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MDExOlB1bGxSZXF1ZXN0ODcwMjUzNTI=
| 14,308 |
PERF: unnecessary materialization of a MultiIndex.values when introspecting memory_usage
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| 1 | 2016-09-27T22:37:23Z | 2016-09-28T18:01:19Z | 2016-09-28T18:01:18Z |
CONTRIBUTOR
| null |
```
In [2]: import string
...: import pandas as pd
...: import numpy as np
...:
...: def memory_usage(f):
...: return f.memory_usage(deep=True).sum()
...:
...: N = 100
...: M = len(string.uppercase)
...: df = pd.DataFrame({'value' : np.random.randn(N*M)},
...: index=pd.MultiIndex.from_product([list(string.uppercase),
...: pd.date_range('20160101',periods=N)],
...: names=['id','date'])
...: )
...:
...:
...: stacked = df.unstack('id')
...:
...: assert df.values.nbytes == stacked.values.nbytes
...:
In [3]: memory_usage(df)
Out[3]: 145600
In [4]: memory_usage(stacked)
Out[4]: 21600
I
n [7]: df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 2600 entries, (A, 2016-01-01 00:00:00) to (Z, 2016-04-09 00:00:00)
Data columns (total 1 columns):
value 2600 non-null float64
dtypes: float64(1)
memory usage: 142.2 KB
In [8]: stacked.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 100 entries, 2016-01-01 to 2016-04-09
Freq: D
Data columns (total 26 columns):
(value, A) 100 non-null float64
(value, B) 100 non-null float64
(value, C) 100 non-null float64
(value, D) 100 non-null float64
(value, E) 100 non-null float64
(value, F) 100 non-null float64
(value, G) 100 non-null float64
(value, H) 100 non-null float64
(value, I) 100 non-null float64
(value, J) 100 non-null float64
(value, K) 100 non-null float64
(value, L) 100 non-null float64
(value, M) 100 non-null float64
(value, N) 100 non-null float64
(value, O) 100 non-null float64
(value, P) 100 non-null float64
(value, Q) 100 non-null float64
(value, R) 100 non-null float64
(value, S) 100 non-null float64
(value, T) 100 non-null float64
(value, U) 100 non-null float64
(value, V) 100 non-null float64
(value, W) 100 non-null float64
(value, X) 100 non-null float64
(value, Y) 100 non-null float64
(value, Z) 100 non-null float64
dtypes: float64(26)
memory usage: 21.1 KB
```
with this PR
```
In [2]: memory_usage(df)
Out[2]: 27088
In [3]: memory_usage(stacked)
Out[3]: 21600
In [4]: df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 2600 entries, (A, 2016-01-01 00:00:00) to (Z, 2016-04-09 00:00:00)
Data columns (total 1 columns):
value 2600 non-null float64
dtypes: float64(1)
memory usage: 26.5 KB
In [5]: stacked.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 100 entries, 2016-01-01 to 2016-04-09
Freq: D
Data columns (total 26 columns):
(value, A) 100 non-null float64
(value, B) 100 non-null float64
(value, C) 100 non-null float64
(value, D) 100 non-null float64
(value, E) 100 non-null float64
(value, F) 100 non-null float64
(value, G) 100 non-null float64
(value, H) 100 non-null float64
(value, I) 100 non-null float64
(value, J) 100 non-null float64
(value, K) 100 non-null float64
(value, L) 100 non-null float64
(value, M) 100 non-null float64
(value, N) 100 non-null float64
(value, O) 100 non-null float64
(value, P) 100 non-null float64
(value, Q) 100 non-null float64
(value, R) 100 non-null float64
(value, S) 100 non-null float64
(value, T) 100 non-null float64
(value, U) 100 non-null float64
(value, V) 100 non-null float64
(value, W) 100 non-null float64
(value, X) 100 non-null float64
(value, Y) 100 non-null float64
(value, Z) 100 non-null float64
dtypes: float64(26)
memory usage: 21.1 KB
```
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MDExOlB1bGxSZXF1ZXN0ODcwMzUwNzk=
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DOC: expand doc for numeric_only
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| 2 | 2016-09-28T00:16:01Z | 2016-09-28T10:08:52Z | 2016-09-28T10:08:52Z |
CONTRIBUTOR
| null |
- [x] closes #10480
- [x] tests not needed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry not needed
Just making the description (hopefully) a little clearer.
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MDU6SXNzdWUxNzk3MTAyMTU=
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SparseDataFrame loc multiple index is slow
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| 10 | 2016-09-28T08:28:36Z | 2019-09-17T21:08:29Z | 2019-09-17T21:08:29Z |
NONE
| null |
``` python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame([[1, 0, 0],[0,0,0]])
# non sparse
In [19]: %time df.loc[0]
CPU times: user 367 µs, sys: 14 µs, total: 381 µs
Wall time: 374 µs
Out[19]:
0 1
1 0
2 0
Name: 0, dtype: int64
In [20]: %time df.loc[[0]]
CPU times: user 808 µs, sys: 57 µs, total: 865 µs
Wall time: 827 µs
Out[20]:
0 1 2
0 1 0 0
In [3]: df = df.to_sparse()
# sparse
In [5]: %time df.loc[0]
CPU times: user 429 µs, sys: 14 µs, total: 443 µs
Wall time: 436 µs
Out[5]:
0 1.0
1 0.0
2 0.0
Name: 0, dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([3], dtype=int32)
In [6]: %time df.loc[[0]]
CPU times: user 4.07 ms, sys: 406 µs, total: 4.48 ms
Wall time: 6.16 ms
Out[6]:
0 1 2
0 1.0 0.0 0.0
```
It seems in SparseDataFrame, selecting as dataframe, the time increased much more than it does in DataFrame, I would expect it runs faster(on my 1m x 500 SparseDataFrame it takes seconds for the loc operation)
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_GB.UTF-8
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 24.0.2
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: None
boto: None
pandas_datareader: None
</details>
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DataFrame.apply(axis=1) to return a series with dictionary as value
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| 3 | 2016-09-28T08:43:05Z | 2016-09-28T15:52:29Z | 2016-09-28T10:18:30Z |
NONE
| null |
``` python
# Your code here
df = pd.util.testing.makeTimeDataFrame(5)
df2 = df.reset_index()
ff = lambda row: {'some key': row['A']}
# this is what I want, but my dataframe is unfortunately like df2
df.apply(ff, axis = 1)
2000-01-03 {u'some key': 1.70177486613}
2000-01-04 {u'some key': -0.833481157829}
2000-01-05 {u'some key': -0.117445006362}
2000-01-06 {u'some key': -1.12638444617}
2000-01-07 {u'some key': -0.981868953754}
Freq: B, dtype: object
# not correct
df2.apply(ff, axis = 1)
index A B C D
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
>>>
```
#### Expected Output
returns me a pandas Series with dictionary as value
#### 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 44 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.0
setuptools: 0.6c11
Cython: 0.21
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.32.1
pandas_datareader: None
<details>
# Paste the output here
</details>
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| 14,312 |
read_html() performance when HTML malformed
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[] | null | 5 | 2016-09-28T09:46:05Z | 2018-12-12T11:22:41Z | 2018-12-11T23:19:38Z |
CONTRIBUTOR
| null |
When parsing thousands of HTML files, I noticed there was a rather large gap in performance when using `pd.read_html` and when parsing by hand using `lxml` - one that could not be attributed to the `pandas` overhead in handling edge cases.
I looked into it a little bit and found out `pandas` uses `lxml`, but only when there are no formatting issues (`recover=False` in the parser settings, let's call it _strict_ mode). It falls back on `BeautifulSoup` in that case, which is an order of magnitude slower. I have recreated this performance test using two public websites and a dummy example of a generated table.
https://gist.github.com/kokes/b97c8324ba664400714a78f5561340fc
(My code in no way replicates `pd.read_html`, but the gap is too big to be explained by edge case detection and proper column naming.)
I would like to find out how `BeautifulSoup` improves upon `lxml` in malformed HTML files, to justify the performance gap. _This may as well be a non-issue_ if `lxml` is known to produce incorrect outputs - you tell me. For example, in the wikipedia example (see the gist above), `lxml` fails to compute in strict mode, because it reaches a `<bdi>` element in one of the cells, which it does not recognise. There aren't any formatting errors, just an unknown element.
(Or maybe there's a C/Cython implementation of `bs4` that could be used, I haven't explored that option, I'm still trying to understand these basics.)
Thank you!
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BUG: resample and apply modify the index type for empty Series
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| 2 | 2016-09-28T10:20:48Z | 2017-08-09T10:37:21Z | 2017-08-09T10:37:21Z |
NONE
| null |
#### A small, complete example of the issue
Consider the following two resamplings on empty Series:
``` python
>>> import pandas as pd
# These work:
>>> pd.Series(index=pd.DatetimeIndex([]), data=[]).resample('H').apply(np.sum).index
DatetimeIndex([], dtype='datetime64[ns]', freq='H')
>>> pd.Series(index=pd.DatetimeIndex([]), data=[], name="foobar").resample('H').apply(np.sum).name
'foobar'
# These don't work properly:
>>> pd.Series(index=pd.DatetimeIndex([]), data=[]).resample('H').apply(lambda x: 1).index
Index([], dtype='object')
>>> pd.Series(index=pd.DatetimeIndex([]), data=[], name="foobar").resample('H').apply(lambda x: 1).name
# no output
```
#### Expected Output
I would have expected that the index in the second example would be the same as in the first one. That is, the expected output:
``` python
>>> pd.Series(index=pd.DatetimeIndex([]), data=[]).resample('H').apply(lambda x: 1).index
DatetimeIndex([], dtype='datetime64[ns]', freq='H')
```
Also, `name` attribute shouldn't get lost:
``` python
>>> pd.Series(index=pd.DatetimeIndex([]), data=[], name="foobar").resample('H').apply(lambda x: 1).name
'foobar'
```
I suspect that there might be other attributes / metadata that is lost too.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.5-gnu-1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0rc1
nose: 1.3.6
pip: 8.1.2
setuptools: 27.3.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: 1.4.5
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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read_sql_query type detection when missing data
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[] | null | 12 | 2016-09-28T10:32:11Z | 2019-06-06T15:28:45Z | 2019-06-06T15:28:44Z |
NONE
| null |
I have choosen Pandas for an ETL project and encountered data type detection problems.
I already posted on StackOverflow and responses were pointing the fact that missing values can make data type detection error in Pandas.
Here is the post containing code example and behaviour :
http://stackoverflow.com/questions/39298989/python-pandas-dtypes-detection-from-sql
Briefly, it appears that while querying a database, if tuple has missing fields, then the whole column type is affected : dates are not correctly interpreted or integers turn into float.
I understand than working with flat csv files can be tricky for type detection.
However, as Pandas works with a whole database layer (SqlAlchemy, cx_Oracle, DB API), when working with a database, it should have access to metadata that describes columns type.
Am I missing something? Is this a bug? Or a function still not implemented?
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-238.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.1
setuptools: 21.2.1
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
Sincerely,
Guillaume
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MDU6SXNzdWUxNzk3NTMwMTQ=
| 14,315 |
to_sql function takes forever to insert in oracle database
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[] | null | 9 | 2016-09-28T12:00:11Z | 2020-05-16T03:49:28Z | null |
NONE
| null |
I am using pandas to do some analysis on a excel file, and once that analysis is complete, I want to insert the resultant dataframe into a database. The size of this dataframe is around 300,000 rows and 27 columns.
I am using `pd.to_sql` method to insert dataframe in the database. When I use a `MySQL` database, insertion in the database takes place around 60-90 seconds. However when I try to insert the same dataframe using the same function in an `oracle` database, the process takes around 2-3 hours to complete.
Relevant code can be found below:
```
data_frame.to_sql(name='RSA_DATA', con=get_engine(), if_exists='append',
index=False, chunksize=config.CHUNK_SIZE)
```
I tried using different `chunk_size`s (from 50 to 3000), but the difference in time was only of the order of 10 minutes.
Any solution to the above problem ?
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| 179,896,002 |
MDU6SXNzdWUxNzk4OTYwMDI=
| 14,316 |
Slicing with reversed datetime index
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| 9 | 2016-09-28T21:32:17Z | 2016-09-30T10:08:02Z | 2016-09-30T10:08:02Z |
NONE
| null |
I'm working with a time series data file from EIA.gov. It's a CSV file with the dates in reverse order (most recent first). Slicing on the dates wasn't working right. I believe these snippets illustrate the problem:
Create dataframe and date reversed dataframe.
``` python
>>> import pandas as pd
>>> dates = pd.date_range('1/1/2016',periods = 5)
>>> df = pd.DataFrame([1,2,3,4,5], index=dates, columns=['Col'])
>>> rdf = df.sort_index(axis=0, ascending=False)
```
First the normal order: Slice on dates, getting what we expect:
``` python
>>> print(df['2016-01-01':'2016-01-03'])
Col
2016-01-01 1
2016-01-02 2
2016-01-03 3
```
Now the reverse order: I expect to get exactly the same answer (same slice on the same dataset), perhaps with rows reversed, but don't:
``` python
>>> print(rdf['2016-01-01':'2016-01-03'])
Empty DataFrame
Columns: [Col]
Index: []
```
Perhaps if I reverse the slice. Note only two rows are returned:
``` python
>>> print(rdf['2016-01-03':'2016-01-01'])
Col
2016-01-03 3
2016-01-02 2
```
This makes sense, but it's still confusing:
``` python
>>> print(df[1:3]), print(rdf[1:3])
Col
2016-01-02 2
2016-01-03 3
Col
2016-01-04 4
2016-01-03 3
```
I could sort (or reverse) my index, but I had no reason to believe I needed to. Furthermore, other operations, such as plotting, work as expected (the time axis is correct).
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 23 Stepping 6, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
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: None
</details>
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BUG: date slicing with reverse sorted index
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| 3 | 2016-09-29T01:08:48Z | 2016-09-30T10:08:06Z | 2016-09-30T10:08:02Z |
CONTRIBUTOR
| null |
- [x] closes #14316
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
cc @shoyer - good diagnosis!
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| 14,318 |
Enforce boolean types
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| 18 | 2016-09-29T06:10:37Z | 2017-01-06T12:33:35Z | 2017-01-06T12:33:21Z |
CONTRIBUTOR
| null |
- [ x ] closes #14189
- [ x ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This PR only addresses the `inplace` argument, though there was a comment on #14189 that identifies the issue as being with the `copy` argument as well. I can build this out further to account for all of the frequently occurring boolean arguments.
