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MDU6SXNzdWUxNzM2NDEyMjg=
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ENH: Add pd.to_period
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| 5 | 2016-08-28T11:11:51Z | 2018-10-26T20:53:52Z | 2018-10-25T22:38:55Z |
MEMBER
| null |
Would return `Period` or `PeriodIndex`, analogous to functions like `to_datetime` and `to_timedelta`.
xref #5886
xref #14048
cc @sinhrks
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BUG: pass *args and **kwargs for wrapped methods in Groupby (#14107)
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[] | null | 11 | 2016-08-28T11:25:36Z | 2017-04-03T15:26:43Z | 2017-04-03T15:26:43Z |
NONE
| null |
- [x] closes #14107
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Methods such as 'hist' that take keyword arguments would now be able to be called via a Groupby object and the keyword args would be passed onwards to matplotlib as the user expects.
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Performance regression in DataFrame.to_csv
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| 1 | 2016-08-28T13:44:06Z | 2016-08-28T13:53:01Z | 2016-08-28T13:52:18Z |
NONE
| null |
Hello,
I recently upgraded from pandas 0.17.1 to 0.18.1 and ran into a performance regression with DataFrame.to_csv:
#### Code Sample, a copy-pastable example if possible
import pandas
import numpy
import tempfile
fd, filename = tempfile.mkstemp(prefix='to_csv_', suffix='.csv')
test = pandas.DataFrame(numpy.random.rand(2000, 5000))
%timeit test.to_csv(filename, sep=';', index=False, header=False)
In pandas **0.17.1**:
1 loop, best of 3: **6.86 s** per loop
In pandas **0.18.1**:
1 loop, best of 3: **21.9 s** per loop
You will find attached the results given by the Spyder profiler running this code in both version.
Most of the time (in pandas 0.18.1) is spent in the pandas.isnull method.
#### output of `pd.show_versions()`
## 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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fr_FR
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
None
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TST: Enable Google BigQuery (pandas.io.gbq) integration testing #11089
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| 20 | 2016-08-28T13:51:32Z | 2016-09-03T13:53:38Z | 2016-08-31T12:47:53Z |
CONTRIBUTOR
| null |
- [ x ] closes #11089
- [ x ] tests added / passed
- [ x ] passes `git diff upstream/master | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0ODMwMTgyOTA=
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Test for segfault in factorize (gh12666)
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| 2 | 2016-08-28T15:09:35Z | 2016-08-31T16:17:19Z | 2016-08-31T16:17:14Z |
CONTRIBUTOR
| null |
- [x] closes #12666
- [x] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
Not sure about the last. It's my first PR.
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DEPR: Deprecated PeriodIndex.to_datetime
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| 6 | 2016-08-28T17:25:56Z | 2016-09-01T08:18:46Z | 2016-09-01T08:03:39Z |
MEMBER
| null |
Continuation of #14096 by deprecating `PeriodIndex.to_datetime` in favour of `PeriodIndex.to_timestamp`.
Also addresses #8254.
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Test for Python 3.5 with C locale
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| 20 | 2016-08-28T17:35:45Z | 2016-09-10T14:39:34Z | 2016-09-10T14:38:17Z |
CONTRIBUTOR
| null |
As @jreback suggested, I'm adding an alternate py3 build to change LOCALE, based on the 3.4 slow build, to reveal some encoding bugs (see #12337)
I'm new to configuring Travis, I just hope this will work as I expect and that the tests will fail.
- [ ] closes #12337
- [x] tests added but not passed?
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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DOC: small update to install.rst page
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| 1 | 2016-08-28T18:34:52Z | 2016-08-29T12:44:19Z | 2016-08-29T12:44:19Z |
MEMBER
| null |
Move some content from the removed content on dependencies from the README (see GH13882)
I was going through the install.rst, and updated some small things, but there was actually no content from README to be moved.
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API: Raise FileNotFoundError in read_csv
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| 10 | 2016-08-29T03:50:30Z | 2016-09-01T08:19:15Z | 2016-09-01T08:19:15Z |
MEMBER
| null |
For a nonexistent file, raise the more specific `FileNotFoundError` for Python >= 3.3 in `read_csv`.
Closes #14086.
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TST: confirming tests for some fixed issues
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| 3 | 2016-08-29T14:10:49Z | 2016-09-01T07:28:07Z | 2016-09-01T07:28:07Z |
MEMBER
| null |
closes #7710, closes #8617, closes #13119
Closes a few issues still tagged with 0.19.0 that only need a confirming test
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BUG: series resample with timedelta values looses dtype (GH13119)
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| 1 | 2016-08-29T16:42:25Z | 2016-08-31T07:52:49Z | 2016-08-31T07:52:49Z |
MEMBER
| null |
- [x] closes #13119
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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unable to set xticks within bar() plotting function
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| 2 | 2016-08-29T20:28:39Z | 2019-09-23T16:34:55Z | 2019-09-23T16:34:55Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
In the code below:
``` python
pd.Series(range(10), index=range(10)).plot.bar(xticks=range(0, 11, 2))
```
`xticks` argument has no effect. However, there's no such problem in line() plot.
#### Expected Output
Output plot is expected to have xticks defined by `xticks` argument, e.g. in the example above it should be set to [0, 2, 4, 6, 8, 10].
#### output of `pd.show_versions()`
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: 0.8.1
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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API: Warn or raise for > 1 char encoded sep
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| 4 | 2016-08-30T02:44:55Z | 2016-08-31T16:10:31Z | 2016-08-31T16:10:31Z |
MEMBER
| null |
The system file encoding can cause a separator to be encoded as more than one character even though it maybe provided as one character. Multi-char separators are not supported by the C engine, so we need to catch this case.
Closes #14065.
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| 174,016,004 |
MDExOlB1bGxSZXF1ZXN0ODMyNjg5MjQ=
| 14,121 |
BUG: #14095. Amend eval() resolvers kwarg to accept lists
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| 6 | 2016-08-30T13:15:33Z | 2016-08-31T16:05:11Z | 2016-08-31T16:05:06Z |
CONTRIBUTOR
| null |
- [x] closes #14095
- [x] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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| 174,158,286 |
MDU6SXNzdWUxNzQxNTgyODY=
| 14,122 |
Saving CSV with backslashed-escaping is not idempotent.
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[] | null | 19 | 2016-08-31T00:35:58Z | 2021-07-03T18:23:12Z | null |
NONE
| null |
@pdbaines and I noticed this bug.
I want Pandas to write a CSV file so that all field data is backslash escaped if the character has a special interpretation (e.g. quotes or backslashes themselves). If a quote is backslashed, it is treated as field data, rather than a special character. This is not the behavior that I am seeing.
Consider the following data frame:
```
df = pd.DataFrame({"text": ["""Hello! Please "help" me. I cannot quote a csv.\\"""], "zoo": ["1"]})
df.to_csv("out.csv", index=False, quoting=csv.QUOTE_NONNUMERIC, encoding="utf-8", escapechar='\\', doublequote=False)
```
When written to a file, it looks something like this:
```
"text","zoo"
"Hello! Please \"help\" me. I cannot quote a csv.\","1"
```
The quotes are properly escaped in `Please "help" me`, but oddly, the end-quote of the field is backslashed, but the start-quote of the field is not back-slashed.
If I read the data frame in again using exactly the same parameters,
```
df2 = pd.read_csv("out.csv", quoting=csv.QUOTE_NONNUMERIC, encoding="utf-8", escapechar='\\', doublequote=False)
```
I get a data frame with both fields concatenated into the first field and the second field is NaN.
```
$ print(df2)
text zoo
0 Hello! Please "help" me. I cannot quote a csv.... NaN
```
If I instead, do the following:
```
df3 = pd.DataFrame({"text": ["""Hello! Please "help" me. I cannot quote a csv.\\\""""], "zoo": ["1"]})
df3.to_csv("outB.csv", index=False, quoting=csv.QUOTE_NONNUMERIC, encoding="utf-8", escapechar='\\', doublequote=False)
df4 = pd.read_csv("outB.csv", quoting=csv.QUOTE_NONNUMERIC, encoding="utf-8", escapechar='\\', doublequote=False)
```
I instead get a file with an odd-number of unescaped quote characters:
```
"text","zoo"
"Hello! Please \"help\" me. I cannot quote a csv.\\"","1"
```
and some unescaped quote characters are treated as data.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-39-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
```
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MDExOlB1bGxSZXF1ZXN0ODMzNzIyNjI=
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WIP: API: Add support for PEP 519 / file system path protocol
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| 9 | 2016-08-31T00:54:10Z | 2017-05-08T09:28:00Z | 2017-05-07T14:03:53Z |
NONE
| null |
- [ ] closes #13823
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWUxNzQyMDMxMDQ=
| 14,124 |
BUG: read_csv() does strange things if data rows have trailing delimiter
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| 5 | 2016-08-31T07:27:22Z | 2020-02-11T02:05:43Z | 2016-11-23T13:21:07Z |
CONTRIBUTOR
| null |
Given a file `t.txt` like this:
```
A,B,C
1,4,7,
2,5,8,
3,6,9,
```
`pandas.read_csv('t.txt')` produces:
```
A B C
1 4 7 NaN
2 5 8 NaN
3 6 9 NaN
```
In other words, it implies a nonexistent header column for what it believes is the index. I would expect it to do the same as this code:
```
pd.read_csv('t.txt', index_col=False).set_index('A')
```
Which is:
```
B C
A
1 4 7
2 5 8
3 6 9
```
The first example has the same problem even if you specify `index_col=0`, which is confusing because saying the first column should be used as the index is pretty much what's being done in the second example.
Files like this are commonly produced by vendors like Bloomberg whose formats we cannot expect to change.
Pandas 0.18.1.
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BUG: read_csv() crashes with engine='c'
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| 2 | 2016-08-31T08:49:58Z | 2017-01-24T23:13:38Z | 2017-01-24T23:13:38Z |
CONTRIBUTOR
| null |
Here's the code (input data is at the end of this message):
```
pd.read_csv('foo.csv', header=None, usecols=[0])
```
It fails with:
```
File "pandas/parser.pyx", line 805, in pandas.parser.TextReader.read (pandas/parser.c:8748)
File "pandas/parser.pyx", line 827, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:9003)
File "pandas/parser.pyx", line 881, in pandas.parser.TextReader._read_rows (pandas/parser.c:9731)
File "pandas/parser.pyx", line 868, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:9602)
File "pandas/parser.pyx", line 1865, in pandas.parser.raise_parser_error (pandas/parser.c:23325)
pandas.io.common.CParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file.
