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https://api.github.com/repos/pandas-dev/pandas/issues/25213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25213/events | https://github.com/pandas-dev/pandas/issues/25213 | 407,702,617 | MDU6SXNzdWU0MDc3MDI2MTc= | 25,213 | BUG: object dtype & Int64Dtype equality comparison issue | {
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} | [] | closed | false | null | [] | null | 4 | 2019-02-07T13:34:44Z | 2019-02-08T09:07:20Z | 2019-02-08T09:07:20Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
d = {'int': [1, 2, 3], 'str': ['a', 'b', 'c']}
df = pd.DataFrame(d)
print(df['int'].dtype == pd.core.arrays.integer.Int64Dtype)
print(df['str'].dtype == pd.core.arrays.integer.Int64Dtype)
```
Example output:
```python
In [1]: import pandas as pd
In [2]: d = {'int': [1, 2, 3], 'str': ['a', 'b', 'c']}
In [3]: df = pd.DataFrame(d)
In [4]: df.dtypes
Out[4]:
int int64
str object
dtype: object
In [5]: df['int'].dtype == pd.core.arrays.integer.Int64Dtype
Out[5]: False
In [6]: df['str'].dtype == pd.core.arrays.integer.Int64Dtype
Out[6]: True
```
#### Problem description
I want to process columns of a dataframe according to their type and have a list of numeric dtypes that I'm interested in (one of which is the newer `Int64` type). However, I'm getting an issue with columns of `object` dtype which also appears to be `in` this list of numeric types.
The simplified code above shows why I'm getting this problem - when the dtype is `object` the equality check against `Int64Dtype` evaluates to `True`. Is this expected behaviour, or a bug?
I'm going to solve my use case in another way (using `pandas.api.types.is_numeric_dtype`), but I'd like to find out whether this is a bug or whether I need improve my understanding!
#### Expected Output
```
Out[5]: False
Out[6]: False
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.1
setuptools: 40.6.2
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25214/events | https://github.com/pandas-dev/pandas/issues/25214 | 407,741,420 | MDU6SXNzdWU0MDc3NDE0MjA= | 25,214 | BUG: SettingWithCopyWarning even if view | {
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} | [] | closed | false | null | [] | null | 0 | 2019-02-07T14:59:41Z | 2019-02-07T15:08:43Z | 2019-02-07T15:08:43Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# df['column_to_assign_to'][assigning_mask] = values_tobe_assigned
```
#### Problem description
the given code example raies a SettingWithCopyWarning but it's no copy but a view!
<details>
python: 3.6.2.final.0
pandas: 0.24.1
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} | 18 | 2019-02-07T15:03:17Z | 2019-07-12T17:20:51Z | 2019-02-11T19:40:17Z | CONTRIBUTOR | null | - [x ] closes #25156
- [x ] tests added / passed
tests not needed - just a doc change
- [x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
not needed - just a doc change
- [x ] whatsnew entry
Not needed - just a doc change
Small typo fixed in Powerpoint and PDF versions of cheat sheet.
Ideally, when merged, new PDF versions should push to current pandas release.
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} | [] | closed | false | null | [] | null | 2 | 2019-02-07T16:04:56Z | 2019-02-07T19:11:48Z | 2019-02-07T19:11:48Z | CONTRIBUTOR | null | Python 2.7 support is now dropped for 0.25, but the CI tests are still run against python 2.7 | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-02-07T18:36:15Z | 2019-02-09T13:59:40Z | 2019-02-09T13:59:39Z | NONE | null | #### Code Sample
```python
import pandas as pd
table_list = pd.read_html("https://af.wikipedia.org/wiki/Lys_van_lande_volgens_geletterdheidskoers")
df = table_list[0].copy()
# Make the first row of the table the column headers.
new_header = df.iloc[0] # Grab the first row for the header.
df = df[1:] # Take the data less the header row.
df.columns = new_header # Set the header row as the df header.
df
```
#### Problem description
The last column (Geletterdheidskoers) has a value for the country Togo that is 532 instead of 53,2. This might be related to the fact that the row before that (for Jemen) incorrectly uses a period instead of a comma to indicate the decimal point.
#### Expected Output
53,2
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.0
pip: 19.0.1
setuptools: 40.6.2
Cython: 0.28.5
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.10.0
xarray: 0.10.8
IPython: 6.5.0
sphinx: 1.7.7
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: 1.5.1
bottleneck: 1.2.1
tables: None
numexpr: None
feather: 0.4.0
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: 1.0.7
lxml: 4.2.4
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.1.6
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25219 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25219/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25219/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25219/events | https://github.com/pandas-dev/pandas/pull/25219 | 407,884,214 | MDExOlB1bGxSZXF1ZXN0MjUxMjU4NDYz | 25,219 | BUG: Fix type coercion in read_json orient='table' (#21345) | {
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} | 6 | 2019-02-07T20:30:33Z | 2019-02-23T19:12:39Z | 2019-02-23T19:12:33Z | CONTRIBUTOR | null | - [X] closes #21345
- [x] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 34 | 2019-02-07T22:21:57Z | 2019-09-18T12:09:35Z | 2019-06-28T20:04:13Z | NONE | null | #### Problem description
`read_table()` got deprecated in favour of using `read_csv()`.
Using `read_csv()` to read tab/space delimited files is counter-intuitive. According to the docs and the related issues, both share the same code and it is not clear why the one function should be preferred over the other, and that change may even break existing code.
In my point of view `read_table()` is the more general function and `read_csv()` is a special case. Why would you deprecate (and then remove) the more useful function? It is already annoying to use `to_csv()` to write space/tab delimited files. And as I can see it, it comes down to two lines of code.
#### Proposed solution
Keep both functions as they are (un-deprecate `read_table()`) or rename the function to have a more general name like `read_txt()` (as in `numpy.genfromtxt()`) or similar. | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 3 | 2019-02-08T00:51:04Z | 2021-06-26T04:57:19Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
>>> branches
KEY Terminal.ConductingEquipment SvVoltage.TopologicalNode_x SvVoltage.TopologicalNode_y Branch.y
1 d8f14106-d89c-4779-bd29-18ad4b4e671d a94e594e-46c8-4cb3-b3d1-f412715bbdb4 ff1fb999-6411-4776-8ce9-0a875afed1ea (0.009999999999999998-0.03j)
2 d8f14106-d89c-4779-bd29-18ad4b4e671d ff1fb999-6411-4776-8ce9-0a875afed1ea a94e594e-46c8-4cb3-b3d1-f412715bbdb4 (0.009999999999999998-0.03j)
# Works OK with strings
>>> branches[["Terminal.ConductingEquipment","SvVoltage.TopologicalNode_x", "SvVoltage.TopologicalNode_y"]].pivot(index = "SvVoltage.TopologicalNode_x", columns = "SvVoltage.TopologicalNode_y")
KEY Terminal.ConductingEquipment
SvVoltage.TopologicalNode_y a94e594e-46c8-4cb3-b3d1-f412715bbdb4 ff1fb999-6411-4776-8ce9-0a875afed1ea
SvVoltage.TopologicalNode_x
a94e594e-46c8-4cb3-b3d1-f412715bbdb4 NaN d8f14106-d89c-4779-bd29-18ad4b4e671d
ff1fb999-6411-4776-8ce9-0a875afed1ea d8f14106-d89c-4779-bd29-18ad4b4e671d NaN
# Fails with complex
>>> branches[["Branch.y","SvVoltage.TopologicalNode_x", "SvVoltage.TopologicalNode_y"]].pivot(index = "SvVoltage.TopologicalNode_x", columns = "SvVoltage.TopologicalNode_y")
Traceback (most recent call last):
File "<interactive input>", line 1, in <module>
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\frame.py", line 5194, in pivot
return pivot(self, index=index, columns=columns, values=values)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 415, in pivot
return indexed.unstack(columns)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\frame.py", line 5534, in unstack
return unstack(self, level, fill_value)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 495, in unstack
return _unstack_frame(obj, level, fill_value=fill_value)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 516, in _unstack_frame
return unstacker.get_result()
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 184, in get_result
values, _ = self.get_new_values()
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 248, in get_new_values
f = getattr(_reshape, "unstack_{name}".format(name=name))
AttributeError: 'module' object has no attribute 'unstack_complex128'
# Same with pivot_table
>>> branches[["Branch.y","SvVoltage.TopologicalNode_x", "SvVoltage.TopologicalNode_y"]].pivot_table(index = "SvVoltage.TopologicalNode_x", columns = "SvVoltage.TopologicalNode_y")
Traceback (most recent call last):
File "<interactive input>", line 1, in <module>
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\frame.py", line 5303, in pivot_table
margins_name=margins_name)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\pivot.py", line 114, in pivot_table
table = agged.unstack(to_unstack)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\frame.py", line 5534, in unstack
return unstack(self, level, fill_value)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 495, in unstack
return _unstack_frame(obj, level, fill_value=fill_value)
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 516, in _unstack_frame
return unstacker.get_result()
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 184, in get_result
values, _ = self.get_new_values()
File "C:\Users\kristjan.vilgo\AppData\Roaming\Python\Python27\site-packages\pandas\core\reshape\reshape.py", line 248, in get_new_values
f = getattr(_reshape, "unstack_{name}".format(name=name))
AttributeError: 'module' object has no attribute 'unstack_complex128'
```
#### Problem description
pivot and pivot_table not able to handle complex numbers
#### Expected Output
pivoted table, as demonstrated above with string values, but with complex values
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: None
pip: 7.1.2
setuptools: 28.8.0
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.4.1
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.0
openpyxl: 2.4.5
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: None
lxml: 4.2.1
bs4: 4.6.3
html5lib: 0.999999999
sqlalchemy: 1.2.15
pymysql: 0.9.3
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 2 | 2019-02-08T01:25:53Z | 2019-02-08T03:44:16Z | 2019-02-08T03:44:02Z | NONE | null | #### Code Sample
```python
>>> df = pd.DataFrame(data=[pd.Timestamp('2017-10-11 07:30:00'),
pd.Timestamp('2017-10-11 08:30:00'),
pd.Timestamp('2017-10-11 09:30:00')],
index=[0,1,2],
columns=['Date_time'])
>>> df.Date_time
>>> 0 2017-10-11 07:30:00
1 2017-10-11 08:30:00
2 2017-10-11 09:30:00
Name: Date_time, dtype: datetime64[ns]
>>> df.Date_time.dt.round('H')
>>> 0 2017-10-11 08:00:00
1 2017-10-11 08:00:00
2 2017-10-11 10:00:00
Name: Date_time, dtype: datetime64[ns]
```
#### Problem description
07:30 and 09:30 are rounded **up** to the hour. 08:30 is rounded **down** to the hour. I find this inconsistent. I think my expected output below is more consistent, where 08:30 is rounded **up** to 09:00.
#### Expected Output
```python
>>> df.Date_time.dt.round('H')
>>> 0 2017-10-11 08:00:00
1 2017-10-11 09:00:00
2 2017-10-11 10:00:00
Name: Date_time, dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-141-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 27 | 2019-02-08T06:31:10Z | 2019-03-13T13:11:40Z | 2019-03-13T13:11:29Z | CONTRIBUTOR | null | - [x] closes #21510
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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the match check in `test_astype_categorical_to_other` is probably too explicit and dependent on random numbers. | {
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} | 1 | 2019-02-08T12:02:47Z | 2020-05-10T15:52:54Z | 2020-05-10T15:52:54Z | NONE | null | #### Problem description
`pd.Grouper`'s `base` argument is almost undocumented ([link](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.Grouper.html)). Despite this, the argument is extremely useful when grouping a dataframe with uneven sampling rate with an arbitrary start time.
Take for example the case of a dataframe representing a 60 minutes experiment with uneven sampling rate starting at '12-01-2019 11:55:23.01938' that we want to split in six 10-minutes group. `base` is our best ally here.