I also wrote tests for the common function `_enforce_bool_type`, but didn't write individual tests for every method with an `inplace` argument. This is something I can add if it makes sense. I wanted to get something reviewed sooner rather than later to get feedback and ensure that I'm on the right track. Feedback is much appreciated -- thanks!
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| 179,964,421 |
MDU6SXNzdWUxNzk5NjQ0MjE=
| 14,319 |
pd.read_sql_query() does not convert NULLs to NaN
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[] | null | 12 | 2016-09-29T06:37:14Z | 2019-06-06T15:29:24Z | 2019-06-06T15:29:24Z |
NONE
| null |
#### A small, complete example of the issue
``` python
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite://')
conn = engine.connect()
conn.execute("create table test (a float)")
for _ in range(5):
conn.execute("insert into test values (NULL)")
df = pd.read_sql_query("select * from test", engine, coerce_float=True)
print(df.a)
```
#### Expected Output
In pandas 0.18.1 this will result in a column of type `object` with `None` values, whereas I needed float("nan"). The coerce_float=True option made no difference. This is most needed, when reading in a float column _chunk-wise_, since there may be sequences of NULLs.
(also http://stackoverflow.com/questions/30652457/adjust-pandas-read-sql-query-null-value-treatment/)
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| 14,320 |
BUG: Frequency not set for empty Series
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| 6 | 2016-09-29T08:27:01Z | 2017-03-03T13:16:14Z | 2017-03-03T13:16:14Z |
NONE
| null |
Pandas doesn't set frequency properly for empty series. This is probably related to #14313.
#### A small, complete example of the issue
It works for non-empty series:
``` python
>>> pd.Series(index=pd.DatetimeIndex(["2016-09-29 11:00"]), data=[3]).asfreq('H').index
DatetimeIndex(['2016-09-29 11:00:00'], dtype='datetime64[ns]', freq='H')
```
But for empty series, `freq` is `None`:
``` python
>>> pd.Series(index=pd.DatetimeIndex([]), data=[]).asfreq('H').index
DatetimeIndex([], dtype='datetime64[ns]', freq=None)
```
#### Expected Output
I expected the following output:
``` python
>>> pd.Series(index=pd.DatetimeIndex([]), data=[]).asfreq('H').index
DatetimeIndex([], dtype='datetime64[ns]', freq='H')
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.5-gnu-1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0rc1
nose: 1.3.6
pip: 8.1.2
setuptools: 27.3.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: 1.4.5
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxODAwMjE2NTg=
| 14,321 |
Expose pandas.json.dump in the public API?
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| 1 | 2016-09-29T11:21:01Z | 2016-09-29T11:36:19Z | 2016-09-29T11:36:07Z |
NONE
| null |
I could not find any public documentation for `pandas.json.dump`, so I guess this makes it private? Or is it public and I can depend on it?
Would you consider making this function public, or at least as public as `pandas.json.dumps` which are at least imported into/as `pandas.io.json.dumps`.
I find the performance of `pandas.json.dump` to be an improvement on the normal json module.
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| 14,322 |
BUG in plotting timeseries data with twinx (different data representation on each ax)
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[] | null | 8 | 2016-09-29T13:35:13Z | 2016-11-26T09:15:56Z | null |
NONE
| null |
Simplified the example.
During issue reporting i updated my pandas version from 0.13 to 0.18.1 - with version 0.13 i was able to add the whole DataFrame to my plot, only after i selected some rows between dates it produces the error. After the update the behaviour became worse - also adding the whole DataFrame to the plot now produces the error.
#### A small, complete example of the issue
``` python
import pandas
import dateutil.parser
import matplotlib.pyplot as plt
p_vals = {
'x_vals' : [
"2006-12-17 00:00:00+01:00",
"2006-12-18 00:00:00+01:00",
"2006-12-19 00:00:00+01:00",
"2006-12-20 00:00:00+01:00",
"2006-12-21 00:00:00+01:00",
"2006-12-22 00:00:00+01:00",
"2006-12-23 00:00:00+01:00",
"2006-12-24 00:00:00+01:00",
"2006-12-25 00:00:00+01:00",
"2006-12-26 00:00:00+01:00",
],
'y_vals' : [
10,9,8,7,6,5,4,3,2,1
]
}
p_vals2 = {
'x_vals' : [
"2006-12-17 00:00:00+01:00",
"2006-12-18 00:00:00+01:00",
"2006-12-19 00:00:00+01:00",
"2006-12-20 00:00:00+01:00",
"2006-12-21 00:00:00+01:00",
],
'y_vals' : [
1,2,3,4,5
]
}
p_vals['x_vals'] = [ dateutil.parser.parse(x) for x in p_vals['x_vals'] ]
p_vals2['x_vals'] = [ dateutil.parser.parse(x) for x in p_vals2['x_vals'] ]
df = pandas.DataFrame(data = [1,2,3,4,5], index=["2006-12-17","2006-12-18","2006-12-19","2006-12-20","2006-12-21"])
df.index = pandas.to_datetime(df.index, format="%Y-%m-%d")
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(p_vals['x_vals'], p_vals['y_vals'], color="r")
#ax2.plot(p_vals2['x_vals'], p_vals2['y_vals'], color="b") # works as intended, see second attached image
df.plot(ax=ax2, color="b") # hides data on ax1, see first image
plt.show()
```
#### Expected Output
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-69-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.1
pip: 8.1.2
setuptools: 3.3
Cython: None
numpy: 1.11.1
scipy: 0.13.3
statsmodels: None
xarray: None
IPython: 1.2.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.5.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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| 14,323 |
DatetimeIndex union fails in 0.19rc1 when constructed from differences in DatetimeIndexes
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| 6 | 2016-09-29T16:24:34Z | 2016-10-24T22:34:21Z | 2016-10-24T22:34:21Z |
CONTRIBUTOR
| null |
When constructing a union of 2 DatetimeIndex objects that themselves were constructed from differences from a third DatetimeIndex, the union operator is ignored. This appears to be new behavior in 0.19rc1; the code functioned correctly under 0.18.1.
``` python
x = pd.DatetimeIndex(['20160923', '20160924'])
y = pd.DatetimeIndex(['20160923', '20160925'])
z = pd.date_range("20160921", "20160930")
a = z.difference(x)
b = z.difference(y)
In [59]: a.union(b)
Out[59]:
DatetimeIndex(['2016-09-21', '2016-09-22', '2016-09-25', '2016-09-26',
'2016-09-27', '2016-09-28', '2016-09-29', '2016-09-30'],
dtype='datetime64[ns]', freq='D')
In [60]: a.union(b).difference(a)
Out[60]: Index([], dtype='object')
#### Expected Output
# a.union(b) should contain 2016-09-24
# ('2016-09-24' exists in b, since it was in z and not in y,
# and thus '2016-09-24' should be in a.union(b)).
In [59]: a.union(b)
Out[59]:
DatetimeIndex(['2016-09-21', '2016-09-22', '2016-09-24', '2016-09-25',
'2016-09-26', '2016-09-27', '2016-09-28', '2016-09-29',
'2016-09-30'],
dtype='datetime64[ns]', freq=None, tz=None)
In [60]: a.union(b).difference(a)
Out[60]: DatetimeIndex(['2016-09-24'], dtype='datetime64[ns]', freq=None, tz=None)
```
#### Output of `pd.show_versions()`
<details>
## 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 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0rc1+0.g497a3bc.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
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: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.4.2
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: 0.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0ODczNjM5ODA=
| 14,324 |
BUG: Patch Checked Add Method
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| 45 | 2016-09-29T18:08:07Z | 2016-10-19T23:25:58Z | 2016-10-19T16:11:15Z |
MEMBER
| null |
Title is self-explanatory. Follow-up to #14237.
Merged in PR #14453
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| 6 | 2016-09-30T01:37:51Z | 2016-09-30T19:44:40Z | 2016-09-30T19:44:34Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
# Your code here
# This is what I have in my project's setup.py
install_requires=['pandas']
```
and my install command is the usual `python setup.py install`
#### Expected Output
pip installs an official release of pandas
#### Actual Output
``` bash
Processing dependencies for pyciss==0.5.2
Searching for pandas
Reading https://pypi.python.org/simple/pandas/
Downloading https://pypi.python.org/packages/d5/fc/8e1ba6427ab71e3e8f84b04db8800c0ea3a9a6f84cf9cc5ba1256b619a82/pandas-0.19.0rc1.zip#md5=1063ac85410a2f6ed70195b16f0e36cb
Best match: pandas 0.19.0rc1
Processing pandas-0.19.0rc1.zip
Writing /tmp/easy_install-oatji_7q/pandas-0.19.0rc1/setup.cfg
Running pandas-0.19.0rc1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-oatji_7q/pandas-0.19.0rc1/egg-dist-tmp-hpr8nxvu
package init file 'pandas/io/tests/sas/__init__.py' not found (or not a regular file)
warning: no files found matching 'README.rst'
no previously-included directories found matching 'doc/build'
warning: no directories found matching 'examples'
warning: no previously-included files matching '*.so' found anywhere in distribution
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '*~' found anywhere in distribution
warning: no previously-included files matching '#*' found anywhere in distribution
warning: no previously-included files matching '.git*' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '*.png' found anywhere in distribution
package init file 'pandas/io/tests/sas/__init__.py' not found (or not a regular file)
package init file 'pandas/io/tests/sas/__init__.py' not found (or not a regular file)
UPDATING build/lib.linux-x86_64-3.5/pandas/_version.py
set build/lib.linux-x86_64-3.5/pandas/_version.py to '0.19.0rc1'
Traceback (most recent call last):
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/sandbox.py", line 156, in save_modules
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/sandbox.py", line 197, in setup_context
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/sandbox.py", line 246, in run_setup
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/sandbox.py", line 276, in run
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/sandbox.py", line 245, in runner
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/sandbox.py", line 47, in _execfile
File "/tmp/easy_install-oatji_7q/pandas-0.19.0rc1/setup.py", line 677, in <module>
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/core.py", line 148, in setup
dist.run_commands()
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/dist.py", line 955, in run_commands
self.run_command(cmd)
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/command/bdist_egg.py", line 161, in run
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/command/bdist_egg.py", line 147, in call_command
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/site-packages/setuptools-27.2.0-py3.5.egg/setuptools/command/install_lib.py", line 11, in run
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/command/install_lib.py", line 107, in build
self.run_command('build_ext')
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/home/travis/miniconda/envs/test-environment/lib/python3.5/distutils/command/build_ext.py", line 338, in run
self.build_extensions()
File "/tmp/easy_install-oatji_7q/pandas-0.19.0rc1/setup.py", line 368, in build_extensions
File "/tmp/easy_install-oatji_7q/pandas-0.19.0rc1/setup.py", line 140, in build_extensions
NameError: name 'tempita' is not defined
```
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| 180,257,754 |
MDExOlB1bGxSZXF1ZXN0ODc0NjAwMzQ=
| 14,326 |
Remove NotImplementedError for parse_dates keyword in read_excel
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| 6 | 2016-09-30T09:18:07Z | 2018-09-13T14:19:38Z | 2017-03-27T18:42:00Z |
MEMBER
| null |
Rebase and update of PR https://github.com/pydata/pandas/pull/12051
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Grouping by index and column fails on DataFrame with single index
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| 0 | 2016-10-01T00:20:55Z | 2016-10-15T19:25:41Z | 2016-10-15T19:25:41Z |
CONTRIBUTOR
| null |
Referenced in #5677
#### Example
The following snippet shows how a MultiIndex DataFrame (`df`) may be grouped by a combination of a column (`B`) and a named index level (`inner`) using a `Grouper` object.
``` python
import pandas as pd
import numpy as np
idx = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3)])
idx.names = ['outer', 'inner']
df = pd.DataFrame({"A": np.arange(6), 'B': ['one', 'one', 'two', 'two', 'one', 'one']}, index=idx)
In [1]: df
Out[1]:
A B
outer inner
a 1 0 one
2 1 one
3 2 two
b 1 3 two
2 4 one
3 5 one
In [2]: df.groupby(['B', pd.Grouper(level='inner')]).mean()
Out [2]:
A
B inner
one 1 0.0
2 2.5
3 5.0
two 1 3.0
3 2.0
```
However, when the DataFrame (`df2`) has only a single index level an `AttributeError` is thrown
``` python
In [3]: df2 = df.reset_index('outer')
In [4]: df2
Out [4]:
outer A B
inner
1 a 0 one
2 a 1 one
3 a 2 two
1 b 3 two
2 b 4 one
3 b 5 one
In [5]: df2.groupby(['B', pd.Grouper(level='inner')]).mean()
...
AttributeError: 'Int64Index' object has no attribute 'labels'
```
#### Expected Output
``` python
In [2]: df2.groupby(['B', pd.Grouper(level='inner')]).mean()
Out [2]:
A
B inner
one 1 0.0
2 2.5
3 5.0
two 1 3.0
3 2.0
```
#### Output of `pd.show_versions()`
<details>
```
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_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: 1.4.1
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
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: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
</details>
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RuntimeWarning: Invalid value encountered in percentile RuntimeWarning
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| 2 | 2016-10-01T03:40:51Z | 2016-10-01T11:27:08Z | 2016-10-01T11:26:36Z |
NONE
| null |
#### A small, complete example of the issue
I am Using Pycharm
PyCharm Community Edition 2016.2.3
Build #PC-162.1967.10, built on September 7, 2016
JRE: 1.8.0_112-release-b343 x86_64
JVM: OpenJDK 64-Bit Server VM by JetBrains s.r.o
``` python
arr = np.array([[1,2,np.nan],[np.nan,3,4]])
dframe1 = DataFrame(arr,index=['A','B'],columns=['One','Two','Three'])
print dframe1.describe()
```
When I run .describe function it throws me an exception
```
.../anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile
RuntimeWarning)
One Two Three
count 1.0 2.000000 1.0
mean 1.0 2.500000 4.0
std NaN 0.707107 NaN
min 1.0 2.000000 4.0
25% NaN 2.250000 NaN
50% NaN 2.500000 NaN
75% NaN 2.750000 NaN
max 1.0 3.000000 4.0
```
#### Expected Output
```
One Two Three
count 1.0 2.000000 1.0
mean 1.0 2.500000 4.0
std NaN 0.707107 NaN
min 1.0 2.000000 4.0
25% NaN 2.250000 NaN
50% NaN 2.500000 NaN
75% NaN 2.750000 NaN
max 1.0 3.000000 4.0
```
#### Output of `pd.show_versions()`
<details>
# Paste the output here
</details>
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MDExOlB1bGxSZXF1ZXN0ODc2MDkwMjA=
| 14,329 |
to_latex encoding issue
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| 4 | 2016-10-01T17:34:21Z | 2016-10-02T11:57:09Z | 2016-10-02T11:56:59Z |
CONTRIBUTOR
| null |
- [x] closes #14275
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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BUG: mixed freq timeseries plotting with shared axes (GH13341)
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| 3 | 2016-10-01T20:05:31Z | 2016-11-26T09:13:05Z | 2016-11-26T09:13:05Z |
MEMBER
| null |
Closes #13341, partly closes #14322 (that example still does not work when first plotting the irregular series)
cc @sinhrks @TomAugspurger
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TST: fix period tests for numpy 1.9.3 (GH14183)
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| 1 | 2016-10-01T20:27:12Z | 2016-10-02T08:45:54Z | 2016-10-02T08:45:53Z |
MEMBER
| null |
Partly addresses #14183
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DOC: fix some sphinx build issues
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| 1 | 2016-10-01T21:33:08Z | 2016-10-02T08:58:11Z | 2016-10-02T08:58:10Z |
MEMBER
| null |
The table here: http://pandas-docs.github.io/pandas-docs-travis/categorical.html#concatenation is apparently not building well. Rst ...