```
Small perturbations of the input file (adding or removing characters) makes it work, as does `engine='python'`. Note that while one row (or more) of the file contains "extra" columns, I have only asked Pandas to read column 0, which it should well be able to do since that column has a consistent, short length.
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-85-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
## DATA
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Make show_versions not to reload modules (GH13684)
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| 2 | 2016-08-31T10:35:26Z | 2016-09-01T07:28:54Z | 2016-09-01T07:28:54Z |
MEMBER
| null |
closes #13684
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MDExOlB1bGxSZXF1ZXN0ODM0MjgxNTA=
| 14,127 |
API/DEPR: Remove +/- as setops for Index (GH8227)
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| 7 | 2016-08-31T10:38:15Z | 2016-09-07T13:24:30Z | 2016-09-06T14:30:58Z |
MEMBER
| null |
xref #13777, deprecations put in place in #8227
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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Possibility to draw random markers in line plots
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[] | null | 0 | 2016-08-31T11:41:57Z | 2016-10-04T09:10:06Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
I often have dataframes with many obervations and want to have a quick glance at the data using a line plot.
The problem is that the colors of the colormap are either repeated over X observations or hard to distinguish e.g. in case of sequential colormaps.
So my idea was to add random markers to the line plot which is where I got stuck.
Here's an example with one markerstyle:
```
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# dataframe with random data
df = pd.DataFrame(np.random.rand(10, 8))
# plot
df.plot(kind='line', marker='d')
plt.show()
```
So far, this only seems to be possible with additional matplotlib code as shown in my related question on stackoverflow:
http://stackoverflow.com/questions/39246115/pandas-dataframe-plot-show-random-markers#39247062
#### Expected Output
It would be nice if one could draw random markers in line plots via a flag, e. g.:
```
df.plot(kind='line', random_markers=True)
```
#### output of `pd.show_versions()`
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.2
setuptools: 20.7.0
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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MDU6SXNzdWUxNzQyOTU0MDA=
| 14,129 |
Change DataFrame to_sql's first parameter to something more descriptive
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[] | null | 2 | 2016-08-31T14:55:22Z | 2021-05-01T23:29:13Z | 2021-05-01T23:29:13Z |
NONE
| null |
This is a high-level suggestion.
`DataFrame.to_sql(name, con, ...)`
It's not intuitive if `name` means name of database or name of table in database.
I think something like `table_name` or `tablename` would better explain what `name` is.
Yes, I know I can Google it every time and eventually after Googling it so many times I'll eventually learn it... but isn't the point to make it intuitive and easy-to-use? Others might not have this same minor issue.
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API: engine kw to .plot to enable selectable backends
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| 31 | 2016-08-31T15:58:03Z | 2019-08-05T06:26:52Z | 2019-06-21T09:37:18Z |
CONTRIBUTOR
| null |
We have had some conversations in the past regarding an `.plot(....., engine=)` kw. in #8018
This would allow pandas to redirect the plotting to a user selectable back-end, keeping `matplotlib` as the default.
see [chartpy here](https://github.com/cuemacro/chartpy), for a way to selectively enable `matplotlib`, `bokeh` and `plotly`.
and generically via [altair](https://github.com/ellisonbg/altair).
Missing from here is when to re-direct to `seaborn`.
So this issue is for discussion:
1) should we do this
2) implementation method and minimalist dependencies to actually do this (ideally pandas would add _NO_ dependences itself, just import for a particular engine, raising if its not available).
3) maybe should spin off much of the current pandas code into a separate repo (`pandas-plot`)?
and this actually should be the default (rather than `matplotlib`), which is of course the dependency. This might allow simply removing the vast majority of the custom plotting code.
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BUG: max rows in read_csv
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| 22 | 2016-08-31T22:04:34Z | 2019-10-21T19:52:16Z | null |
NONE
| null |
I have 5e9 rows csv file that I'm trying to load with pandas, which appears to load it only partially and produces 2852516352 rows data frame.
``` py
#Python 2.7.3 (default, Jun 22 2015, 19:33:41)
#[GCC 4.6.3] on linux2
#Type "help", "copyright", "credits" or "license" for more information.
import pandas as pd
import os
import gc
import timeit
x = pd.read_csv("X5e9_2c.csv")
x.head()
# KEY X2
#0 2632858426 -4008534609
#1 3176486913 -3394302982
#2 3235589527 -4982919314
#3 1017229071 3793469468
#4 1039519144 2740983791
gc.collect()
#21
t_start = timeit.default_timer()
ans = x.sort_values('KEY')
print ans.shape[0]
#2852516352
t = timeit.default_timer() - t_start
x.shape[0]
#2852516352
#t
6073.774994134903
```
Expected behaviour would be either properly read csv or an error, so it would save ~100 minutes of processing in my case.
my `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-61-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.0.3
setuptools: 17.0
Cython: None
numpy: 1.11.1
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: None
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: 2.38.0
pandas_datareader: None
```
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| 174,438,285 |
MDExOlB1bGxSZXF1ZXN0ODM1NzE2MDM=
| 14,132 |
BUG: Don't print stray newline with MultiIndex
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| 2 | 2016-09-01T05:15:08Z | 2016-09-02T11:19:50Z | 2016-09-02T11:19:42Z |
MEMBER
| null |
Title is self-explanatory.
Closes #6618.
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bug: double print statement in groupby / apply
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[] | null | 1 | 2016-09-01T10:09:28Z | 2016-09-01T10:11:59Z | 2016-09-01T10:11:58Z |
NONE
| null |
#### Code Sample
This is based on http://stackoverflow.com/questions/39268147/understand-pandas-groupby-apply-behaviour/39268538#39268538.
```
import seaborn as sns
iris = sns.load_dataset('iris')
iris.ix[1:100:10].groupby(["species"]).apply(lambda x: print(len(x), '\n***\n', x)) ## first try
iris.ix[1:100:10].groupby(["species"]).apply(lambda x: print(len(x), x.name, '\n***\n', x)) ## second try
```
#### Expected Output
The first line prints one of the groups doubled, which is unexpected. The second line doesn't double.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.2.0-b1
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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MDU6SXNzdWUxNzQ1MDE5OTA=
| 14,134 |
DataFrame.groupby.expanding duplicates grouped dimensions
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[] | null | 1 | 2016-09-01T11:40:02Z | 2016-09-01T11:47:46Z | 2016-09-01T11:46:34Z |
NONE
| null |
While using a `groupby`, applying an `expanding` function duplicates the grouped dimensions. I would expect the result of this to work like `groupby.cumsum` where the `expanding` function is applied over the groups and then the result has the same index as the original data frame.
#### Code Sample, a copy-pastable example if possible
My Data Frame sample
```
>>> dataframe
one
cont cat1
0 a 0
b 1
1 a 2
b 3
2 a 4
b 5
3 a 6
b 7
4 a 8
b 9
5 a 10
b 11
6 a 12
b 13
7 a 14
b 15
```
Example of the function call and incorrect output.
```
>>> dataframe.groupby(level=1).expanding(min_periods=1).mean()
one
cat1 cont cat1
a 0 a 0.0
1 a 1.0
2 a 2.0
3 a 3.0
4 a 4.0
5 a 5.0
6 a 6.0
7 a 7.0
b 0 b 1.0
1 b 2.0
2 b 3.0
3 b 4.0
4 b 5.0
5 b 6.0
6 b 7.0
7 b 8.0
```
#### Expected Output
```
>>> dataframe.groupby(level=1).cumsum()
one
cont cat1
0 a 0.0
b 1.0
1 a 1.0
b 2.0
2 a 2.0
b 3.0
3 a 3.0
b 4.0
4 a 4.0
b 5.0
5 a 5.0
b 6.0
6 a 6.0
b 7.0
7 a 7.0
b 8.0
```
#### output of `pd.show_versions()`
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 18.1
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
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read_fwf does not use comment character if colspecs argument does not include first column
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| 5 | 2016-09-02T01:31:37Z | 2021-11-01T13:14:51Z | 2021-11-01T13:14:51Z |
NONE
| null |
When reading fixed-width files using the `read_fwf` function, it is possible to specify a comment character using the `comment` argument. I expected that all lines beginning with the comment character would be ignored. However, if you do not specify the first column in the file in any column in `colspecs`, the comment character does not appear to be used.
#### Code Sample
``` python
import io
import pandas as pd
# Two input files, first line is comment, second line is data.
# Second file has a column (with the letter A)
# that I don't want at start of data.
string = "#\n1K\n"
off_string = "#\nA1K\n"
# When using skiprows to skip commented row, both work.
pd.read_fwf(io.StringIO(string), colspecs = [(0,1), (1,2)], skiprows = 1, header = None)
# 0 1
#0 1 K
pd.read_fwf(io.StringIO(off_string), colspecs = [(1,2), (2,3)], skiprows = 1, header = None)
# 0 1
#0 1 K
# If a comment character is specified, it only works when the colspecs
# includes the column with the comment character.
pd.read_fwf(io.StringIO(string), colspecs = [(0,1), (1,2)], comment = '#', header = None)
# 0 1
#0 1 K
pd.read_fwf(io.StringIO(off_string), colspecs = [(1,2), (2,3)], comment = '#', header = None)
# 0 1
#0 NaN NaN
#1 1.0 K
```
#### Expected Output
I expect that the final line should have the same result as the other three that is:
``` python
pd.read_fwf(io.StringIO(off_string), colspecs = [(1,2), (2,3)], comment = '#', header = None)
```
Should result in
``` python
# 0 1
#0 1 K
```
I first [reported this on StackOverflow](http://stackoverflow.com/questions/39235377/read-fwf-in-pandas-in-python-does-not-use-comment-character-if-colspecs-argument).
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.5-x86_64-linode71
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 3.3
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: None
boto: None
pandas_datareader: None
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| 174,748,520 |
MDU6SXNzdWUxNzQ3NDg1MjA=
| 14,136 |
transpose subplots
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| 3 | 2016-09-02T12:29:54Z | 2016-09-04T11:31:43Z | 2016-09-02T14:10:55Z |
NONE
| null |
Right now its easy to plot columns as rows like this:
df.plot(subplots=True, layout = (15, 1),legend=False) # for a 300x15 dataframe
which gives very nice row plots like this:
However sometimes it is needed to also plot this as columns instead - so basically swap x and y axes on each subplot. ie. its not enough to just transpose the df before plotting because its the plotting that needs to be transposed and not the data.
Is this possible already somehow? If not, could this possibly be implemented ?