Despite this, in the current implementation `base` represents a floating number of **minutes**. It's however quite hard to understand what this number refers to, is quite cumbersome to convert the above '11:55:23.01938' into a number of minutes and gives you little flexibility regarding when the grouping will start.
This can also cause rounding error issues as descibed [here](https://github.com/pandas-dev/pandas/issues/25161).
I suggest replacing (or extend) this allowing it to be a datetime object so that the edge of each group is simply
`base + N * freq`
where N is an integer number. This is much cleaner and easy to understand.
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https://api.github.com/repos/pandas-dev/pandas/issues/25227 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25227/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25227/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25227/events | https://github.com/pandas-dev/pandas/pull/25227 | 408,136,770 | MDExOlB1bGxSZXF1ZXN0MjUxNDUyNTk1 | 25,227 | Specify build requirements in pyproject.toml (PEP 517) | {
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... | closed | false | null | [] | null | 27 | 2019-02-08T12:43:44Z | 2019-06-27T03:34:02Z | 2019-06-27T03:34:02Z | CONTRIBUTOR | null | - [x] closes #25193
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Verified this using the Dockefile:
```
FROM alpine:3.8
RUN apk update
RUN apk add g++ libstdc++ python3-dev bash git
RUN pip3 install --upgrade pip
COPY dist/pandas-0.25.0.dev0+86.gba77fb900.tar.gz /
RUN ls -l /
RUN pip3 install -vvv /pandas-0.25.0.dev0+86.gba77fb900.tar.gz
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] | closed | false | null | [] | null | 2 | 2019-02-08T15:07:22Z | 2019-02-08T22:04:21Z | 2019-02-08T22:04:13Z | NONE | null | ```
df_test = pd.read_csv('utterances_test.txt', sep='\t', names=['id', 'label'])
```
Above code results in "df_test" having 19988 lines, but the original file "utterances_test.txt" has exactly 20000 lines.
This can be confirmed by running
```
cat utterances_test.txt | wc -l
```
in the terminal.
Also reading the file in myself with python also results in 20000 lines.
Above file attached.
[utterances_test.txt](https://github.com/pandas-dev/pandas/files/2845609/utterances_test.txt)
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https://api.github.com/repos/pandas-dev/pandas/issues/25229 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25229/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25229/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25229/events | https://github.com/pandas-dev/pandas/issues/25229 | 408,200,614 | MDU6SXNzdWU0MDgyMDA2MTQ= | 25,229 | DISCUSS: What would an ORC reader/writer API look like? | {
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} | 13 | 2019-02-08T15:28:21Z | 2021-07-16T22:24:30Z | 2019-12-11T08:11:52Z | CONTRIBUTOR | null | cc @mrocklin for dask.dataframe visibility
I'm one of the developers of https://github.com/rapidsai/cudf and we're working on adding GPU-accelerated file readers / writers to our library. It seems most of the standard formats are covered quite nicely in the Pandas API, but ORC isn't. Before we went off defining our own API I wanted to open a discussion for defining what that API would look like so we can be consistent with the Pandas and Pandas-like community.
At the top level, I imagine it would look almost identical to Parquet in something like the following:
```
def read_orc(path, engine='auto', columns=None, **kwargs):
"""
Load an orc object from the file path, returning a DataFrame.
Parameters
----------
path : string
File path
columns : list, default=None
If not None, only these columns will be read from the file.
engine : {'auto', 'pyarrow'}, default 'auto'
Orc library to use. If 'auto', then the option
``io.orc.engine`` is used. The default ``io.orc.engine``
behavior is to use 'pyarrow'.
kwargs are passed to the engine
Returns
-------
DataFrame
"""
...
def to_orc(self, fname, engine='auto', compression='snappy', index=None,
partition_cols=None, **kwargs):
"""
Write a DataFrame to the binary orc format.
This function writes the dataframe as a `orc file
<https://orc.apache.org/>`_. You can choose different orc
backends, and have the option of compression. See
:ref:`the user guide <io.orc>` for more details.
Parameters
----------
fname : str
File path or Root Directory path. Will be used as Root Directory
path while writing a partitioned dataset.
engine : {'auto', 'pyarrow'}, default 'auto'
Orc library to use. If 'auto', then the option
``io.orc.engine`` is used. The default ``io.orc.engine``
behavior is to use 'pyarrow'.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output.
If ``False``, they will not be written to the file. If ``None``,
the behavior depends on the chosen engine.
partition_cols : list, optional, default None
Column names by which to partition the dataset
Columns are partitioned in the order they are given
**kwargs
Additional arguments passed to the orc library. See
:ref:`pandas io <io.orc>` for more details.
"""
...
```
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} | 6 | 2019-02-08T15:32:29Z | 2019-02-22T14:37:04Z | 2019-02-09T16:54:10Z | CONTRIBUTOR | null | - [X] closes #25196
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
With this change the snippets reported in the issue work as expected:
```
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(np.zeros((100, 15)))
>>> df.T.apply(dict)
0 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
1 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
2 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
3 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
4 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
5 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
6 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
7 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
8 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
9 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
10 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
11 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0....
...
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} | 3 | 2019-02-08T18:18:35Z | 2019-12-22T13:18:26Z | null | CONTRIBUTOR | null | #### Problem description
In order to have a continuous check by the CI on validation error `PR08` (Parameter description should start with a capital letter), fixing them in the code base enables the addition to the CI for automated testing in the future.
`PR08` has 156 occurrences (see [overview of the errors](https://gist.github.com/198cfa552c727d71bd7360fda66fdbb4)), including:
method | file | file_line | github_link
-- | -- | -- | --
pandas.Timestamp.ceil | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.day_name | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.floor | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.fromordinal | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.month_name | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.round | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.tz_localize | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Period.to_timestamp | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.DataFrame.insert | pandas/core/frame.py | 3434.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L3434
pandas.DataFrame.apply | pandas/core/frame.py | 6284.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L6284
pandas.DataFrame.pipe | pandas/core/generic.py | 4924.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L4924
pandas.DataFrame.corr | pandas/core/frame.py | 6960.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L6960
pandas.DataFrame.corrwith | pandas/core/frame.py | 7151.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L7151
pandas.DataFrame.rank | pandas/core/generic.py | 8287.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L8287
pandas.DataFrame.drop_duplicates | pandas/core/frame.py | 4579.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L4579
pandas.DataFrame.duplicated | pandas/core/frame.py | 4614.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L4614
pandas.DataFrame.idxmax | pandas/core/frame.py | 7564.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L7564
pandas.DataFrame.idxmin | pandas/core/frame.py | 7528.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L7528
pandas.DataFrame.rename | pandas/util/_decorators.py | 3922.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/util/_decorators.py#L3922
pandas.DataFrame.fillna | pandas/core/frame.py | 4007.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L4007
pandas.DataFrame.unstack | pandas/core/frame.py | 5905.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L5905
pandas.DataFrame.from_csv | pandas/core/frame.py | 1838.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L1838
pandas.DataFrame.to_feather | pandas/core/frame.py | 2083.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L2083
pandas.DataFrame.to_msgpack | pandas/core/generic.py | 2380.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L2380
pandas.DataFrame.to_clipboard | pandas/core/generic.py | 2596.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L2596
pandas.merge_ordered | pandas/core/reshape/merge.py | 132.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/reshape/merge.py#L132
pandas.merge_asof | pandas/core/reshape/merge.py | 236.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/reshape/merge.py#L236
pandas.to_numeric | pandas/core/tools/numeric.py | 14.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/tools/numeric.py#L14
pandas.to_datetime | pandas/core/tools/datetimes.py | 401.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/tools/datetimes.py#L401
pandas.to_timedelta | pandas/core/tools/timedeltas.py | 18.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/tools/timedeltas.py#L18
... | ... | ... | ...
pandas.Series.rename | pandas/core/series.py | 3726.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/series.py#L3726
pandas.Series.fillna | pandas/core/series.py | 3893.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/series.py#L3893
pandas.Series.reorder_levels | pandas/core/series.py | 3301.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/series.py#L3301
pandas.Series.dt.tz_localize | pandas/core/accessor.py | 94.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94
pandas.Series.dt.round | pandas/core/accessor.py | 94.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94
pandas.Series.dt.floor | pandas/core/accessor.py | 94.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94
pandas.Series.dt.ceil | pandas/core/accessor.py | 94.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94
pandas.Series.str.findall | pandas/core/strings.py | 1755.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/strings.py#L1755
pandas.Series.str.match | pandas/core/strings.py | 2526.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/strings.py#L2526
pandas.Series.str.replace | pandas/core/strings.py | 2531.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/strings.py#L2531
pandas.Series.cat.rename_categories | pandas/core/accessor.py | 94.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94
pandas.Series.hist | pandas/plotting/_core.py | 2418.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_core.py#L2418
pandas.Series.to_msgpack | pandas/core/generic.py | 2380.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L2380
pandas.Series.to_clipboard | pandas/core/generic.py | 2596.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L2596
pandas.io.formats.style.Styler | pandas/io/formats/style.py | 55.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L55
pandas.io.formats.style.Styler.apply | pandas/io/formats/style.py | 565.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L565
pandas.io.formats.style.Styler.applymap | pandas/io/formats/style.py | 623.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L623
pandas.io.formats.style.Styler.where | pandas/io/formats/style.py | 650.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L650
pandas.io.formats.style.Styler.set_properties | pandas/io/formats/style.py | 987.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L987
pandas.io.formats.style.Styler.highlight_max | pandas/io/formats/style.py | 1143.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L1143
pandas.io.formats.style.Styler.highlight_min | pandas/io/formats/style.py | 1163.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L1163
pandas.io.formats.style.Styler.background_gradient | pandas/io/formats/style.py | 881.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L881
pandas.io.formats.style.Styler.use | pandas/io/formats/style.py | 742.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L742
pandas.core.window.Rolling.apply | pandas/core/window.py | 1699.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/window.py#L1699
pandas.core.window.Expanding.apply | pandas/core/window.py | 1972.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/window.py#L1972
pandas.Panel.fillna | pandas/core/panel.py | 1288.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/panel.py#L1288
pandas.Panel.fromDict | pandas/core/panel.py | 243.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/panel.py#L243
pandas.Panel.pipe | pandas/core/generic.py | 4924.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L4924
pandas.Panel.rank | pandas/core/generic.py | 8287.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L8287
pandas.Panel.to_msgpack | pandas/core/generic.py | 2380.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L2380
Todo:
* [ ] get rid of the errors in the code base
* [ ] update the `code_check.sh` script to take into account the `SS02` type of errors | {
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} | 3 | 2019-02-08T19:40:18Z | 2019-02-09T02:24:32Z | 2019-02-08T23:34:57Z | MEMBER | null | Is it the case that LegacyTable and AppendableTable are never used directly, only their subclasses? If so, some further simplifications are available. | {
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} | 4 | 2019-02-09T01:44:06Z | 2019-02-11T12:52:50Z | 2019-02-11T12:52:39Z | CONTRIBUTOR | null | - [x] closes #25184
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 5 | 2019-02-09T01:51:47Z | 2019-02-09T17:25:59Z | 2019-02-09T17:25:59Z | MEMBER | null | Having seen this on a couple PRs now it looks like the following bechmarks are causing build failures as they fail to run
frame_methods.Repr.time_html_repr_trunc_mi
<details>
```python-traceback
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 1039, in main_run_server
main_run(run_args)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 913, in main_run
result = benchmark.do_run()
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 412, in do_run
return self.run(*self._current_params)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 506, in run
min_run_count=self.min_run_count)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 569, in benchmark_timing
timing = timer.timeit(number)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/timeit.py", line 176, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py", line 226, in time_html_repr_trunc_mi
self.df3._repr_html_()
File "/home/vsts/work/1/s/pandas/core/frame.py", line 651, in _repr_html_
import IPython
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/IPython/__init__.py", line 55, in <module>
from .terminal.embed import embed
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/IPython/terminal/embed.py", line 16, in <module>
from IPython.terminal.interactiveshell import TerminalInteractiveShell
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py", line 81, in <module>
if not _stream or not hasattr(_stream, 'isatty') or not _stream.isatty():
ValueError: I/O operation on closed file
```
</details>
frame_methods.Repr.time_html_repr_trunc_si
<details>
```python-traceback
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 1039, in main_run_server
main_run(run_args)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 913, in main_run
result = benchmark.do_run()
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 412, in do_run
return self.run(*self._current_params)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 506, in run
min_run_count=self.min_run_count)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 569, in benchmark_timing
timing = timer.timeit(number)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/timeit.py", line 176, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py", line 229, in time_html_repr_trunc_si
self.df4._repr_html_()
File "/home/vsts/work/1/s/pandas/core/frame.py", line 651, in _repr_html_
import IPython
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/IPython/__init__.py", line 55, in <module>
from .terminal.embed import embed
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/IPython/terminal/embed.py", line 16, in <module>
from IPython.terminal.interactiveshell import TerminalInteractiveShell
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py", line 81, in <module>
if not _stream or not hasattr(_stream, 'isatty') or not _stream.isatty():
ValueError: I/O operation on closed file
```
</details>
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} | 13 | 2019-02-09T02:47:34Z | 2020-11-10T03:20:06Z | 2020-11-10T03:20:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
idx = ['str1', 'str2', 'str3']
df_full.set_index(idx, drop=False, inplace=True)
for i, rc in df.iterrows():
df.at[i, 'prev_state'] = df_full.loc[tuple(rc[e] for e in idx)]['state']
## df is a dataframe with columns in idx and prev_state
```
#### Problem description
The above piece of code works in 0.23.4, yet after I upgraded to 0.24.1, it throws
``` python
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py", line 1494, in __getitem__
return self._getitem_tuple(key)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py", line 873, in _getitem_tuple
self._has_valid_tuple(tup)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py", line 221, in _has_valid_tuple
raise IndexingError('Too many indexers')
pandas.core.indexing.IndexingError: Too many indexers
```
I did compare that part of source code in both 0.23.4 and 0.24.1, and they look like the same.(maybe I am wrong) Please help.
