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DOC: Standardize references to pandas in the documentation
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| 2020-02-28T00:54:20Z
| 2020-10-04T20:13:32Z
| 2020-10-04T20:13:32Z
|
MEMBER
| null |
In our documentation (files in `doc/source/../*.rst`), we refer to pandas as `Pandas`, `pandas`, and `*pandas*` (sphinx italics).
For documentation consistency, we should standardize these to one form. `pandas` is the preferable form in our documentation files.
Unfortunately I don't think we create a lint rule for this pattern since we have to be flexible to references and hyperlinks.
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REF: test_first_valid_index
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| 2020-02-28T01:33:24Z
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MEMBER
| null |
For this one I went ahead and combined the two files, split tests, and parametrized. We end up covering a few more cases now than in the status quo (Series with index of strings, DataFrame with DTI)
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API: Named aggregations for groupby.transform()
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| 2020-02-28T02:20:44Z
| 2021-07-29T04:48:13Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: df = pd.DataFrame({
...: 'A': [1, 1, 2, 2, 3, 3],
...: 'B': [4, 5, 6, 7, 8, 9]
...: })
In [3]: df
Out[3]:
A B
0 1 4
1 1 5
2 2 6
3 2 7
4 3 8
5 3 9
In [4]: df.groupby('A').agg(maximum=('B', 'max'))
Out[4]:
maximum
A
1 5
2 7
3 9
In [5]: df.groupby('A').transform(maximum=('B', 'max'))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-10080a262c70> in <module>
----> 1 df.groupby('A').transform(maximum=('B', 'max'))
TypeError: transform() missing 1 required positional argument: 'func'
In [6]: df['maximum'] = df.groupby('A')['B'].transform('max')
In [7]: df
Out[7]:
A B maximum
0 1 4 5
1 1 5 5
2 2 6 7
3 2 7 7
4 3 8 9
5 3 9 9
```
#### Problem description
[Named aggregation](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#named-aggregation) streamlines the process of creating columns as a result of `groupby.agg()`. A similar (identical?) API for `groupby.transform()` would provide the same benefit when creating new columns from `groupby.transform()`.
This issue may be related to #28380.
#### Expected Output
```python
In [2]: df = pd.DataFrame({
...: 'A': [1, 1, 2, 2, 3, 3],
...: 'B': [4, 5, 6, 7, 8, 9]
...: })
In [3]: df.groupby('A').transform(maximum=('B', 'max'))
Out[3]:
A B maximum
0 1 4 5
1 1 5 5
2 2 6 7
3 2 7 7
4 3 8 9
5 3 9 9
```
#### Output of ``pd.show_versions()``
<details>
In [8]: pd.show_versions()
C:\Users\dwales\AppData\Local\Continuum\anaconda3\lib\site-packages\fastparquet\dataframe.py:5: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
from pandas.core.index import CategoricalIndex, RangeIndex, Index, MultiIndex
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.3
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzgxMTc1NDY1
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CLN: remove _igetitem_cache
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MEMBER
| null |
Only used once (recent bugfixes got rid of the user uses IIRC) and its just an extra layer that isnt needed.
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MDExOlB1bGxSZXF1ZXN0MzgxMTgzNDI3
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BUG: parse_dates may have columns not in dataframe
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| 2020-02-28T03:28:41Z
| 2020-03-17T03:49:49Z
| 2020-03-17T00:03:12Z
|
CONTRIBUTOR
| null |
read_csv will raise ValueError when columnes used for parse_dates are found in the dataframe.
- [x] closes #31251
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 32,321
|
Cant Read Panda
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| 2020-02-28T03:48:49Z
| 2020-02-28T04:17:33Z
| 2020-02-28T04:17:32Z
|
NONE
| null |
```
first_concurrent_all_table = pd.read_html(requests.get(url).text)
first_concurrent_first_table = first_concurrent_all_table[0]
print(first_concurrent_first_table)
```
The error is the following :
irst_concurrent_all_table = pd.read_html(requests.get(url).text)
File "/usr/local/lib/python3.7/dist-packages/pandas/io/html.py", line 1100, in read_html
displayed_only=displayed_only,
File "/usr/local/lib/python3.7/dist-packages/pandas/io/html.py", line 915, in _parse
raise retained
File "/usr/local/lib/python3.7/dist-packages/pandas/io/html.py", line 895, in _parse
tables = p.parse_tables()
File "/usr/local/lib/python3.7/dist-packages/pandas/io/html.py", line 213, in parse_tables
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
File "/usr/local/lib/python3.7/dist-packages/pandas/io/html.py", line 545, in _parse_tables
raise ValueError("No tables found")
ValueError: No tables found
When I tried this yesterday it was fine. The website had not changed anything regarding their code.
Desired output is the table.
