} | 12 | "2013-05-11T11:35:34Z" | "2013-09-12T16:05:48Z" | "2013-09-12T16:05:48Z" | CONTRIBUTOR | null | The HDFStore seemed to not accepting timedelta's objects. The code
```
import datetime as dt
import pandas as pd
s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
td = pd.Series([ dt.timedelta(days=i) for i in range(3) ])
df = pd.DataFrame(dict(A = s, B = td))
print df
print df.dtypes
store = pd.HDFStore('store.h5')
store['df'] = df
store.close()
```
results in the `ValueError: unknown type: 'timedelta64[ns]'`.
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-27-b5619cf79ed9> in <module>()
9
10 store = pd.HDFStore('store.h5')
---> 11 store['df'] = df
12 store.close()
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in __setitem__(self, key, value)
239
240 def __setitem__(self, key, value):
--> 241 self.put(key, value)
242
243 def __delitem__(self, key):
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in put(self, key, value, table, append, **kwargs)
534 table
535 """
--> 536 self._write_to_group(key, value, table=table, append=append, **kwargs)
537
538 def remove(self, key, where=None, start=None, stop=None):
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_to_group(self, key, value, index, table, append, complib, **kwargs)
869 raise ValueError('Compression not supported on non-table')
870
--> 871 s.write(obj = value, append=append, complib=complib, **kwargs)
872 if s.is_table and index:
873 s.create_index(columns = index)
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in write(self, obj, **kwargs)
2003 blk = data.blocks[i]
2004 # I have no idea why, but writing values before items fixed #2299
-> 2005 self.write_array('block%d_values' % i, blk.values)
2006 self.write_index('block%d_items' % i, blk.items)
2007
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in write_array(self, key, value)
1805 self.write_array_empty(key, value)
1806 else:
-> 1807 self._handle.createArray(self.group, key, value)
1808
1809 getattr(self.group, key)._v_attrs.transposed = transposed
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/file.pyc in createArray(self, where, name, object, title, byteorder, createparents)
778 parentNode = self._getOrCreatePath(where, createparents)
779 return Array(parentNode, name,
--> 780 object=object, title=title, byteorder=byteorder)
781
782
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/array.pyc in __init__(self, parentNode, name, object, title, byteorder, _log, _atom)
165 # Ordinary arrays have no filters: leaf is created with default ones.
166 super(Array, self).__init__(parentNode, name, new, Filters(),
--> 167 byteorder, _log)
168
169
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/leaf.pyc in __init__(self, parentNode, name, new, filters, byteorder, _log)
261 # is a lazy property that automatically handles their loading.
262
--> 263 super(Leaf, self).__init__(parentNode, name, _log)
264
265
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/node.pyc in __init__(self, parentNode, name, _log)
248 # Create or open the node and get its object ID.
249 if new:
--> 250 self._v_objectID = self._g_create()
251 else:
252 self._v_objectID = self._g_open()
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/array.pyc in _g_create(self)
198 # on
199 (self._v_objectID, self.shape, self.atom) = self._createArray(
--> 200 nparr, self._v_new_title, self.atom)
201 except: #XXX
202 # Problems creating the Array on disk. Close node and re-raise.
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/hdf5Extension.so in tables.hdf5Extension.Array._createArray (tables/hdf5Extension.c:8214)()
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/atom.pyc in from_dtype(class_, dtype, dflt)
368 return class_.from_kind('string', itemsize, dtype.shape, dflt)
369 # Most NumPy types have direct correspondence with PyTables types.
--> 370 return class_.from_type(basedtype.name, dtype.shape, dflt)
371
372 @classmethod
/Users/user/.virtual_envs/system/lib/python2.7/site-packages/tables/atom.pyc in from_type(class_, type, shape, dflt)
392
393 if type not in all_types:
--> 394 raise ValueError("unknown type: %r" % (type,))
395 kind, itemsize = split_type(type)
396 return class_.from_kind(kind, itemsize, shape, dflt)
ValueError: unknown type: 'timedelta64[ns]'
```
OS: OSX 10.8.3
python: 2.7.4
pandas: 0.11.0
numpy: 1.7.1
tables: 2.4.0
UPDATE:
It seemed to be a pytables issue.