#### Output of ``pd.show_versions() for 0.23.4``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 17.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: None
pip: 9.0.1
setuptools: 40.6.2
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.4
pytz: 2018.6
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
None
</details>
#### Output of ``pd.show_versions() for 0.24.1``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 17.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 9.0.1
setuptools: 40.6.2
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.4
pytz: 2018.6
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
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} | 3 | 2019-02-09T03:20:39Z | 2019-02-09T22:59:05Z | 2019-02-09T17:25:59Z | CONTRIBUTOR | null | This should fix the `asv dev` errors seen in #24953, though this may add a lot of runtime overhead so input on approaches appreciated.
When running `asv dev`, the default `--launch-method=fork` calls `sys.stdin.close()` in the main process where all benchmarks run. The function `DataFrame._repr_html_()` attempts to import `IPython`, which itself attempts a call on `sys.stdin` as part of `IPython.__init__`, which fails due to it having been closed by `asv`. This leads to the following output:
```
[ 30.00%] ··· frame_methods.Repr.time_html_repr_trunc_mi failed
[ 30.00%] ···· Traceback (most recent call last):
File "/home/chris/code/asv/asv/benchmark.py", line 1170, in main_run_server
main_run(run_args)
File "/home/chris/code/asv/asv/benchmark.py", line 1044, in main_run
result = benchmark.do_run()
File "/home/chris/code/asv/asv/benchmark.py", line 523, in do_run
return self.run(*self._current_params)
File "/home/chris/code/asv/asv/benchmark.py", line 617, in run
min_run_count=self.min_run_count)
File "/home/chris/code/asv/asv/benchmark.py", line 680, in benchmark_timing
timing = timer.timeit(number)
File "/home/chris/anaconda3/lib/python3.7/timeit.py", line 176, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
File "/home/chris/code/pandas/asv_bench/benchmarks/frame_methods.py", line 226, in time_html_repr_trunc_mi
self.df3._repr_html_()
File "/home/chris/code/pandas/pandas/core/frame.py", line 652, in _repr_html_
import IPython
File "/home/chris/anaconda3/lib/python3.7/site-packages/IPython/__init__.py", line 55, in <module>
from .terminal.embed import embed
File "/home/chris/anaconda3/lib/python3.7/site-packages/IPython/terminal/embed.py", line 16, in <module>
from IPython.terminal.interactiveshell import TerminalInteractiveShell
File "/home/chris/anaconda3/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py", line 81, in <module>
if not _stream or not hasattr(_stream, 'isatty') or not _stream.isatty():
ValueError: I/O operation on closed file
```
Long term, this needs to be changed upstream in `asv`. Short term, I see a couple options:
* Disable these benchmarks
* Use a different launchmethod that does not trigger the `sys.stdin.close()` call
This PR implements the latter by using the `--launch-method=spawn`, but it's to be seen how much overhead that introduces.
The command `asv dev` is shorthand for `asv run --quick --show-stderr --python=same`. Unfortunately, the `asv dev` command does not expose the `--launch-method` flag, so we have to substitute with the extended form.
- [ ] closes #25235
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @WillAyd | {
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} | 5 | 2019-02-09T04:01:22Z | 2019-02-11T15:30:02Z | 2019-02-11T13:21:56Z | MEMBER | null | There's a kludge for test_pickle and test_packers to ignore the "panel" keys in the legacy files. Longer-term we'll need to re-generate the legacy files without the Panels in them. | {
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"id": 211840... | closed | false | null | [] | null | 4 | 2019-02-09T15:12:42Z | 2021-06-26T05:01:37Z | 2021-06-26T05:01:37Z | CONTRIBUTOR | null | #### Problem description
The `freq` attribute of the `pandas.Timestamp` class is not well explained on the class's documentation page.
For background, there is an existing issue #15146 which proposes removing the `freq` attribute. I agree with that proposal, although I don't know how much technical complexity weighs against that.
If pandas decides not to deprecate/remove it, the documentation for it should be improved. The current (version 0.24.1) documentation reads:
```
ts_input : datetime-like, str, int, float
Value to be converted to Timestamp
freq : str, DateOffset <----
Offset which Timestamp will have <----
tz : str, pytz.timezone, dateutil.tz.tzfile or None
Time zone for time which Timestamp will have.
```
The `Timestamp` class or `datetime.datetime` class represents a point in time, an instant. There is no "offset" that a time point has, semantically speaking.
As far as I can tell, the role of `freq` is (1) to provide an implicit timedelta for Timestamp + integer operations, which are now deprecated, and (2) to provide a default in `Timestamp.to_period()` when that method's `freq` argument is None.
The `freq` value does not influence equality comparisons between Timestamp objects, and does not influence the hash of a Timestamp object. It is dropped in a few Timestamp transformations like tz localization
It's hard for me to suggest what the documentation should be here, since the attribute seems like a vestige from the days before a Period class, and since it doesn't influence the semantic meaning of a timestamp.
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"description": "String extension data... | open | false | null | [] | null | 4 | 2019-02-09T15:15:29Z | 2021-06-12T01:04:51Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
import pandas as pd
s1 = pd.Series(['AA/aa', 'BB/bb', 'CC/cc'])
s2 = s1.str.split('/')
s2.str[0]
Result:
0 AA
1 BB
2 CC
dtype: object
#### Problem description
In this example, the second 'str' attribute is applyied to a Series of list and not to a Series of string.
Then the [] operator works fine with each list an retrieve their first element...
As it is an unexpected working behavior, one may wonder if it is secure to code like this (instead of working with an apply + lambda for instance). This works also with Series of dict, and probably with any object implementing the [] operator.
#### Expected Output
Warning or error?
Or a word in the documentation?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.3.2
pip: 19.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.2
scipy: 1.0.0
pyarrow: None
xarray: 0.10.9
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.2.3
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 1 | 2019-02-09T15:45:51Z | 2019-02-11T13:09:36Z | 2019-02-11T13:09:36Z | CONTRIBUTOR | null | The `pandas.Timestamp.tz_localize()` and `pandas.Timestamp.tz_convert()` methods do not preserve the `freq` attribute. For background, the `freq` attribute is mostly a "helpful label", and it seems semantically ill-defined to me; cf. issue #15146, which proposes removing the attribute.
#### Code Sample
```
>>> import pandas
>>> import pytz
>>> t1 = pandas.Timestamp('2019-01-01 10:00', freq='H')
>>> print(t1.tz_localize(pytz.utc).freq)
None
>>> t2 = pandas.Timestamp('2019-01-02 12:00', tz=pytz.utc, freq='T')
>>> print(t2.tz_convert(pytz.utc).freq)
None
```
#### Problem description
I don't think that `pandas.Timestamp` should actually have a `freq` attribute, but so long as it does, it should be propagated to the new timestamp value returned by `tz_localize()` or `tz_convert`. In the `tz_convert` case, the method should produce an "equivalent" timestamp with a different tz.
#### Expected Output
`t1.tz_localize(pytz.utc).freq` should equal `t1.freq`, and `t2.tz_convert(pytz.utc).freq` should equal `t2.freq`.
#### Output of ``pd.show_versions()``
<details>
No module named 'dask'
INSTALLED VERSIONS
------------------
commit: 04df22fbeec782f474bcff20480eded04e0d1d82
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+89.g04df22fbe
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.7.3
Cython: 0.29.4
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.11.1
xarray: 0.11.0
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.14
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.17
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: 0.1.2
</details>
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} | 1 | 2019-02-09T15:56:01Z | 2019-03-19T23:32:57Z | 2019-03-19T23:32:57Z | MEMBER | null | #### Problem description
xref: #24782
See the comment in https://github.com/pandas-dev/pandas/pull/24819
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} | 5 | 2019-02-09T15:56:56Z | 2019-03-19T23:32:22Z | 2019-03-19T23:32:09Z | MEMBER | null | - [ ] closes #25242
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-02-09T18:33:57Z | 2019-02-11T16:08:55Z | 2019-02-11T16:08:48Z | CONTRIBUTOR | null | closes #24014
xref #10633
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Before this change, when the `Series.interpolate()` method was called with invalid arguments, exceptions with informative messages would be raised internally, but then caught and suppressed. An exception with a potentially misleading message would be raised instead.
For example, when interpolate is called with `method='spline'`, an `order` argument must also be supplied. This is checked internally, but when the `order` argument was missing, a confusing error would be raised:
```
>>> import pandas as pd
>>> s = pd.Series([0, 1, pd.np.nan, 3, 4])
>>> s.interpolate(method='spline')
Traceback (most recent call last):
...
ValueError: invalid method 'spline' to interpolate.
```
This problem was reported as issues #10633 and #24014.
After this change, a specific and correct exception (previously caught and discarded internally) is raised:
```
>>> import pandas as pd
>>> s = pd.Series([0, 1, pd.np.nan, 3, 4])
>>> s.interpolate(method='spline')
...
ValueError: You must specify the order of the spline or polynomial.
```
In addition, light validation is now performed on the `order` parameter before it is passed to a `scipy` class. Previously, `pandas` would check if `order` was truthy in the Python sense. If `order` was non-zero and invalid, a scipy error would propagate to the user:
```
>>> s.interpolate(method='spline', order=-1)
Traceback (most recent call last):
...
dfitpack.error: (1<=k && k<=5) failed for 3rd argument k: fpcurf0:k=-1
```
After this change, a more understandable exception is raised in this case:
```
>>> s.interpolate(method='spline', order=-1)
Traceback (most recent call last):
...