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Series/DataFrame breaks truth-checking
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| 6 | 2016-09-02T14:28:39Z | 2016-09-05T22:42:56Z | 2016-09-02T21:05:50Z |
CONTRIBUTOR
| null |
This behavior is obviously by design. Why choose to break substitutability?
```
>>> bool([])
False
>>> bool(numpy.array([]))
False
>>> bool(pandas.Series(numpy.array([])))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/generic.py", line 892, in __nonzero__
.format(self.__class__.__name__))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
I ask, because I'd like to write idomatic Python that works for all containers, without need to special-case Pandas Series and DataFrames. There's no ambiguity for `[False]` nor `numpy.array([False])` ... oh.
```
>>> bool([False])
True
>>> bool(numpy.array([False]))
False
```
Ok, NumPy made an interesting choice here. But Pandas could reverse it and go back to standard truthiness: `len() == 0` means `False` and `len() > 0` means `True`.
I don't expect to actually cause a change here, but I'm curious as to what discussion happened on this point. If there's an archived email thread about this, could someone point me to it?
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Feature request: Advanced masking (e.g. `0 < pd.Series(x) < 1`)
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| 4 | 2016-09-02T19:33:12Z | 2016-09-02T22:37:44Z | 2016-09-02T21:03:49Z |
NONE
| null |
**Can there be a way to to do masking that is dependent on more than one boolean?**
```
0 < pd.Series(x) < 1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-132-06fa1595460a> in <module>()
----> 1 0 < pd.Series(x) < 1
/Users/jespinoz/anaconda/lib/python3.5/site-packages/pandas/core/generic.py in __nonzero__(self)
890 raise ValueError("The truth value of a {0} is ambiguous. "
891 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
--> 892 .format(self.__class__.__name__))
893
894 __bool__ = __nonzero__
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
My solution for it is:
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Renaming an empty MultiIndex fails
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| 1 | 2016-09-02T20:23:39Z | 2016-09-02T21:17:23Z | 2016-09-02T21:16:54Z |
NONE
| null |
``` python
s = pd.Series([0], index=pd.MultiIndex.from_tuples([('a', 1)]))
# This works:
s.rename(lambda x: x)
# This fails:
s.iloc[0:0].rename(lambda x: x)
```
Renaming an empty index should not throw an error. It should just return the original empty index. It appears to be failing because `_transform_index` calls `MultiIndex.from_tuples`, which doesn't accept an empty list of tuples.
I would imagine that this could instead call `from_arrays`, which `from_tuples` calls anyway, or it could check for the special case of an empty index.
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TST: windows failures
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| 25 | 2016-09-02T22:37:52Z | 2016-09-08T22:12:53Z | 2016-09-08T22:12:53Z |
CONTRIBUTOR
| null |
```
======================================================================
FAIL: test_c_engine (pandas.io.tests.parser.test_unsupported.TestUnsupportedFeatures)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\conda\Documents\pandas3.5\pandas\io\tests\parser\test_unsupported.py", line 64, in test_c_engine
read_table(StringIO(data), engine='c', sep='\xa7')
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 2437, in __exit__
raise AssertionError("{0} not raised.".format(name))
AssertionError: ValueError not raised.
```
closed in #14140
```
======================================================================
FAIL: test_append_zero (pandas.sparse.tests.test_list.TestSparseList)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\conda\Documents\pandas3.5\pandas\sparse\tests\test_list.py", line 64, in test_append_zero
tm.assert_sp_array_equal(sparr, SparseArray(arr, fill_value=0))
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1392, in assert_sp_array_equal
assert_numpy_array_equal(left.sp_values, right.sp_values)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1083, in assert_numpy_array_equal
assert_attr_equal('dtype', left, right, obj=obj)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 878, in assert_attr_equal
left_attr, right_attr)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
Attribute "dtype" are different
[left]: int64
[right]: int32
======================================================================
FAIL: test_dataframe_dummies_prefix_str (pandas.tests.test_reshape.TestGetDummies)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\conda\Documents\pandas3.5\pandas\tests\test_reshape.py", line 327, in test_dataframe_dummies_prefix_str
assert_frame_equal(result, expected)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1313, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1154, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 878, in assert_attr_equal
left_attr, right_attr)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Attributes are different
Attribute "dtype" are different
[left]: int64
[right]: int32
======================================================================
FAIL: test_dataframe_dummies_prefix_str (pandas.tests.test_reshape.TestGetDummiesSparse)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\conda\Documents\pandas3.5\pandas\tests\test_reshape.py", line 327, in test_dataframe_dummies_prefix_str
assert_frame_equal(result, expected)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1313, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1154, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 878, in assert_attr_equal
left_attr, right_attr)
File "C:\Users\conda\Documents\pandas3.5\pandas\util\testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Attributes are different
Attribute "dtype" are different
[left]: int64
[right]: int32
```
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TST: sparse / dummy array comparisons on windows, xref #14140
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| 0 | 2016-09-02T23:35:24Z | 2016-09-03T00:00:37Z | 2016-09-03T00:00:37Z |
CONTRIBUTOR
| null |
partial on #14140
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TST: fix blosc version
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| 0 | 2016-09-02T23:48:14Z | 2016-09-02T23:52:39Z | 2016-09-02T23:52:39Z |
CONTRIBUTOR
| null |
something wrong with update for blosc=1.4.3 (latest via pip).
|
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| 174,866,590 |
MDU6SXNzdWUxNzQ4NjY1OTA=
| 14,143 |
TST: blosc>1.4.1 failing
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| 1 | 2016-09-02T23:49:40Z | 2016-09-03T14:50:08Z | 2016-09-03T14:50:08Z |
CONTRIBUTOR
| null |
xref: https://github.com/pydata/pandas/pull/14142
seems this update just hit and failing 1 tests [here](https://travis-ci.org/pydata/pandas/jobs/157218567)
worked [here](https://travis-ci.org/pydata/pandas/jobs/157068571)
seems like a back-compat issue (new blosc not reading older blosc?)
```
======================================================================
ERROR: pandas.io.tests.test_packers.TestMsgpack.test_msgpack('0.17.1',)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/travis/build/pydata/pandas/pandas/io/tests/test_packers.py", line 873, in read_msgpacks
self.compare(vf, version)
File "/home/travis/build/pydata/pandas/pandas/io/tests/test_packers.py", line 817, in compare
data = read_msgpack(vf, encoding='latin-1')
File "/home/travis/build/pydata/pandas/pandas/io/packers.py", line 202, in read_msgpack
return read(fh)
File "/home/travis/build/pydata/pandas/pandas/io/packers.py", line 187, in read
l = list(unpack(fh, encoding=encoding, **kwargs))
File "_unpacker.pyx", line 459, in pandas.msgpack._unpacker.Unpacker.__next__ (pandas/msgpack/_unpacker.cpp:4709)
File "_unpacker.pyx", line 390, in pandas.msgpack._unpacker.Unpacker._unpack (pandas/msgpack/_unpacker.cpp:3843)
File "/home/travis/build/pydata/pandas/pandas/io/packers.py", line 628, in decode
blocks = [create_block(b) for b in obj[u'blocks']]
File "/home/travis/build/pydata/pandas/pandas/io/packers.py", line 615, in create_block
b[u'compress']), b[u'shape'])
File "/home/travis/build/pydata/pandas/pandas/io/packers.py", line 329, in unconvert
_move_into_mutable_buffer(decompress(values)),
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/blosc/toplevel.py", line 485, in decompress
return _ext.decompress(bytesobj, as_bytearray)
error: Error -1 while decompressing data
----------------------------------------------------------------------
```
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MDExOlB1bGxSZXF1ZXN0ODM4OTYwMDE=
| 14,144 |
Add the steps to setup gbq integration testing to the contributing docs
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| 16 | 2016-09-03T13:43:30Z | 2016-09-07T13:23:03Z | 2016-09-07T13:22:53Z |
CONTRIBUTOR
| null |
Pull requests from forked repositories cannot access the existing secure Google BigQuery credentials which are on Travis at pydata/pandas. In order for contributors to run Google BigQuery integration tests on Travis, they need to create a pull request on their forked repository of pandas with their own secure Google BigQuery credentials.
I've updated the contributing documentation to include the steps required to run Google BigQuery integration tests on a forked repository of pandas.
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CLN/BUG: fix ndarray assignment may cause unexpected cast
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| 7 | 2016-09-04T01:10:37Z | 2017-05-21T19:49:10Z | 2017-05-21T19:49:10Z |
MEMBER
| null |
- [x] closes #14001
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixed a bug with assignment against block may be coerced incorrectly.
```
s = pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2012-01-01')])
s[1] = 1
s
#0 2011-01-01 00:00:00.000000000
#1 1970-01-01 00:00:00.000000001
# dtype: datetime64[ns]
```
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BUG: DatetimeTZBlock can't assign values near dst boundary
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| 3 | 2016-09-04T01:14:48Z | 2016-09-10T14:34:57Z | 2016-09-10T14:34:57Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Value assignment doesn't work if data contains DST boundary because of internal `.localize(None)`.
```
s = pd.Series(pd.date_range('2016-11-06', freq='H', periods=3, tz='US/Eastern'))
s[1] = pd.Timestamp('2011-01-01', tz='US/Eastern')
s
#0 2016-11-06 00:00:00-04:00
#1 2016-11-06 01:00:00-04:00
#2 2016-11-06 01:00:00-05:00
# dtype: datetime64[ns, US/Eastern]
```
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MAINT: flake8 *.pyx files
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| 11 | 2016-09-04T06:43:56Z | 2016-09-06T10:18:35Z | 2016-09-06T10:18:08Z |
MEMBER
| null |
closes #12995
flake8-ed *.pyx files and fixed errors.
Removed the E226 check because that inhibits pointers (e.g. char*). In addition, the check is not even universally accepted in Python.