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Bug: Grouping by index and column fails on DataFrame with single index (GH14327)
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| 12 | 2016-10-02T01:31:15Z | 2016-10-14T23:41:45Z | 2016-10-14T23:35:59Z |
CONTRIBUTOR
| null |
- [x] closes #14327
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Don't know if this is too late for 0.19.0 but I went ahead and added the whatsnew entry there for now.
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Error when key-only Grouper is passed to groupby in a list
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| 2 | 2016-10-03T00:13:34Z | 2016-10-25T10:51:36Z | 2016-10-25T10:51:36Z |
CONTRIBUTOR
| null |
#### Overview
A `Grouper` object configured with only a `key` specification may be passed to `groupby` to group a DataFrame by a particular column. For example:
``` python
In [19]: import pandas as pd
In [20]: df = pd.DataFrame({'A': [0, 0, 0, 1, 1, 1],
...: 'B': [1, 1, 2, 2, 3, 3],
...: 'C': [1, 2, 3, 4, 5, 6]})
In [26]: df.groupby(pd.Grouper(key='A')).count()
Out[26]:
B C
A
0 3 3
1 3 3
```
However, when this key-only `Grouper` is passed to `groupby` inside a list, a `TypeError` is thrown. The following example is for a scalar list containing the same `Grouper` object as in the previous example.
``` python
In [27]: df.groupby([pd.Grouper(key='A')]).count()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-27-f4f86763ebfc> in <module>()
----> 1 df.groupby([pd.Grouper(key='A')]).count()
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/core/generic.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, **kwargs)
3776 return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
3777 sort=sort, group_keys=group_keys, squeeze=squeeze,
-> 3778 **kwargs)
3779
3780 def asfreq(self, freq, method=None, how=None, normalize=False):
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/core/groupby.py in groupby(obj, by, **kwds)
1425 raise TypeError('invalid type: %s' % type(obj))
1426
-> 1427 return klass(obj, by, **kwds)
1428
1429
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/core/groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, **kwargs)
352 level=level,
353 sort=sort,
--> 354 mutated=self.mutated)
355
356 self.obj = obj
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/core/groupby.py in _get_grouper(obj, key, axis, level, sort, mutated)
2400 sort=sort,
2401 in_axis=in_axis) \
-> 2402 if not isinstance(gpr, Grouping) else gpr
2403
2404 groupings.append(ping)
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/core/groupby.py in __init__(self, index, grouper, obj, name, level, sort, in_axis)
2197
2198 # get the new grouper
-> 2199 grouper = self.grouper._get_binner_for_grouping(self.obj)
2200 self.obj = self.grouper.obj
2201 self.grouper = grouper
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/core/groupby.py in _get_binner_for_grouping(self, obj)
290 group_axis = obj._get_axis(self.axis)
291 return Grouping(group_axis, None, obj=obj, name=self.key,
--> 292 level=self.level, sort=self.sort, in_axis=False)
293
294 @property
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/core/groupby.py in __init__(self, index, grouper, obj, name, level, sort, in_axis)
2213 t = self.name or str(type(self.grouper))
2214 raise ValueError("Grouper for '%s' not 1-dimensional" % t)
-> 2215 self.grouper = self.index.map(self.grouper)
2216 if not (hasattr(self.grouper, "__len__") and
2217 len(self.grouper) == len(self.index)):
/Users/measejm1/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py in map(self, mapper)
2238 applied : array
2239 """
-> 2240 return self._arrmap(self.values, mapper)
2241
2242 def isin(self, values, level=None):
pandas/src/generated.pyx in pandas.algos.arrmap_int64 (pandas/algos.c:94003)()
TypeError: 'NoneType' object is not callable
```
The same error occurs for lists containing multiple key-only `Groupers`. e.g. `[pd.Grouper(key='A'), pd.Grouper(key='B')]`
#### Expected Output
``` python
In [27]: df.groupby([pd.Grouper(key='A')]).count()
Out[27]:
B C
A
0 3 3
1 3 3
```
#### Output of `pd.show_versions()`
<details>
## 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_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: 1.4.1
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
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: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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Feature Request: add explicit support for double (multiple) inequalities
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| 2 | 2016-10-03T13:34:18Z | 2016-10-04T14:34:43Z | 2016-10-03T13:45:15Z |
CONTRIBUTOR
| null |
This will improve readability:
As is:
``` python
df[(8 < df.foo + df.bar) & (df.foo + df.bar <= 42)].measure.sum()
```
To be:
``` python
df[8 < df.foo + df.bar <= 42].measure.sum()
```
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API: add DataFrame.nunique() and DataFrameGroupBy.nunique()
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| 5 | 2016-10-03T13:45:59Z | 2017-01-23T13:41:51Z | 2017-01-23T13:41:25Z |
CONTRIBUTOR
| null |
When exploring a data set, I often need to `df.apply(pd.Series.nunique)` or `df.apply(lambda x: x.nunique())`. How about adding this as `nunique()`-method parallel to `DataFrame.count()` (`count` and `unique` are also the two most basic infos displayed by `DataFrame.describe()`)?
I think there are also use cases for this as a `groupby`-method, for example when checking a candidate primary key for different lines (values):
``` python
>>> import pandas as pd
>>> df = pd.DataFrame({'id': ['spam', 'eggs', 'eggs', 'spam'], 'value': [1, 5, 5, 2]})
>>> df.groupby('id').filter(lambda g: (g.apply(pd.Series.nunique) > 1).any())
id value
0 spam 1
3 spam 2
```
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Wrong dtype when mixed dtype DataFrame is accessed with complete indexer
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| 2 | 2016-10-03T15:12:49Z | 2016-10-03T15:48:19Z | 2016-10-03T15:47:56Z |
MEMBER
| null |
#### A small, complete example of the issue
``` python
In [2]: df = pd.DataFrame({'float' : range(4),
...: 'int' : range(4)},
...: index=pd.MultiIndex.from_product([[1,2]]*2))
In [3]: df['float'] = df['float'].astype(float)
In [4]: df.loc[(1,), 'int']
Out[4]:
1 0
2 1
Name: int, dtype: int64
```
Right!
``` python
In [5]: df.loc[(1,), 'int'].loc[1]
Out[5]: 0
```
Right!
``` python
In [6]: df.loc[(1,1), 'int']
Out[6]: 0.0
```
Wrong!
#### Expected Output
Still an integer.
#### Output of `pd.show_versions()`
<details>
In [7]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+1.g96b364a
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+f80669e
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.5
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
</details>
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| 14,338 |
PERF: Period factorization very slow in 0.19.0
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| 10 | 2016-10-03T16:12:34Z | 2016-10-15T19:27:11Z | 2016-10-15T19:27:11Z |
CONTRIBUTOR
| null |
``` python
df = DataFrame(data={'data': np.random.randint(0, 100, size=5500000),
'date': [dt(2016, 1, 1)] * 5500000})
for period, g in df.groupby(pd.DatetimeIndex(df.date).to_period('D')):
print(g)
```
#### Expected Output
outputs dataframe
#### Output of `pd.show_versions()`
0.19.0
The output is not the issue, the issue is that in any version before 0.19.0, this was incredibly fast, like ~1 second or less. With 0.19.0, after waiting many minutes I just give up.
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BUG: DataFrame.drop can't find row label when iterating through dict
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| 2 | 2016-10-03T20:53:29Z | 2016-11-23T13:09:47Z | 2016-11-23T13:09:47Z |
NONE
| null |
When using a for loop with a dictionary of dataframes, DataFrame.drop() raises an unexpected error, apparently unable to find the label, even when that label exists and is handled correctly in a single df.
#### A small, complete example of the issue
``` python
>>> for key in dfs2:
>>> dfs2[key].drop('0',axis=0,inplace=True)
---------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-179-3b991fac4b08> in <module>()
1 for key in dfs2:
----> 2 dfs2[key].drop('0',axis=0,inplace=True)
/Library/Python/2.7/site-packages/pandas/core/generic.pyc in drop(self, labels, axis, level, inplace, errors)
1875 new_axis = axis.drop(labels, level=level, errors=errors)
1876 else:
-> 1877 new_axis = axis.drop(labels, errors=errors)
1878 dropped = self.reindex(**{axis_name: new_axis})
1879 try:
/Library/Python/2.7/site-packages/pandas/indexes/base.pyc in drop(self, labels, errors)
3049 if errors != 'ignore':
3050 raise ValueError('labels %s not contained in axis' %
-> 3051 labels[mask])
3052 indexer = indexer[~mask]
3053 return self.delete(indexer)
ValueError: labels ['0'] not contained in axis
```
Iterating through a dict with DataFrame.drop _does_ behave as expected (viz. successfully drop the row) when using `errors='ignore'`. However, I expected iteration with drop to behave as drop does with a single dataframe from the same dict:
#### Expected Output
``` python
>>> dfs2[key]
A B C D
a 1 2 3 4
b 2 3 4 5
0 3 4 5 6
d 4 5 6 7
e 5 6 7 8
>>> dfs2[key].drop('0',axis=0,inplace=True)
>>> dfs2[key]
A B C D
a 1 2 3 4
b 2 3 4 5
d 4 5 6 7
e 5 6 7 8
```
If different behavior for .drop when iterating is intended, a note in the docs might be helpful.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 1.1.6
Cython: None
numpy: 1.8.0rc1
scipy: 0.13.0b1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 1.5
pytz: 2013.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0ODc4MDcyMTE=
| 14,340 |
BUG: Frequency not set on empty series
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| 5 | 2016-10-03T22:57:17Z | 2016-10-31T14:54:50Z | 2016-10-19T23:26:55Z |
CONTRIBUTOR
| null |
- [x] closes #14320
- [x] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
|
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CLN: different way of doing nogil blocks in templates?
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| 7 | 2016-10-04T00:04:26Z | 2018-11-16T03:20:03Z | 2018-11-16T03:20:02Z |
CONTRIBUTOR
| null |
Right now we have blocks in the `pxi.in` files that look this, which are somewhat error prone to edit/write.
``` python
{{nogil_str}}
{{tab}}prev = arr[0]
{{tab}}for i in range(1, n):
{{tab}} cur = arr[i]
{{tab}} if timelike and cur == iNaT:
{{tab}} is_monotonic_inc = 0
{{tab}} is_monotonic_dec = 0
```
This is the best alternative I could come up with, where `maybe_nogil` is a formatting function; `tempita` uses `|` as a pipe operator. It's somewhat annoying to look at in that the whole block gets syntax highlighted as a string, but may be cleaner and plays nicer with an editor than what's being done now - thoughts? cc @sinhrks
``` python
{{'''
prev = arr[0]
for i in range(1, n):
cur = arr[i]
if timelike and cur == iNaT:
is_monotonic_inc = 0
is_monotonic_dec = 0
''' | maybe_nogil }}}
```
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Bug: Error when key-only Grouper is passed to groupby in a list (GH14334)
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| 9 | 2016-10-04T00:22:28Z | 2016-10-26T12:12:19Z | 2016-10-25T10:51:36Z |
CONTRIBUTOR
| null |
- [x] closes #14334
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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BUG: astype falsely converts inf to integer, patch for Numpy (GH14265)
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| 7 | 2016-10-04T05:09:10Z | 2016-12-11T22:23:58Z | 2016-12-11T22:23:50Z |
CONTRIBUTOR
| null |
- [x] closes #14265
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Bug in Numpy causes inf values to be falsely converted to integers. I added a ValueError exception similar to the exception for trying to convert NaN to an integer.
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DOC: Correct uniqueness of index for Series
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| 9 | 2016-10-04T05:54:18Z | 2016-11-30T02:38:51Z | 2016-11-25T10:05:30Z |
NONE
| null |
closes #7808
Just wanted to fix the docstring to reflect the fact that the index labels neither need to be unique ~~nor hashable~~.
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| 180,925,247 |
MDU6SXNzdWUxODA5MjUyNDc=
| 14,345 |
TST: failing test at tseries/tests/test_base.py TestDatetimeIndexOps test_nat
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| 1 | 2016-10-04T15:25:20Z | 2016-10-06T10:23:46Z | 2016-10-06T10:23:45Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
======================================================================
FAIL: test_nat (pandas.tseries.tests.test_base.TestDatetimeIndexOps)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/matt/pandas-mattrijk/pandas/tseries/tests/test_base.py", line 878, in test_nat
tm.assert_numpy_array_equal(idx._nan_idxs, np.array([], dtype=np.int64))
File "/home/matt/pandas-mattrijk/pandas/util/testing.py", line 1084, in assert_numpy_array_equal
assert_attr_equal('dtype', left, right, obj=obj)
File "/home/matt/pandas-mattrijk/pandas/util/testing.py", line 878, in assert_attr_equal
left_attr, right_attr)
File "/home/matt/pandas-mattrijk/pandas/util/testing.py", line 1019, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
Attribute "dtype" are different
[left]: int32
[right]: int64
----------------------------------------------------------------------
Ran 92 tests in 3.580s
FAILED (failures=1)
Process finished with exit code 0
```
#### Expected Output
# failing test
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02'], tz=tz)
tm.assert_numpy_array_equal(idx._nan_idxs, np.array([], dtype=np.int64))
idx = pd.DatetimeIndex(['2011-01-01', 'NaT'], tz=tz)
tm.assert_numpy_array_equal(idx._nan_idxs, np.array([1], dtype=np.int64))
# test passes
tm.assert_numpy_array_equal(idx._nan_idxs, np.array([], dtype=np.int32))
In Ipython on 32bit the values look fine.