ValueError: order needs to be specified and greater than 0
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} | 4 | 2019-02-09T18:55:27Z | 2019-02-11T13:10:05Z | 2019-02-11T13:09:37Z | MEMBER | null | - [ ] closes #25241
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} | 4 | 2019-02-09T20:17:01Z | 2019-02-11T19:39:30Z | 2019-02-11T19:39:27Z | CONTRIBUTOR | null | Followup to a previous [PR](https://github.com/pandas-dev/pandas/pull/24954).
Continue refactoring cython function implemented in tempita into fused types. | {
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"d... | closed | false | null | [] | null | 6 | 2019-02-09T22:05:12Z | 2019-06-27T21:24:45Z | 2019-06-27T21:24:33Z | MEMBER | null | #25238 introduced a kludge in test_pickle and test_msgpack to ignore the Panel objects when unpickling/unpacking files produced using older versions. The right way to do this is to re-write those files without Panels in them to begin with. | {
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"description": "Incorrec... | closed | false | null | [] | null | 5 | 2019-02-09T22:13:29Z | 2021-06-26T05:16:01Z | 2021-06-26T05:16:01Z | CONTRIBUTOR | null | I wrote a package that monkey patches pandas to allow support of writing concise error-trapping field transformations (https://github.com/inside-track/pandas-mapper). Would there be any interest in integrating this directly in pandas (or any objection)?
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In order to have a continuous check by the CI on validation error `PR08` (Parameter description should start with a capital letter), fixing them in the code base enables the addition to the CI for automated testing in the future.
Please see [overview of the errors](https://gist.github.com/198cfa552c727d71bd7360fda66fdbb4).
Todo:
* [ ] get rid of the errors in the code base
* [ ] update the `code_check.sh` script to take into account the `PR08` type of errors | {
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} | 3 | 2019-02-09T23:39:43Z | 2019-05-12T08:02:49Z | 2019-02-11T13:08:16Z | CONTRIBUTOR | null | Reverts pandas-dev/pandas#25237 | {
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- [x] closes #20257
- [x] tests added / passed
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- [x] whatsnew entry
Includes some related-ish timezone documentation issues. | {
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- [ ] tests added / passed
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- [ ] This change avoids making an entire copy of the dataframe while writing to SQL DBs with a chunksize. Instead we convert slices of the dataframe into a data list and write to the DB. Saves significant memory when writing large dataframes to SQL DBs.
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} | 12 | 2019-02-10T01:08:56Z | 2019-02-28T13:35:04Z | 2019-02-28T13:35:01Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The `validate_docstrings.py` script dynamically loads modules/classes/functions from its command-line argument.
Currently, if the user supplies an invalid argument, an UnboundLocalError can be obtained:
```
Traceback (most recent call last):
...
File "./scripts/validate_docstrings.py", line 275, in _load_obj
obj = getattr(obj, part)
UnboundLocalError: local variable 'obj' referenced before assignment
```
This PR fixes a check within the relevant function, so that a more informative error can be raised:
```
$ ./scripts/validate_docstrings.py tslibs.Timestamp
Traceback (most recent call last):
...
ImportError: No module can be imported from "tslibs.Timestamp"
```
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} | 9 | 2019-02-10T06:29:24Z | 2020-07-13T12:55:12Z | 2020-07-13T12:55:12Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({
'timestamp': [pd.Timestamp('2017-08-27 01:00:00.709949+0000', tz='UTC')],
})
pd.concat([df]) # works
pd.concat([df, df]) # works
pd.concat([df], copy=False) # fails
# Edit: added full traceback
# Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# File ".../pandas/core/reshape/concat.py", line 229, in concat
# return op.get_result()
# File ".../pandas/core/reshape/concat.py", line 426, in get_result
# copy=self.copy)
# File ".../pandas/core/internals/managers.py", line 2055, in concatenate_block_managers
# b = b.make_block_same_class(values, placement=placement)
# File ".../pandas/core/internals/blocks.py", line 235, in make_block_same_class
# klass=self.__class__, dtype=dtype)
# File ".../pandas/core/internals/blocks.py", line 3095, in make_block
# return klass(values, ndim=ndim, placement=placement)
# File ".../pandas/core/internals/blocks.py", line 1680, in __init__
# values = self._maybe_coerce_values(values)
# File ".../pandas/core/internals/blocks.py", line 2266, in _maybe_coerce_values
# raise ValueError("cannot create a DatetimeTZBlock without a tz")
# ValueError: cannot create a DatetimeTZBlock without a tz
```
#### Problem description
Running `pd.concat` with a single dataframe and `copy=False` fails, giving an error about `cannot create a DatetimeTZBlock without a tz`.
Closely related:
- https://github.com/pandas-dev/pandas/issues/19420
- https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3AReshaping+label%3ATimezones
#### Expected Output
The original dataframe, since I'm only `concat`-ing a single dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-14-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.6.3
Cython: 0.29.4
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.11.1
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.4.10
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.2
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25258 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25258/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25258/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25258/events | https://github.com/pandas-dev/pandas/issues/25258 | 408,521,224 | MDU6SXNzdWU0MDg1MjEyMjQ= | 25,258 | IOError: [Errno 2] while building pandas doc | {
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... | open | false | null | [] | null | 3 | 2019-02-10T10:25:17Z | 2020-04-19T21:40:01Z | null | NONE | null | **Describe the bug**
Got an `IOError: [Errno 2]` when I tried to build the [pandas docs](https://github.com/pandas-dev/panda) in Ubuntu 18.04 LTS (Bionic Beaver) running in Windows 10. I initially posted this at `sphinx-doc` issues: [#6049](https://github.com/sphinx-doc/sphinx/issues/6049#issue-408502477) and [#6049-reply](https://github.com/sphinx-doc/sphinx/issues/6049#issuecomment-462114284) and it was suggested there that I post it here (see additional reply in [#6049](https://github.com/sphinx-doc/sphinx/issues/6049#issue-408502477)).
**To Reproduce**
Steps to reproduce the behavior:
```
$ git clone htps://github.com/pandas-dev/pandas
$ cd pandas
$ python setup.py build_ext --inplace --force
$ cd doc
$ python make.py clean
$ python make.py html
```
**Expected behavior**
Docs get built.
**Your project**
[None]
**Screenshots**
[None]
**Environment info**
- OS: Ubuntu 18.04 LTS (Bionic Beaver) running in Windows 10
- Python version: 2.7.15rc1 (CPython)
- Sphinx version: 1.8.4
- Docutils version: 0.14
- Jinja2 version: 2.10
**Additional context**
[*NOTE: Both the terminal output and .log file given below have been edited to allow anonymity*.]
The terminal output after `python make.py html`:
```bash
$ python make.py html
Running Sphinx v1.8.4
making output directory...
[autosummary] generating autosummary for: development/contributing.rst, development/contributing_docstring.rst, development/developer.rst, development/extending.rst, development/index.rst, development/internals.rst, ecosystem.rst, getting_started/10min.rst, getting_started/basics.rst, getting_started/comparison/comparison_with_r.rst, ..., whatsnew/v0.6.0.rst, whatsnew/v0.6.1.rst, whatsnew/v0.7.0.rst, whatsnew/v0.7.1.rst, whatsnew/v0.7.2.rst, whatsnew/v0.7.3.rst, whatsnew/v0.8.0.rst, whatsnew/v0.8.1.rst, whatsnew/v0.9.0.rst, whatsnew/v0.9.1.rst
WARNING: [autosummary] failed to import u'pandas.Timestamp.fold': no module named pandas.Timestamp.fold
[autosummary] generating autosummary for: /path/to/pandas/doc/source/reference/api/pandas.
...
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 106 source files that are out of date
updating environment: 2781 added, 0 changed, 0 removed
/path/to/pandas/pandas/core/generic.py:docstring of pandas.DataFrame.asof:28: WARNING: Unexpected indentation.
/path/to/pandas/pandas/core/generic.py:docstring of pandas.DataFrame.asof:29: WARNING: Block quote ends without a blank line; unexpected unindent.
/path/to/pandas/pandas/core/generic.py:docstring of pandas.DataFrame.ewm:19: WARNING: Unexpected indentation.
/path/to/pandas/pandas/core/generic.py:docstring of pandas.DataFrame.ewm:17: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/path/to/pandas/pandas/core/accessor.py:docstring of pandas.DatetimeIndex.ceil:29: WARNING: Block quote ends without a blank line; unexpected unindent.
/path/to/pandas/pandas/core/accessor.py:docstring of pandas.DatetimeIndex.floor:29: WARNING: Block quote ends without a blank line; unexpected unindent.
/path/to/pandas/pandas/core/accessor.py:docstring of pandas.DatetimeIndex.round:29: WARNING: Block quote ends without a blank line; unexpected unindent.
/path/to/pandas/pandas/core/accessor.py:docstring of pandas.DatetimeIndex.tz_localize:36: WARNING: Block quote ends without a blank line; unexpected unindent.
/path/to/pandas/pandas/core/dtypes/dtypes.py:docstring of pandas.DatetimeTZDtype:36: WARNING: toctree references unknown document u'reference/api/pandas.DatetimeTZDtype.type'
/path/to/pandas/pandas/core/arrays/integer.py:docstring of pandas.Int16Dtype:34: WARNING: toctree references unknown document u'reference/api/pandas.Int16Dtype.type'
Exception occurred:
File "/usr/lib/python2.7/inspect.py", line 563, in findsource
raise IOError('could not find class definition')
IOError: could not find class definition
The full traceback has been saved in /tmp/sphinx-err-lTiOie.log, if you want to report the issue to the developers.
Please also report this if it was a user error, so that a better error message can be provided next time.
A bug report can be filed in the tracker at <https://github.com/sphinx-doc/sphinx/issues>. Thanks!
Traceback (most recent call last):
File "make.py", line 339, in <module>
sys.exit(main())
File "make.py", line 335, in main
return getattr(builder, args.command)()
File "make.py", line 226, in html
self._add_redirects()
File "make.py", line 209, in _add_redirects
with open(path, 'w') as moved_page_fd:
IOError: [Errno 2] No such file or directory: /path/to/pandas/doc/build/html/generated/pandas.api.extensions.ExtensionArray.argsort.html'
```
The .log file is: [sphinx-err-lTiOie-EDITED.log](https://github.com/pandas-dev/pandas/files/2848738/sphinx-err-lTiOie-EDITED.log)
Thank you very much for whatever help you can extend! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25259 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25259/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25259/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25259/events | https://github.com/pandas-dev/pandas/issues/25259 | 408,547,241 | MDU6SXNzdWU0MDg1NDcyNDE= | 25,259 | replace method with comma separator does not work in pandas 0.24.1 | {
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} | 2 | 2019-02-10T15:40:42Z | 2019-02-28T13:55:33Z | 2019-02-28T13:55:33Z | NONE | null | ### The latest version (0.24.1) does not replace comma separator, but 0.23.4 does.
```python
import pandas as pd
df = pd.DataFrame({'one': ["1,000", "2,000"], 'two': ["3,000", "4,000"]})
df = df.replace({',': ''}, regex=True)
print(pd.__version__)
print(df)
```
```
0.23.4
one two
0 1000 3000
1 2000 4000
```
```
0.24.1
one two
0 1,000 3,000
1 2,000 4,000
```
If other characters are used with a comma, it works normally
```
df = df.replace({'1,': ''}, regex=True)
```
```
one two
0 000 3,000
1 2,000 4,000
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} | 6 | 2019-02-10T23:33:06Z | 2019-03-11T19:17:27Z | 2019-02-11T18:53:43Z | MEMBER | null | - [x] closes #23931
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 14 | 2019-02-11T00:15:51Z | 2021-01-28T15:07:14Z | 2021-01-28T15:07:14Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import matplotlib.pyplot as plt
import pandas as pd
fig, axes = plt.subplots(2, constrained_layout=True)
times = pd.date_range(start='now', periods=10)
pd.DataFrame({'a': np.arange(10)}, index=times).plot(style='x', ax=axes[0])
```
#### Problem description
Plotting time-series uses pandas-internally a subplots_adjust, but due to this I am unable to use the new matplotlib constrained_layout that would take care of these things automatically.