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} | 3 | "2013-05-11T19:23:47Z" | "2014-05-08T13:23:06Z" | "2014-05-08T13:23:06Z" | NONE | null | related is #4636
I'm having an issue drawing a Pandas boxplot within a subplot. Based on the two ways I'm trying, creating the boxplot either removes all the subplots that I've already created, or plots the boxplot after the subplot grid. But I can't seem to draw it within the subplot grid.
import matplotlib.pyplot as plt
import pandas
from pandas import DataFrame, Series
data = {'day' : Series([1, 1, 1, 2, 2, 2, 3, 3, 3]),
'val' : Series([3, 4, 5, 6, 7, 8, 9, 10, 11])}
df = pandas.DataFrame(data)
The first thing I've tried is the following:
plt.figure()
plt.subplot(2, 2, 1)
plt.plot([1, 2, 3])
plt.subplot(2, 2, 4)
df.boxplot('val', 'day')
But this simply creates the plot outside of the subplots:
So, I then tried supplying the axis by hand:
plt.figure()
plt.subplot(2, 2, 1)
plt.plot([1, 2, 3])
plt.subplot(2, 2, 4)
ax = plt.gca()
df.boxplot('val', 'day', ax=ax)
It seems that an internal call to subplot is messing up my desired subplot structure, preventing me from getting my boxplot image to appear in the bottom right grid in the subplots (the one that's empty in the first set of images)?
See this question on StackOverflow:
http://stackoverflow.com/questions/16500079/issue-with-pandas-boxplot-within-a-subplot
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} | 4 | "2013-05-11T19:27:40Z" | "2013-05-13T18:56:24Z" | "2013-05-13T18:56:24Z" | CONTRIBUTOR | null | I have found this while working with period-indexed data frames on Windows XP 32 bits:
```
import numpy as np
import pandas as pd
index = pd.period_range(start='1999-01', periods=5, freq='M')
s1 = pd.Series(np.random.rand(len(index)), index=index)
s2 = pd.Series(np.random.rand(len(index)), index=index)
series = [('s1', s1), ('s2',s2)]
df = pd.DataFrame.from_items(series)
grouped = df.groupby(df.index.month)
list(grouped)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\Python27\lib\site-packages\pandas\core\groupby.py", line 595, in get_iterator
for key, (i, group) in izip(keys, splitter):
File "D:\Python27\lib\site-packages\pandas\core\groupby.py", line 2214, in __iter__
sdata = self._get_sorted_data()
File "D:\Python27\lib\site-packages\pandas\core\groupby.py", line 2231, in _get_sorted_data
return self.data.take(self.sort_idx, axis=self.axis)
File "D:\Python27\lib\site-packages\pandas\core\frame.py", line 2891, in take
new_index = self.index.take(indices)
File "D:\Python27\lib\site-packages\pandas\tseries\period.py", line 1110, in take
taken = self.values.take(indices, axis=axis)
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
```
This happens with the last stable version 0.11.0. However iterating over grouped.s1 or grouped.s2 just works.
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} | 3 | "2013-05-11T21:17:37Z" | "2018-07-06T20:21:00Z" | "2016-09-29T22:31:03Z" | MEMBER | null | Is this something that is worth pursuing? Personally, I don't use Panel really ever since DataFrame is basically N-dimensional, but this might be good for API consistency.
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https://api.github.com/repos/pandas-dev/pandas/issues/3582 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3582/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3582/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3582/events | https://github.com/pandas-dev/pandas/issues/3582 | 14,227,694 | MDU6SXNzdWUxNDIyNzY5NA== | 3,582 | Surprising behavior of DataFrame.replace | {
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} | 21 | "2013-05-11T22:07:43Z" | "2013-05-22T13:45:59Z" | "2013-05-22T13:45:59Z" | MEMBER | null | This seems a bit surprising:
``` python
>>> from string import ascii_lowercase as letters
>>> from pandas import DataFrame
>>> df = DataFrame(list(letters[:4]), columns=['a'])
>>> df
a
0 a
1 b
2 c
3 d
>>> df.replace({'a': 'b'})
a
0 a
1 a
2 c
3 d
>>> df.replace({'a': 'c'})
a
0 a
1 b
2 b
3 d
```
Does this have to do with padding?
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} | 6 | "2013-05-12T01:36:52Z" | "2014-06-26T17:48:41Z" | "2013-06-11T19:27:46Z" | MEMBER | null | This extension works perfectly fine on Python 2.x and all platforms to my knowledge except win32 on MinGW (it's fine with VC2008+ AFAIK). It might have problems on some of @yarikoptic's esoteric Debian platforms, we should identify which ones those are and disable the extension in the setup.py until we have a chance to sort out what's wrong in the C code. I don't think that's a bad compromise for now in the interest of having a usable extension for a lot of folks.