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Datatime stored in DataFrame loses tz info and it goes to hour:minute
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| 2 | 2016-09-04T16:27:14Z | 2016-09-04T18:26:22Z | 2016-09-04T17:46:15Z |
NONE
| null |
```
#With this code:
#-----------------------------
from datetime import datetime
import pytz
from pandas import DataFrame
original_date = datetime(2016, 9, 2, 0, 0, 0, 0, pytz.timezone('America/Manaus'))
dataframe = DataFrame(columns = ['stored_date'])
dataframe.loc['A'] = [original_date]
print("I expect it shows '2016-09-02T00:00:00-04:00', but it shows '2016-09-02 04:00:00'...")
print("original_date: " + original_date.isoformat())
print("stored_date in dataframe: " + str(dataframe.loc['A', 'stored_date']))
#-----------------------------
```
```
#I get this output:
#-----------------------------
I expect it shows '2016-09-02T00:00:00-04:00', but it shows '2016-09-02 04:00:00'...
original_date: 2016-09-02T00:00:00-04:00
stored_date in dataframe: 2016-09-02 04:00:00
#-----------------------------
```
```
#Output from pd.show_versions()
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: pt_BR.utf8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 26.0.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
```
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NaT treatment in pandas groupby/nunique
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| 4 | 2016-09-04T19:09:06Z | 2021-05-01T23:35:37Z | 2021-05-01T23:35:37Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Calling nunique on a column that contains NaT results in this FutureWarning from numpy:
```
python35\lib\site-packages\pandas\core\groupby.py:2854
FutureWarning: In the future, NAT != NAT will be True rather than False.
inc = np.r_[1, val[1:] != val[:-1]]
```
It might be worth updating the relevant lines in a way that does not depend on != comparison of NaT, otherwise `nunique()` behavior will unexpectedly change when [numpy is upgraded to 1.13](http://docs.scipy.org/doc/numpy-dev/release.html#future-changes).
#### Expected Output
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.24.1
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.4
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.14
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
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| 14,150 |
SettingWithCopy dependence on reference count
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| 9 | 2016-09-04T23:06:59Z | 2020-09-06T18:50:04Z | null |
NONE
| null |
As a follow-up to my [question](http://stackoverflow.com/questions/39321914/unpredictable-pandas-slice-assignment-behavior-with-no-settingwithcopywarning), I believe the current approach is that pandas assumes that the programmer intends assignment to a child DF/S to be propagated to the parent DF/S _as long as the parent has a non-zero reference count_.
The problems like the ones I described would be avoided if child DataFrame always warned on assignment regardless of the reference count of the parent object - UNLESS a `copy() method` or `DataFrame` constructor was explicitly applied to the slice. (If desired `query` and/or `filter` can be documented as `copy` methods as well.)
(Making ref count incremented when the child is a view is not enough because the code that works normally might one day stop working - with SettingWithCopyWarning that but might be ignored if it appear on the client site - simply due to the change in the input data.)
If this approach is followed, the documentation can also be clarified as follows:
> When applying indexing, `filter` (???) and some other operations (???) to a parent DF/S pandas may create a view or copy of the parent depending on conditions that are too complicated to describe. This creates a possibility of a subtle bug in the code when assigning to the child DF/S. To help catch such potential bugs, pandas assumes that the programmer intends assignment to a child DF/S to be propagated to the parent DF/S unless it is explicitly copied (using .copy(), .query(), `DataFrame` constructor, etc??? ); when pandas cannot guarantee such behavior, it generates `SettingWithCopyWarning`. Note that this happens regardless of whether the parent DF/S is reachable in the current context or even whether it still exists as an object.
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BUG/TST: Empty input arrays in cartesian_product and MultiIndex (#12258)
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| 4 | 2016-09-05T04:46:36Z | 2016-09-27T10:36:44Z | 2016-09-27T10:36:44Z |
CONTRIBUTOR
| null |
- [x] closes #12258
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This commit:
1. fixes logic (and division by 0) in `cartesian_product` when some input arrays are empty
2. adds tests for MultiIndex empty level construction with `.from_arrays` and `.from_product`
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Feature request: Access multiindex by name
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| 4 | 2016-09-05T08:27:19Z | 2016-09-06T10:44:29Z | 2016-09-05T15:07:12Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
I have not found an easy way to access a column of a multi index by name. Consider the following code:
```
import pandas as pd
df = pd.DataFrame([[1,1,10,200],
[1,2,11,201],
[1,3,12,202],
[2,1,13,210],
[2,2,14,230]],
columns=list('ABCD')).set_index(['A','B'])
ii = df.index
```
To access the values of column B i can e.g. do:
```
df.reset_index('B').B.tolist()
```
or
```
[v[1] for v in ii.tolist()]
```
Both of there are somewhat cumbersome What I'm proposing is to add similar shorcut access to a multiindex just like a dataframe. E.g.
```
ii.B
```
or
```
ii['B']
```
that would return the same list as in the two examples above.
#### output of `pd.show_versions()`
0.18.1
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| 14,153 |
Fix typo (change 'n' to 'k' in get_dummies documentation)
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[] | null | 2 | 2016-09-05T12:19:02Z | 2016-09-05T14:16:35Z | 2016-09-05T14:16:31Z |
CONTRIBUTOR
| null |
Just changing an `n` to a `k`.
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MDU6SXNzdWUxNzUwOTI2OTc=
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ENH: allow usecols to be case insensitive
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| 9 | 2016-09-05T15:24:54Z | 2016-12-06T11:38:06Z | 2016-12-06T11:38:06Z |
CONTRIBUTOR
| null |
I have a wide delimited file that includes columns with predictable names but unpredictable capitalisation, as shown in this toy code.
``` python
import pandas as pd
from io import StringIO
data = """1,1,1,1
2,2,2,2
3,3,3,3
4,4,4,4
5,5,5,5"""
names = ['col1', 'col2', 'COL3', 'Col4']
df = pd.read_csv(StringIO(data), names=names, usecols=None)
df.head()
```
| | col1 | col2 | COL3 | Col4 |
| --- | --- | --- | --- | --- |
| **0** | 1 | 1 | 1 | 1 |
| **1** | 2 | 2 | 2 | 2 |
| **2** | 3 | 3 | 3 | 3 |
| **3** | 4 | 4 | 4 | 4 |
| **4** | 5 | 5 | 5 | 5 |
I want to pass a list of column names to `usecols` when importing the file, but I won't be able to unless I match the names exactly:
``` python
df = pd.read_csv(StringIO(data), names=names, usecols=['col3', 'col4'])
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-92d038c5ac8c> in <module>()
1 cols = ['col3', 'col4']
----> 2 df1 = pd.read_csv('test.csv', usecols=['col3', 'col4'])
3 df1.head()
/Users/alexchase/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
560 skip_blank_lines=skip_blank_lines)
561
--> 562 return _read(filepath_or_buffer, kwds)
563
564 parser_f.__name__ = name
/Users/alexchase/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
313
314 # Create the parser.
--> 315 parser = TextFileReader(filepath_or_buffer, **kwds)
316
317 if (nrows is not None) and (chunksize is not None):
/Users/alexchase/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
643 self.options['has_index_names'] = kwds['has_index_names']
644
--> 645 self._make_engine(self.engine)
646
647 def close(self):
/Users/alexchase/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
797 def _make_engine(self, engine='c'):
798 if engine == 'c':
--> 799 self._engine = CParserWrapper(self.f, **self.options)
800 else:
801 if engine == 'python':
/Users/alexchase/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1255
1256 if len(self.names) < len(self.usecols):
-> 1257 raise ValueError("Usecols do not match names.")
1258
1259 self._set_noconvert_columns()
ValueError: Usecols do not match names.
```
I see two problems here. First, the error text would be more helpful if it specified what column name(s) is/are wrong. That's pretty easy to fix. Second, if I know that capitalisation might be a problem, I have to check every column name individually before I can pass a list to `usecols`. One possible solution would be to modify [this part](https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1421) and [this part](https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L2193) of `parsers.py` to case-transform the lists of column names before matching. This behaviour could be made the default, or it could be optional via an `ignore_col_case` keyword in the various `read` functions.
---
> ## INSTALLED VERSIONS
>
> commit: None
> python: 3.5.2.final.0
> python-bits: 64
> OS: Darwin
> OS-release: 15.6.0
> machine: x86_64
> processor: i386
> byteorder: little
> LC_ALL: None
> LANG: en_GB.UTF-8
>
> pandas: 0.18.1
> nose: 1.3.7
> pip: 8.1.2
> setuptools: 25.1.6
> Cython: 0.24.1
> numpy: 1.11.1
> scipy: 0.18.0
> statsmodels: 0.6.1
> xarray: None
> IPython: 5.1.0
> sphinx: 1.4.1
> patsy: 0.4.1
> dateutil: 2.5.3
> pytz: 2016.6.1
> blosc: None
> bottleneck: 1.1.0
> tables: 3.2.3.1
> numexpr: 2.6.1
> matplotlib: 1.5.1
> openpyxl: 2.3.2
> xlrd: 1.0.0
> xlwt: 1.1.2
> xlsxwriter: 0.9.2
> lxml: 3.6.4
> bs4: 4.4.1
> html5lib: None
> httplib2: None
> apiclient: None
> sqlalchemy: 1.0.13
> pymysql: 0.7.6.None
> psycopg2: None
> jinja2: 2.8
> boto: 2.40.0
> pandas_datareader: None
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MDU6SXNzdWUxNzUwOTc0ODg=
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Bug when assigning timedelta64 to a series (or dataframe) with an indexer
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| 2 | 2016-09-05T15:55:32Z | 2016-09-08T10:22:35Z | 2016-09-08T10:22:35Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: s = pd.Series(10*[np.timedelta64(10, 'm')])
In [5]: s
Out[5]:
0 00:10:00
1 00:10:00
2 00:10:00
3 00:10:00
4 00:10:00
5 00:10:00
6 00:10:00
7 00:10:00
8 00:10:00
9 00:10:00
dtype: timedelta64[ns]
In [6]: s.ix[[1,2,3]] = np.timedelta64(20, 'm')
In [7]: s
Out[7]:
0 00:10:00
1 00:00:00.000000
2 00:00:00.000000
3 00:00:00.000000
4 00:10:00
5 00:10:00
6 00:10:00
7 00:10:00
8 00:10:00
9 00:10:00
dtype: timedelta64[ns]
```
#### Expected Output
```
In [7]: s
Out[7]:
0 00:10:00
1 00:20:00
2 00:20:00
3 00:20:00
4 00:10:00
5 00:10:00
6 00:10:00
7 00:10:00
8 00:10:00
9 00:10:00
dtype: timedelta64[ns]
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 18.2
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 4.0.3
sphinx: 1.4.6
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.15
pymysql: 0.7.5.None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
```
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| 3 | 2016-09-05T17:09:20Z | 2018-05-21T11:06:04Z | 2018-05-21T11:06:04Z |
CONTRIBUTOR
| null |
I think this is rounding prematurely, though not entirely sure. Could also just be loss of precision when converting from a float -> int64.