In [3]: idx = pd.DatetimeIndex(['2016-01-01', 'NaT'], tz=None)
In [4]: idx.dtype
Out[4]: dtype('<M8[ns]')
In [5]: idx._nan_idxs
Out[5]: array([], dtype=int64)
In [6]: arry = np.array([], dtype=np.int64)
In [7]: arry
Out[7]: array([], dtype=int64)
In [8]: import pandas.util.testing as tm
In [9]: tm.assert_numpy_array_equal(idx._nan_idxs, arry))
Out[9]: True
This fixes it but I think theres more going on.
import platform
if platform.architecture()[0] == '32bit':
tm.assert_numpy_array_equal(idx._nan_idxs, np.array([], dtype=np.int32))
else:
tm.assert_numpy_array_equal(idx._nan_idxs, np.array([], dtype=np.int64))
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 32
OS: Linux
OS-release: 3.13.0-92-generic
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: None
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: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.6.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
</details>
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BUG: GH14323 Union of differences from DatetimeIndex incorrect
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| 4 | 2016-10-04T23:40:04Z | 2016-10-24T22:34:25Z | 2016-10-24T22:34:21Z |
CONTRIBUTOR
| null |
- [ ] closes #14323
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Sets freq to None when doing a difference operation on a DatetimeIndex
or TimedeltaIndex, rather than retaining the frequency (which can cause
problems with downstream operations). Frequency of PeriodIndex is
retained.
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TST: #14345 fixes TestDatetimeIndexOps test_nat AssertionErrors on 32-bit
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| 2 | 2016-10-05T00:01:22Z | 2016-10-06T10:23:46Z | 2016-10-06T10:23:46Z |
CONTRIBUTOR
| null |
- [ ] closes #14345
Changed tm.assert_numpy_array_equal(idx._nan_idxs, np.array([], dtype=np.int64)
np.int64 to npintp
All tests now pass on 32bit linux install.
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PERF: period factorization
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| 8 | 2016-10-05T02:15:35Z | 2016-10-13T20:18:45Z | 2016-10-13T20:13:34Z |
CONTRIBUTOR
| null |
Merged in #14419
---
- [x] closes #14338
- [x] tests not needed / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
asv
```
before after ratio
[c41c6511] [96b364a4]
- 2.44s 46.28ms 0.02 groupby.groupby_period.time_groupby_sum
```
cc @sinhrks , @bmoscon
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| 14,349 |
ERR: fail fast with non-supported dtypes on construction
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| 14 | 2016-10-05T05:11:46Z | 2016-10-10T04:46:39Z | 2016-10-10T04:46:39Z |
CONTRIBUTOR
| null |
#### Example Code
`print(pd.DataFrame({'a': np.zeros(1000, 'V4')}))`
#### Results
Non-deterministic behavior. Sometimes you get all zeros, sometimes you get garbage like this:
```
a
0 [1, 0, 0, 0]
1 [0, 0, 0, 0]
2 [8, 0, 0, 0]
3 [0, 0, 0, 0]
4 [48, -92, 71, -60]
5 [-27, 127, 0, 0]
6 [5, 0, 0, 0]
7 [0, 0, 0, 0]
...
```
That is despite the fact that the bytes are actually all zero, and NumPy prints all rows as `[0, 0, 0, 0]`.
Sometimes when printing a wider DataFrame containing such a column, it segfaults with this stack trace:
```
#0 BYTE_copyswap (dst=0x7fffebdbe868, src=0x7fffe9f0d054, __NPY_UNUSED_TAGGEDswap=0, __NPY_UNUSED_TAGGEDarr=0x7fffea15f990)
at numpy/core/src/multiarray/arraytypes.c.src:1911
#1 0x00007ffff5d82075 in PyArray_Scalar (data=0x7fffe9f0d054, descr=0x7fffea1612d0, base=<optimized out>) at numpy/core/src/multiarray/scalarapi.c:835
#2 0x00007ffff5d52df0 in array_item (self=0x7fffea15f990, i=<optimized out>) at numpy/core/src/multiarray/mapping.c:1371
```
#### Expected Output
All rows `[0, 0, 0, 0]` - just as NumPy prints it.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.1
python-bits: 64
OS: Linux
OS-release: 3.13.0
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
pip: 8.1.2
setuptools: 27.2.0
numpy: 1.11.1
</details>
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DOC/ERR: update error message / doc-string for to_datetime with non-convertible object and unit kw
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| 3 | 2016-10-05T07:18:57Z | 2017-11-20T11:10:06Z | 2017-11-20T11:10:06Z |
NONE
| null |
#### A small, complete example of the issue
```
import pandas as pd
pd.to_datetime(datetime.datetime(2016,1,1), unit='s')
Traceback (most recent call last):
File "/home/julienv/.pycharm_helpers/pydev/pydevd_exec2.py", line 3, in Exec
exec(exp, global_vars, local_vars)
File "<input>", line 1, in <module>
File "/usr/local/lib/python3.4/dist-packages/pandas/util/decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/pandas/tseries/tools.py", line 424, in to_datetime
return _convert_listlike(np.array([arg]), box, format)[0]
File "/usr/local/lib/python3.4/dist-packages/pandas/tseries/tools.py", line 330, in _convert_listlike
errors=errors)
File "pandas/tslib.pyx", line 2144, in pandas.tslib.array_with_unit_to_datetime (pandas/tslib.c:39248)
File "pandas/tslib.pyx", line 2255, in pandas.tslib.array_with_unit_to_datetime (pandas/tslib.c:38492)
ValueError: non convertible value 2016-01-01 00:00:00with the unit 's'
```
#### Expected Output
Timestamp('2016-01-01 00:00:00')
#### Output of `pd.show_versions()`
<details>
> > > pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-96-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0.19.0
nose: None
pip: 1.5.4
setuptools: 3.3
Cython: 0.20.1post0
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: None
sphinx: 1.2.2
patsy: None
dateutil: 2.5.3
pytz: 2016.7
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: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
jinja2: 2.7.3
boto: None
pandas_datareader: None
</details>
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merge_asof timeseries don't preserve categories Pandas 0.19.0
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| 2 | 2016-10-05T07:49:25Z | 2016-10-05T11:15:07Z | 2016-10-05T11:15:07Z |
NONE
| null |
from the timeseries realease example: [http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#merge-asof-for-asof-style-time-series-joining](http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#merge-asof-for-asof-style-time-series-joining)
making ticker as category:
``` python
trades.ticker = trades.ticker.astype('category')
quotes.ticker = quotes.ticker.astype('category')
merge = pd.merge_asof(trades, quotes,
on='time',
by='ticker')
```
#### Expected Output
merge.dtypes
Out[31]:
time datetime64[ns]
ticker category
price float64
quantity int64
bid float64
ask float64
geting :
time datetime64[ns]
ticker object
price float64
quantity int64
bid float64
ask float64
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ADMIN: backports branch
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[] | null | 14 | 2016-10-05T11:13:40Z | 2017-09-24T13:39:00Z | 2017-09-24T13:39:00Z |
CONTRIBUTOR
| null |
As discussed on the mailing list, we should outline how we are going to do backports. Goals would be to provide a 0.19.1 bug-fix only release and at the same time allow master to proceed as 0.20/1.0 with all bug-fixes and API changes.
This issue is for how to do this, as we are ready to merge some 0.19.1 fixes. So need to put some machinery in place ASAP.
- create a 0.19.1 (or .x?) branch
- `scripts/merge-py.py` merges to master
- then cherry-pick to 0.19.1?
Some questions
- how to ensure that we are getting the appropriate commits. Do we need a comparison script?
- how do we deal with the duplicated whatsnew issue (IOW, should commiters be directed to 0.20, then we copy / fix to put _also_ in 0.19.1)?
- testing, I _think_ travis/appveyor will build both branches (master and 0.19.x)
would be nice to link some examples of how other projects do this. We have quite a lot of activity and don't want this to become a burden.
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| 1 | 2016-10-05T12:49:01Z | 2018-01-05T19:15:38Z | 2018-01-05T19:15:38Z |
CONTRIBUTOR
| null |
#### Background
During the review of @jreback's PR last year to cleanup the sorting API (#10726) there was some discussion of how the DataFrame API could eventually support sorting by a combination of columns and index levels. I'm interested in working on implementing this soon and would like to continue the discussion of where this should fit into the DataFrame sorting API.
In https://github.com/pydata/pandas/pull/10726#issuecomment-128066486 @jorisvandenbossche made the following suggestion
> If we want to add this enhancement to simultaneously specify to sort on index levels and columns (the 5d option of above), then the question is: where do we add this functionality and how? In sorted, sort_index or both? I would then lean towards saying: only add it in sorted, where the by keyword can also denote a index level name.
This approach makes good sense to me. Each object passed to the `by` keyword of `sort_values` (referred to as `sorted` in the quote above) could refer to either a column or an index level. For backwards compatibility, column references would take precedence. And my assumption is that we would want to preserve the index when sorting by a combination of columns and index levels this way.
This proposal is the sorting analog of the groupby proposal in #5677 (which I will be working on soon)
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| 4 | 2016-10-05T13:10:21Z | 2016-10-27T13:53:09Z | 2016-10-27T13:53:09Z |
CONTRIBUTOR
| null |
'left' and 'right' changed meaning in `DatetimeIndex._maybe_cast_slice_bound`. Was this intentional?
### Pandas 0.18
``` python
In [1]: import pandas as pd
In [2]: index = pd.DatetimeIndex(freq='1H', periods=0, end='2015')
In [3]: index._maybe_cast_slice_bound('2015-01-02', 'right', 'loc')
Out[3]: Timestamp('2015-01-02 23:59:59.999999999')
In [4]: index._maybe_cast_slice_bound('2015-01-02', 'left', 'loc')
Out[4]: Timestamp('2015-01-02 00:00:00')
```
### Pandas 0.19
``` python
In [1]: import pandas as pd
In [2]: index = pd.DatetimeIndex(freq='1H', periods=0, end='2015')
In [3]: index._maybe_cast_slice_bound('2015-01-02', 'right', 'loc')
Out[3]: Timestamp('2015-01-02 00:00:00')
In [4]: index._maybe_cast_slice_bound('2015-01-02', 'left', 'loc')
Out[4]: Timestamp('2015-01-02 23:59:59.999999999')
```
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| 6 | 2016-10-05T14:00:26Z | 2017-12-01T17:29:25Z | 2017-12-01T16:45:20Z |
CONTRIBUTOR
| null |
- [x] `.groupby` #5677
- [ ] `.sort_values` #14353, though this is directly in combat with `.sort_index` and non-explict
- [x] `.merge` (this issue)
#### Overview
@jorisvandenbossche
As a part of the [Pandas 1.0](https://docs.google.com/document/d/151ct8jcZWwh7XStptjbLsda6h2b3C0IuiH_hfZnUA58/edit#) goal to "Make the index/column distinction less painful (#5677, #8162)" I propose that the `df.merge` method support merging DataFrames on a combination of columns and index levels.
This could be accomplished in the API by allowing the `on`, `left_on`, and `right_on` keywords to accept a combination of column names and index level names. Any index levels that are joined on would be preserved as index levels in the resulting merged DataFrame, while all other index levels would be removed.
This proposal is in the spirit of #5677 for `df.groupby` and #14353 for `df.sort_values`.
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| 4 | 2016-10-05T15:25:01Z | 2016-10-11T07:21:09Z | 2016-10-11T07:21:09Z |
CONTRIBUTOR
| null |
I'm getting this error after I installed pandas and run the tests
```
======================================================================
ERROR: test_readonly_axis_zlib_to_sql (pandas.io.tests.test_packers.TestCompression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/matt/pandas-mattrijk/pandas/io/tests/test_packers.py", line 738, in test_readonly_axis_zlib_to_sql
df.to_sql('test', eng, if_exists='append')
File "/home/matt/pandas-mattrijk/pandas/core/generic.py", line 1200, in to_sql
chunksize=chunksize, dtype=dtype)
File "/home/matt/pandas-mattrijk/pandas/io/sql.py", line 460, in to_sql
pandas_sql = pandasSQL_builder(con, schema=schema, flavor=flavor)
File "/home/matt/pandas-mattrijk/pandas/io/sql.py", line 530, in pandasSQL_builder
return SQLDatabase(con, schema=schema, meta=meta)
File "/home/matt/pandas-mattrijk/pandas/io/sql.py", line 960, in __init__
meta = MetaData(self.connectable, schema=schema)
TypeError: __init__() got an unexpected keyword argument 'schema'
----------------------------------------------------------------------
Ran 11101 tests in 1117.090s
FAILED (SKIP=255, errors=1)
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: 5033a4a799c77fdc7e868a9f332384eabcc332b8
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0rc1+39.g5033a4a.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: None
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: 1.5.3
openpyxl: 2.0.3
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.4.6
lxml: 3.6.4
bs4: 4.2.1
html5lib: 1.0b2
httplib2: None
apiclient: None
sqlalchemy: 0.7.1
pymysql: 0.6.2.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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ValueError when trying to compute Quantile
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| 9 | 2016-10-05T18:29:37Z | 2016-11-02T13:01:04Z | 2016-11-02T13:01:04Z |
NONE
| null |
``` python
In [7]: df = pd.DataFrame(np.random.randn(10, 2))
In [8]: df.iloc[1, 1] = np.nan
In [9]: df.quantile(.5)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-66d518aa86c6> in <module>()
----> 1 df.quantile(.5)
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/core/frame.py in quantile(self, q, axis, numeric_only, interpolation)
5152 axis=1,
5153 interpolation=interpolation,
-> 5154 transposed=is_transposed)
5155
5156 if result.ndim == 2:
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/core/internals.py in quantile(self, **kwargs)
3142
3143 def quantile(self, **kwargs):
-> 3144 return self.reduction('quantile', **kwargs)
3145
3146 def setitem(self, **kwargs):
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/core/internals.py in reduction(self, f, axis, consolidate, transposed, **kwargs)
3071 for b in self.blocks:
3072 kwargs['mgr'] = self
-> 3073 axe, block = getattr(b, f)(axis=axis, **kwargs)
3074
3075 axes.append(axe)
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas-0.19.0rc1+21.ge596cbf-py3.5-macosx-10.11-x86_64.egg/pandas/core/internals.py in quantile(self, qs, interpolation, axis, mgr)
1325 values = _block_shape(values[~mask], ndim=self.ndim)
1326 if self.ndim > 1:
-> 1327 values = values.reshape(result_shape)
1328
1329 from pandas import Float64Index
ValueError: total size of new array must be unchanged
```
original post follows
---
I have a simple dataframe that I created as follows:
`df[df['Week of'] == week]`
where `week` is a week name I'm filtering by
I have been taking the quartile values of this dataframe as follows:
`df[df['Week of'] == week].quantile(.25)`
However since the update to Pandas 0.19 I am receiving the error (this code worked fine before):
`values = values.reshape(result_shape)`
`ValueError: total size of new array must be unchanged`
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read_json Raises AttributeError with Valid JSON as Input
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| 2 | 2016-10-05T18:53:17Z | 2020-05-03T10:37:35Z | null |
NONE
| null |
#### A small, complete example of the issue
``` python
pd.read_json('[{}, null]') # This raises `AttributeError`
pd.read_json('[null, {}]') # This does not
```
While this isn't a big problem, the output is not as expected. The only way I see this causing issues for me is for parsing JSON received on a server; the raised error does not signify bad JSON (which it isn't) nor an appropriate ValueError. I don't think this should raise an error at all actually, correct me if I'm wrong.