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} | 8 | "2013-05-13T10:51:23Z" | "2013-05-31T13:14:17Z" | "2013-05-13T17:35:52Z" | NONE | null | In the following code, my data are corrupted by the sequence set_index().reset_index(). The value of "QC" is actually changed to 1 from NaN where it should be NaN.
Btw, for symmetry I added the ".reset_index()", but the data corruption is introduced by set_index.
This problem exists in both version '0.10.1' and '0.11.0'
```
bug=pd.DataFrame({'PRuid': {17: 'nonQC', 18: 'nonQC', 19: 'nonQC', 20: '10', 21: '11', 22: '12', 23: '13', 24: '24', 25: '35', 26: '46', 27: '47', 28: '48', 29: '59', 30: '10'}, 'QC': {17: 0.0, 18: 0.0, 19: 0.0, 20: nan, 21: nan, 22: nan, 23: nan, 24: 1.0, 25: nan, 26: nan, 27: nan, 28: nan, 29: nan, 30: nan}, 'data': {17: 7.9544899999999998, 18: 8.0142609999999994, 19: 7.8591520000000008, 20: 0.86140349999999999, 21: 0.87853110000000001, 22: 0.8427041999999999, 23: 0.78587700000000005, 24: 0.73062459999999996, 25: 0.81668560000000001, 26: 0.81927080000000008, 27: 0.80705009999999999, 28: 0.81440240000000008, 29: 0.80140849999999997, 30: 0.81307740000000006}, 'year': {17: 2006, 18: 2007, 19: 2008, 20: 1985, 21: 1985, 22: 1985, 23: 1985, 24: 1985, 25: 1985, 26: 1985, 27: 1985, 28: 1985, 29: 1985, 30: 1986}})
print bug
#print bug.set_index(['year','PRuid','QC'])
print bug.set_index(['year','PRuid','QC']).reset_index()
```
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} | 0 | "2013-05-13T13:29:29Z" | "2014-06-29T12:28:29Z" | "2013-05-13T17:35:20Z" | CONTRIBUTOR | null | closes #3586
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} | 5 | "2013-05-13T13:57:24Z" | "2014-06-17T11:23:40Z" | "2013-05-19T16:55:29Z" | NONE | null | The 'pivot' function does not properly handle NaNs. E.g.:
```
import numpy as np, pandas as pd
test = pd.DataFrame({"a":['R1', 'R2', 'R3', 'R4'], "b":["C1", "C3", np.nan , "C4"], "c":[10, 15, np.nan , 20]})
test.head(2).pivot('a', 'b', 'c')
b C1 C3
a
R1 10 NaN
R2 NaN 15
```
(No NaNs, works properly)
But:
```
test.pivot('a', 'b', 'c')
b C1 C3 C4
a
R1 NaN NaN NaN
R2 NaN 10 15
R3 NaN NaN NaN
R4 NaN NaN 20
```
(Incorrect results, does not crash)
Bug is present in versions 0.8.1 and 0.10.1
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} | 1 | "2013-05-13T15:10:54Z" | "2013-12-04T00:46:48Z" | "2013-06-24T15:57:43Z" | MEMBER | null | A script to automate some of the parts of updating docs when you add a feature would be great. @lodrago had a great idea regarding a gh issue sphinx extension.