```
In [12]: pd.to_datetime(1434743731.8770001,unit='s')
Out[12]: Timestamp('2015-06-19 19:55:31.877000')
In [13]: pd.to_datetime(1434743731.8770001,unit='s').value
Out[13]: 1434743731877000000
In [14]: (pd.Timestamp('2015-06-19 19:55:15.877000')+pd.Timedelta('1us')).value
Out[14]: 1434743715877001000
```
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ENH: function to take DatetimeIndex and adjust for leapseconds
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| 1 | 2016-09-05T17:16:19Z | 2017-12-15T01:18:37Z | 2017-12-15T01:18:37Z |
CONTRIBUTOR
| null |
[leapseconds list here](https://github.com/eggert/tz/blob/master/leap-seconds.list).
[SO discussion here](http://stackoverflow.com/questions/19332902/extract-historic-leap-seconds-from-tzdata). This info is actually available from the tzinfo, though I don't think `pytz` is reading it.
[astropy docs](http://docs.astropy.org/en/stable/api/astropy.time.TimeGPS.html)
This is just a matter of do a timedelta addition against this list (which could be hardcoded) and returning a new DTI. In theory this could be in the 'leapseconds' tz `right/UTC`, but that might require more work. Just thinking of a simple.
```
index = pd.date_range('.....')
index.adjusted_for_leapseconds()
```
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| 3 | 2016-09-05T17:28:46Z | 2016-09-06T14:24:32Z | 2016-09-05T17:34:42Z |
CONTRIBUTOR
| null |
Hi,
I have been currently working with dataset mostly consisting of binary features (column has only 1, 0 or NaN). I know there is a `category` dtype, but that is not usable in some cases, e.g. when you want to store it HDF and can't use `tables` format (e.g. because of to wide tables). I have datasets with about 50k rows and 7k columns and it is wasting a lot of memory storing 0/1 as `float64` (because `int64` can't handle NaNs - but it is still terribly big). In memory size is about 15 GB, while reduced is about 4GB and HDF files are of course much smaller.
So I use this function to reduce a Series to smallest possible dtype, effectively reducing the size of the dataset (up to 8 times from 64b -> 8b):
``` python
import numpy as np
def safely_reduce_dtype(ser): # pandas.Series or numpy.array
orig_dtype = "".join([x for x in ser.dtype.name if x.isalpha()]) # float/int
mx = 1
for val in ser.values:
new_itemsize = np.min_scalar_type(val).itemsize
if mx < new_itemsize:
mx = new_itemsize
new_dtype = orig_dtype + str(mx * 8)
return new_dtype # or converts the pandas.Series by ser.astype(new_dtype)
```
it's far from perfect and probably some edge cases may occur. It could be definitely enhanced somehow, take this as a first proposal.
I think it could be added as a utility function, something like `pd.to_numeric` or `pd.to_datetime`.
What do you think?
## Example
``` python
>>> import pandas
>>> serie = pd.Series([1,0,1,0], dtype='int32')
>>> safely_reduce_dtype(serie)
dtype('int8')
>>> float_serie = pd.Series([1,0,1,0])
>>> safely_reduce_dtype(float_serie)
dtype('float8') # from float64
```
or when returning `ser.astype(new_type)`:
``` python
>>> import numpy as np
>>> import pandas as pd
>>> rands = np.random.randint(1,100, 10000)
>>> ser_orig = pd.Series(rands)
>>> ser_reduced = safely_reduce_dtype(ser_orig)
>>> print(ser_orig.memory_usage(), ser_reduced.memory_usage())
80080 10080
```
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WIP/ENH: reading older Stata versions, #11526
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| 7 | 2016-09-05T19:43:57Z | 2016-09-11T13:55:44Z | 2016-09-11T13:55:39Z |
CONTRIBUTOR
| null |
A simple file seems to read OK if we just treat it like other "old format" (version 104-115) files. However I do not currently have any documentation for this format. The only relevant resource I can find is the file `foreign/src/stataread.c` in the R `foreign` package sources:
https://cran.r-project.org/src/contrib/foreign_0.8-66.tar.gz
It suggests some special handling for version 111 but I haven't had time to figure out exactly what it is doing.
- [x] closes #11526
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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BUG : assignment of non-ns timedelta64 value (GH14155)
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| 9 | 2016-09-06T01:51:49Z | 2016-09-08T10:22:40Z | 2016-09-08T10:22:35Z |
NONE
| null |
- [x] closes #14155
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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TST: Make encoded sep check more locale sensitive
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| 8 | 2016-09-06T02:08:59Z | 2016-09-08T22:13:08Z | 2016-09-08T22:12:53Z |
MEMBER
| null |
Follow-up to #14120 to make the `sep` check more locale sensitive. Closes #14140.
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Unexpected results for the mean of a DataFrame of ufloat from the uncertainties package.
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| 14 | 2016-09-06T07:31:50Z | 2021-05-01T23:37:23Z | null |
NONE
| null |
Related to #6898.
I find it very convenient to use a DataFrame of `ufloat` from the `uncertainties` package. Each entry consists of (value, error) and could represent the result of Monte Carlo simulations or an experiment.
At present taking sums along both axes gives the expected result, but taking the mean does not.
``` python
import pandas as pd
import numpy as np
from uncertainties import unumpy
value = np.arange(12).reshape(3,4)
err = 0.01 * np.arange(12).reshape(3,4) + 0.005
data = unumpy.uarray(value, err)
df = pd.DataFrame(data, index=['r1', 'r2', 'r3'], columns=['c1', 'c2', 'c3', 'c4'])
```
Examples:
``` python
print (df)
c1 c2 c3 c4
r1 0.000+/-0.005 1.000+/-0.015 2.000+/-0.025 3.000+/-0.035
r2 4.00+/-0.04 5.00+/-0.06 6.00+/-0.07 7.00+/-0.08
r3 8.00+/-0.09 9.00+/-0.10 10.00+/-0.11 11.00+/-0.12
df.sum(axis=0) # This works
c1 12.00+/-0.10
c2 15.00+/-0.11
c3 18.00+/-0.13
c4 21.00+/-0.14
dtype: object
df.sum(axis=1) # This works
r1 6.00+/-0.05
r2 22.00+/-0.12
r3 38.00+/-0.20
dtype: object
df.mean(axis=0) # This does not work
Series([], dtype: float64)
Expected (`df.apply(lambda x: x.sum() / x.size)`)
c1 4.000+/-0.032
c2 5.00+/-0.04
c3 6.00+/-0.04
c4 7.00+/-0.05
dtype: object
df.mean(axis=1) # This does not work
r1 NaN
r2 NaN
r3 NaN
dtype: float64
Expected (`df.T.apply(lambda x: x.sum() / x.size)`)
r1 1.500+/-0.011
r2 5.500+/-0.031
r3 9.50+/-0.05
dtype: object
```
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ERR: improves message raised for bad names in usecols (GH14154)
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| 7 | 2016-09-06T13:59:11Z | 2017-02-27T21:05:09Z | 2017-02-27T16:04:20Z |
CONTRIBUTOR
| null |
- [X] partial fix for #14154
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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API/DEPR: Remove +/- as setops for DatetimeIndex/PeriodIndex (GH9630)
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| 4 | 2016-09-06T15:53:21Z | 2016-09-07T13:24:30Z | 2016-09-07T13:11:04Z |
MEMBER
| null |
xref #13777, deprecations put in place in #9630
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MDU6SXNzdWUxNzUzNDg3Mzk=
| 14,165 |
BUG: pandas.Series.astype('category', categories=[list_of_categories]) returns all NaNs
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| 1 | 2016-09-06T20:51:29Z | 2016-09-06T21:06:08Z | 2016-09-06T21:05:08Z |
CONTRIBUTOR
| null |
The following code returns all NaN's instead of "given" categories:
#### Code Sample, a copy-pastable example if possible
```
In [25]: df = pd.DataFrame({'a':['aa','bb','cc','dd']})
In [26]: df
Out[26]:
a
0 aa
1 bb
2 cc
3 dd
In [27]: df.a.astype('category', categories=[0,1,2,3])
Out[27]:
0 NaN
1 NaN
2 NaN
3 NaN
Name: a, dtype: category
Categories (4, int64): [0, 1, 2, 3]
In [28]: df.a.astype('category', categories=['0','1','2','3'])
Out[28]:
0 NaN
1 NaN
2 NaN
3 NaN
Name: a, dtype: category
Categories (4, object): [0, 1, 2, 3]
```
#### Expected Output
```
a
0 0
1 1
2 2
3 3
Name: a, dtype: category
Categories (4, object): [0, 1, 2, 3]
```
#### output of `pd.show_versions()`
```
In [29]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.1
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.4
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: 3.6.1
bs4: 4.5.0
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
```
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Sparse becomes float under fancy indexing
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| 2 | 2016-09-06T23:45:19Z | 2016-09-07T03:30:53Z | 2016-09-07T00:15:32Z |
CONTRIBUTOR
| null |
``` python
>>> pd.SparseSeries([False, True], dtype=bool)[[0, 1]]
0 0.0
1 1.0
dtype: float64
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: array([1], dtype=int32)
```
#### Expected output
```
0 False
1 True
dtype: bool
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: array([1], dtype=int32)
```
#### Output of `pd.versions()`
pandas: 0.18.1
should alone be relevant.
Apologies I've not checked if this is fixed by #13849 or elsewhere. Just passing on issues from
https://github.com/scikit-learn/scikit-learn/issues/7352.
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MDU6SXNzdWUxNzUzNzk0Njk=
| 14,167 |
SparseSeries.__array__ only returns non-fills
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| 17 | 2016-09-06T23:51:36Z | 2019-02-09T09:18:54Z | 2018-10-17T13:50:11Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
``` python
>>> np.array(pd.SparseSeries([0, 1], fill_value=0))
array([1])
```
#### Expected Output
```
array([0, 1])
```
this should really be consistent with `Series` rather than just returning the non-fill values (i.e. rather than being equivalent to `np.array(ps.SparseArray([np.nan, 1]))`.
#### output of `pd.show_versions()`
Pandas 0.18.1
should alone be relevant.
Apologies I've not checked if this is fixed in master. Just passing on issues from
scikit-learn/scikit-learn#7352.
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| 0 | 2016-09-07T02:31:26Z | 2016-09-07T03:36:28Z | 2016-09-07T03:36:07Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
#### Expected Output
#### output of `pd.show_versions()`
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Passing psycopg2 DictRow raises TypeError
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| 2 | 2016-09-07T06:23:45Z | 2018-05-16T13:39:45Z | 2016-09-07T10:39:47Z |
NONE
| null |
Maybe something you want to look into:
http://stackoverflow.com/questions/35604186/convert-psycopg2-dictrow-query-to-pandas-dataframe
```
curs = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
curs.execute("SELECT * FROM mytable")
data = curs.fetchall()
print pd.DataFrame(list(data))
```
Will produce a TypeError.