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MDExOlB1bGxSZXF1ZXN0MzgxMjY4OTUz
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|
Fix exception causes all over the code
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| 2
| 2020-02-28T08:37:25Z
| 2020-06-24T08:34:18Z
| 2020-02-28T10:22:52Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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STY: spaces in wrong place
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| 2020-02-28T09:33:22Z
| 2020-02-28T10:28:34Z
| 2020-02-28T10:15:59Z
|
MEMBER
| null |
- [x] ref #30755
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MEMBER
| null |
xref #31867
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ENH: Syntactic sugar for DatetimeIndex datetime slicing
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| 2020-02-28T10:03:06Z
| 2020-09-18T17:09:57Z
| 2020-09-18T17:09:56Z
|
NONE
| null |
```python
import pandas as pd
from datetime import datetime
index = pd.date_range(start=datetime(2019, 1, 1), end=datetime(2020, 1, 1), freq='D')
ts = pd.Series(index=index, dtype=float)
start_date = datetime(2019, 6, 1)
index_from_ts_slice = ts[start_date:].index
# this errors:
try:
index_from_index_slice = index[start_date:]
except TypeError as e:
print(e)
# I can use slice_indexer to get what I want but it is uglier
index_from_slice_indexer = index[index.slice_indexer(start_date)]
assert (index_from_ts_slice == index_from_slice_indexer).all()
```
When a Series or a DataFrame has a sorted DatetimeIndex, one can slice these structures using datetimes and square brackets notation. However this kind of slicing is not supported directly on a DatetimeIndex.
Basically I really like the datetime slicing notation and I'd like index[start_date:] to be able to return the same slice of the index as ts[start_date:].index or index[index.slice_indexer(start_date)]
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.93-boot2docker
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.14.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.9999999
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.4.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 2.0.0
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 0.17.1
sqlalchemy : None
tables : 3.4.2
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
numba : 0.35.0
</details>
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Unexpected behaviour of df.to_json(compression="gzip")
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| 2020-05-08T21:26:01Z
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NONE
| null |
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": ["A", "B", "C", "D"],})
json_df = df.to_json(lines=True, orient="records", compression="gzip")
print(json_df)
```
#### Problem description
The behavior of the "compression" argument is unexpected. It doesn't have any effect. Although [it is documented](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_json.html) that it is only applied if there is a filename argument, there should at very least be a warning if compression is set, but no filename is given.
Also: Why doesn't it have an effect? Couldn't we output a bytes object?
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local test failure - pandas/tests/io/test_parquet.py::TestBasic::test_compression[fastparquet-snappy]
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MEMBER
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```
$ conda list snappy
# packages in environment at C:\Users\simon\Anaconda3\envs\pandas-dev:
#
# Name Version Build Channel
python-snappy 0.5.4 py37hd25c944_1 conda-forge
snappy 1.1.8 he025d50_1 conda-forge
```
```
pandas/tests/io/test_parquet.py::TestBasic::test_compression[fastparquet-snappy] Windows fatal exception: code 0xc000001d
Current thread 0x00002074 (most recent call first):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\snappy\snappy.py", line 92 in uncompress
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\compression.py", line 44 in snappy_decompress
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\compression.py", line 145 in decompress_data
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 25 in _read_page
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 99 in read_data_page
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 245 in read_col
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 331 in read_row_group_arrays
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 354 in read_row_group
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\api.py", line 228 in read_row_group
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\api.py", line 399 in to_pandas
File "C:\Users\simon\pandas\pandas\io\parquet.py", line 195 in read
File "C:\Users\simon\pandas\pandas\io\parquet.py", line 300 in read_parquet
File "C:\Users\simon\pandas\pandas\tests\io\test_parquet.py", line 168 in compare
File "C:\Users\simon\pandas\pandas\tests\io\test_parquet.py", line 174 in check_round_trip
File "C:\Users\simon\pandas\pandas\tests\io\test_parquet.py", line 317 in test_compression
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\python.py", line 167 in pytest_pyfunc_call
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\python.py", line 1445 in runtest
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 134 in pytest_runtest_call
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 210 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 237 in from_call
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 210 in call_runtest_hook
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 185 in call_and_report
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 99 in runtestprotocol
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 84 in pytest_runtest_protocol
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 271 in pytest_runtestloop
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 247 in _main
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 197 in wrap_session
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 240 in pytest_cmdline_main
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\config\__init__.py", line 93 in main
File "C:\Users\simon\Anaconda3\envs\pandas-dev\Scripts\pytest-script.py", line 10 in <module>
Fatal Python error: Illegal instruction