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} | 4 | "2013-05-13T15:32:17Z" | "2013-06-26T00:04:19Z" | "2013-05-14T21:48:10Z" | NONE | null | In python:
3 % 0
is undefined and returns "ZeroDivisionError: integer division or modulo by zero"
But in pandas 0.11, that gives 0. I believe that is incorrect. It ought to give NaN or Inf
p = pandas.DataFrame({ 'first' : [3,4,5,8], 'second' : [0,0,0,3] })
p['mod'] = p['first'] % p['second']
p
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https://api.github.com/repos/pandas-dev/pandas/issues/3591 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3591/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3591/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3591/events | https://github.com/pandas-dev/pandas/pull/3591 | 14,273,377 | MDExOlB1bGxSZXF1ZXN0NTY4MTAzMg== | 3,591 | BUG: fix take platorm issue with PeriodIndex (GH3579) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3592 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3592/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3592/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3592/events | https://github.com/pandas-dev/pandas/issues/3592 | 14,276,958 | MDU6SXNzdWUxNDI3Njk1OA== | 3,592 | resample with period='m' does not work on localized indexes for multiple methods in "how" | {
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} | [] | closed | false | null | [] | null | 1 | "2013-05-13T19:18:58Z" | "2014-02-18T15:18:06Z" | "2013-05-13T19:26:22Z" | CONTRIBUTOR | null | For the following code:
```
rng = pd.date_range('1/1/2011', periods=200, freq='D')
rng = rng.tz_localize('UTC')
ts = pd.Series(cumsum(randn(len(rng))), index=rng)
```
the following resample:
`ts.resample('m', how='mean')`
works fine, but this one:
`ts.resample('m', how={'Mean': 'mean', 'Std': 'std'})`
fails with the error: `TypeError: can't compare offset-naive and offset-aware datetimes`
However if we choose other interval of resampling (e.g. daily or quarterly or weekly):
`ts.resample('D', how={'Mean': 'mean', 'Std': 'std'})`
or
`ts.resample('Q', how={'Mean': 'mean', 'Std': 'std'})`
there would be no error.
OS: OSX 10.8.3
python: 2.7.4
pandas: 0.11.0
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} | 5 | "2013-05-13T20:03:25Z" | "2013-05-14T04:07:41Z" | "2013-05-13T22:50:08Z" | NONE | null | Method `combine_first` performs an unwanted conversion of `Timestamp` to `long`. Pandas version is "0.11.0". Here is an IPython session demonstrating the behavior, which is IMHO a bug:
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.11.0'
In [4]: from datetime import datetime
In [5]: df0 = pd.DataFrame({"a":[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]})
In [6]: df0
Out[6]:
a
0 2000-01-01 00:00:00
1 2000-01-02 00:00:00
2 2000-01-03 00:00:00
In [7]: df1 = pd.DataFrame({"a":[None, None, None]})
In [8]: df1
Out[8]:
a
0 None
1 None
2 None
In [9]: df2 = df1.combine_first(df0)
In [10]: df2
Out[10]:
a
0 946684800000000000
1 946771200000000000
2 946857600000000000
In [11]: type(df2["a"][0])
Out[11]: long
In [12]: type(df0["a"][0])
Out[12]: pandas.tslib.Timestamp
```
I think `df2` should contain time stamps like `df0`.
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} | 2 | "2013-05-13T21:17:30Z" | "2013-05-14T10:51:38Z" | "2013-05-14T10:51:38Z" | NONE | null | version 0.11.0 has introduced a confusing behavior when importing data via read_fwf (and, I'm pretty sure, read_table)
<PRE>
import pandas as pd
from cStringIO import StringIO
from datetime import datetime
tzlist = [1,10,20,30,60,80,100]
ntz = len(tzlist)
tcolspecs = [16]+[8]*ntz
tcolnames = ['SST'] + ["T%03d" % z for z in tzlist[1:]]
data = ''' 2009164202000 9.5403 9.4105 8.6571 7.8372 6.0612 5.8843 5.5192
2009164203000 9.5435 9.2010 8.6167 7.8176 6.0804 5.8728 5.4869
2009164204000 9.5873 9.1326 8.4694 7.5889 6.0422 5.8526 5.4657