#### Expected Output
A good layout that respects my constrained_layout setting to plt.subplots()
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.2.0
pip: 19.0.2
setuptools: 40.7.3
Cython: 0.29.5
numpy: 1.16.1
scipy: 1.2.0
pyarrow: None
xarray: 0.11.3
IPython: 7.1.1
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.3
lxml.etree: 4.3.1
bs4: None
html5lib: None
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-02-11T00:23:41Z | 2019-03-13T21:11:32Z | 2019-02-11T12:55:05Z | MEMBER | null | follow-on from #24377
xref #25197
in #24377, the `pytest_generate_tests` hook was used to parametrize the base tests over date_range, period_range and timedelta_range indexes.
to make it easier to understand the code and use less pytest magic, this PR uses a simpler approach of applying markers to the tests instead of having the test name ending in _all_ts. | {
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- [x] closes #25211
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beginning of the string but spans all the characters within. The
behaviour is then consistent with versions prior to 0.24.0.
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} | 2 | 2019-02-11T12:53:23Z | 2019-02-11T13:59:47Z | 2019-02-11T13:59:47Z | NONE | null | Backport PR #25234: BUG: Duplicated returns boolean dataframe | {
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} | 7 | 2019-02-11T14:31:59Z | 2019-02-19T13:46:12Z | 2019-02-19T13:46:05Z | CONTRIBUTOR | null | - closes #24570
- 1 test added
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
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} | 4 | 2019-02-11T14:41:45Z | 2019-02-11T16:16:06Z | 2019-02-11T14:46:16Z | CONTRIBUTOR | null | Hi,
I don't really know if this matter should be posted in pandas but here is the issue I noticed.
If you save a DataFrame into pickle (using `to_pickle`) on a virtualenv that has `numpy>=1.16` and want to open it with another virtualenv with `numpy<=1.15.4`, the following error is thrown :
```
ModuleNotFoundError: No module named 'numpy.core._multiarray_umath'
```
Which is a feature of `numpy=1.16`. This issue could be of interest because the numpy version downloaded through anaconda is still 1.15.4 (unless you use conda-forge) while it is 1.16 using pip.
My pandas version is `0.24.1` in both cases. | {
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```python
import pandas as pd
import numpy as np
ary = np.array([ [1, 0, 0, 3],
[1, 0, 2, 0],
[0, 4, 0 ,0] ])
df = pd.DataFrame(ary)
df.columns = [1, 2, 3, 4]
dfs = pd.SparseDataFrame(df,
default_fill_value=0)
# DOES NOT WORK:
dfs.to_coo() # raises KeyError: 0
# WORKS (1)
dfs2 = dfs.copy()
dfs2.columns = [0, 1, 2, 3]
dfs2.to_coo()
# WORKS (2)
dfs3 = dfs.copy()
dfs3.columns = [str(i) for i in dfs3.columns]
dfs3.to_coo()
```
#### Problem description
In the example above, the Pandas SparseDataFrame method `to_coo()` (and possibly others) cannot handle sparse dataframes if the column names are integer types and don't start at 0. If the column names start at 0 or are string types, this is not an issue.
#### Expected Output
```python
<3x4 sparse matrix of type '<class 'numpy.int64'>'
with 5 stored elements in COOrdinate format>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 1 | 2019-02-11T17:55:40Z | 2019-02-20T08:04:32Z | 2019-02-20T08:04:32Z | MEMBER | null | As called out by @gfyoung in review there are some easy opportunities to clean up and simplify the existing Excel IO modules. These are mostly minor loop refactors and docstring updates
https://github.com/pandas-dev/pandas/pull/25153#pullrequestreview-200888051
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} | 6 | 2019-02-11T18:13:03Z | 2019-03-05T21:02:54Z | 2019-02-11T19:37:54Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25273 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25273/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25273/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25273/events | https://github.com/pandas-dev/pandas/issues/25273 | 408,922,153 | MDU6SXNzdWU0MDg5MjIxNTM= | 25,273 | REF: Refactor private/circular parts out of dtypes.concat | {
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"de... | closed | false | null | [] | null | 0 | 2019-02-11T18:44:36Z | 2021-06-17T03:06:21Z | 2021-06-17T03:06:21Z | MEMBER | null | A lot of core.dtypes.concat arguably doesn't belong in core.dtypes. In particular functions that induce circular imports, private functions that are used externally, and functions that are more about arrays/indexes than they are about dtypes.
Some of these (_get_series_result_type, _get_frame_result_type) are used in only core.reshape.concat, could go either directly in that module or into a helper module in core.reshape.
Others are class-specific (concat_rangeindex_same_dtype, _concat_sparse, concat_index_asobject) and make more sense in the modules corresponding to those classes (avoiding circular imports). union_categoricals and _concat_categorical probably belong in this group too.
_concat_compat is used in 6 other files, probably shouldn't be private. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25274 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25274/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25274/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25274/events | https://github.com/pandas-dev/pandas/issues/25274 | 408,929,321 | MDU6SXNzdWU0MDg5MjkzMjE= | 25,274 | Broken behaviour on read_excel on 0.24.0 | {
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"descript... | closed | false | null | [] | null | 3 | 2019-02-11T19:03:28Z | 2019-02-15T00:28:13Z | 2019-02-12T06:24:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
wget https://github.com/pandas-dev/pandas/files/2852618/file.xlsx
pip install pandas==0.23.4
>>import pandas as pd
>>df = pd.read_excel('file.xlsx', index_row=[0, 1, 2], header=[0, 1, 2])
>>print(df.columns.names)
pip uninstall pandas
pip install pandas==0.24.0
>>import pandas as pd
>>df = pd.read_excel('file.xlsx', index_row=[0, 1, 2], header=[0, 1, 2])
>>print(df.columns.names)
```
#### Problem description
The columns names are not identified by versions 0.24+.
#### Expected Output
Both print lines should print the same thing:
['top_level', 'middle_level', 'low_level']
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 1 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 40.6.2
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 1 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.0
pytest: None
pip: 18.1
setuptools: 40.6.2
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 7 | 2019-02-11T21:02:24Z | 2019-02-20T08:42:25Z | 2019-02-20T08:04:32Z | CONTRIBUTOR | null | - [x] closes #25271
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"id... | closed | false | null | [] | null | 6 | 2019-02-11T21:08:46Z | 2019-02-12T21:47:20Z | 2019-02-12T21:47:19Z | CONTRIBUTOR | null | I believe it was with the release of version 0.24.0 that changed how the arithmetic between pandas periods works, but prior to that version, if we started with
```python
x = pd.Period('2017-01')
y = pd.Period('2016-01')
diff = x-y
print(diff, ' ', type(diff))
```
The result would be:
```
12 <class 'int'>
```
diff would be the int `12`. But using version 0.24.0 and running the same code, the result is:
```
<12 * MonthEnds> <class 'pandas.tseries.offsets.MonthEnd'>
```
And there is seemingly no easy way to convert that back to an int. One solution posted on StackOverflow (https://stackoverflow.com/questions/42822768/pandas-number-of-months-between-two-dates) suggest that we do:
```python
(diff/np.timedelta64(1, 'M')).astype(int)
```
But this is a weird way to do something that I'd imagine is needed fairly often. It also causes issues if you need to support multiple pandas versions in your codebase because this code will break when diff is an int. So that leaves me with the best solution I could think of being:
```python
try:
(diff/np.timedelta64(1, 'M')).astype(int)
except:
pass
```
But this seems overly convoluted. Is there a better way to do this? Or could you add a `.astype(int)` method to the MonthsEnds class? | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [n/a ] whatsnew entry
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"de... | open | false | null | [] | null | 2 | 2019-02-12T01:47:13Z | 2021-06-26T05:25:57Z | null | NONE | null | #### Code Sample
Do a `crosstab` with an ordered categorical variable, with the order being different than the alphabetic default:
```python
df = pd.DataFrame({'First': ['B', 'B', 'C', 'A', 'B', 'C'],
'Second': ['C', 'B', 'B', 'B', 'C', 'A']})
df.First = df.First.astype(CategoricalDtype(ordered=True))
# Default order is alphabetic
df.First.cat.categories
>> Index(['A', 'B', 'C'], dtype='object')
# Change the order
df.First = df.First.cat.reorder_categories(['C', 'A', 'B'])
df.First.cat.categories
>> Index(['C', 'A', 'B'], dtype='object')
# Do a simple crosstab with margins
pd.crosstab(df.First, df.Second, margins=True)
>> Second A B C All
>> First
>> C 1 1 0 1
>> A 0 1 0 3
>> B 0 1 2 2
>> All 1 3 2 6
```
#### Problem description
The margins are showing the wrong values. In fact, they're showing the values that would be expected from the default ordering of the categories. This might be related to #20496, but the output here surely looks like a bug
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-1032-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.7.3
Cython: 0.29.4
numpy: 1.16.1
scipy: 1.2.0
pyarrow: 0.12.0
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.14
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: 0.9.0
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-02-12T02:34:00Z | 2019-03-11T19:17:22Z | 2019-02-12T13:40:08Z | MEMBER | null | (cherry picked from commit a9a03a25d82ee285b6da48bfb20b9057d20bb9e7)
xref https://github.com/pandas-dev/pandas/pull/25260#issuecomment-462448610
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} | 3 | 2019-02-12T05:47:56Z | 2019-02-28T07:23:11Z | 2019-02-13T13:48:28Z | MEMBER | null | This is a logical follow up to #25233 which should also help resolve the build failures in #24953 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25282 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25282/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25282/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25282/events | https://github.com/pandas-dev/pandas/issues/25282 | 409,149,773 | MDU6SXNzdWU0MDkxNDk3NzM= | 25,282 | Sum of TimedeltaIndex raises TypeError: len() of unsized object | {
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} | 6 | 2019-02-12T07:02:04Z | 2019-02-20T10:52:02Z | 2019-02-20T10:52:02Z | NONE | null | #### Example code
```python
import pandas as pd
import numpy as np
dti = pd.date_range('2018-01-01', periods=100, freq='T')
dtd = dti[1:] - dti[:-1]
print(np.sum(dtd))
```
#### Problem description
Summing a TimedeltaIndex when using pandas 0.24.0 or newer with any version of numpy, or pandas 0.23.4 with any version of numpy newer than 0.15.4 fails, `np.sum(dtd)` gives the following output:
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-d64fe48d0fec> in <module>
6
7 print(np.mean(dtd)) # works
----> 8 print(np.sum(dtd)) # breaks
~/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py in sum(a, axis, dtype, out, keepdims, initial)
2074
2075 return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
-> 2076 initial=initial)
2077
2078
~/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
84 return reduction(axis=axis, out=out, **passkwargs)
85
---> 86 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
87
88
~/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py in __array_wrap__(self, result, context)
658 attrs = self._get_attributes_dict()
659 attrs = self._maybe_update_attributes(attrs)
--> 660 return Index(result, **attrs)
661
662 @cache_readonly
~/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
301 (dtype is not None and is_timedelta64_dtype(dtype))):
302 from pandas.core.indexes.timedeltas import TimedeltaIndex
--> 303 result = TimedeltaIndex(data, copy=copy, name=name, **kwargs)
304 if dtype is not None and _o_dtype == dtype:
305 return Index(result.to_pytimedelta(), dtype=_o_dtype)
~/.local/lib/python3.6/site-packages/pandas/core/indexes/timedeltas.py in __new__(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity)
250
251 # check that we are matching freqs
--> 252 if verify_integrity and len(data) > 0:
253 if freq is not None and not freq_infer:
254 index = cls._simple_new(data, name=name)
TypeError: len() of unsized object
```
Other methods such as `np.mean()` and `np.median()` continue to function normally
#### Expected Output
`5940000000000 nanoseconds`
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.2
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25283 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25283/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25283/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25283/events | https://github.com/pandas-dev/pandas/issues/25283 | 409,242,330 | MDU6SXNzdWU0MDkyNDIzMzA= | 25,283 | Pandas dataFrame - selecting data - 'Interval' Recursion error | {
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} | 1 | 2019-02-12T11:05:25Z | 2019-02-20T03:51:05Z | 2019-02-20T03:51:05Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'SPCName': ['IntervalXxxxxxxxxxxxxxxxxxxxxxxxx']})
df[df.SPCName == 'IntervalA']
```
#### Problem description
I am getting a recursion error from the above code ( RuntimeError: maximum recursion depth exceeded while calling a Python object )
This issue did not occur in version 0.23.4 but occurs when upgrading to 0.24.1.