Current thread 0x00002074 (most recent call first):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\snappy\snappy.py", line 92 in uncompress
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\compression.py", line 44 in snappy_decompress
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\compression.py", line 145 in decompress_data
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 25 in _read_page
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 99 in read_data_page
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 245 in read_col
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 331 in read_row_group_arrays
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\core.py", line 354 in read_row_group
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\api.py", line 228 in read_row_group
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\api.py", line 399 in to_pandas
File "C:\Users\simon\pandas\pandas\io\parquet.py", line 195 in read
File "C:\Users\simon\pandas\pandas\io\parquet.py", line 300 in read_parquet
File "C:\Users\simon\pandas\pandas\tests\io\test_parquet.py", line 168 in compare
File "C:\Users\simon\pandas\pandas\tests\io\test_parquet.py", line 174 in check_round_trip
File "C:\Users\simon\pandas\pandas\tests\io\test_parquet.py", line 317 in test_compression
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\python.py", line 167 in pytest_pyfunc_call
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\python.py", line 1445 in runtest
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 134 in pytest_runtest_call
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 210 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 237 in from_call
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 210 in call_runtest_hook
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 185 in call_and_report
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 99 in runtestprotocol
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\runner.py", line 84 in pytest_runtest_protocol
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 271 in pytest_runtestloop
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 247 in _main
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 197 in wrap_session
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 240 in pytest_cmdline_main
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 86 in <lambda>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 92 in _hookexec
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\_pytest\config\__init__.py", line 93 in main
File "C:\Users\simon\Anaconda3\envs\pandas-dev\Scripts\pytest-script.py", line 10 in <module>
```
<details>
INSTALLED VERSIONS
------------------
commit : bd2ee0fa1f1244aa4f0d9f1a7fea0d9e66ce72b4
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 1.1.0.dev0+631.gbd2ee0fa1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200209
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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| 572,696,158
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MDExOlB1bGxSZXF1ZXN0MzgxMzQxMjM4
| 32,328
|
CLN: Removed unused variables defenition
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| 1
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| 2020-02-29T10:24:23Z
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MEMBER
| null |
I don't see ```found``` and ```count``` being used anywhere in this function.
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MDExOlB1bGxSZXF1ZXN0MzgxMzY2MDQw
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CLN: _libs.interval looping with cdef index
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| 2
| 2020-02-28T12:28:47Z
| 2020-02-29T10:25:01Z
| 2020-02-28T16:41:26Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU1NzI3MzAyODI=
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|
How to tell if Accessor is Registered
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[] | null | 2
| 2020-02-28T12:37:24Z
| 2021-07-29T06:12:23Z
| null |
NONE
| null |
I have a simple question:
How do you know if your accessor namespace has already been registered with the extension framework?
Let's say I do:
```python
import pandas as pd
import coolaccessor # registers dataframe accessor/namespace
```
Now later on in the code, a person does:
```python
def cool_function(a,b):
import coolaccessor
return pd.cool.shibby(a,b)
```
It will try to re-register the accessor. This produces a warning during testing if that case occurs. So how can I check that if on re-import the registration doesn't need to occur again?
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|
Local test failure plotting.test_datetimelike.test_ts_plot_with_tz
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[] | null | 6
| 2020-02-28T13:47:36Z
| 2020-07-01T16:59:44Z
| 2020-07-01T16:59:44Z
|
MEMBER
| null |
After running:
```
(venv-pandas) $ pytest pandas/tests/plotting/test_datetimelike.py -k "test_ts_plot_with_tz"
```
I got a test failure.
Test log:
<details>
```
================================================== FAILURES ==================================================
___________________________________ TestTSPlot.test_ts_plot_with_tz['UTC'] ___________________________________
self = <pandas.tests.plotting.test_datetimelike.TestTSPlot object at 0x63f623915bb0>, tz_aware_fixture = 'UTC'
@pytest.mark.slow
def test_ts_plot_with_tz(self, tz_aware_fixture):
# GH2877, GH17173, GH31205, GH31580
tz = tz_aware_fixture
index = date_range("1/1/2011", periods=2, freq="H", tz=tz)
ts = Series([188.5, 328.25], index=index)
with tm.assert_produces_warning(None):
_check_plot_works(ts.plot)
ax = ts.plot()
xdata = list(ax.get_lines())[0].get_xdata()
# Check first and last points' labels are correct
> assert (xdata[0].hour, xdata[0].minute) == (0, 0)
E AttributeError: 'numpy.datetime64' object has no attribute 'hour'
pandas/tests/plotting/test_datetimelike.py:57: AttributeError
```
</details>
---
Output of ```pd.show_versions()```:
<details>
INSTALLED VERSIONS
------------------
commit : 4e5e73e2318a1221eb56ae7e68688278ca87ca6f
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.19.a-1-hardened
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+635.g4e5e73e23
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.3
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzgxNDAyNTQ3