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| 1 | 2016-09-07T06:55:32Z | 2016-09-07T10:33:17Z | 2016-09-07T10:31:52Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame({'a': [1,2,3,4], 'b':[None, None, None, None]})
chunks = df.groupby('a')
assert len([g for g, chunk in chunks]) == 4
chunks = df.groupby(['a', 'b'])
# failed here, it's emtpy
assert len([g for g, chunk in chunks]) == 4
# however, chunks.groups shows that there are 4 groups
#{(1, nan): [0], (2, nan): [1], (3, nan): [2], (4, nan): [3]}
# but if we fillna('') first then it could pass
chunks = df.fillna('').groupby(['a', 'b'])
assert len([g for g, chunk in chunks]) == 4
# In[11]:
```
#### Expected Output
I don't see why have NaN in the grouped by key should fail the groupby. Especially if you use `chunks.groups`, you do have them grouped like this `{(1, nan): [0], (2, nan): [1], (3, nan): [2], (4, nan): [3]}
In [18]:`
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL:
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.2
setuptools: 24.0.0
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 1.0b8
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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Series.str.contains doesn't correct if series contains only nan values.
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| 3 | 2016-09-07T08:50:42Z | 2016-09-09T22:29:16Z | 2016-09-09T22:29:16Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
import re
CONTAINS_REG_EXP = ur'{0}'
value = '3'
t1 = pd.Series(data=[np.nan, np.nan, np.nan], dtype=np.object)
t2 = t1.str.contains(CONTAINS_REG_EXP.format(re.escape(value)),
flags=re.U, na=False)
print t2
```
#### Expected Output
```
0 False
1 False
2 False
dtype: bool
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-42-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.3
setuptools: 18.0.1
Cython: 0.23.4
numpy: 1.10.1
scipy: None
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
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MDExOlB1bGxSZXF1ZXN0ODQyNjkyNjQ=
| 14,172 |
DOC: cleanup build warnings
|
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| 2 | 2016-09-07T09:50:46Z | 2016-09-07T13:57:22Z | 2016-09-07T13:57:22Z |
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| 14,173 |
BUG: union_categoricals w/Series & CategoricalIndex
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| 8 | 2016-09-07T10:11:28Z | 2016-09-10T14:30:49Z | 2016-09-10T14:30:49Z |
CONTRIBUTOR
| null |
```
In [25]: from pandas.types.concat import union_categoricals
In [26]: s1 = pd.Series(['a', 'b'], dtype='category')
...: s3 = pd.Series(['a', 'b', 'c'], dtype='category')
In [27]: s1
Out[27]:
0 a
1 b
dtype: category
Categories (2, object): [a, b]
In [28]: s3
Out[28]:
0 a
1 b
2 c
dtype: category
Categories (3, object): [a, b, c]
```
I don't see why this shouldn't work. This should return a Categorical.
```
In [29]: union_categoricals([s1,s3])
AttributeError: 'Series' object has no attribute 'categories'
```
actual categoricals are combinable
```
In [30]: union_categoricals([s1.values,s3.values])
Out[30]:
[a, b, a, b, c]
Categories (3, object): [a, b, c]
```
This works fine
```
In [31]: union_categoricals([s1.values,pd.CategoricalIndex(s3.values)])
Out[31]:
[a, b, a, b, c]
Categories (3, object): [a, b, c]
```
But this is broken
```
In [32]: union_categoricals([pd.CategoricalIndex(s1.values),pd.CategoricalIndex(s3.values)])
AttributeError: 'CategoricalIndex' object has no attribute 'is_dtype_equal'
```
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MDExOlB1bGxSZXF1ZXN0ODQyNzQ5MzE=
| 14,174 |
Fix trivial typo in comment
|
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| 1 | 2016-09-07T10:28:45Z | 2016-09-07T10:49:41Z | 2016-09-07T10:49:24Z |
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TypeError bug when using pandas.to_timedelta (astype() unexpected argument 'copy')
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| 5 | 2016-09-07T10:36:18Z | 2016-09-07T10:58:10Z | 2016-09-07T10:41:33Z |
NONE
| null |
#### Code Sample
Running the following code:
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(6,4),columns=list('ABCD'))
pd.to_timedelta(df.index,unit='s')
```
returns a `TypeError`:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-cf2e27d56944> in <module>()
1 df = pd.DataFrame(np.random.randn(6,4),columns=list('ABCD'))
----> 2 pd.to_timedelta(df.index,unit='s')
~/anaconda/lib/python3.5/site-packages/pandas/util/decorators.py in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
~/anaconda/lib/python3.5/site-packages/pandas/tseries/timedeltas.py in to_timedelta(arg, unit, box, errors, coerce)
91 return Series(values, index=arg.index, name=arg.name, dtype='m8[ns]')
92 elif isinstance(arg, ABCIndexClass):
---> 93 return _convert_listlike(arg, box=box, unit=unit, name=arg.name)
94 elif is_list_like(arg) and getattr(arg, 'ndim', 1) == 1:
95 return _convert_listlike(arg, box=box, unit=unit)
~/anaconda/lib/python3.5/site-packages/pandas/tseries/timedeltas.py in _convert_listlike(arg, box, unit, name)
73 elif is_integer_dtype(arg):
74 value = arg.astype('timedelta64[{0}]'.format(
---> 75 unit)).astype('timedelta64[ns]', copy=False)
76 else:
77 value = tslib.array_to_timedelta64(
TypeError: astype() got an unexpected keyword argument 'copy'
```
#### Expected Output
Should instead return the following:
```
TimedeltaIndex(['00:00:00', '00:00:01', '00:00:02', '00:00:03', '00:00:04',
'00:00:05'],
dtype='timedelta64[ns]', freq=None)
```
which is what I get if I manually (for testing) remove the copy argument in the `timedeltas.py`
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
```
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DOC: clean-up 0.19.0 whatsnew file
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| 3 | 2016-09-07T10:50:38Z | 2016-09-07T19:15:38Z | 2016-09-07T19:15:38Z |
MEMBER
| null |
WIP (it's actually not critical for rc, as we do link to the dev docs (which are updated after rc release) anyway)
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API: union_categoricals in concat
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[] | null | 5 | 2016-09-07T13:20:54Z | 2021-05-01T23:38:21Z | null |
MEMBER
| null |
Follow up of https://github.com/pydata/pandas/pull/13767 (this part was left out of the final merged version). Summary copied from there:
**Discussion: addition of new keyword to `concat` to union categoricals with different categories**
PR #13767 proposed to add a `union_categoricals` keyword to `concat`, which lets you concatenate categoricals with different categories (like the `union_categoricals` function does). Default would be False.
There was some discussion about the name to use. @wesm suggested to use a dict to pass type specific options. But that is maybe something we should first explore further to see if there are other use cases for such a construct?
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DOC: source links in API reference
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| 5 | 2016-09-07T14:51:28Z | 2016-09-14T09:17:09Z | 2016-09-12T07:57:26Z |
CONTRIBUTOR
| null |
For example, from numpy.
http://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html
I don't know enough about sphinx to know how hard/easy this is, but it may be nice.
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setitem type coercion fails when setting a datetime column in a single row dataframe
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| 3 | 2016-09-07T22:40:35Z | 2020-01-28T01:24:10Z | 2017-10-30T10:56:42Z |
NONE
| null |
xref #6942
xref #12499
``` python
import datetime
import pandas as pd
df1 = pd.DataFrame({'a': [0,1], 'b': [datetime.datetime.now(), datetime.datetime.now()]})
# next line works as expected
df1.loc[:,'b'] = [datetime.datetime.now(), datetime.datetime.now()]
df1 = pd.DataFrame({'a': [0], 'b': [datetime.datetime.now()]})
# next line blows up
df1.loc[:,'b'] = [datetime.datetime.now()]
```
Exception:
/Users/<snip>/anaconda/lib/python2.7/site-packages/pandas/core/internals.py in _try_coerce_args(self, values, other)
2103 other_mask = isnull(other)
2104
-> 2105 other = other.astype('i8', copy=False).view('i8')
2106 except ValueError:
2107
TypeError: long() argument must be a string or a number, not 'datetime.datetime'
Incidentally, df1['b'] = [datetime.datetime.now()] will work, but i'm avoiding that as the performance is so poor when it's a copy (in my particular case the copy is dead and gc.collect(2) gets called for each column set operation, blowing out the runtime to 3x what it was pre-copy checking - should I open a separate issue for that?).
pd.show_versions():
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 12.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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| 175,625,092 |
MDU6SXNzdWUxNzU2MjUwOTI=
| 14,180 |
DOC: clarify dangers of fast apply in GroupBy apply docs
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| 2 | 2016-09-07T22:48:20Z | 2021-05-01T23:39:07Z | null |
NONE
| null |
I recently had to solve an issue where some code was behaving inconsistently. Effectively, I tracked this issue to a function that was being passed to apply which occasionally, under certain circumstances, mutated the input for a group - and not always the first group. Because fast_apply only checks the first group this meant that the output was unreliable. It didn't take me long to work out, but from a user's point of view it would be good to extend the docs so that they clarify that mutating the input is either not a good idea, or it should occur consistently for each group (or at the very least always for the first group).
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MAINT: Use __module__ in _DeprecatedModule.
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| 10 | 2016-09-08T03:44:04Z | 2016-09-14T08:31:15Z | 2016-09-14T08:31:04Z |
MEMBER
| null |
Follow-up to #14105. Uses the `__module__` method to correctly determine the location of the alternative
module to use.
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BUG: fix str.contains for series containing only nan values
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| 2 | 2016-09-08T09:54:44Z | 2016-09-09T22:29:29Z | 2016-09-09T22:29:16Z |
CONTRIBUTOR
| null |
- [x] closes #14171
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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TST: failing tests at pandas-wheels
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| 8 | 2016-09-08T10:56:21Z | 2017-03-22T11:53:46Z | 2017-03-22T11:53:46Z |
MEMBER
| null |
A bunch of tests are failing in the wheel-building repo (https://github.com/MacPython/pandas-wheels): https://travis-ci.org/MacPython/pandas-wheels/builds/158414872
It are mainly issues with 32 bit linux (see eg https://travis-ci.org/MacPython/pandas-wheels/jobs/158414875), but also some tests are failing for 64bit:
On 2.7:
```
======================================================================
FAIL: test_abs (pandas.tests.test_panel4d.TestPanel4d)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/local/lib/python2.7/site-packages/pandas/tests/test_panel4d.py", line 303, in test_abs
assert_frame_equal(result, expected)
File "/usr/lib/python2.7/contextlib.py", line 24, in __exit__
self.gen.next()
File "/venv/local/lib/python2.7/site-packages/pandas/util/testing.py", line 2538, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['RuntimeWarning', 'RuntimeWarning', 'RuntimeWarning'].