2009164205000 9.5810 9.0896 8.4009 7.4652 6.0322 5.8189 5.4379
2009164210000 9.6034 9.0897 8.3822 7.4905 6.0908 5.7904 5.4039'''
dftemp = pd.read_fwf(StringIO(data),
index_col=0,
header=None,
names=tcolnames,
widths=tcolspecs,
parse_dates=True,
date_parser=lambda s: datetime.strptime(s,'%Y%j%H%M%S'))
</PRE>
## With version 0.10.1
<PRE>
In [1]: pd.__version__
Out[1]: '0.10.1'
In [2]: dftemp
Out[2]:
SST T010 T020 T030 T060 T080 T100
2009-06-13 20:20:00 9.5403 9.4105 8.6571 7.8372 6.0612 5.8843 5.5192
2009-06-13 20:30:00 9.5435 9.2010 8.6167 7.8176 6.0804 5.8728 5.4869
2009-06-13 20:40:00 9.5873 9.1326 8.4694 7.5889 6.0422 5.8526 5.4657
2009-06-13 20:50:00 9.5810 9.0896 8.4009 7.4652 6.0322 5.8189 5.4379
2009-06-13 21:00:00 9.6034 9.0897 8.3822 7.4905 6.0908 5.7904 5.4039
In [3]: dftemp.T030
Out[3]:
2009-06-13 20:20:00 7.8372
2009-06-13 20:30:00 7.8176
2009-06-13 20:40:00 7.5889
2009-06-13 20:50:00 7.4652
2009-06-13 21:00:00 7.4905
Name: T030, dtype: float64
In [4]: dftemp.T060
Out[4]:
2009-06-13 20:20:00 6.0612
2009-06-13 20:30:00 6.0804
2009-06-13 20:40:00 6.0422
2009-06-13 20:50:00 6.0322
2009-06-13 21:00:00 6.0908
Name: T060
In [5]: dftemp.T080
Out[5]:
2009-06-13 20:20:00 5.8843
2009-06-13 20:30:00 5.8728
2009-06-13 20:40:00 5.8526
2009-06-13 20:50:00 5.8189
2009-06-13 21:00:00 5.7904
Name: T080
In [6]: dftemp.T100
Out[6]:
2009-06-13 20:20:00 5.5192
2009-06-13 20:30:00 5.4869
2009-06-13 20:40:00 5.4657
2009-06-13 20:50:00 5.4379
2009-06-13 21:00:00 5.4039
Name: T100, dtype: float64
</PRE>
## and, with version 0.11.0
<PRE>
In [1]: pd.__version__
Out[1]: '0.11.0'
In [2]: dftemp
Out[2]:
SST T010 T020 T030 T060 T080 T100
2009-06-13 20:20:00 9.5403 9.4105 8.6571 7.8372 6.0612 5.8843 5.5192
2009-06-13 20:30:00 9.5435 9.2010 8.6167 7.8176 6.0804 5.8728 5.4869
2009-06-13 20:40:00 9.5873 9.1326 8.4694 7.5889 6.0422 5.8526 5.4657
2009-06-13 20:50:00 9.5810 9.0896 8.4009 7.4652 6.0322 5.8189 5.4379
2009-06-13 21:00:00 9.6034 9.0897 8.3822 7.4905 6.0908 5.7904 5.4039
In [3]: dftemp.T030
Out[3]:
2009-06-13 20:20:00 7.8372
2009-06-13 20:30:00 7.8176
2009-06-13 20:40:00 7.5889
2009-06-13 20:50:00 7.4652
2009-06-13 21:00:00 7.4905
Name: T030, dtype: float64
In [4]: dftemp.T060
Out[4]:
Empty DataFrame
Columns: [SST, T010, T020, T030, T060, T080, T100]
Index: []
In [5]: dftemp.T080
Out[5]:
Empty DataFrame
Columns: [SST, T010, T020, T030, T060, T080, T100]
Index: []
In [6]: dftemp.T100
Out[6]:
2009-06-13 20:20:00 5.5192
2009-06-13 20:30:00 5.4869
2009-06-13 20:40:00 5.4657
2009-06-13 20:50:00 5.4379
2009-06-13 21:00:00 5.4039
Name: T100, dtype: float64
</PRE>
No matter how many columns I've been importing (up to 32 in some cases), it seems like it is always the 5th and 6th columns getting hit.
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https://api.github.com/repos/pandas-dev/pandas/issues/3595 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3595/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3595/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3595/events | https://github.com/pandas-dev/pandas/pull/3595 | 14,284,463 | MDExOlB1bGxSZXF1ZXN0NTY4NzIyOA== | 3,595 | BUG: (GH3593) fixed a bug in the incorrect conversion of datetime64[ns] in combine_first | {
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} | 3 | "2013-05-13T23:00:24Z" | "2013-05-15T20:19:46Z" | "2013-05-15T20:19:46Z" | NONE | null | In version 0.11.0
``` python
A = DataFrame([[1,1],[2,1]],columns=['X','Y']).groupby('X').count()
```
returns a DataFrame, when there are 2+ grouping levels...
but,
``` python
B = DataFrame([[1,1],[1,1]],columns=['X','Y']).groupby('X').count()
```
returns a Series, when there is only 1 grouping level.
In version 0.10.0, B would also be a DataFrame.
Don't know if this was by design, but it seems awkward to check the type before using B downstream.