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BUG: Rolling groupby should not maintain the by column in the resulting DataFrame
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[] | null | 5
| 2020-02-28T13:55:34Z
| 2020-10-10T14:14:53Z
| 2020-03-22T21:08:34Z
|
MEMBER
| null |
- [x] closes #32262
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Does this seem like the correct idea? If so, I'll expand on tests / write whatsnew / think about a cleaner patch
EDIT
----
lots of unwanted changes here, just seeing what tests fail
EDIT
----
need to check the types make sense and see what's the proper way to write
```
kwargs["exclusions"] = self.exclusions
```
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DOC: Fixed reference to `convert_dtypes` in `to_numeric` (#32295)
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| 1
| 2020-02-28T14:37:28Z
| 2020-03-02T10:20:40Z
| 2020-02-29T03:07:24Z
|
CONTRIBUTOR
| null |
- closes #32295
- just changed the reference from convert_dtypes to DataFrame.convert_dtypes
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DataFrame.sort_index() with ascending passed as a list on a single level index gives wrong result
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| 2020-02-28T15:44:13Z
| 2020-10-01T18:32:25Z
| 2020-10-01T18:32:25Z
|
CONTRIBUTOR
| null |
I found some problems while using data_frame sort_index, first, create dataframe
```
data = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])
data = data.set_index('a')
data.sort_index(level=['a'], ascending=[False])
```
Then I want to sort index "a" in descending order,
```
data.sort_index(level=['a'], ascending=[False])
```
The above code does not sort index a in descending order, I found the reason is that this dataframe is a single index structure, so when ascending = list is passed, it does not take effect
https://github.com/pandas-dev/pandas/blob/bf613c14f41b624b432d0d6b9a29007e7990d460/pandas/core/frame.py#L4777-L4783
Because when it is used, the index may be single index or multi-index. In order to use the effect uniformly,I think there should be a judgment here. If index is a single index and ascending type == list, ascending should be equal to ascending [0]，This may be more user friendly
If yes, can I mention pr
|
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MDExOlB1bGxSZXF1ZXN0MzgxNDYwMDAz
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ENH: implement fill_value for df.add(other=Series) #13488
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| 2020-02-28T15:53:18Z
| 2020-03-11T02:17:59Z
| 2020-03-11T02:17:58Z
|
NONE
| null |
- [ ] closes #13488
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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CLN: Don't create _join_functions
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| 2
| 2020-02-28T16:04:25Z
| 2020-04-09T02:37:43Z
| 2020-03-03T03:09:21Z
|
MEMBER
| null |
This seems more clear than defining `_join_functions` far away from where it's actually used
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MDExOlB1bGxSZXF1ZXN0MzgxNTA1NzEz
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|
BUG: fixes unhandled NAType when plotting (#32073)
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| 2020-03-01T17:58:51Z
| 2020-03-01T17:58:51Z
|
CONTRIBUTOR
| null |
- [x] closes #32073
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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TST/REF: move tools test files
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| 5
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MEMBER
| null |
These files are in the index tests, but they're not really testing the index objects.
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PERF: short-circuit (left == right).all() comparisons
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[] | null | 4
| 2020-02-28T19:20:46Z
| 2021-07-29T06:13:31Z
| null |
MEMBER
| null |
In places like `equals` methods and `array_equivalent`, we do things like `(left == right).all()` or `((left == right) | (isna(left) & isna(right))).all()`. For large arrays that are not equal, we can do much better with something like:
```
def all_match(left, right) -> bool:
if left.dtype.kind != "i":
# viewing as i8 will make NaNs be treated as equal
return _all_match_i8(left.view("i8"), right.view("i8"))
return _all_match_i8(left, right)
cdef bint _all_match_i8(const int64_t[:] left, const int64_t[:] right):
cdef:
Py_ssize_t i, n = len(left)
for i in range(n):
if left[i] != right[i]:
return False
return True
```
Some profiling results:
```
In [2]: arr = np.arange(10**6)
In [3]: arr2 = arr.copy()
In [4]: arr2[0] = -1
In [5]: %timeit np.array_equal(arr, arr2)
831 µs ± 42.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [6]: %timeit all_match(arr, arr2)
1.27 µs ± 58.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [7]: %timeit np.array_equal(arr, arr)
416 µs ± 16.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [8]: %timeit all_match(arr, arr)
812 µs ± 5.84 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
So in cases that short circuit early, we can get massive speedups, but this implementation is actually 2x slower in cases that dont short-circuit (for reasons that are not clear to me).
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BUG: None / Timedelta incorrectly returning NaT
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| 3
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| 2021-11-20T23:21:50Z
| 2020-03-03T02:05:29Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xref #31869, arguable whether or not this closes that.
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CLN: setitem_with_indexer cleanups
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| 1
| 2020-02-28T23:37:52Z
| 2020-03-03T02:07:33Z
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MEMBER
| null |
working on fixing some significant bugs in setitem_with_indexer, this breaks off some easier cleanups
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[] | null | 7
| 2020-02-28T23:49:01Z
| 2021-05-28T03:53:15Z
| 2021-05-28T03:53:15Z
|
CONTRIBUTOR
| null |
In our case this arose via `.cumsum()`:
```
pd.Series([0, 1], dtype='int64').cumsum()
0 0
1 1
dtype: int64
pd.Series([0, 1], dtype='Int64').cumsum()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-31-2ebc3e3cf698> in <module>
----> 1 pd.Series([0, 1], dtype='Int64').cumsum()
...