I am not aware why using 'IntervalA' in a Boolean lookup or when using the loc indexer should result in RuntimeError
This issue appears to occur regardless of what follows the str 'Interval' for example 'IntervalABC' also fails.
#### Full traceback
python2.7/site-packages/pandas/core/ops.pyc in wrapper(self, other, axis)
1726
1727 elif (is_extension_array_dtype(self) or
-> 1728 (is_extension_array_dtype(other) and not is_scalar(other))):
1729 # Note: the `not is_scalar(other)` condition rules out
1730 # e.g. other == "category"
python2.7/site-packages/pandas/core/dtypes/common.pyc in is_extension_array_dtype(arr_or_dtype)
1747 dtype = getattr(arr_or_dtype, 'dtype', arr_or_dtype)
1748 return (isinstance(dtype, ExtensionDtype) or
-> 1749 registry.find(dtype) is not None)
1750
1751
python2.7/site-packages/pandas/core/dtypes/dtypes.pyc in find(self, dtype)
87 for dtype_type in self.dtypes:
88 try:
---> 89 return dtype_type.construct_from_string(dtype)
90 except TypeError:
91 pass
python2.7/site-packages/pandas/core/dtypes/dtypes.pyc in construct_from_string(cls, string)
936 (string.startswith('interval') or
937 string.startswith('Interval'))):
--> 938 return cls(string)
939
940 msg = "a string needs to be passed, got type {typ}"
python2.7/site-packages/pandas/core/dtypes/dtypes.pyc in __new__(cls, subtype)
897
898 try:
--> 899 subtype = pandas_dtype(subtype)
900 except TypeError:
901 raise TypeError("could not construct IntervalDtype")
python2.7/site-packages/pandas/core/dtypes/common.pyc in pandas_dtype(dtype)
2002
2003 # registered extension types
-> 2004 result = registry.find(dtype)
2005 if result is not None:
2006 return result
... last 4 frames repeated, from the frame below ...
python2.7/site-packages/pandas/core/dtypes/dtypes.pyc in find(self, dtype)
87 for dtype_type in self.dtypes:
88 try:
---> 89 return dtype_type.construct_from_string(dtype)
90 except TypeError:
91 pass
#### Expected Output
Empty dataframe because the str 'IntervalA' doesnt exist in the dataframe col SPCName.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.14.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-642.6.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
LOCALE: None.None
pandas: 0.24.1
pytest: 3.6.4
pip: 18.0
setuptools: 40.2.0
Cython: None
numpy: 1.15.1
scipy: None
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.12
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"description": "Related to indexing on s... | open | false | null | [] | null | 1 | 2019-02-12T15:23:35Z | 2021-06-26T05:26:43Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
Example 1:
```python
import pandas as pd
import numpy as np
index = ['col1:sub1', 'col1:sub2', 'col2']
df = pd.DataFrame(np.random.random(size=(10, len(index))), columns = index)
df.columns = df.columns.str.split(':', expand=True)
for key in df.columns.levels[0]:
print(key, df.loc[:, key].shape)
index = ['col1:sub1', 'col1:sub2']
df = pd.DataFrame(np.random.random(size=(10, len(index))), columns = index)
df.columns = df.columns.str.split(':', expand=True)
df.loc[:, 'col2'] = np.random.random(size=(10))
for key in df.columns.levels[0]:
print(key, df.loc[:, key].shape)
```
Example 2:
```python
import pandas as pd
import numpy as np
index = pd.MultiIndex.from_tuples([
('col1', ''),
('col2', np.nan),
('col3', 'sub1'), ('col3', 'sub2'),
('col4', ''), ('col4', 'sub2'),
('col5', np.nan), ('col5', 'sub2')])
df = pd.DataFrame(np.random.random(size=(10, len(index))), columns = index)
for key in df.columns.levels[0]:
print(key, df.loc[:, key].shape)
```
#### Problem description
maybe related to #25154
This issue arises when one wants to work with mixed length MultiIndex. For example if I have a name and pH at different concentration, I would like to access `df.loc[:, 'name']` that would give me a Series, and `df.loc[:, 'pH']` that would give me a DataFrame.
To achieve that, pandas uses `''` as a subindex. Intuitively I would have thought that `nan` would have been a better choice! For example in example 1, if one uses `split` with `expand=True`, the missing values are filled with `nan`, while adding a column at a latter stage adds a `''`. Example 2 show that if the subindex is not alone, It is considered to be a DataFrame anyway.
In my opinion, it would be more intuitive if `('col2', np.nan)` was acting like `('col2', '')`. Example1 would show the same result for both techniques.
Perhaps more controversial, I think that the subindex of `df.loc[:, 'col2'] = np.random.random(size=(10))` should probably be `nan`, as this is the standard way of indicating missing data, and no data has been provided.
#### Expected Output
Example 1
```
col1 (10, 2)
col2 (10,)
col1 (10, 2)
col2 (10,)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.2.0
pip: 18.0
setuptools: 40.4.1
Cython: 0.28.5
numpy: 1.15.2
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: 1.8.1
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-02-12T15:47:27Z | 2019-02-14T01:50:05Z | 2019-02-14T01:49:50Z | CONTRIBUTOR | null | fix for the docstring of `DataFrame.stack()` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25286 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25286/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25286/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25286/events | https://github.com/pandas-dev/pandas/issues/25286 | 409,402,246 | MDU6SXNzdWU0MDk0MDIyNDY= | 25,286 | Ability to Override Default Titles for Missing Column Headers in read_csv | {
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... | closed | false | null | [] | null | 6 | 2019-02-12T16:59:24Z | 2021-06-26T05:29:49Z | 2021-06-26T05:29:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
index = pd.MultiIndex.from_tuples([('col1', ''), ('col2', 'a1'), ('col2', 'a2')])
df = pd.DataFrame(np.random.random(size=(10, 3)), columns=index)
df.to_csv('test.cvs')
df2 = pd.read_csv('test.cvs', header=[0, 1], index_col=0)
print(df.columns)
print(df2.columns)
```
#### Problem description
`pd.read_csv` renames empty cells to "Unnamed: {i}_level_{level}" on line
https://github.com/pandas-dev/pandas/blob/4d44a2aff08c3075d9472e361bd7448a0472494e/pandas/io/parsers.py#L2542
It would be great to have an option of changing this behaviour to replacing with `''` or even a generic solution.
#### Expected Output
```
MultiIndex(levels=[['col1', 'col2'], ['', 'a1', 'a2']],
codes=[[0, 1, 1], [0, 1, 2]])
MultiIndex(levels=[['col1', 'col2'], ['', 'a1', 'a2']],
codes=[[0, 1, 1], [0, 1, 2]])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.2.0
pip: 18.0
setuptools: 40.4.1
Cython: 0.28.5
numpy: 1.15.2
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: 1.8.1
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
weird_string = u"føøbår"
x = pd.DataFrame({"a" : [weird_string, "xx"]})
bool_series = (x["a"].eq(weird_string))
print(x[bool_series].to_string())
assert len(x[bool_series]) == 1
```
#### Problem description
The code snippet above crashes. This makes querying dataframes with non-ascii text quite difficult.
Exception
```
bug.py
bool_series = (x["a"].eq(weird_string))
.env\lib\site-packages\pandas\core\ops.py:1728: in wrapper
(is_extension_array_dtype(other) and not is_scalar(other))):
.env\lib\site-packages\pandas\core\dtypes\common.py:1749: in is_extension_array_dtype
registry.find(dtype) is not None)
.env\lib\site-packages\pandas\core\dtypes\dtypes.py:89: in find
return dtype_type.construct_from_string(dtype)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xf8' in position 2: ordinal not in range(128)
```
I'm using pandas 0.24.1
#### Expected Output
No crash :)
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.7.3
Cython: None
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: 0.11.3
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.14
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 22 | 2019-02-12T19:47:57Z | 2021-10-13T00:00:48Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
sample_data = []
sample_data.append({"integer_column":None})
sample_data.append({"integer_column":1})
sample_data.append({"integer_column":2})
df = pd.DataFrame(sample_data)
# Previous type is object
# df.dtypes
df.loc[:,'integer_column'] = df.loc[:,'integer_column'].astype('Int64')
# Check new type is Int64, nullable
# df.dtypes
df.fillna('null_string')
```
#### Problem description
Using the new nullable type Int64, it is not possible to fill "NaN" values with other value.
### Error raised
> TypeError: <U11 cannot be converted to an IntegerDtype
#### Expected Output
The new dataframe should have replaced it's NaN values with the desired input of .fillna() method.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.1
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.1.18
pymysql: None
psycopg2: None
jinja2: 2.8.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-02-12T21:01:45Z | 2019-02-24T03:47:53Z | 2019-02-24T03:47:24Z | CONTRIBUTOR | null | - [x] closes #25183
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"color": "20... | closed | false | null | [] | null | 6 | 2019-02-12T22:21:18Z | 2021-06-26T05:38:09Z | 2021-06-26T05:38:09Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I uses to be able to assign to a series like so:
```
import pandas as pd
pd.Series()["aaa"]= True # regular indexing
pd.Series().loc["aaa"]= True # regular indexing
pd.Series()[("aaa",123)]= True # Multi indexing
pd.Series().loc[("aaa",123)]= True # Multi indexing
```
but in pandas 0.24.1:
```
pd.Series()[("aaa",123)]= True
pd.Series().loc[("aaa",123)]= True
```
both give me:
```
KeyError: u"None of [Index([u'aaa', 1], dtype='object')] are in the [index]"
```
Was assignment of new rows by multiindex intentionally removed?
#### Problem description
Assignment by multiindex no longer works after upgrading from 0.24.1 from 0.22.0
#### Expected Output
Should not assert.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.candidate.1
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 18.1
setuptools: 40.6.2
Cython: 0.29.1
numpy: 1.16.1
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.5.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 2.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"color": "AD7FA8"... | closed | false | null | [] | null | 17 | 2019-02-12T22:55:12Z | 2019-09-20T14:42:35Z | 2019-09-20T14:42:35Z | CONTRIBUTOR | null | - [ ] closes #25006
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25292 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25292/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25292/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25292/events | https://github.com/pandas-dev/pandas/issues/25292 | 409,564,279 | MDU6SXNzdWU0MDk1NjQyNzk= | 25,292 | read_parquet segmentation fault with pyarrow | {
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Loading a dataframe with `pandas.read_parquet` with `engine='pyarrow'` can semi-randomly result in a segmentation fault.
This only seems to affect certain dataframes, others always seem to work. Unfortunately I can't share an affected dataframe because the problematic ones have sensitive data.
This happens with or without compression, and with or without `memory_map`. It happens consistently for certain data, even if I load it and save it to a different format (e.g. changing from compressed to uncompressed).