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https://api.github.com/repos/pandas-dev/pandas/issues/3597 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3597/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3597/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3597/events | https://github.com/pandas-dev/pandas/pull/3597 | 14,288,892 | MDExOlB1bGxSZXF1ZXN0NTY4OTU4MQ== | 3,597 | BUG: Fixed bug where a time-series was being selected in preference to an actual column name | {
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} | 1 | "2013-05-14T07:58:11Z" | "2013-07-28T05:43:36Z" | "2013-06-26T13:38:50Z" | NONE | null | If I want to plot 2 series ('A' and 'B') with different y-scales using a **different y-axis** for each one of them, I can use `secondary_y`:
```
df = pd.DataFrame(np.random.uniform(size=10).reshape(5,2),columns=['A','B'])
df['A'] = df['A'] * 100
df.plot(secondary_y=['A'])
```
but if I want to create bar graphs, the equivalent command **is ignored** (it doesn't put different scales on the y-axis), so the bars from 'A' are so big that the bars from 'B' cannot be distinguished:
```
df.plot(kind='bar',secondary_y=['A'])
```
The last command should produce a graph with equally sized bars.
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https://api.github.com/repos/pandas-dev/pandas/issues/3599 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3599/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3599/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3599/events | https://github.com/pandas-dev/pandas/pull/3599 | 14,306,013 | MDExOlB1bGxSZXF1ZXN0NTY5ODAxNA== | 3,599 | BUG: Add squeeze keyword to groupby to allow reduction in returned type | {
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} | 4 | "2013-05-14T11:54:01Z" | "2014-07-06T08:10:53Z" | "2013-05-15T20:19:14Z" | CONTRIBUTOR | null | This will allow a reduction in the returned type from DataFrame -> Series
if the groups are unique.
This is a fix for a regression from 0.10.1.
Allows functionaility in #2893, by specifying `squeeze=True`
in groupby call. #3596 functionaility is back as the default
This returns a Series because we are passing `squeeze=True`
```
In [9]: df2 = DataFrame([{"val1": 1, "val2" : 20},
{"val1":1, "val2": 19},{"val1":1, "val2": 27}, {"val1":1, "val2": 12}])
In [10]: df2
Out[10]:
val1 val2
0 1 20
1 1 19
2 1 27
3 1 12
In [11]: def func(dataf):
....: return dataf["val2"] - dataf["val2"].mean()
....:
In [12]: df2.groupby("val1", squeeze=True).apply(func)
Out[12]:
0 0.5
1 -0.5
2 7.5
3 -7.5
Name: 1, dtype: float64
```
Traditionally returns a DataFrame (even though have unique groups)
Implicity (`squeeze=False`)
```
In [13]: df = DataFrame([[1,1],[1,1]],columns=['X','Y'])
In [14]: df
Out[14]:
X Y
0 1 1
1 1 1
In [15]: df.groupby('X').count()
Out[15]:
X Y
X
1 2 2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3600 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3600/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3600/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3600/events | https://github.com/pandas-dev/pandas/pull/3600 | 14,312,284 | MDExOlB1bGxSZXF1ZXN0NTcwMTMyNQ== | 3,600 | BUG: Fix integer modulo and division to make integer and float dtypes work similarly for invalid values | {
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} | 1 | "2013-05-14T14:28:08Z" | "2014-07-16T08:09:04Z" | "2013-05-14T21:48:10Z" | CONTRIBUTOR | null | closes #3590
This is a numpy oddity that treats them differently.
```
In [131]: p = DataFrame({ 'first' : [3,4,5,8], 'second' : [0,0,0,3] })
In [132]: p % 0
Out[132]:
first second
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
In [133]: p % p
Out[133]:
first second
0 0 NaN
1 0 NaN
2 0 NaN
3 0 0
In [134]: p / p
Out[134]:
first second
0 1 inf
1 1 inf
2 1 inf
3 1 1.000000
In [135]: p / 0
Out[135]:
first second
0 inf inf
1 inf inf
2 inf inf
3 inf inf
```
Numpy does this (on integers), floats are as like above
```
In [3]: x
Out[3]:
array([[3, 0],
[4, 0],
[5, 0],
[8, 3]])
In [4]: x % 0
Out[4]:
array([[0, 0],
[0, 0],
[0, 0],
[0, 0]])
In [5]: x % x
Out[5]:
array([[0, 0],
[0, 0],
[0, 0],
[0, 0]])
In [6]: x / x
Out[6]:
array([[1, 0],
[1, 0],
[1, 0],
[1, 1]])
In [7]: x / 0
Out[7]:
array([[0, 0],
[0, 0],
[0, 0],
[0, 0]])
```
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