~/model/.venv/lib/python3.7/site-packages/pandas/core/generic.py in na_accum_func(blk_values)
11324 np.putmask(result, mask, mask_b)
11325 else:
> 11326 result = accum_func(blk_values.T, axis)
11327
11328 # transpose back for ndarray, not for EA
AttributeError: 'IntegerArray' object has no attribute 'T'
```
But it's also true that just calling `.values.T` on a regular `int` array works (just a no-op), whereas on an `Int64` array it does not. Same is true for
Any reason not to just add a no-op `.T` on the base ExtensionArray class..? cc @TomAugspurger in case you have an opinion 🙂
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C Functionality for Accessing DataFrame Values in Column Order
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| 2020-02-29T00:28:10Z
| 2020-03-04T01:06:46Z
| 2020-03-04T01:06:46Z
|
MEMBER
| null |
This is very rough and was a little hesitant to even push here, but figured worth opening up to feedback.
The main motivation for this PR is to get block management out of the JSON serializer, but it could potentially be extended beyond that for external uses
The gist of this is that we can hold a struct `PdOrderedArrays` which gives the length of the DataFrame and sequential access to Numpy arrays that mirror what the user sees when dealing with a DataFrame, all by deconstructing the blocks that already exist down in some C functions
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| 32,344
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BUG: Groupby.transform with tshift giving incorrect result
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| 2020-02-29T01:09:22Z
| 2021-07-29T06:15:06Z
| null |
CONTRIBUTOR
| null |
Came across this inconsistency whilst trying to write a test for `tshift` in #32069
```
>>> import pandas as pd
>>> df = pd.DataFrame({'A':[121, 121, 121, 121, 231, 231, 676], 'B':[1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0], "C": pd.date_range("2013-11-03", periods=7)})
>>> df
A B C
0 121 1.0 2013-11-03
1 121 2.0 2013-11-04
2 121 2.0 2013-11-05
3 121 3.0 2013-11-06
4 231 3.0 2013-11-07
5 231 3.0 2013-11-08
6 676 4.0 2013-11-09
>>> g = df.set_index("C").groupby("A")
>>> g.transform(lambda x: x.tshift(2, "D"))
B
C
2013-11-03 1.0
2013-11-04 2.0
2013-11-05 2.0
2013-11-06 3.0
2013-11-07 3.0
2013-11-08 3.0
2013-11-09 4.0
>>> g.transform("tshift", *[2, "D"])
B
C
2013-11-03 1.0
2013-11-04 2.0
2013-11-05 2.0
2013-11-06 3.0
2013-11-07 3.0
2013-11-08 3.0
2013-11-09 4.0
```
#### Problem description
Using `tshift` in `groupby.transform` seems to drop `A` from the index. Also, this seems to be leaving the dates unshifted as seen in the results above.
#### Expected Output
Would expect something like below which is achieved correctly using `groupby.tshift `
```
>>> g.tshift(2, "D")
B
A C
121 2013-11-05 1.0
2013-11-06 2.0
2013-11-07 2.0
2013-11-08 3.0
231 2013-11-09 3.0
2013-11-10 3.0
676 2013-11-11 4.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 94befe6bc2522d9441b95e55602dbe746f63c1bc
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0.dev0+3348.g94befe6.dirty
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.56.3
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.46.0
</details>
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TYP: Update type naming in formatter
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CONTRIBUTOR
| null |
- [x] part of #26792, #28480
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: assignment with enlargement gives object dtype with ExtensionArrays
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| 2021-01-30T18:09:35Z
| null |
MEMBER
| null |
From https://github.com/pandas-dev/pandas/issues/32271, when setting with enlargment, the dtype gets converted into object dtype (while for normal integer or boolean dtype this is not the case):
```
In [10]: s = pd.Series([1, 2, 3], dtype="Int64")
In [11]: s[3] = 4
In [12]: s
Out[12]:
0 1
1 2
2 3
3 4
dtype: object
In [13]: s = pd.Series([1, 2, 3], dtype="int64")
In [14]: s[3] = 4
In [15]: s
Out[15]:
0 1
1 2
2 3
3 4
dtype: int64
```
It also happens with eg DecimalArray from our tests, so suppose it is a general issue with ExtensionArrays.