~This only happens when reading a file multiple times in a single python interpreter. Loading a file once seems to always work.~\
**EDIT:** This USUALLY happens only when reading a file multiple times in a single python interpreter. Loading a file once USUALLY works. Not always
## Examples
### Loading a file several times in one script
```python
# seg_fault.py
import pandas as pd
if __name__ == '__main__':
for i in range(100):
print('ITERATION {}: '.format(i), end='', flush=True)
x = pd.read_parquet('test-comp.parquet', engine='pyarrow', memory_map=False)
print('success', flush=True)
print('SUCCESS -- all runs succeeded')
```
#### output of seg_fault.py
```bash
ibackus@ilearn:$ python -u seg_fault.py
ITERATION 0: success
ITERATION 1: success
ITERATION 2: success
ITERATION 3: success
ITERATION 4: success
Segmentation fault (core dumped)
ibackus@ilearn:$ python -u seg_fault.py
ITERATION 0: success
ITERATION 1: success
ITERATION 2: success
ITERATION 3: success
ITERATION 4: success
ITERATION 5: success
ITERATION 6: success
ITERATION 7: success
ITERATION 8: success
ITERATION 9: success
ITERATION 10: success
ITERATION 11: success
ITERATION 12: Segmentation fault (core dumped)
ibackus@ilearn:$ python -u seg_fault.py
ITERATION 0: success
ITERATION 1: success
ITERATION 2: Segmentation fault (core dumped)
```
### Loading a file once per script
```python
# load.py
import pandas as pd
if __name__ == '__main__':
x = pd.read_parquet('test-comp.parquet', engine='pyarrow', memory_map=False)
```
```
#!/bin/bash -e
# load.sh
for run in {1..50}; do
echo "Run: $run"
python -u load.py
done
echo "ALL RUNS SUCCEEDED"
```
#### output of load.sh
```bash
ibackus@ilearn:$ ./load.sh
Run: 1
Run: 2
Run: 3
Run: 4
Run: 5
Run: 6
Run: 7
Run: 8
Run: 9
Run: 10
Run: 11
Run: 12
Run: 13
Run: 14
Run: 15
Run: 16
Run: 17
Run: 18
Run: 19
Run: 20
Run: 21
Run: 22
Run: 23
Run: 24
Run: 25
Run: 26
Run: 27
Run: 28
Run: 29
Run: 30
Run: 31
Run: 32
Run: 33
Run: 34
Run: 35
Run: 36
Run: 37
Run: 38
Run: 39
Run: 40
Run: 41
Run: 42
Run: 43
Run: 44
Run: 45
Run: 46
Run: 47
Run: 48
Run: 49
Run: 50
ALL RUNS SUCCEEDED
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-1032-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.1.0
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.12.0
xarray: None
IPython: 7.2.0
sphinx: 1.8.3
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.6
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-02-13T01:17:57Z | 2019-03-03T02:33:36Z | 2019-03-03T02:33:35Z | MEMBER | null | The travis logs have been failing recently as a result of -OO optimization failure. I cannot reproduce this locally so may just be a Windows issue?
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=8270&view=logs&jobId=ae5f5554-4e02-5fb5-2f57-d6e8aefc10c0&taskId=1f8b548e-2577-5189-1750-f82ca6ecc932&lineStart=690&lineEnd=691&colStart=1&colEnd=1 | {
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"descript... | closed | false | null | [] | null | 4 | 2019-02-13T01:47:04Z | 2019-02-13T12:29:54Z | 2019-02-13T08:07:34Z | CONTRIBUTOR | null | I posted a question about this on [StackOverflow](https://stackoverflow.com/questions/54650913/why-doesnt-pandas-update-a-variables-dtype-when-populating-a-dataframe), but though it might be something worth reporting here.
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(columns = ("Name", "Age"))
df.loc[1] = "Jane", 5
df.loc[2] = "Riley", 24
df.dtypes
```
#### Problem description
In my test:
- with Python 3.5 and Pandas 0.18.1, populating the dataframe does update the "object" dtype of the Age variable to "float64"
- with Python 3.7 and Pandas 0.23.4, populating the dataframe does not update the "object" dtype of the Age variable
Why is that? I couldn't find an explanation in the documentation.
#### Expected Output
The dtype of an ampty variable gets updated when populating it for the first time, similarly to what `infer_objects()` does.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-lowlatency
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.23.4
pytest: 4.0.1
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: 2.5.11
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 6 | 2019-02-13T03:08:35Z | 2019-02-16T17:29:49Z | 2019-02-16T17:27:50Z | MEMBER | null | This PR fixes a variety of docstring errors found in different parts of the code base (mostly incorrect names for data types, missing punctuation, PEP8 in examples, etc.). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25296 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25296/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25296/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25296/events | https://github.com/pandas-dev/pandas/issues/25296 | 409,615,840 | MDU6SXNzdWU0MDk2MTU4NDA= | 25,296 | Document Using Regex for str.split | {
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```python
import pandas as pd
df = pd.DataFrame({'col': ['a-b-c+e=d,f#t']*5})
df.col.str.split('+|=', expand=True)
```
#### Problem description
While passing two patterns separating with `|` to `str.split()` method, if one of them is `+`, `panads` returns the following error:
```
---------------------------------------------------------------------------
error Traceback (most recent call last)
in
2 import pandas as pd
3 df = pd.DataFrame({'col': ['a-b-c+e=d,f#t']*5})
----> 4 df.col.str.split('+|=', expand=True)
~\Anaconda3\lib\site-packages\pandas\core\strings.py in split(self, pat, n, expand)
2328 @copy(str_split)
2329 def split(self, pat=None, n=-1, expand=False):
-> 2330 result = str_split(self._data, pat, n=n)
2331 return self._wrap_result(result, expand=expand)
2332
~\Anaconda3\lib\site-packages\pandas\core\strings.py in str_split(arr, pat, n)
1458 if n is None or n == -1:
1459 n = 0
-> 1460 regex = re.compile(pat)
1461 f = lambda x: regex.split(x, maxsplit=n)
1462 res = _na_map(f, arr)
~\Anaconda3\lib\re.py in compile(pattern, flags)
231 def compile(pattern, flags=0):
232 "Compile a regular expression pattern, returning a pattern object."
--> 233 return _compile(pattern, flags)
234
235 def purge():
~\Anaconda3\lib\re.py in _compile(pattern, flags)
299 if not sre_compile.isstring(pattern):
300 raise TypeError("first argument must be string or compiled pattern")
--> 301 p = sre_compile.compile(pattern, flags)
302 if not (flags & DEBUG):
303 if len(_cache) >= _MAXCACHE:
~\Anaconda3\lib\sre_compile.py in compile(p, flags)
560 if isstring(p):
561 pattern = p
--> 562 p = sre_parse.parse(p, flags)
563 else:
564 pattern = None
~\Anaconda3\lib\sre_parse.py in parse(str, flags, pattern)
853
854 try:
--> 855 p = _parse_sub(source, pattern, flags & SRE_FLAG_VERBOSE, 0)
856 except Verbose:
857 # the VERBOSE flag was switched on inside the pattern. to be
~\Anaconda3\lib\sre_parse.py in _parse_sub(source, state, verbose, nested)
414 while True:
415 itemsappend(_parse(source, state, verbose, nested + 1,
--> 416 not nested and not items))
417 if not sourcematch("|"):
418 break
~\Anaconda3\lib\sre_parse.py in _parse(source, state, verbose, nested, first)
614 if not item or (_len(item) == 1 and item[0][0] is AT):
615 raise source.error("nothing to repeat",
--> 616 source.tell() - here + len(this))
617 if item[0][0] in _REPEATCODES:
618 raise source.error("multiple repeat",
error: nothing to repeat at position 0
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.7.1
pip: 18.1
setuptools: 40.2.0
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: 0.11.0
IPython: 7.1.1
sphinx: 1.7.6
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.5
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.5
lxml: 4.2.4
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.10
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 3 | 2019-02-13T07:42:48Z | 2019-02-16T22:34:09Z | 2019-02-16T17:22:41Z | MEMBER | null | - [x] closes #22660
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2019-02-13T08:03:41Z | 2021-10-05T12:37:29Z | null | NONE | null | I am trying to index data frame with a `MultiIndex` with constituent `IntervalIndex`
#### Example
```python
ii = pd.IntervalIndex.from_arrays([1, 5, 2, 6, 3, 7], [4, 8, 5, 9, 6, 10])
mi = pd.MultiIndex.from_arrays([["aa", "aa", "bb", "bb", "cc", "cc"], ii])
data = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12)]
df = pd.DataFrame(data, columns=['k', 'l'], index=mi)
df.loc[("bb", 3)]
```
Above code throws `TypeError` as follows:
```python
TypeError: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [3] of <class 'int'>
```
I was expecting above to produce same result as:
```python
df.loc[("bb")].loc[3]
# k 5
# l 6
# Name: (2, 5], dtype: int64
```
Is this a bug or I'm not using it properly? (newbie here)
#### Version Details:
```
python: Python 3.6.7 | packaged by conda-forge | (default, Nov 21 2018, 03:09:43)
pandas: '0.23.4'
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} | 7 | 2019-02-13T09:15:34Z | 2019-02-16T18:50:48Z | 2019-02-16T18:50:48Z | NONE | null | I have a Pandas DataFrame with one Serie containing ordered Categorical data. Some value of this Serie may be missing (NaN). I want to get the minimum without taking into account NaNs but I obtained strange results ...
#### Code:
```python
raw_cat = pd.Categorical(["a", "b", "c", "a"],
categories=["b", "c", "d"],
ordered=True)
s = pd.Series(raw_cat)
raw_cat.min(numeric_only=True), s.min(numeric_only=True)
```
#### Output:
```python
('b', nan)
```
#### Expected utput:
```python
('b', 'b')
```
I am getting the desired output when running this code with pandas 0.23.4 but not with pandas 0.24.0 and above.
Is this an issue or a misunderstanding? Thank you for your help. | {
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} | 1 | 2019-02-13T10:10:02Z | 2019-02-13T11:49:46Z | 2019-02-13T11:49:36Z | NONE | null | Column `from` gets automatically renamed when DataFrame is converted to dictionary.
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.ones((5, 2)), columns=['from', 'sum'])
print(df.to_dict(orient='records'))
```
Obove code returns:
```
[{'_0': 1.0, 'sum': 1.0}, {'_0': 1.0, 'sum': 1.0}, {'_0': 1.0, 'sum': 1.0}, {'_0': 1.0, 'sum': 1.0}, {'_0': 1.0, 'sum': 1.0}]
```
instead of:
```
[{'from': 1.0, 'sum': 1.0}, {'from': 1.0, 'sum': 1.0}, ...
```
Is this a bug or intended behaviour?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-1027-gcp
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.0
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 7.1.1
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: 2.5.9
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.10
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: 0.2.0
</details>
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} | [] | closed | false | null | [] | null | 4 | 2019-02-13T12:35:49Z | 2019-02-13T14:09:39Z | 2019-02-13T14:09:39Z | NONE | null | The current stable documentation page for eq doesn't actually feature eq in the example:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.eq.html
I presume the last execution in the example should be something like this, rather than the add example.
```
>>> a.eq(b)
a True
b False
c False
d False
e False
dtype: bool
```
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} | 3 | 2019-02-13T13:27:01Z | 2019-03-03T02:31:38Z | 2019-03-03T02:31:38Z | CONTRIBUTOR | null | anyone know why the wheel building is timing out, not all of them though: https://travis-ci.org/MacPython/pandas-wheels. I have restarted on multiple days.
cc @TomAugspurger @matthew-brett | {
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} | 7 | 2019-02-13T13:36:51Z | 2019-12-02T10:37:12Z | 2019-12-02T10:37:12Z | MEMBER | null | Consider an ordered Categorical with missing values:
```
In [32]: cat = pd.Categorical(['a', np.nan, 'b', 'a'], ordered=True)
In [33]: cat.min()
Out[33]: nan
In [34]: cat.max()
Out[34]: 'b'
In [35]: cat.min(numeric_only=True)
Out[35]: 'a'
In [36]: cat.max(numeric_only=True)
Out[36]: 'b'
In [37]: cat.min(numeric_only=False)
Out[37]: nan
In [38]: cat.max(numeric_only=False)
Out[38]: 'b'
```
So from the observation above (and from the code: https://github.com/pandas-dev/pandas/blob/a89e19d59e0bff2d02e4647af1904e2c9701dd5f/pandas/core/arrays/categorical.py#L2199), it seems that `numeric_only` means that only the actual categories should be considered, and not the missing values (so codes that are not -1).