#### Expected Output
``` python
# Raises `AttributeError`
0
0 {}
1 None
# This is as expected
0
0 None
1 {}
```
#### Actual Output
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas/io/json.py", line 211, in read_json
date_unit).parse()
File "/usr/local/lib/python2.7/dist-packages/pandas/io/json.py", line 279, in parse
self._parse_no_numpy()
File "/usr/local/lib/python2.7/dist-packages/pandas/io/json.py", line 496, in _parse_no_numpy
loads(json, precise_float=self.precise_float), dtype=None)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 263, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 5355, in _to_arrays
coerce_float=coerce_float, dtype=dtype)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 5471, in _list_of_dict_to_arrays
columns = lib.fast_unique_multiple_list_gen(gen)
File "pandas/lib.pyx", line 504, in pandas.lib.fast_unique_multiple_list_gen (pandas/lib.c:10190)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 5470, in <genexpr>
gen = (list(x.keys()) for x in data)
AttributeError: 'NoneType' object has no attribute 'keys'
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-38-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: 25.1.1
Cython: None
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: None
tables: None
numexpr: None
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: None
jinja2: 2.8
boto: None
pandas_datareader: None
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| 14,359 |
block mutation of read-only array in series
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| 8 | 2016-10-05T19:26:55Z | 2016-10-24T22:46:44Z | 2016-10-24T22:43:56Z |
CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
reproduction case:
``` python
In [1]: array = np.array([1, 2, 3])
In [2]: array.flags.writeable = False
In [3]: series = pd.Series(array)
In [4]: series[0] = -1
In [5]: series
Out[5]:
0 -1
1 2
2 3
dtype: int64
In [6]: array
Out[6]: array([-1, 2, 3])
```
In the case I was running into I was passing a `numpy.memmap` which is a subclass of `ndarray` but was over a read only memory segment so attempting the setitem was causing a segfault. The new behavior matches the numpy behavior:
``` python
In [4]: series[0] = -1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-2ad0997d5cf2> in <module>()
----> 1 series[0] = -1
...
ValueError: assignment destination is read-only
In [5]: array[0] = -1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-f67d1d011a13> in <module>()
----> 1 array[0] = -1
ValueError: assignment destination is read-only
```
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Inconsistent date_range behavior when using DateOffset
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| 1 | 2016-10-05T21:54:52Z | 2016-10-07T10:37:34Z | 2016-10-07T10:37:34Z |
NONE
| null |
#### A small, complete example of the issue
pd.date_range returns a different number of dates when used with different dates and the same DateOffset. Note that both dates are month end dates and that 2008 is a leap year (potential source of bug).
``` python
In [1]:
month = pd.to_datetime('2007-02-28')
pd.date_range(month, month + pd.DateOffset(months=12), freq='M')
Out[1]:
DatetimeIndex(['2007-02-28', '2007-03-31', '2007-04-30', '2007-05-31',
'2007-06-30', '2007-07-31', '2007-08-31', '2007-09-30',
'2007-10-31', '2007-11-30', '2007-12-31', '2008-01-31'],
dtype='datetime64[ns]', freq='M')
In [2]:
month = pd.to_datetime('2007-01-31')
pd.date_range(month, month + pd.DateOffset(months=12), freq='M')
Out[2]:
DatetimeIndex(['2007-01-31', '2007-02-28', '2007-03-31', '2007-04-30',
'2007-05-31', '2007-06-30', '2007-07-31', '2007-08-31',
'2007-09-30', '2007-10-31', '2007-11-30', '2007-12-31',
'2008-01-31'],
```
However, we see that using the to-be deprecated datetools gives consistent behavior:
``` python
In [2]:
month = pd.to_datetime('2007-02-28')
pd.date_range(month, month + pd.datetools.MonthEnd(12), freq='M')
Out[2]:
DatetimeIndex(['2007-02-28', '2007-03-31', '2007-04-30', '2007-05-31',
'2007-06-30', '2007-07-31', '2007-08-31', '2007-09-30',
'2007-10-31', '2007-11-30', '2007-12-31', '2008-01-31',
'2008-02-29'], dtype='datetime64[ns]', freq='M')
In [3]:
month = pd.to_datetime('2007-01-31')
pd.date_range(month, month + pd.datetools.MonthEnd(12), freq='M')
Out[3]:
DatetimeIndex(['2007-01-31', '2007-02-28', '2007-03-31', '2007-04-30',
'2007-05-31', '2007-06-30', '2007-07-31', '2007-08-31',
'2007-09-30', '2007-10-31', '2007-11-30', '2007-12-31',
'2008-01-31'], dtype='datetime64[ns]', freq='M')
```
#### Expected Output
``` python
In [1]:
month = pd.to_datetime('2007-02-28')
pd.date_range(month, month + pd.DateOffset(months=12), freq='M')
Out[2]:
DatetimeIndex(['2007-02-28', '2007-03-31', '2007-04-30', '2007-05-31',
'2007-06-30', '2007-07-31', '2007-08-31', '2007-09-30',
'2007-10-31', '2007-11-30', '2007-12-31', '2008-01-31',
'2008-02-29'], dtype='datetime64[ns]', freq='M')
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.19-29.55.amzn1.x86_64
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: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
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: 2.6.0
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: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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Selecting an element or row of mixed int/float DataFrame returns all floats
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| 3 | 2016-10-05T23:45:22Z | 2016-10-06T22:54:03Z | 2016-10-06T00:57:30Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
In [37]: df = pd.DataFrame(dict(a=[0,1,2,3],b=[0.0,1.0,2.0,3.0]))
In [38]: df
Out[38]:
a b
0 0 0.0
1 1 1.0
2 2 2.0
3 3 3.0
In [39]: df.dtypes
Out[39]:
a int64
b float64
dtype: object
In [40]: df.iloc[0]
Out[40]:
a 0.0
b 0.0
Name: 0, dtype: float64
In [41]: df.loc[1,'a']
Out[41]: 1.0
```
#### Expected Output
Any values from column 'a' should still be ints.
#### Output of `pd.show_versions()`
<details>
## 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: 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: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
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: 2.6.0
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: None
</details>
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| 181,301,305 |
MDU6SXNzdWUxODEzMDEzMDU=
| 14,362 |
BUG: clipboard functions windows 10 py3.5
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| 1 | 2016-10-06T00:54:38Z | 2016-11-18T22:43:10Z | 2016-11-18T22:42:37Z |
CONTRIBUTOR
| null |
This seems to work ok in the current version of pyperclip, but is broken in our vendored version, so likely just need to update.
https://github.com/asweigart/pyperclip
https://github.com/asweigart/pyperclip/pull/41
```
# Your code here
In [1]: df = pd.DataFrame(np.random.randn(10, 2))
In [2]: df.to_clipboard()
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-2-1f8b11f0ff98> in <module>()
----> 1 df.to_clipboard()
c:\users\chris\documents\python-dev\pandas\pandas\core\generic.py in to_clipboard(self, excel, sep, **kwargs)
1236 """
1237 from pandas.io import clipboard
-> 1238 clipboard.to_clipboard(self, excel=excel, sep=sep, **kwargs)
1239
1240 def to_xarray(self):
c:\users\chris\documents\python-dev\pandas\pandas\io\clipboard.py in to_clipboard(obj, excel, sep, **kwargs)
96 else:
97 objstr = str(obj)
---> 98 clipboard_set(objstr)
c:\users\chris\documents\python-dev\pandas\pandas\util\clipboard.py in _copyWindows(text)
83 len(text.encode('utf-16-le')) + 2)
84 pchData = d.kernel32.GlobalLock(hCd)
---> 85 ctypes.cdll.msvcrt.wcscpy(ctypes.c_wchar_p(pchData), text)
86 d.kernel32.GlobalUnlock(hCd)
87 d.user32.SetClipboardData(CF_UNICODETEXT, hCd)
OSError: exception: access violation writing 0x0000000000000000
In [4]: pd.read_clipboard()
<segfault>
```
#### Output of `pd.show_versions()`
<details>
# Paste the output here
## 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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0+5.g855357b
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.6.0
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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BLD/CI: cython cache pxd files
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| 3 | 2016-10-06T01:22:24Z | 2016-10-12T09:15:58Z | 2016-10-12T09:15:49Z |
CONTRIBUTOR
| null |
Currently the cython cache on travis doesn't pick up change in `.pxd` files Most of this commit history is trial and error - but 479c311 shows this working
https://travis-ci.org/pydata/pandas/jobs/166041112
```
$ ci/prep_cython_cache.sh
cython_files.tar motd.legal-displayed pip pyxfiles
Cache available - checking pyx diff
util.pxd has changed:
--- /home/travis/build/pydata/pandas/pandas/src/util.pxd 2016-10-08 13:01:48.255250369 +0000 +++ /home/travis/.cache/pyxfiles/home/travis/build/pydata/pandas/pandas/src/util.pxd 2016-10-06 11:04:00.000000000 +0000 @@ -97,5 +97,6 @@ cdef inline bint _checknan(object val): return not cnp.PyArray_Check(val) and val != val + cdef inline bint is_period_object(object val): return getattr(val, '_typ', '_typ') == 'period'
In a PR
Rebuilding cythonized files
Use cache (Blank if not set) = true
Clear cache (1=YES) = 1
```
xref #14359
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Inconsistent behaviour on empty RangeIndex intersection
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| 1 | 2016-10-06T07:58:45Z | 2016-10-24T22:20:45Z | 2016-10-24T22:20:45Z |
NONE
| null |
## Description of the issue
Two empty RangeIndex objects with same starts and stops will have a non-null intersection.
## Code example
``` python
import pandas as pd
# Two empty rangeindexes
a = pd.RangeIndex(0, 0)
b = pd.RangeIndex(0, 0)
# Also empty but different start and stops
c = pd.RangeIndex(1, 1)
# OK, this returns an empty Index
print(a.intersection(c))
# Strangely, this one returns an index of [0, 1[
print(a.intersection(b))
```
### Expected output
```
RangeIndex(start=0, stop=0, step=1)
RangeIndex(start=0, stop=0, step=1)
```
### Output
```
RangeIndex(start=0, stop=0, step=1)
RangeIndex(start=0, stop=1, step=1)
```
## Output of `pd.show_versions()`
Not the most recent version of Pandas, but I believe this behaviour is still present in the master branch
after browsing the code. If somebody could confirm.
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: None
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
setuptools: 24.0.2
numpy: 1.10.1
dateutil: 1.5
pytz: 2015.4
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DOC: Remove old warning from dsintro.rst
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| 6 | 2016-10-06T09:00:24Z | 2016-10-06T15:46:36Z | 2016-10-06T09:25:23Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
The warning is about something that have been fixed for almost 3 years. Every time a new user excited about pandas start reading the docs, they have to waste brain-cycles ignoring that big red warning bubble.
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DOC: add 0.19.1 whatsnew file
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| 2 | 2016-10-06T09:16:03Z | 2016-10-07T19:25:31Z | 2016-10-07T19:25:31Z |
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BUG/ENH : Column name mangling doesn't strip white space
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| 3 | 2016-10-06T16:15:00Z | 2016-10-07T10:37:01Z | 2016-10-07T10:36:58Z |
NONE
| null |
#### A small, complete example of the issue
```
a ,a ,a
1,2,3
4,5,6
7,8,9
```
note the white space next to the char.
``` python
>>> import pandas
>>> df = pandas.read_table('test.csv', sep=',')
>>> df.columns
Index([u'a ', u'a .1', u'a .2'], dtype='object')
```
again, note the white space between the char and the num.
A work around previously was to set `mangle_dupe_cols` to `False`, manually strip the columns and then clean up the column names. With `pandas 0.19.0`, `mangle_dupe_cols=False` raises a `ValueError`.
This issue is more of a question as to what the spec of `mangle_dupe_cols` is and what a full implementation, mentioned in https://github.com/pydata/pandas/issues/13262 , desires/is going to be.
#### Expected Output
``` python
Index([u'a', u'a.1', u'a.2'], dtype='object')
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.11.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
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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BUG: sorting of category dtypes
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| 7 | 2016-10-06T17:35:11Z | 2019-10-27T18:26:21Z | null |
CONTRIBUTOR
| null |
```
In [2]: df = pd.DataFrame({'A' : pd.Series(list('aabc')).astype('category'), 'B' : range(4)})
In [3]: df
Out[3]:
A B
0 a 0
1 a 1
2 b 2
3 c 3
In [4]: df.dtypes
Out[4]:
A category
B int32
dtype: object
In [5]: df.dtypes.value_counts()
Out[5]:
category 1
int32 1
dtype: int64
In [6]: df.dtypes.value_counts().sort_index()
TypeError: data type not understood
```
I think the dtype itself is missing a comparison operator, it can effectively be stringified though
e.g.
```
In [9]: sorted(df.dtypes.value_counts().index, key=lambda x: str(x))
Out[9]: [category, dtype('int32')]
```
is fine
might be able to just define things like `__lt__` and friend and make this work.
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concat with axis='rows'
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| 3 | 2016-10-06T19:55:48Z | 2016-10-15T19:59:53Z | 2016-10-15T19:59:53Z |
NONE
| null |
#### A small, complete example of the issue
Is there something special going on here that `pd.concat` seems to interpret `axis='rows'` as `axis=1`?
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.19.0'
In [3]: import numpy as np
In [5]: df1 = pd.DataFrame({'A':np.random.normal(size=5)}, index=range(5))
In [6]: df2 = pd.DataFrame({'A':np.random.normal(size=5)}, index=range(5))
In [7]: pd.concat([df1,df2], axis=0)
Out[7]:
A
0 1.302686
1 -0.463573
2 0.057750
3 0.908621
4 -0.637590
0 -1.876701
1 0.946551
2 -1.696579
3 0.793796
4 1.403455
In [8]: pd.concat([df1,df2], axis='rows')
Out[8]:
A A
0 1.302686 -1.876701
1 -0.463573 0.946551
2 0.057750 -1.696579
3 0.908621 0.793796
4 -0.637590 1.403455
```
#### Expected Output
I would think that `axis='rows'` and `axis=0` do the same thing.