```
On 3.5:
```
======================================================================
FAIL: test_pi_ops_errors (pandas.tseries.tests.test_period.TestPeriodIndexSeriesMethods)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/lib/python3.5/site-packages/pandas/tseries/tests/test_period.py", line 4184, in test_pi_ops_errors
np.add(ng, obj)
File "/venv/lib/python3.5/site-packages/pandas/util/testing.py", line 2450, in __exit__
raise AssertionError("{0} not raised.".format(name))
AssertionError: TypeError not raised.
======================================================================
FAIL: test_pi_sub_period (pandas.tseries.tests.test_period.TestPeriodIndexSeriesMethods)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/lib/python3.5/site-packages/pandas/tseries/tests/test_period.py", line 4299, in test_pi_sub_period
tm.assert_index_equal(result, exp)
File "/venv/lib/python3.5/site-packages/pandas/util/testing.py", line 756, in assert_index_equal
assertIsInstance(left, Index, '[index] ')
File "/venv/lib/python3.5/site-packages/pandas/util/testing.py", line 947, in assertIsInstance
raise AssertionError(err_msg.format(msg, cls, type(obj)))
AssertionError: [index] Expected type <class 'pandas.indexes.base.Index'>, found <class 'NotImplementedType'> instead
```
I can't directly reproduce, but that last one is possibly an issue with the numpy version
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ENH: Added multicolumn/multirow support for latex
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| 11 | 2016-09-08T12:14:10Z | 2017-03-03T09:17:22Z | 2017-03-03T09:16:46Z |
NONE
| null |
- [x] closes #13508
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
Print names of MultiIndex columns.
Added "multicol" and "multirow" flags to to_latex
which trigger the corresponding feature.
Multirow adds clines to visually separate sections.
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DOC: minor typo in 0.19.0 whatsnew file
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| 1 | 2016-09-08T14:36:38Z | 2016-09-08T15:02:19Z | 2016-09-08T15:02:13Z |
CONTRIBUTOR
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groupby TimeStamp bug
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| 1 | 2016-09-08T20:47:06Z | 2016-09-08T22:12:00Z | 2016-09-08T22:11:44Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
x = pd.DataFrame(index = [1,1,2,2,2], data = pd.date_range("7:00", "9:00", freq="30min", tz = 'US/Eastern'))
x:
1 2016-09-08 07:00:00-04:00
1 2016-09-08 07:30:00-04:00
2 2016-09-08 08:00:00-04:00
2 2016-09-08 08:30:00-04:00
2 2016-09-08 09:00:00-04:00
In [59]: x.groupby(level=0).first()
Out[59]:
0
1 2016-09-08 11:00:00-04:00
2 2016-09-08 12:00:00-04:00
```
#### Expected Output
```
1 2016-09-08 07:00:00-04:00
2 2016-09-08 08:00:00-04:00
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.14.1.el7.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: 8.1.2
setuptools: 23.0.0
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
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: 2.6.1 (dt dec pq3 ext)
Jinja2: None
```
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MDU6SXNzdWUxNzU4Nzg1NzY=
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date_range produces wrong values when overflowing
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| 2 | 2016-09-08T22:15:00Z | 2018-10-28T03:14:14Z | 2018-10-28T03:14:14Z |
MEMBER
| null |
In the following case, you now get a RuntimeWarning. But shouldn't we rather error on this?
```
In [23]: pd.date_range('1/1/2000', periods=100000, freq='D')
/home/joris/scipy/pandas/pandas/tseries/index.py:1944: RuntimeWarning: overflow encountered in long_scalars
e = b + np.int64(periods) * stride
/home/joris/scipy/pandas/pandas/tseries/index.py:1954: RuntimeWarning: overflow encountered in long_scalars
data = np.arange(b, e, stride, dtype=np.int64)
Out[23]:
DatetimeIndex([ '2000-01-01 00:00:00',
'2000-01-02 00:00:00',
'2000-01-03 00:00:00',
'2000-01-04 00:00:00',
'2000-01-05 00:00:00',
'2000-01-06 00:00:00',
'2000-01-07 00:00:00',
'2000-01-08 00:00:00',
'2000-01-09 00:00:00',
'2000-01-10 00:00:00',
...
'1689-03-17 00:25:26.290448384',
'1689-03-18 00:25:26.290448384',
'1689-03-19 00:25:26.290448384',
'1689-03-20 00:25:26.290448384',
'1689-03-21 00:25:26.290448384',
'1689-03-22 00:25:26.290448384',
'1689-03-23 00:25:26.290448384',
'1689-03-24 00:25:26.290448384',
'1689-03-25 00:25:26.290448384',
'1689-03-26 00:25:26.290448384'],
dtype='datetime64[ns]', length=100000, freq='D')
In [24]: pd.__version__
Out[24]: '0.19.0rc1+2.gd8cd33b'
In [25]: np.__version__
Out[25]: '1.11.1'
```
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| 175,884,144 |
MDU6SXNzdWUxNzU4ODQxNDQ=
| 14,188 |
reindex(fill_value=None) fills with np.NaN instead of None
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| 9 | 2016-09-08T22:47:30Z | 2016-09-09T10:14:33Z | 2016-09-09T00:55:57Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
``` python
In [12]: import pandas as pd
In [13]: s = pd.Series(['a', 'b'])
In [14]: s.reindex([0,1,2], fill_value=None)
Out[14]:
0 a
1 b
2 NaN
dtype: object
```
#### Expected Output
``` python
0 a
1 b
2 None
dtype: object
```
#### output of `pd.show_versions()`
``` ------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.2
setuptools: 25.2.0
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 1.1.0
sphinx: 1.2b1
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2013b
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: None
openpyxl: 2.3.0
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
jinja2: 2.7
boto: None
```
#### in source
This is happening in BlockManager.reindex_indexer()
https://github.com/pydata/pandas/blob/8af626474f6f314527a9ad3f15403aa2dd8c402d/pandas/core/internals.py#L3820-L3822
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| 175,891,207 |
MDU6SXNzdWUxNzU4OTEyMDc=
| 14,189 |
Pandas inplace argument is not explicit True or False
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| 5 | 2016-09-08T23:36:01Z | 2017-01-06T12:33:21Z | 2017-01-06T12:33:21Z |
NONE
| null |
Any Serie or DataFrame method with arguments inplace != [0, False, None, [], {}] process inplace operation.
Is not explicit True or False like in the documentation.
Other methods tested: where, mask, sort_index
Any bool(arg) == True is accepted
Like: inplace='Fail' , inplace=3, inplace=[0]
Maybe is not a bug, but can generate problems if someone is not careful using positional arguments, like me.
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
df.where(lambda x: x > 4, lambda x: x + 10, 'fail')
```
#### Expected Output
```
Out[72]:
A B C
0 11 14 7
1 12 5 8
2 13 6 9
```
#### output of `pd.show_versions()`
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: AMD64 Family 18 Model 1 Stepping 0, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: 0.8.1
IPython: 5.1.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.0
```
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| 14,190 |
BUG: Categorical constructor is not idempotent with datetime with tz
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| 3 | 2016-09-08T23:51:56Z | 2016-09-09T19:33:37Z | 2016-09-09T19:33:37Z |
CONTRIBUTOR
| null |
This isn't a huge deal, but popped up adding tests for #14173
Works fine with plain datetimes
```
In [9]: dti = pd.date_range('2014-01-01', '2014-01-05')
In [10]: pd.Categorical(pd.Categorical(dti))
Out[10]:
[2014-01-01, 2014-01-02, 2014-01-03, 2014-01-04, 2014-01-05]
Categories (5, datetime64[ns]): [2014-01-01, 2014-01-02, 2014-01-03, 2014-01-04, 2014-01-05]
```
Does not with a tz
```
In [11]: dti = pd.date_range('2014-01-01', '2014-01-05', tz='US/Central')
In [12]: pd.Categorical(pd.Categorical(dti))
Out[12]:
[NaT, NaT, NaT, NaT, NaT]
Categories (5, datetime64[ns, US/Central]): [2014-01-01 00:00:00-06:00, 2014-01-02 00:00:00-06:00, 2014-01-03 00:00:00-06:00, 2014-01-04 00:00:00-06:00, 2014-01-05 00:00:00-06:00]
```
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BUG: Categorical constructor not idempotent with ext dtype
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| 3 | 2016-09-09T00:07:11Z | 2016-09-09T19:33:37Z | 2016-09-09T19:33:37Z |
CONTRIBUTOR
| null |
- [x] closes #14190
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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TST: Difference in drop_duplicates for numeric Series and DataFrames
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| 4 | 2016-09-09T13:54:12Z | 2016-12-15T09:02:51Z | 2016-12-15T09:02:51Z |
NONE
| null |
#### Code Sample
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'a':[1,1,1,'one', 'one'],
'b': [2,2,np.nan, np.nan, np.nan],
'c': [3,3,np.nan, np.nan, 'three']
})
print df[['b']].drop_duplicates()
print df['b'].drop_duplicates()
```
#### Expected Output
Why the result is not consistent?
For a dataframe (first case), the NA values are considered equal (we get 2 unique values), while for a series (second case), the NA values are not (similarly to numpy.unique, we get 3 unique values)
Curiously, in the case of a mixed type column, `len(df[['c']].drop_duplicates() ) == len(df['c'].drop_duplicates())`
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 14,193 |
Kernel dies when exporting large dataframe to json
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| 1 | 2016-09-09T15:46:47Z | 2016-09-09T19:29:23Z | 2016-09-09T19:29:13Z |
NONE
| null |
### Problem
The kernel dies when I try to export relatively large data to json. No problems with short test data, it gets exported as it should. Also, exporting a large df to_csv works just fine, but to_json fails.
These are datasets of either tweets or newspaper articles, so at least one of the fields contains strings of different lengths.
#### Code Sample
> df
> [...]