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REF: avoid using internals methods for to_timestamp, to_period
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Getting the wrong version of pandas when installing from requirements.txt
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| 2020-02-29T03:33:00Z
| 2020-03-01T17:42:02Z
| 2020-03-01T17:42:02Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
$ python3 -m pip install --user -r requirements.txt | grep -i pandas | grep -vi 'Installing collected packages'
below cmd output started 2020 Sat Feb 29 03:20:45 AM UTC
Collecting pandas==0.23.4 (from -r requirements.txt (line 43))
Using cached https://files.pythonhosted.org/packages/e1/d8/feeb346d41f181e83fba45224ab14a8d8af019b48af742e047f3845d8cff/pandas-0.23.4-cp36-cp36m-manylinux1_x86_64.whl
above cmd output done 2020 Sat Feb 29 03:21:55 AM UTC
dstromberg@TransientTracking-2:~/src/grok/just-master x86_64-pc-linux-gnu 126478
$ python3 -m pip freeze | grep pandas
below cmd output started 2020 Sat Feb 29 03:22:46 AM UTC
pandas==1.0.1
above cmd output done 2020 Sat Feb 29 03:22:47 AM UTC
dstromberg@TransientTracking-2:~/src/grok/just-master x86_64-pc-linux-gnu 126478
$ grep pandas requirements.txt
below cmd output started 2020 Sat Feb 29 03:22:59 AM UTC
#pandas
pandas==0.23.4
above cmd output done 2020 Sat Feb 29 03:22:59 AM UTC
dstromberg@TransientTracking-2:~/src/grok/just-master x86_64-pc-linux-gnu 126478
```
#### Problem description
I'm looking at a large codebase that needs an old version of pandas - but for some reason pip install isn't giving it to me.
If I delete pandas and install it manually with the following, I get the right version of pandas. There's something about requirements.txt that isn't quite right.
```
$ python3 -m pip install pandas==0.23.4 | grep -i pandas | grep -v 'Installing collected packages'
below cmd output started 2020 Sat Feb 29 03:30:27 AM UTC
Collecting pandas==0.23.4
Using cached https://files.pythonhosted.org/packages/e1/d8/feeb346d41f181e83fba45224ab14a8d8af019b48af742e047f3845d8cff/pandas-0.23.4-cp36-cp36m-manylinux1_x86_64.whl
above cmd output done 2020 Sat Feb 29 03:30:35 AM UTC
dstromberg@TransientTracking-2:~/src/grok/just-master x86_64-pc-linux-gnu 126478
$ python3 -m pip freeze | grep pandas
below cmd output started 2020 Sat Feb 29 03:30:47 AM UTC
pandas==0.23.4
above cmd output done 2020 Sat Feb 29 03:30:48 AM UTC
dstromberg@TransientTracking-2:~/src/grok/just-master x86_64-pc-linux-gnu 126478
```
Any suggestions?
Thanks for the cool software :)
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REF: Remove BlockManager.rename_axis
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MEMBER
| null |
Better to do it using NDFrame methods
cc @toobaz I know you're on board for getting index/axis stuff out of BlockManager.
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Implement BlockManager.iset
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MEMBER
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Preliminary to the PR that fixes `setitem_with_indexer` (#22036, #15686)
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DOC: Fix SS06 formatting errors in merge_asof docstrings
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| 2
| 2020-02-29T04:58:31Z
| 2020-03-04T04:54:52Z
| 2020-02-29T17:27:50Z
|
CONTRIBUTOR
| null |
Errors fixed (from the list in #29254 ):
pandas.merge_asof
Validated the fixes with python scripts/validate_docstrings.py
Refrencing issue: #29254
Fix : pandanistas/pandanistas_sprint_ui2020#11
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MDExOlB1bGxSZXF1ZXN0MzgxNzUwMTc3
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docs: fix SS06, PR08, RT02, RT03, SA04 in pandas.DatetimeIndex.indexe…
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NONE
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…r_at_time
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Fix : https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/8
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DOC : fix errors docstrings pandas.to_numeric
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| 2020-03-08T10:55:55Z
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
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added docstrings information about arg descriptions, downcast type 'int', and Return type
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MDExOlB1bGxSZXF1ZXN0MzgxNzUyMzYx
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Fix PR08, RT02, RT03, and SA01 on pandas.Index.fillna
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| 2020-02-29T05:15:36Z
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MDExOlB1bGxSZXF1ZXN0MzgxNzY0NDg4
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DOC: Fixed ES01, PR07, SA04 error in pandas.core.groupby.DataFrameGroupBy.shift
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| 2020-03-03T14:26:56Z
| 2020-03-02T22:52:15Z
|
CONTRIBUTOR
| null |
- [x] closes: https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/14
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Output of python scripts/validate_docstrings.py pandas.core.groupby.DataFrameGroupBy.shift:
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
No examples section found
```
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DOC: Update the pandas.Index.is_ docstring
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[] | null | 3
| 2020-02-29T05:51:37Z
| 2020-06-20T08:24:55Z
| 2020-03-10T14:36:28Z
|
NONE
| null |
- [x] closes https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/6
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output of `python scripts/validate_docstrings.py pandas.Index.is_`:
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
No examples section found
```
|
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| 573,196,724
|
MDExOlB1bGxSZXF1ZXN0MzgxNzY2NjQx
| 32,358
|
DOC: Fixed PR09 error in pandas.testing.assert_series_equal
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| 2
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| 2020-03-04T08:55:38Z
| 2020-02-29T17:55:18Z
|
CONTRIBUTOR
| null |
- [X] closes https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/15
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output of `python scripts/validate_docstrings.py pandas.HDFStore.put`:
```
################################################################################
####################### Docstring (pandas.HDFStore.put) #######################
################################################################################
Store object in HDFStore.