#### Output of `pd.show_versions()`
<details>
## 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_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
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: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 181,583,510 |
MDU6SXNzdWUxODE1ODM1MTA=
| 14,370 |
inconsistant result shape from dataframe.apply depending on if a date column is present
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| 3 | 2016-10-07T04:33:12Z | 2017-09-20T10:11:30Z | 2016-10-07T10:35:57Z |
NONE
| null |
#### A small, complete example of the issue
``` python
import pandas as pd
df1 = pd.DataFrame.from_items([('A', [1,2,3]), ('B', [4, 5, 6])])
df2 = pd.DataFrame.from_items([('A', [pd.datetime(1970,1,1), pd.datetime(1970,1,1), pd.datetime(1970,1,1)]), ('B', [4, 5, 6])])
f = lambda row: [row.B + 3]
r1 = df1.apply(f, axis=1)
r2 = df2.apply(f, axis=1)
# bug: r1 and r2 are different and different in shape.
# expect: r1 and r2 to be the same values and shape.
r1
r2
# a clue to the difference:
(df1._is_mixed_type,df1._is_datelike_mixed_type)
(df2._is_mixed_type,df2._is_datelike_mixed_type)
```
#### Expected Output
``` python
0 [7]
1 [8]
2 [9]
```
#### Output of `pd.show_versions()`
<details>
## 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 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 20.3
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: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0ODgzODkyNTM=
| 14,371 |
BUG: Fix linux Qt clipboard QApplication() creation
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| 4 | 2016-10-07T06:59:09Z | 2016-12-07T09:02:27Z | 2016-12-06T21:28:58Z |
CONTRIBUTOR
| null |
Fixes #14372
A Qt application cannot instantiate multiple `QApplication` instances,
so we create a new `QApplication` only when the global
`QApplication.instance()` is None.
Failing sample:
```
from PyQt4.QtGui import QApplication
myapp = QApplication([])
from pandas.util.clipboard import clipboard_get # <--- ERROR
File "prefix/lib/python2.7/site-packages/pandas/util/clipboard.py", line 164, in <module>
app = qt4.QtGui.QApplication([])
RuntimeError: A QApplication instance already exists.
```
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MDU6SXNzdWUxODE1OTg2ODc=
| 14,372 |
BUG : Check for and Use QApplication instance : clipboard
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| 0 | 2016-10-07T07:05:48Z | 2017-03-20T13:47:14Z | 2017-03-20T13:47:14Z |
NONE
| null |
reading data from clipboard fails on Linux because pandas doesn't check if there's already a `QApplication` running - https://github.com/pydata/pandas/blob/master/pandas/util/clipboard.py#L247
``` python
import pandas
pandas.read_clipboard()
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-2-4c32fab697e6> in <module>()
----> 1 pandas.read_clipboard()
/home/rporuri/.canopy/envs/User/lib/python2.7/site-packages/pandas/io/clipboard.pyc in read_clipboard(**kwargs)
15 parsed : DataFrame
16 """
---> 17 from pandas.util.clipboard import clipboard_get
18 from pandas.io.parsers import read_table
19 text = clipboard_get()
/home/rporuri/.canopy/envs/User/lib/python2.7/site-packages/pandas/util/clipboard.py in <module>()
245 elif qtBindingInstalled:
246 _functions = 'PyQt4 module' # for debugging
--> 247 app = QtGui.QApplication([])
248 cb = QtGui.QApplication.clipboard()
249 paste = _pasteQt
RuntimeError: A QApplication instance already exists.
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IN
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.4
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
</details>
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MDU6SXNzdWUxODE2Mzg2Nzc=
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problem with building pandas - not using gcc as compiler
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| 14 | 2016-10-07T10:49:09Z | 2016-10-17T15:15:18Z | 2016-10-08T11:31:08Z |
CONTRIBUTOR
| null |
"""the example is merely
pip install pandas - which fails, so download and run
python ./setup.py build
```
I fear it is a specific behavior of gcc compared to not gcc/gnu 'features' (aka NGNU for 'Not "GNU Not Unix"')
I will attach the .cpp, as well as a .i (-E) output, as well as verbose include file processing (.info file)
The build stops with the following messages:
root@x064:[/data/prj/python/pipbuilds/pandas-0.19.0]das/msgpack/_unpacker.cpp -o build/temp.aix-5.3-2.7/pandas/msgpack/_unpacker.o <
"pandas/msgpack/../src/msgpack/unpack_template.h", line 173.13: 1540-0063 (S) The text "..." is unexpected.
"pandas/msgpack/../src/msgpack/unpack_template.h", line 75.19: 1540-0700 (I) The previous message was produced while processing "unpack_execute<1>(unpack_context *, const char *, size_t, size_t *)".
"pandas/msgpack/../src/msgpack/unpack_template.h", line 468.45: 1540-0700 (I) The previous message was produced while processing "unpack_construct".
"pandas/msgpack/../src/msgpack/unpack_template.h", line 173.13: 1540-0063 (S) The text "..." is unexpected.
"pandas/msgpack/../src/msgpack/unpack_template.h", line 75.19: 1540-0700 (I) The previous message was produced while processing "unpack_execute<0>(unpack_context *, const char *, size_t, size_t *)".
"pandas/msgpack/../src/msgpack/unpack_template.h", line 469.40: 1540-0700 (I) The previous message was produced while processing "unpack_skip".
"pandas/msgpack/../src/msgpack/unpack_template.h", line 433.29: 1540-0063 (S) The text "..." is unexpected.
"pandas/msgpack/../src/msgpack/unpack_template.h", line 412.19: 1540-0700 (I) The previous message was produced while processing "unpack_container_header<144,220>(unpack_context *, const char *, size_t, size_t *)".
"pandas/msgpack/../src/msgpack/unpack_template.h", line 470.46: 1540-0700 (I) The previous message was produced while processing "read_array_header".
"pandas/msgpack/../src/msgpack/unpack_template.h", line 433.29: 1540-0063 (S) The text "..." is unexpected.
"pandas/msgpack/../src/msgpack/unpack_template.h", line 412.19: 1540-0700 (I) The previous message was produced while processing "unpack_container_header<128,222>(unpack_context *, const char *, size_t, size_t *)".
"pandas/msgpack/../src/msgpack/unpack_template.h", line 471.44: 1540-0700 (I) The previous message was produced while processing "read_map_header".
reading carefully - it comes down to errors while processing these four lines:
+468 static const execute_fn unpack_construct = &unpack_execute<true>;
+469 static const execute_fn unpack_skip = &unpack_execute<false>;
+470 static const execute_fn read_array_header = &unpack_container_header<0x90, 0xdc>;
+471 static const execute_fn read_map_header = &unpack_container_header<0x80, 0xde>;
These become (from the .i file)
#line 468
static const execute_fn unpack_construct = &unpack_execute<1>;
static const execute_fn unpack_skip = &unpack_execute<0>;
static const execute_fn read_array_header = &unpack_container_header<0x90, 0xdc>;
static const execute_fn read_map_header = &unpack_container_header<0x80, 0xde>;
#line 314 "pandas/msgpack/_unpacker.cpp"
And - whether it is pandas or perhaps cython - my knowledge of C does not understand how:
&unpack_execute<1> and &unpack_execute<0>
are useable against:
static inline int unpack_execute(unpack_context* ctx, const char* data, size_t len, size_t* off)
FYI: _packer.cpp is fine.
Will try again to attach file - getting message "We don't support that file type", even after putting them into a zip file.
```
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ExcelWriter not able to save using PyQt4
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[] | null | 5 | 2016-10-07T10:50:30Z | 2016-10-08T00:27:20Z | 2016-10-08T00:27:20Z |
NONE
| null |
I am using python2.7 from anaconda and I have a gui I created in pyqt4 that has a function to call a module that is supposed to write into an excel sheet using pandas excelwriter. For some reason, it creates the worksheet but does not write anything into it. It just crashes my qui without any errors..when I run it in debug mode, it apparently goes through without any issues. I have been debugging this for the past few weeks and now point the issue between pyqt and excelwriter.
Is there a known issue that pyqt does not like pandas excelwriter?
```
import sys
from MAIN_GUI import *
if __name__=="__main__":
app = Qt.Gui.QApplication(sys.argv)
class MAIN_GUI(QtGui.QMainWindow):
def __init__self:
super(MAIN_GUI, self.__init__:
self.uiM=Ui_MainWindow
self.uiM.setupUi(self)
self.connect(self.uiM.updateALL_Button,QtCore.SIGNAL('clicked()'),self.updateALLEXCEL)
def updateALLEXCEL(self):
import excel_dummy
main_gui = MAIN_GUI()
main_gui.show()
main_gui.raise_()
sys.exit(app.exec_())
```
---excel_dummy.py---
```
import pandas as pd
from pandas import ExcelWriter
def excelify():
with ExcelWriter('/home/Desktop/Excelified/final.xlsx', engine='xlsxwriter') as writer:
workbook=writer.book
worksheet=workbook.add_worksheet()
worksheet.write(2,2,'just write something')
writer.save()
excelify()
```
---MAIN_GUI.py---
```
from PyQt4 import QtCore,QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.unicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName(_fromUtf8("MainWindow"))
MainWindow.resize(320,201)
self.centralwidget = QtGui.QWidget(MainWindow)
self.centralwidget.setObjectName(_fromUtf8("centralwidget"))
self.updateALL_Button = QtGui.QPushButton(self.centralwidget)
self.updateALL_Button.setGeometry(QtCore.QRect(40,110,161,27))
self.updateALL_Button.setFocusPolicy(QtCore.Qt.NoFocus)
self.updateAll_Button.setObjectName(_fromUtf8("Options_updateALL_Button"))
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtGui.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 320, 24))
self.menubar.setObjectName(_fromUtf8("menubar"))
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtGui.QStatusBar(MainWindow)
self.statusbar.setObjectName(_fromUtf8("statusbar"))
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self,MainWindow):
MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow", None))
self.updateALL_Button.setText(_translate("MainWindow", "updateALL", None))
```
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Could pandas be used as an alternative SQL backend?
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| 1 | 2016-10-07T11:29:53Z | 2016-10-07T22:16:44Z | 2016-10-07T22:16:44Z |
NONE
| null |
I am so used to manipulating data with pandas that I don't want to fall back to SQL unless absolutely necessary. In fact, I'd rather not use SQL. Nonetheless SQL works better for out-of-core size data (obviously, that is not the primary focus of pandas, and yes I am aware of Dask).
Would it be possible to use pandas on data that resides in an SQL database without importing it in memory. I am not asking whether it would be possible to access the data, but whether it would be possible to use pandas as backend to SQL. For instance: I write pandas code and SQL is executed in the background. I want to count all the values in a column, but I write `.value_counts()`. Do you see what I mean?
Does that make any sense? Apologies if my question is off-topic or naive?
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API: add DataFrame.nunique() and DataFrameGroupBy.nunique()
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| 12 | 2016-10-07T19:09:05Z | 2017-01-23T20:45:41Z | 2017-01-23T13:41:26Z |
CONTRIBUTOR
| null |
- [x] closes #14336
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
|
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| 181,771,717 |
MDU6SXNzdWUxODE3NzE3MTc=
| 14,377 |
Odd behavior with floats and dateranges
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| 2 | 2016-10-07T21:30:03Z | 2016-10-07T22:11:48Z | 2016-10-07T22:11:27Z |
CONTRIBUTOR
| null |
When creating a daterange with a decimal number of minutes, pandas truncates everything before the decimal point. This leads to odd results e.g. if you're expecting to generate something every half minute and instead end up with something every 5 minutes, and even odder results if you have a float that is a whole number.
A less contrived example is if pandas is used to process the results of some API call where you don't know what unit you'll have to go to to get to a whole number. It would be safe, I guess, to convert everything to nanoseconds, but not especially readable.
#### A small, complete example of the issue
``` python
>>> import pandas as pd
>>> pd.date_range(end='2016-10-07T21:08:58.490180', periods=3, freq='0.5min')
DatetimeIndex(['2016-10-07 20:58:58.490180', '2016-10-07 21:03:58.490180',
'2016-10-07 21:08:58.490180'],
dtype='datetime64[ns]', freq='5T')
>>> pd.date_range(end='2016-10-07T21:08:58.490180', periods=3, freq='10080.0min')
DatetimeIndex([], dtype='datetime64[ns]', freq='0T')
```
#### Expected Output
For the first example:
`DatetimeIndex(['2016-10-07 21:07:58.490180', '2016-10-07 21:08:28.490180',
'2016-10-07 21:08:58.490180'],
dtype='datetime64[ns]', freq='30S')`
For the second:
`DatetimeIndex(['2016-09-23 21:08:58.490180', '2016-09-30 21:08:58.490180',
'2016-10-07 21:08:58.490180'],
dtype='datetime64[ns]', freq='10080T')`
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.1
pip: 1.5.4
setuptools: 26.0.0
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.41.0
pandas_datareader: None
</details>
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| 181,835,222 |
MDExOlB1bGxSZXF1ZXN0ODg1NTExOTA=
| 14,378 |
BUG: Convert float freqstrs to ints at finer resolution
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| 4 | 2016-10-08T16:32:21Z | 2019-01-18T18:25:57Z | 2016-12-14T16:08:04Z |
CONTRIBUTOR
| null |
- [x] closes #8419
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Passing `'0.5min'` as a frequency string should generate 30 second
intervals, rather than five minute intervals. By recursively increasing
resolution until one is found for which the frequency is an integer,
this commit ensures that that's the case for resolutions from days to
microsecond
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MDU6SXNzdWUxODE4NDIxMjQ=
| 14,379 |
In read_csv, setting index_col breaks functionality of converters when engine='python'
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| 1 | 2016-10-08T18:46:12Z | 2016-10-09T17:07:43Z | 2016-10-09T17:07:43Z |
NONE
| null |
I am experiencing an issue with read_csv
When setting index_col, and a converters dict, output of engine='c' is correct and output of engine='python' is wrong
``` python
def first(s):
return s[0]
io = StringIO('col1,col2\n1_,a\n2_,b\n')
print pd.read_csv(io, index_col=0, converters = {0 : first})
col2
col1
1 a
2 b
io = StringIO('col1,col2\n1_,a\n2_,b\n')
print pd.read_csv(io, index_col=0, converters = {0 : first}, engine='python')
col2
col1
1_ a
2_ b
```
Expected output: for second example to be like the first
Note: If I leave out index_col=0, converter works as expected
#### Output of `pd.show_versions()`:
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.11-23.53.amzn1.x86_64
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: 12.2
Cython: None
numpy: 1.11.1
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MDExOlB1bGxSZXF1ZXN0ODg1NjEzNzc=
| 14,380 |
BUG: pivot_table may raise TypeError without values
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| 4 | 2016-10-08T22:40:07Z | 2016-10-20T10:59:49Z | 2016-10-20T10:59:43Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`pivot_table` raises `TypeError` when `index` or `columns` is array-like and `values` is not specified.