> [557807 rows x 25 columns]
> df.to_json('data.json')
> It seems the kernel died unexpectedly. Use 'Restart kernel' to continue using this console.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.16.2
nose: 1.3.7
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 2.4.1
sphinx: 1.3.2
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.99999
httplib2: None
apiclient: None
sqlalchemy: 1.0.6
pymysql: None
psycopg2: None
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MDU6SXNzdWUxNzYwNjk3NDU=
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ENH: DataFrame.style option for hiding the index
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| 0 | 2016-09-09T17:56:31Z | 2017-11-19T16:15:34Z | 2017-11-19T16:15:34Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
``` python
df = pd.DataFrame(np.random.randn(10, 2))
df.style.set_include_index(False) # name TBD
```
#### Expected Output
Same output, but without the index
Would maybe want a way to hide the columns too.
---
Current workaround
``` python
df = pd.DataFrame(np.random.randn(10, 2))
df.style.set_table_styles([
{'selector': '.row_heading, .blank', 'props': [('display', 'none;')]}
])
```
This doesn't work if there's a `df.index.name` and almost surely doesn't work with MultiIndexes.
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MDU6SXNzdWUxNzYxMDE2NjI=
| 14,195 |
Re: DataFrame.to_csv(quoting=csv.QUOTE_NONNUMERIC) quotes numeric values
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| 0 | 2016-09-09T20:27:20Z | 2016-10-06T16:04:57Z | 2016-10-06T16:04:57Z |
NONE
| null |
As per the issue outlined in #12922, applying a float_format or having a NaN value with a float would continue to treat the value as non-numeric when quoting=csv.QUOTE_NONNUMERIC is used. The format string resulting in the float treated like a string should be documented as per @jreback in #12922.
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| 176,150,739 |
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| 14,196 |
Fix minor spacing issue
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[] | null | 4 | 2016-09-10T02:34:40Z | 2016-10-27T14:54:07Z | 2016-10-27T14:54:00Z |
NONE
| null |
Continuation of #13350
- [x] closes #13032
- [ ] tests added / passed
- [x] passes `pep8radius master --diff`
- [x] whatsnew entry - not necessary
See https://github.com/pydata/pandas/pull/13350 for more details, mucked up my pull request. Tests will need a lot of work.
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BUG: numpy RuntimeWarning with Series.round()
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| 17 | 2016-09-10T09:07:48Z | 2019-02-27T14:40:33Z | 2019-02-27T14:40:33Z |
CONTRIBUTOR
| null |
Maybe needs wrapping in `numpy.errstate` (in pandas)?
``` python
>>> import pandas as pd
>>> pd.__version__
u'0.19.0rc1'
>>> pd.Series([pd.np.nan]).round()
Warning (from warnings module):
File "C:\Program Files\Python27\lib\site-packages\pandas\core\series.py", line 1340
result = _values_from_object(self).round(decimals)
RuntimeWarning: invalid value encountered in rint
0 NaN
dtype: float64
```
Same for `DataFrame.round()`.
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Fix: F999 dictionary key '2000q4' repeated with different values
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[] | null | 4 | 2016-09-10T13:15:18Z | 2016-09-10T19:00:13Z | 2016-09-10T19:00:13Z |
CONTRIBUTOR
| null |
Removal of duplicate line to fix lint error
```
pandas/tseries/tests/test_tslib.py:685:18: F999 dictionary key '2000q4' repeated with different values
pandas/tseries/tests/test_tslib.py:690:18: F999 dictionary key '2000q4' repeated with different values
```
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BUG: union_categorical with Series and cat idx
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| 2 | 2016-09-10T13:31:36Z | 2016-09-10T14:30:49Z | 2016-09-10T14:30:49Z |
CONTRIBUTOR
| null |
- [x] closes #14173
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry (not needed)
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DOC: add source links to api docs
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| 4 | 2016-09-10T14:42:28Z | 2016-09-12T07:57:46Z | 2016-09-12T07:57:26Z |
CONTRIBUTOR
| null |
- [x] closes #14178
- [x] tests not needed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew not needed
cc @jorisvandenbossche - like you mentioned, this doesn't work for everything (properties, accessors, some generated methods)
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retrieving dataframe from HDFStore: row index "nan" incorrectly becomes NaN
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| 7 | 2016-09-11T00:09:33Z | 2016-09-12T13:15:27Z | 2016-09-11T13:58:37Z |
NONE
| null |
Retrieving a dataframe previously stored in a HDFStore with a row label "nan" (string) is incorrectly retrieved as NaN.
My blog talks more about this issue: [http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/09/pandas-dataframe-hdfstore-bug.html](http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/09/pandas-dataframe-hdfstore-bug.html)
Note: to_pickle() fails for lathe dataframes, hence HDF5
# create dataframe and store it
```
import pandas
df1 = pandas.DataFrame()
df1.ix['apple', '001'] = 0.1
df1.ix['banana', '001'] = 0.2
df1.ix['apple', '002'] = 0.3
df1.ix['banana', '002'] = 0.7
df1.ix['nan', '001'] = 0.5
df1
001 002
apple 0.1 0.3
banana 0.2 0.7
nan 0.5 NaN
s = pandas.HDFStore('test')
s['index'] = df1
s.close()
```
# now retrieve it
```
import pandas
s = pandas.HDFStore('test')
s
<class 'pandas.io.pytables.HDFStore'>
File path: test
/index frame (shape->[3,2])
df2 = s['index']
s.close()
del s
df2
001 002
apple 0.1 0.3
banana 0.2 0.7
NaN 0.5 NaN
```
That **NaN** should be a string "_nan_" just like "_banana_" and "_apple_".
# workaround
```
df2.index = df2.index.astype(str)
df2
001 002
apple 0.1 0.3
banana 0.2 0.7
nan 0.5 NaN
```
#### output of `pd.show_versions()`
This is the latest anaconda python 3.5 64bit for MacOSX.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
```
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In gbq.to_gbq allow the DataFrame column order to differ from schema
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| 5 | 2016-09-11T16:31:45Z | 2016-09-13T10:24:04Z | 2016-09-13T10:24:03Z |
CONTRIBUTOR
| null |
- [x] closes #11359
- [x] tests added / passed. [Link to Travis-CI build with my gbq credentials](https://travis-ci.org/parthea/pandas/builds/159134242)
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWUxNzYyNTU3MzY=
| 14,203 |
BUG: Accept column indices for na_values in read_csv
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| 1 | 2016-09-11T17:54:38Z | 2016-12-16T23:32:49Z | 2016-12-16T23:32:48Z |
MEMBER
| null |
``` python
>>> from pandas.compat import StringIO
>>> from pandas import read_csv
>>> data = 'a\nfoo\n1'
>>>
>>> read_csv(StringIO(data), na_values={0: 'foo'}, engine='c')
...
TypeError: Expected list, got set
>>> read_csv(StringIO(data), na_values={0: 'foo'}, engine='python')
a
0 foo # Should be NaN
1 1
```
This behaviour is slightly inconsistent with what we do with `usecols` for example, so it would be nice to be able to process column indices with `na_values` too.
xref #7119.
|
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| 14,204 |
pandas-0.19.0rc1: 'global name tempita is not defined'
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| 15 | 2016-09-11T23:32:41Z | 2016-10-24T22:24:19Z | 2016-10-24T22:24:18Z |
NONE
| null |
I am running a Travis CI build in a python 2.7 min environment. Not sure why it is pulling from an unreleased version of pandas but it looks like the tempita needs to be imported.
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| 14,205 |
Datatype of Integer changes depending on indexing method on a dataframe with an integer and a float column
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| 1 | 2016-09-12T10:39:36Z | 2016-09-12T10:52:09Z | 2016-09-12T10:52:09Z |
NONE
| null |
Take a dataframe that has one integer and one float column, then use various methods to get the first integer element. In certain cases the datatype will be given back correctly as int64, in other cases however it's changed to float64. Indexing using ix even does both, depending on how it is used:
```
import pandas as pd
DD = {'I':[1,2,3,4,5,6,7,8,9,10],'F':[1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10.1]}
DF = pd.DataFrame(DD)
print "Direct index:"
print DF['I'][0]
print DF['I'][0].dtype
print "==========="
print "ix with name:"
print DF.ix[0,'I']
print DF.ix[0,'I'].dtype
print "==========="
print "loc of column:"
print DF['I'].loc[0]
print DF['I'].loc[0].dtype
print "==========="
print "iloc of column:"
print DF['I'].iloc[0]
print DF['I'].iloc[0].dtype
print "==========="
print "ix of column:"
print DF['I'].ix[0]
print DF['I'].ix[0].dtype
print "====================="
print "Values:"
print DF.values[0,1]
print DF.values[0,1].dtype
print "==========="
print "ix with index:"
print DF.ix[0,1]
print DF.ix[0,1].dtype
print "==========="
print "loc:"
print DF.loc[0,'I']
print DF.loc[0,'I'].dtype
print "==========="
print "iloc:"
print DF.iloc[0,1]
print DF.iloc[0,1].dtype
```
#### Expected Output
```
Direct index:
1
int64
===========
ix with name:
1
int64
===========
loc of column:
1
int64
===========
iloc of column:
1
int64
===========
ix of column:
1
int64
=====================
Values:
1.0
float64
===========
ix with index:
1.0
float64
===========
loc:
1.0
float64
===========
iloc:
1.0
float64
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: de_DE
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
```
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pandas.DataFrame.unstack() documentation example has errors
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| 3 | 2016-09-12T16:21:40Z | 2016-09-13T23:00:15Z | 2016-09-13T23:00:15Z |
NONE
| null |
The last part of the documentation says
``` python
>>> df = s.unstack(level=0)
>>> df.unstack()
one a 1.
b 3.
two a 2.
b 4.
```
although we'd have
``` python
>>> import pandas as pd
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
... ('two', 'a'), ('two', 'b')])
>>> import numpy as np
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
>>> s.unstack(level=-1)
a b
one 1.0 2.0
two 3.0 4.0
>>> s.unstack(level=0)
one two
a 1.0 3.0
b 2.0 4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
```
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| 176,445,314 |
MDU6SXNzdWUxNzY0NDUzMTQ=
| 14,207 |
API: allow dependent assignment?
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| 19 | 2016-09-12T18:14:26Z | 2018-02-10T16:20:18Z | 2018-02-10T16:20:18Z |
CONTRIBUTOR
| null |
Since kwarg order will be [guaranteed](https://docs.python.org/3.6/whatsnew/3.6.html#pep-468-preserving-keyword-argument-order) in python 3.6, we could allow this...though would only want to try with a 3.6+ version check, otherwise code could more or less randomly work/not work, depending on dict order.
``` python
In [44]: df = pd.DataFrame({'a': [1,2,3]})
In [45]: df.assign(b=1, c=lambda x: x['b'] * 2)
```
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