Parameters
----------
key : str
value : {Series, DataFrame}
format : 'fixed(f)|table(t)', default is 'fixed'
fixed(f) : Fixed format
Fast writing/reading. Not-appendable, nor searchable.
table(t) : Table format
Write as a PyTables Table structure which may perform
worse but allow more flexible operations like searching
/ selecting subsets of the data.
append : bool, default False
This will force Table format, append the input data to the
existing.
data_columns : list, default None
List of columns to create as data columns, or True to
use all columns. See `here
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
encoding : str, default None
Provide an encoding for strings.
dropna : bool, default False, do not write an ALL nan row to
The store settable by the option 'io.hdf.dropna_table'.
################################################################################
################################## Validation ##################################
################################################################################
8 Errors found:
No extended summary found
Parameters {'min_itemsize', 'index', 'complevel', 'errors', 'complib', 'append', 'nan_rep'} not documented
Unknown parameters {'dropna ', 'append '}
Parameter "key" has no description
Parameter "value" has no description
Parameter "format" description should start with a capital letter
See Also section not found
No examples section found
(pandas-dev) D:\Keluarga\Tolhas\Kuliah\Semester 4\DSC Pandas\pandas-tolhassianipar>python scripts/validate_docstrings.py pandas.HDFStore.put
################################################################################
####################### Docstring (pandas.HDFStore.put) #######################
################################################################################
Store object in HDFStore.
Parameters
----------
key : str
value : {Series, DataFrame}
format : 'fixed(f)|table(t)', default is 'fixed'
fixed(f) : Fixed format
Fast writing/reading. Not-appendable, nor searchable.
table(t) : Table format
Write as a PyTables Table structure which may perform
worse but allow more flexible operations like searching
/ selecting subsets of the data.
append : bool, default False
This will force Table format, append the input data to the
existing.
data_columns : list, default None
List of columns to create as data columns, or True to
use all columns. See `here
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
encoding : str, default None
Provide an encoding for strings.
dropna : bool, default False, do not write an ALL nan row to
The store settable by the option 'io.hdf.dropna_table'.
################################################################################
################################## Validation ##################################
################################################################################
8 Errors found:
No extended summary found
Parameters {'errors', 'index', 'nan_rep', 'append', 'min_itemsize', 'complevel', 'complib'} not documented
Unknown parameters {'append ', 'dropna '}
Parameter "key" has no description
Parameter "value" has no description
Parameter "format" description should start with a capital letter
See Also section not found
No examples section found
```
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MDExOlB1bGxSZXF1ZXN0MzgxNzc1NTE5
| 32,359
|
Fixing RT02 pandas.Index.dropna and PR08 pandas.Index.fillna
|
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| 1
| 2020-02-29T06:19:05Z
| 2020-03-06T17:00:16Z
| 2020-03-06T17:00:10Z
|
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/17
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzgxNzg0NTU2
| 32,360
|
DOC: Update the pandas.DatetimeIndex docstring
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| 1
| 2020-02-29T06:43:41Z
| 2020-03-09T09:22:50Z
| 2020-03-07T21:01:14Z
|
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/7
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Output of ```python scripts/validate_docstrings.py pandas.DatetimeIndex```
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
No examples section found
```
|
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| 573,219,152
|
MDExOlB1bGxSZXF1ZXN0MzgxNzg3NjUz
| 32,361
|
Changed kind parameter from integer to int, Added example
|
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| 1
| 2020-02-29T06:52:17Z
| 2020-02-29T17:43:54Z
| 2020-02-29T17:43:46Z
|
CONTRIBUTOR
| null |
- [x] closes [Fix PR06 error in pandas.arrays.SparseArray](https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/1)
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
```
################################################################################
################################## Validation ##################################
################################################################################
3 Errors found:
Parameter "sparse_index" has no description
Parameter "index" has no description
See Also section not found
```
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| 573,222,351
|
MDExOlB1bGxSZXF1ZXN0MzgxNzkwNjM2
| 32,362
|
DOC: Fixed errors in pandas.DataFrame.asfreq PR07, RT02, RT03, SA04
|
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| 5
| 2020-02-29T07:00:18Z
| 2020-03-11T04:03:23Z
| 2020-03-11T02:00:36Z
|
CONTRIBUTOR
| null |
- [X] closes https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/19
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output of `python scripts/validate_docstrings.py pandas.DataFrame.asfreq`:
```
################################################################################
##################### Docstring (pandas.DataFrame.asfreq) #####################
################################################################################
Convert TimeSeries to specified frequency.