```
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]},
index=pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-01-02', '2011-01-01', '2011-01-02']))
df.pivot_table(index=df.index.month, columns=df.index.day)
# TypeError: unhashable type: 'numpy.ndarray'
```
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Dataframe constructor fails when given dict with None value
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| 7 | 2016-10-09T01:01:11Z | 2016-10-31T20:53:51Z | 2016-10-31T20:53:51Z |
NONE
| null |
#### A small, complete example of the issue
``` python
# Your code here
import pandas as pd
pd.Dataframe(dict(a=None), index= [0])
```
```
In [3]: pd.DataFrame(dict(a=None),index=[0])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-20b65f605ca3> in <module>()
----> 1 pd.DataFrame(dict(a=None),index=[0])
miniconda2/envs/readout2/lib/python2.7/site-packages/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
264 dtype=dtype, copy=copy)
265 elif isinstance(data, dict):
--> 266 mgr = self._init_dict(data, index, columns, dtype=dtype)
267 elif isinstance(data, ma.MaskedArray):
268 import numpy.ma.mrecords as mrecords
miniconda2/envs/readout2/lib/python2.7/site-packages/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
400 arrays = [data[k] for k in keys]
401
--> 402 return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
403
404 def _init_ndarray(self, values, index, columns, dtype=None, copy=False):
miniconda2/envs/readout2/lib/python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
5382
5383 # don't force copy because getting jammed in an ndarray anyway
-> 5384 arrays = _homogenize(arrays, index, dtype)
5385
5386 # from BlockManager perspective
miniconda2/envs/readout2/lib/python2.7/site-packages/pandas/core/frame.pyc in _homogenize(data, index, dtype)
5693 v = lib.fast_multiget(v, oindex.values, default=NA)
5694 v = _sanitize_array(v, index, dtype=dtype, copy=False,
-> 5695 raise_cast_failure=False)
5696
5697 homogenized.append(v)
miniconda2/envs/readout2/lib/python2.7/site-packages/pandas/core/series.pyc in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
2917
2918 # scalar like
-> 2919 if subarr.ndim == 0:
2920 if isinstance(data, list): # pragma: no cover
2921 subarr = np.array(data, dtype=object)
AttributeError: 'NoneType' object has no attribute 'ndim'
```
#### Expected Output
This previously worked with a sensible output in 0.18.1:
In [2]: pd.DataFrame(dict(a=None),index=[0])
Out[2]:
a
0 None
#### Output of `pd.show_versions()`
<details>
Working version:
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24
numpy: 1.11.2
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.5
lxml: 3.6.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
Broken version:
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24
numpy: 1.11.2
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.5
lxml: 3.6.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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| 14,382 |
BUG:to_csv error when the file include space and '(' ')'
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| 2 | 2016-10-09T10:19:32Z | 2016-10-10T11:55:21Z | 2016-10-10T10:56:30Z |
NONE
| null |
#### A small, complete example of the issue
``` python
# Your code here
# -*- coding: utf-8 -*-
import pandas as pd
from pandas import DataFrame
f = pd.read_excel('./123.xlsx',index_col = None)
f2 = pd.DataFrame()
for i in range(0,len(f)):
f2.loc[i,'Term'] = f.loc[i,'Term']
if f.loc[i,'P-value'] <= 0.05:
for gene in f.loc[i,'Genes'].split(';'):
f2.loc[i,gene] = 1
f2.fillna(0,inplace = True)
f2.to_csv('./123.csv',index = False)
```
#### Expected Output
#### Output of `pd.show_versions()`
<details>
the input file
Term gene
ABCD (ZXCV) gene1,gene2,gene3
the output file f2['Term'] include space and '()' example: f2.loc[0,'Term'] = ABCD (ZXCV)
the csv file it output is
Term gene1
ABCD (ZXCV)
' (123)' come to the 'gene1' columns and cover the existing data,example(which i wanted):
Term gene1
ABCD 1
</details>
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ENH: feather support in the pandas IO api
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| 15 | 2016-10-10T00:34:48Z | 2016-12-29T12:06:01Z | 2016-12-26T22:49:17Z |
CONTRIBUTOR
| null |
closes #13092
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BUG: Fix issue with inserting duplicate columns in a dataframe (GH14291)
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[] | null | 4 | 2016-10-10T01:02:27Z | 2016-10-15T22:18:27Z | 2016-10-15T22:18:27Z |
CONTRIBUTOR
| null |
- [x] closes #14291
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I've been sitting on this simple fix because it seems a little kludgy. SparseDataFrame doesn't do anything parallel to broadcasting in its _sanitize_column method... should it?
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Python pandas use method of merge， return empty DataFrame
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NONE
| null |
#### I use pd.merge to link two excel, but it returns empty dataFrame
``` python
#
df1 = pd.read_excel('gz.xlsx')
df2 = pd.read_excel('dyml.xlsx')
pd.merge(df1, df2, on = 'name')
```
#### df1 and df2 have the same data in column name,but it can't merge
#### Output of `Empty DataFrame`
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Support exporting to JSON Table Schema
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| 18 | 2016-10-10T14:03:41Z | 2017-04-16T08:23:42Z | 2017-04-15T16:20:30Z |
CONTRIBUTOR
| null |
For Jupyter based frontends, we would love to see a common tabular format in JSON that we can render (in addition to or in lieu of the current HTML). This would provide us the flexibility to style and format according to data type, as well as have better hooks for theming of tabular data on frontends. Everyone has an opinion, let's give them flexibility to apply it.
It's important to us to support a common JSON format so that for R, Julia, and other languages also can display their DataFrames with similar formatting and styling out of the box.
The best one I've seen so far, with a great amount of discussion and collaboration, is the [JSON Table Schema](http://specs.frictionlessdata.io/json-table-schema/).
Update: In order to include both data + schema, we're using [data resource](http://specs.frictionlessdata.io/data-resource/) which has media type `application/vnd.dataresource+json`.
/cc @captainsafia @ellisonbg @jreback @TomAugspurger
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DOC: Series.sum skipna= param defaults to None
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NONE
| null |
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sum.html
Docs for pandas.Series.sum state that "skipna=None" in the function name, but then reads "skipna : boolean, default True" in the "parameters" section.
**It is not clear whether the default is "None" or "True".**
Many thanks.
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DOC: Add details to DataFrame groupby transform
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| 6 | 2016-10-10T17:21:22Z | 2018-04-22T21:12:07Z | 2017-03-25T18:40:39Z |
CONTRIBUTOR
| null |
- [X] closes #13543
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Add requirements for user function in groupby transform
closes #13543
[skip ci]
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BUG: Allow concat to take string axis names
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| 6 | 2016-10-10T21:58:56Z | 2016-10-13T19:27:59Z | 2016-10-13T14:35:45Z |
CONTRIBUTOR
| null |
Continued in #14416
---
- [x] closes #14369
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This uses [`_get_axis_number`](https://github.com/pydata/pandas/blob/master/pandas/core/generic.py#L327-L338) to convert a string `axis` parameter to an integer. This will allow the `concat` method to use any other aliases given to dataframes axes in the future while not disrupting other uses of `axis` in the `concat` method. If this pattern works well, it could be used for the other [merge functions](https://github.com/pydata/pandas/blob/master/pandas/tools/merge.py) also.
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BUG: Line delimited json is breaks if string includes `}`
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| 1 | 2016-10-10T22:34:52Z | 2016-10-15T13:38:33Z | 2016-10-15T13:38:33Z |
CONTRIBUTOR
| null |
In 0.19.0 exporting json with new lines breaks if a string contains a closing curly brace.
#### Example
``` python
import pandas as pd
pd.DataFrame([{'a': 'foo}', 'b': 'bar'}, {'a': 'foo2', 'b': 'bar2'}]).to_json(orient='records', lines=True)
'{"a":"foo}"\n"b":"bar"},{"a":"foo2"\n"b":"bar2"}'
```
#### Expected Output
``` python
'{"a":"foo}","b":"bar"}\n{"a":"foo2","b":"bar2"}'
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.3
sqlalchemy: None
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: 0.2.1
</details>
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[BUG] handle } in line delimited json
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| 10 | 2016-10-10T23:08:01Z | 2016-10-15T19:18:43Z | 2016-10-15T13:38:33Z |
CONTRIBUTOR
| null |
- [x] closes #14390
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0ODg3NTgxMDc=
| 14,392 |
BUG: Dataframe constructor when given dict with None value
|
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| 4 | 2016-10-11T01:38:12Z | 2016-10-31T20:54:03Z | 2016-10-31T20:53:51Z |
CONTRIBUTOR
| null |
- [x] closes #14381
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
|
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| 14,393 |
Dataframe constructor does not coerce data=[None] to np.nan
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| 5 | 2016-10-11T01:45:08Z | 2016-10-26T10:51:14Z | 2016-10-26T10:51:13Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
As called out in #14381, if `dtype` is not specified, values of `None` should be coerced to `np.nan`. However, when a list of only `None` is passed to `data`, the `None` remains.
``` python
>>> pd.DataFrame([None])
0
0 None
>>> pd.DataFrame([None, None])
0
0 None
1 None
```
#### Expected Output
``` python
>>> pd.DataFrame([None])
0
0 NaN
>>> pd.DataFrame([None, None])
0
0 NaN
1 NaN
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-77-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.4
pip: 8.1.2
setuptools: 5.8
Cython: 0.21
numpy: 1.11.2
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.5.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: 2.32.1
pandas_datareader: None
</details>
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read_msgpack is returning empty list
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| 2 | 2016-10-11T02:42:19Z | 2016-10-11T11:32:39Z | 2016-10-11T11:32:20Z |
NONE
| null |
#### A small, complete example of the issue
``` python
"""Example of Pandas bug."""
from numpy.random import randn
from pandas import DataFrame, read_msgpack
POWER = 6
ROWS = 10 ** POWER
COLS = 280
SIZE = ROWS * COLS
# COL_NAMES = ['Columns']
print('rows: {:,}; cols: {}; data points: {:,}; float memory (bytes): {:,}; '
'float memory (GiB): {:.3}'.format(ROWS, COLS, SIZE, SIZE * 8,
float(SIZE * 8) / 1024 / 1024 / 1024))
DTF = DataFrame(randn(ROWS, COLS)) # , columns=COL_NAMES
print(DTF)
DTF.to_msgpack('foobar_{}.msg'.format(POWER))
print('Reading exercise')
DTF2 = read_msgpack('foobar_{}.msg'.format(POWER))
print(DTF2)
```
#### Current Output
```
rows: 1,000,000; cols: 280; data points: 280,000,000; float memory (bytes): 2,240,000,000; float memory (GiB): 2.09
0 1 2 3 4 5 6 \
0 -0.026045 1.102096 0.702563 -1.508039 0.308464 0.504351 2.887272
7 8 9 ... 270 271 272 273 \
0 -0.700034 -0.11521 -0.447198 ... -0.566173 0.16131 -0.581333 -0.362
274 275 276 277 278 279
0 -1.433336 0.60287 0.799355 0.628572 0.542312 -0.410359
[1 rows x 280 columns]
Reading exercise
Traceback (most recent call last):
File "../bug/pandas_bug6.py", line 20, in <module>
print(DTF2.head(1))
AttributeError: 'list' object has no attribute 'head'
```
#### Expected Output
```
rows: 1,000,000; cols: 280; data points: 280,000,000; float memory (bytes): 2,240,000,000; float memory (GiB): 2.09
0 1 2 3 4 5 6 \
0 -0.026045 1.102096 0.702563 -1.508039 0.308464 0.504351 2.887272
7 8 9 ... 270 271 272 273 \
0 -0.700034 -0.11521 -0.447198 ... -0.566173 0.16131 -0.581333 -0.362
274 275 276 277 278 279
0 -1.433336 0.60287 0.799355 0.628572 0.542312 -0.410359
[1 rows x 280 columns]
Reading exercise
0 1 2 3 4 5 6 \
0 -0.026045 1.102096 0.702563 -1.508039 0.308464 0.504351 2.887272
7 8 9 ... 270 271 272 273 \
0 -0.700034 -0.11521 -0.447198 ... -0.566173 0.16131 -0.581333 -0.362
274 275 276 277 278 279
0 -1.433336 0.60287 0.799355 0.628572 0.542312 -0.410359
[1 rows x 280 columns]
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 16.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 18.5
Cython: None
numpy: 1.8.0rc1
scipy: 0.13.0b1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 1.5
pytz: 2013.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
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: None
boto: None
pandas_datareader: None
None
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Unexpected Pandas Division Behavior
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| 2 | 2016-10-11T02:54:45Z | 2016-10-11T07:19:58Z | 2016-10-11T04:24:24Z |
NONE
| null |
Test Code
```
import pandas as pd
import numpy as np
a = pd.DataFrame(np.random.randint(0,100,size=(100, 4)))
a.tail(1)/a.head(1)
```
Expected Behavior:
some float point value
Actual Behavior:
```
0 1 2 3
0 NaN NaN NaN NaN
99 NaN NaN NaN NaN
```
Confirmed on OSX python3, Pandas v0.19.0 v0.18.1
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| 14,396 |
pandas.*.sample may return empty objects if frac <= 2^{-axis_length}
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[] | null | 6 | 2016-10-11T12:18:37Z | 2017-12-22T14:01:33Z | 2017-12-22T14:01:33Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
import pandas as pd
axis_length = 1
df = pd.Series([0])
df.sample(frac=2**(-axis_length))
Series([], dtype: int64)
```
#### Expected Output
Series([1], dtype: int64)
#### Why?
I tried to sample fractions of groups, some groups containted only one entry and were not returned in the sampled frame ( I used `groupby(key).apply(lambda x:x.sample)`).
A potential fix may be easy: in generic.py line 2634 might be changed from
``` python
n = int(round(frac * axis_length))
```
to
``` python
n = max(int(round(frac * axis_length)),1)
```
If this behaviour is not desired, maybe one could add to the API some keyword like, min_sample_size
#### Further Remarks
The example can be generalized using np.arange(axis_length). The bound $2^{-axis_length}$ seems to be hard. Increasing the value would return at least one sample entry.