This struck me as strange, for the following reasons:
* The fact that -1 is used as the code for missing data is rather an implementation detail, but now actually determines min/max behaviour (missing value is always the minimum, but never the maximum, unless there are only missing values)
* This behaviour is different than the default for other data types in pandas, which is skipping missing values by default:
```
In [1]: s = pd.Series([1, np.nan, 2, 1])
In [2]: s.min()
Out[2]: 1.0
In [3]: s.astype(pd.CategoricalDtype(ordered=True)).min()
Out[3]: nan
In [5]: s.min(skipna=False)
Out[5]: nan
```
* The keyword in pandas to determine whether NaNs should be skipped or not for reductions is `skipna=True/False`, not `numeric_only` (this also means the `skipna` keyword for categorical series is broken / has no effect).
Apart from that, the name "numeric_only" is also strange to me to mean this (and is also not documented).
* The `numeric_only` keyword in reductions methods of DataFrame actually means something entirely different: should full columns be excluded from the result based on their dtype.
```
In [63]: cat = pd.Categorical(['a', np.nan, 'b', 'a'], ordered=True)
In [64]: pd.Series(cat).min(numeric_only=True)
Out[64]: 'a'
In [65]: pd.DataFrame({'cat': cat}).min(numeric_only=True)
Out[65]: Series([], dtype: float64)
```
From the above list, I don't see a good reason for having `numeric_only=False` as 1) the default behaviour and 2) altogether as an option (instead of skipna). But it seems this was implemented rather from the beginning that Categoricals were introduced.
*Am I missing something?*
*Is there a reason we don't skip NaNs by default for Categorical?*
Would it be an idea to deprecate `numeric_only` in favor of `skipna` and deprecate the default?
cc @jreback @jankatins
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} | 14 | 2019-02-13T14:10:56Z | 2019-02-16T18:49:58Z | 2019-02-16T18:49:51Z | CONTRIBUTOR | null | See https://github.com/pandas-dev/pandas/issues/25303 and https://github.com/pandas-dev/pandas/issues/25299
- [x] closes https://github.com/pandas-dev/pandas/issues/25299
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 5 | 2019-02-13T14:37:24Z | 2019-02-15T04:07:59Z | 2019-02-15T04:07:55Z | CONTRIBUTOR | null | Signed-off-by: Takuya Noguchi <takninnovationresearch@gmail.com> | {
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} | 5 | 2019-02-13T19:12:48Z | 2021-11-21T00:50:06Z | null | CONTRIBUTOR | null | #### Problem description
In order to have a continuous check by the CI on validation error `SS03` (Summary does not end with a period), fixing them in the code base enables the addition to the CI for automated testing in the future.
`SS03` has 178 occurrences (see [overview of the errors](https://gist.github.com/198cfa552c727d71bd7360fda66fdbb4)), including:
method | file | file_line | github_link
-- | -- | -- | -- |
pandas.Timestamp | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.max | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.min | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.resolution | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.tz | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.ceil | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.combine | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.floor | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.fromordinal | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.isoweekday | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.replace | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.round | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.to_datetime64 | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timestamp.weekday | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.components | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.view | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.ceil | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.floor | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.isoformat | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.round | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.to_pytimedelta | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.to_timedelta64 | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Timedelta.total_seconds | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Period | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Period.asfreq | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Period.strftime | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Period.to_timestamp | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Interval.closed | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Interval.left | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Interval.length | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
... | ... | ... | ...
pandas.tseries.offsets.CBMonthEnd.normalize | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.tseries.offsets.CBMonthBegin.normalize | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.tseries.offsets.CDay.normalize | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.tseries.frequencies.to_offset | pandas/tseries/frequencies.py | 59.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/frequencies.py#L59
pandas.Panel.axes | pandas/core/generic.py | 441.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L441
pandas.Panel.shape | pandas/core/generic.py | 434.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L434
pandas.Panel.__iter__ | pandas/core/generic.py | 1819.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L1819
pandas.Panel.iteritems | pandas/core/generic.py | 1832.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L1832
pandas.Panel.lt | pandas/core/ops.py | 2170.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/ops.py#L2170
pandas.Panel.gt | pandas/core/ops.py | 2170.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/ops.py#L2170
pandas.Panel.le | pandas/core/ops.py | 2170.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/ops.py#L2170
pandas.Panel.ge | pandas/core/ops.py | 2170.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/ops.py#L2170
pandas.Panel.ne | pandas/core/ops.py | 2170.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/ops.py#L2170
pandas.Panel.eq | pandas/core/ops.py | 2170.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/ops.py#L2170
pandas.Panel.swaplevel | pandas/core/generic.py | 932.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L932
pandas.Panel.transpose | pandas/core/panel.py | 1269.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/panel.py#L1269
pandas.plotting.deregister_matplotlib_converters | pandas/plotting/_converter.py | 88.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_converter.py#L88
pandas.plotting.register_matplotlib_converters | pandas/plotting/_converter.py | 55.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_converter.py#L55
pandas.core.resample.Resampler.first | pandas/core/resample.py | 857.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L857
pandas.core.resample.Resampler.last | pandas/core/resample.py | 857.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L857
pandas.core.resample.Resampler.max | pandas/core/resample.py | 857.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L857
pandas.core.resample.Resampler.min | pandas/core/resample.py | 857.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L857
pandas.core.resample.Resampler.prod | pandas/core/resample.py | 846.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L846
pandas.core.resample.Resampler.sum | pandas/core/resample.py | 846.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L846
pandas.io.formats.style.Styler.loader | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.io.formats.style.Styler.set_caption | pandas/io/formats/style.py | 778.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/style.py#L778
pandas.Panel.items | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Panel.keys | pandas/core/generic.py | 1824.0 | https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L1824
pandas.Panel.major_axis | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
pandas.Panel.minor_axis | None | NaN | https://github.com/pandas-dev/pandas/blob/master/None#LNone
Todo:
* [ ] get rid of the errors in the code base
* [ ] update the `code_check.sh` script to take into account the `SS03` type of errors | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25307/events | https://github.com/pandas-dev/pandas/issues/25307 | 409,964,393 | MDU6SXNzdWU0MDk5NjQzOTM= | 25,307 | Wrong result for float32 Series when using bottleneck | {
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] | closed | false | null | [] | null | 8 | 2019-02-13T19:29:17Z | 2020-03-08T18:13:09Z | 2020-03-08T18:13:08Z | NONE | null | #### Minimal example
Requires bottleneck, numpy and pandas to be installed:
```python
>>> import numpy as np
>>> import pandas as pd
>>> import bottleneck as bn
>>> data = np.ones(2**25, dtype=np.float32)
>>> pd.Series(data).mean() # wrong
0.5
>>> bn.nanmean(data) # wrong
0.5
>>> data.mean() # correct
1.0
```
#### Problem description
The `mean()` of large float32 Series is wrong when [bottleneck](https://github.com/kwgoodman/bottleneck/) is used. Uninstalling bottleneck or using float64 is a valid workaround. xarray is or has been affected too, see pydata/xarray#1346.
<s>Bottleneck's documentation explicitly mentions that [no error is raised in case of an overflow](https://kwgoodman.github.io/bottleneck-doc/reference.html?highlight=overflow#bottleneck.nanmean), not sure if this is still to be considered as bug in bottleneck.</s> Anyhow since it seems quite severe, I want to raise attention here too.
**Update:** This is not an overflow, it's a numerical error (which is very high because bottleneck does not use [pairwise summation](https://en.wikipedia.org/wiki/Pairwise_summation)).
[Bottleneck's implementation of mean()](https://github.com/kwgoodman/bottleneck/blob/master/bottleneck/src/reduce_template.c#L162).
#### Related issues
* same thing in xarray: pydata/xarray#1346
* same thing in aospy: spencerahill/aospy#217
* similar bug in pandas with bottleneck, but related to int not float: pandas-dev/pandas#15453 and kwgoodman/bottleneck#163
* another very similar but older bug in pandas with bottleneck but related to int not float: pandas-dev/pandas#6915 kwgoodman/bottleneck#83
* something different, not to be confused - race conditions when reading netcdf files: dask/dask#2095
* probably not related, but who knows: kwgoodman/bottleneck#164
#### Expected Output
```python
>>> data = np.ones(2**25, dtype=np.float32)
>>> pd.Series(data).mean()
1.0
```
#### Output of ``pd.show_versions()``
<details>
```bash
$ pip3 freeze
Bottleneck==1.2.1
numpy==1.16.1
pandas==0.24.1
...
```
```python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-13-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.2
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: 0.11.3
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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} | 5 | 2019-02-13T19:55:07Z | 2019-02-28T17:39:01Z | 2019-02-28T17:38:48Z | MEMBER | null | - [x] closes #23683
- [x] closes #21603
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 8 | 2019-02-13T20:41:40Z | 2019-02-14T20:37:45Z | 2019-02-14T19:18:09Z | CONTRIBUTOR | null | - [x] closes #25108
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] updates CI script to check for future testing automation | {
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} | 7 | 2019-02-13T22:10:35Z | 2019-02-19T18:55:20Z | 2019-02-19T13:32:42Z | CONTRIBUTOR | null | - [x] closes #9236
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The referenced issue seems to be solved in the current master. So added a test for the edge case. | {
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} | 8 | 2019-02-13T23:42:04Z | 2019-03-11T16:56:29Z | 2019-03-11T15:26:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df.to_csv('uncompressed.csv')
df.to_csv('compressed-wrong-line-terminator.csv.gz')
df.to_csv('compressed-good-line-terminator.csv.gz', line_terminator='\n')
```
#### Problem description
Current line_terminator defaults when using compression and when not using compression are different (Windows OS, pandas 0.24.1).
When uncompressing the gzip file created using the default line_terminator, we can clearly see that the files are different (compressed-wrong-line-terminator.csv vs uncompressed.csv); only when using the explicit line_termintor='\n' the uncompressed file is identical to the not compressed file (compressed-good-line-terminator.csv.gz vs. uncompressed.csv).
It is emphasized that if we use the explicit line_terminator='\n' for non-compressed files, the output file is different than the ones created without explicit assignment of the line_terminator - forcing the user the need to explicitly specify the line_terminator only for compressed files.
This behavior is problematic, especially using the latest pandas version, where compression is inferred from the file extension, and one would expect that also the line_separator will undergo the same inference.
#### Expected Output
As stated above, it is expected that the command in python line 2 (after uncompressing it) will produce the same file as produced by the command in python line 1.
However, we see that only the command in python line 3 (after uncompressing it) produces the same file as produced by the command in python line 1.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.1
setuptools: 40.4.3
Cython: None
numpy: 1.15.2
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25312/events | https://github.com/pandas-dev/pandas/issues/25312 | 410,097,797 | MDU6SXNzdWU0MTAwOTc3OTc= | 25,312 | Pandas graphing time series issue | {
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import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib qt
exampl = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]
dates = pd.date_range('2016/10/29 5:30pm', periods = 12, freq='H')
series = pd.Series(exampl, dates)
series15m = series.resample('15min').interpolate(method = 'cubic')
series15m.plot()
series.plot()
plt.legend()
plt.show()
```
#### Problem description
I tried this code and the graph it ends up is messed up, I figured it was matplotlib and I asked them for help and the lead developer told me it probably wasn't matplotlib.
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