Edit: Depending on the discussion, I'd love to provide a pull request by myself. I think, it's a good start to support pandas and it fits my skill level
#### Output of `pd.show_versions()`
Series([], dtype: int64)
<details>
# Paste the output here
## 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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.4.0
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: 1.1.0
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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first_valid_index for all NaN DataFrame
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| 1 | 2016-10-11T12:32:52Z | 2016-10-11T16:13:00Z | 2016-10-11T16:12:41Z |
NONE
| null |
Similar to [this one](https://github.com/pydata/pandas/issues/9752), but on a non-empty DataFrame whose values are all NaN.
This raises an error, instead of returning None
``` python
pd.Series([np.nan]).first_valid_index() # this returns None
pd.DataFrame([np.nan]).first_valid_index() # raise error
>>> pd.__version__
u'0.18.1'
```
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Incompatibility between pandas.infer_freq and pandas.to_timedelta
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| 3 | 2016-10-11T13:04:22Z | 2016-10-12T15:19:10Z | 2016-10-12T15:19:10Z |
NONE
| null |
#### A small, complete example of the issue
The output of `pd.infer_freq` is "D" while `pd.to_timedelta` expects "1D".
``` python
import pandas as pd
dates = pd.date_range(start='2016-10-01', end='2016-10-10', freq='1D')
freq = pd.infer_freq(dates)
delta = pd.to_timedelta(freq)
```
#### Expected Output
delta = Timedelta('1 days 00:00:00')
``` python
>>> import pandas as pd
>>> dates = pd.date_range(start='2016-10-01', end='2016-10-10', freq='1D')
>>> dates
DatetimeIndex(['2016-10-01', '2016-10-02', '2016-10-03', '2016-10-04',
'2016-10-05', '2016-10-06', '2016-10-07', '2016-10-08',
'2016-10-09', '2016-10-10'],
dtype='datetime64[ns]', freq='D')
>>> freq = pd.infer_freq(dates)
>>> freq
'D'
>>> delta = pd.to_timedelta(freq)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/iivanov/anaconda2/lib/python2.7/site-packages/pandas/util/decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "/home/iivanov/anaconda2/lib/python2.7/site-packages/pandas/tseries/timedeltas.py", line 102, in to_timedelta
box=box, errors=errors)
File "/home/iivanov/anaconda2/lib/python2.7/site-packages/pandas/tseries/timedeltas.py", line 148, in _coerce_scalar_to_timedelta_type
result = tslib.convert_to_timedelta(r, unit, errors)
File "pandas/tslib.pyx", line 2941, in pandas.tslib.convert_to_timedelta (pandas/tslib.c:52631)
File "pandas/tslib.pyx", line 3256, in pandas.tslib.convert_to_timedelta64 (pandas/tslib.c:56028)
File "pandas/tslib.pyx", line 3188, in pandas.tslib.parse_timedelta_string (pandas/tslib.c:54877)
ValueError: unit abbreviation w/o a number
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-42-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: 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: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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ENH: Create limitation on HDF5 queries such that users do not go over RAM
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[] | null | 4 | 2016-10-11T22:46:34Z | 2021-05-08T00:09:01Z | 2021-05-08T00:09:01Z |
NONE
| null |
#### A small, complete example of the issue
Create a "max_size" parameter for `HDFStore.select()` which stops the user from executing queries over RAM limit, or any limit set by user. At the moment, if users try to create a pandas `df` from this query larger than RAM, the script simply freezes.
This issue was discussed on StackOverflow today:
http://stackoverflow.com/questions/39986786/how-to-limit-the-size-of-pandas-queries-on-hdf5-so-it-doesnt-go-over-ram-limit
To quote from the OP comments:
""
Let's say I try df = store.select('df',columns=['column1', 'column2'], where=['column1==5']) and it's larger than some limit in terms of RAM---if the limit is the limit set by the computer's hardward, the program will just freeze. Let's say I wanted to set an arbitrary limit, e.g. 4 GB. The HDF5 might be +TB or PB, so df could easily exceed RAM if a user were to query this object. What limitations could I put in place to stop "bad things" from happening? ""
``` python
# Your code here
```
#### Expected Output
Perhaps a warning could be thrown? A parameter should be set in `HDFStore.select()` which is a byte-size limit---over this limit, an error is thrown.
#### Output of `pd.show_versions()`
True for all versions.
<details>
# Paste the output here
</details>
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| 14,400 |
[ENH] Need a best dtype method
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| 12 | 2016-10-12T19:55:10Z | 2016-10-21T03:47:17Z | 2016-10-12T20:05:49Z |
NONE
| null |
#### Introduction
Basically, the `pandas.read_*` methods and constructors are awesome at [assigning the highest level of dtype](http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes) that can include all values of a column. But such functionality is lacking for DataFrames created by other methods (stack, unstack are prime examples).
There has been a lot of discussion about dtypes here (ref. #9216, #5902 and especially #9589), and I understand it is a well rehearsed topic, but with no general consensus. An unfortunate result of those discussions was the deprecation of the `.convert_objects` method for being too forceful. However, the undercurrent in those discussions (IMHO) point to, and my needs often require a (DataFrame and Series) method which will intelligently assign the lowest generic dtype based on the data.
The method may optionally take a list of dtypes or a dictionary of column names, dtypes to assign user specified dtypes. Note that I am proposing this in addition to the existing `to_*` methods. The following example will help illustrate:
```
In [1]: df = pd.DataFrame({'c1' : list('AAABBBCCC'),
'c2' : list('abcdefghi'),
'c3' : np.random.randn(9),
'c4' : np.arange(9)})
df.dtypes
Out[1]: c1 object
c2 object
c3 float64
c4 int64
dtype: object
In [2]: df = df.stack().unstack()
df.dtypes
Out[2]: c1 object
c2 object
c3 object
c4 object
dtype: object
```
#### Expected Output
Define a method `.set_dtypes` which does the following:
1. Either takes a boolean keyword argument `infer` to infer and reset the column dtype to the least general dtype such that values are not lost.
2. Or takes a list or dictionary of dtypes to force each column into user specified dtypes, with an optional `errors` keyword argument to handle casting errors.
As illustrated below:
```
In [3]: df.set_dtypes(infer=True).dtypes
Out[3]: c1 object
c2 object
c3 float64
c4 int64
dtype: object
In [4]: df.set_dtypes(types=[np.int64]*4, errors='coerce').dtypes
Out[4]: c1 int64
c2 int64
c3 int64
c4 int64
dtype: object
In [5]: df.set_dtypes(types=[np.int64]*4, errors='coerce') # Note loss of data
Out[5]: c1 c2 c3 c4
0 NaN NaN 1 0
1 NaN NaN 1 1
2 NaN NaN 0 2
3 NaN NaN 0 3
4 NaN NaN 0 4
5 NaN NaN 0 5
6 NaN NaN 2 6
7 NaN NaN 0 7
8 NaN NaN 1 8
In [6]: df.set_dtypes(types=[np.int64]*4, errors='ignore').dtypes
Out[6]: c1 object
c2 object
c3 object
c4 int64
dtype: object
```
#### Additional Notes
I understand that date and time types will be a little difficult to infer. However, following the logic powering `pandas.read_*`, date and time types are not automatically inferred, but explicitly passed by the user.
It would be a one-size-fits-all solution if users were allowed to pass `True`, and `False` in addition to dtype to force when specifying dtypes per column. `True` in this case would indicate infer automatically (set the best dtype), while `False` would indicate ignore column from conversion.
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| 14,401 |
BUG: pd.to_numeric(..., downcast='unsigned') should accept 0
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| 2 | 2016-10-12T20:09:50Z | 2016-12-14T16:37:00Z | 2016-11-17T13:10:59Z |
CONTRIBUTOR
| null |
with 0.19.0.
```
In [28]: pd.to_numeric(pd.Series([1,2,3]), downcast='unsigned')
Out[28]:
0 1
1 2
2 3
dtype: uint8
In [29]: pd.to_numeric(pd.Series([0,1,2,3]), downcast='unsigned')
Out[29]:
0 0
1 1
2 2
3 3
dtype: int64
```
```
In [30]: np.iinfo(np.uint8).min
Out[30]: 0
```
I think 0 is a legit uint8, so [29] should be uint8.
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AmbiguousTimeError: Cannot infer dst time from Timestamp('2015-11-01 01:00:03'), try using the 'ambiguous' argument
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| 5 | 2016-10-12T20:39:34Z | 2016-10-13T10:14:19Z | 2016-10-13T10:14:19Z |
NONE
| null |
In Pandas 0.19.0, there seems to be an error when setting the timezone for times close to the dst cutoff.
This issue appears very similar to #11619 and #11626, both of which have been closed. The traceback is below.
``` python
df['Timestamp'] = df['Timestamp Date'] + " " + df['Timestamp Time']
df['Timestamp'] = pd.to_datetime(df['Timestamp']).dt.tz_localize('US/Central')
---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
in ()
----> 1 df['Timestamp'] = pd.to_datetime(df['Timestamp']).dt.tz_localize('US/Central')
/Users/adam/anaconda/lib/python2.7/site-packages/pandas/core/base.pyc in f(self, *args, **kwargs)
208
209 def f(self, *args, **kwargs):
--> 210 return self._delegate_method(name, *args, **kwargs)
211
212 f.__name__ = name
/Users/adam/anaconda/lib/python2.7/site-packages/pandas/tseries/common.pyc in _delegate_method(self, name, *args, **kwargs)
130
131 method = getattr(self.values, name)
--> 132 result = method(*args, **kwargs)
133
134 if not is_list_like(result):
/Users/adam/anaconda/lib/python2.7/site-packages/pandas/util/decorators.pyc 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
/Users/adam/anaconda/lib/python2.7/site-packages/pandas/tseries/index.pyc in tz_localize(self, tz, ambiguous, errors)
1823 new_dates = tslib.tz_localize_to_utc(self.asi8, tz,
1824 ambiguous=ambiguous,
-> 1825 errors=errors)
1826 new_dates = new_dates.view(_NS_DTYPE)
1827 return self._shallow_copy(new_dates, tz=tz)
pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:70327)()
AmbiguousTimeError: Cannot infer dst time from Timestamp('2015-11-01 01:00:03'), try using the 'ambiguous' argument
```
Here is some relevant information on the data frame.
```
df[['Timestamp Date', 'Timestamp Time', 'Timestamp']].head()
Timestamp Date Timestamp Time Timestamp
0 01-04-2016 07:10:03 AM 01-04-2016 07:10:03 AM
1 01-04-2016 07:20:04 AM 01-04-2016 07:20:04 AM
2 01-04-2016 07:30:03 AM 01-04-2016 07:30:03 AM
3 01-04-2016 07:40:03 AM 01-04-2016 07:40:03 AM
4 01-04-2016 07:50:03 AM 01-04-2016 07:50:03 AM
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 286834 entries, 0 to 286833
Timestamp Date 286834 non-null object
Timestamp Time 286834 non-null object
Timestamp 286834 non-null object
```
I also had this same issue in Pandas 0.18.0 before upgrading to 0.19.0.
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Add decimal option to read_excel
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| 26 | 2016-10-12T20:41:23Z | 2021-11-05T13:05:22Z | 2021-11-05T13:05:22Z |
NONE
| null |
The `pd.read_csv()` function has a `decimal` option, which allows it to parse European dates very easily:
```
pd.read_csv("afile.csv", decimal=',')
```
This option is not present in `pd.read_excel()`, but it could also be very useful there.
It would be even more useful if this option was a list of characters to interpret as decimal delimiters, instead of a single character.
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Bug: Made it so that 0 was included in uint8
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[] | null | 4 | 2016-10-12T21:17:48Z | 2016-10-13T04:23:00Z | 2016-10-13T04:23:00Z |
NONE
| null |
- [ ] closes #14401
- [ ] tests added / passed
- [ ] passes git diff upstream/master | flake8 --diff
- [ ] whatsnew entry
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BUG: Bug in localizing an ambiguous timezone when a boolean is passed
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| 0 | 2016-10-12T21:37:07Z | 2016-10-13T10:14:19Z | 2016-10-13T10:14:19Z |
CONTRIBUTOR
| null |
closes #14402
```
In [1]: s = pd.Series([Timestamp('2015-11-01 01:00:03')])
In [2]: s
Out[2]:
0 2015-11-01 01:00:03
dtype: datetime64[ns]
In [3]: s.dt.tz_localize('US/Central', ambiguous=False)
Out[3]:
0 2015-11-01 01:00:03-06:00
dtype: datetime64[ns, US/Central]
In [4]: s.dt.tz_localize('US/Central', ambiguous=True)
Out[4]:
0 2015-11-01 01:00:03-05:00
dtype: datetime64[ns, US/Central]
In [5]: s.dt.tz_localize('US/Central')
AmbiguousTimeError: Cannot infer dst time from Timestamp('2015-11-01 01:00:03'), try using the 'ambiguous' argument
```
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Convert readthedocs links for their .org -> .io migration for hosted projects
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| 3 | 2016-10-12T21:40:28Z | 2016-10-12T22:10:37Z | 2016-10-12T22:10:31Z |
CONTRIBUTOR
| null |
As per [their blog post of the 27th April](https://blog.readthedocs.com/securing-subdomains/) ‘Securing subdomains’:
> Starting today, Read the Docs will start hosting projects from subdomains on the domain readthedocs.io, instead of on readthedocs.org. This change addresses some security concerns around site cookies while hosting user generated data on the same domain as our dashboard.
Test Plan: Manually visited all the links I’ve modified.
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| 182,645,704 |
MDU6SXNzdWUxODI2NDU3MDQ=
| 14,407 |
BUG: need to block split when filling na
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| 0 | 2016-10-12T22:03:46Z | 2021-05-02T00:32:25Z | null |
CONTRIBUTOR
| null |
xref #14400
```
In [49]: df = pd.DataFrame({'c1' : list('ABC'),
...: 'c2' : list('123'),
...: 'c3' : np.random.randn(3),
...: 'c4' : np.arange(3)})
In [50]: df
Out[50]:
c1 c2 c3 c4
0 A 1 0.645628 0
1 B 2 -0.841708 1
2 C 3 0.207423 2
In [51]: df.dtypes
Out[51]:
c1 object
c2 object
c3 float64
c4 int64
dtype: object
In [52]: df.apply(lambda x: pd.to_numeric(x, errors='coerce')).fillna(df).dtypes
Out[52]:
c1 object
c2 int64
c3 object
c4 int64
dtype: object
In [53]: df.apply(lambda x: pd.to_numeric(x, errors='coerce')).fillna(df).apply(lambda x: pd.to_numeric(x, errors='ignore')).dtypes
Out[53]:
c1 object
c2 int64
c3 float64
c4 int64
dtype: object
```
so [52] should be [53] already, but since the filled block was originally float, I don't think the splitting is happening.
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