Optionally provide filling method to pad/backfill missing values.
Returns the original data conformed to a new index with the specified
frequency. ``resample`` is more appropriate if an operation, such as
summarization, is necessary to represent the data at the new frequency.
Parameters
----------
freq : DateOffset or str
Frequency DateOffset or string.
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill.
how : {'start', 'end'}, default end
For PeriodIndex only (see PeriodIndex.asfreq).
normalize : bool, default False
Whether to reset output index to midnight.
fill_value : scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
Returns
-------
converted
Same type as caller.
See Also
--------
reindex : Conform DataFrame to new index with optional filling logic.
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30S', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30S', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0
################################################################################
################################## Validation ##################################
################################################################################
```
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TYP/cln: generic._make_*_function
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| 1
| 2020-02-29T08:57:49Z
| 2020-03-04T08:53:31Z
| 2020-03-04T08:19:31Z
|
CONTRIBUTOR
| null |
Give calls to these funcs named parameters for better clarity + type up the make_stats functions.
|
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|
TST: Removed import of itertools
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| 1
| 2020-02-29T12:27:05Z
| 2020-03-07T12:09:19Z
| 2020-03-07T12:01:24Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: Replaced "bool_t" with "builtins.bool"
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Backport PR #32284 on branch 1.0.x (CI: nested DataFrames in npdev)
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NONE
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Backport PR #32284: CI: nested DataFrames in npdev
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CLN: some code cleanups to pandas/_libs/missing.pyx
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MEMBER
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Silence warnings when compiling pandas/_libs/parsers.pyx
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MEMBER
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- [ ] closes #xxxx
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This is getting rid of this warning:
```
pandas/_libs/parsers.c: In function ‘__pyx_f_6pandas_5_libs_7parsers_10TextReader__get_header’:
pandas/_libs/parsers.c:9313:27: warning: ‘__pyx_v_data_line’ may be used uninitialized in this function [-Wmaybe-uninitialized]
9313 | __pyx_t_5numpy_uint64_t __pyx_v_data_line;
```
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CLN: Some code cleanups in pandas/_libs/parsers.pyx
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MEMBER
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There are __a lot__ of cdef unused variables in ```pandas/_libs/parsers.pyx``` this PR is covering *some* of the unused variables.
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PR #32068 follow up - Print merged df result in doc
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CONTRIBUTOR
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- [x] follow up to PR #32068 which closed #12550 - small fix - see https://github.com/pandas-dev/pandas/pull/32068#discussion_r386029773
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TYP/CLN: Optional[Hashable] -> pandas._typing.Label
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|
Having problem with 'pandas.DataFrame.loc' for multi type and size dataframes.
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[] | null | 1
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| 2021-07-29T06:16:01Z
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NONE
| null |
The problem:
When I make a data frame with pandas that has columns with different length and type, I cannot re-assign an element inside it using loc. I can explain it better with an example:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'a': [np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9]), np.array([10,11,12]), np.array([13,14,15])], 'b':[5,5,12,123,5]})
df.loc[2,'a']= np.array([53,23,4])
```
The error that I receive is :
```python
Traceback (most recent call last):
File "...", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-67-4741bddaf261>", line 1, in <module>
df.loc[0,'a']= np.array([53,23,4])
File "...", line 205, in __setitem__
self._setitem_with_indexer(indexer, value)
File "...", line 547, in _setitem_with_indexer
"Must have equal len keys and value "
ValueError: Must have equal len keys and value when setting with an iterable
```
Now If I remove the second column ('b') and make my data frame as
```python
df = pd.DataFrame({'a': [np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9]), np.array([10,11,12]), np.array([13,14,15])]})
df.loc[2,'a']= np.array([53,23,4])
```
I will not get any error. I also noticed that doing the assignment with
```python
df['a'][2]= np.array([53,23,4])
```
will do the work but throw a "SettingWithCopyWarning". However, I can go around the whole thing and use the following code to not get the error or the warning (thanks to ALS777 from [stackoverflow](https://stackoverflow.com/questions/60443110/why-in-pandas-dataframe-with-mix-elements-types-when-i-change-an-element-i-get)):
```python
df.at[2,'a'] = np.array([52,23,34])
```
#### Problem description
I am guessing that it should work with loc if it works with direct indexing like df['a'][2]. Also, why changing the other column effect the process of updating another column?
My specs are:
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-40-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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REF: collect+parametrize reorder_levels tests
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MDU6SXNzdWU1NzMzOTcyNTA=
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Having Problems with detection of tables
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| 2020-03-01T16:12:53Z
| 2020-03-01T16:12:23Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
first_concurrent = requests.get(url, proxies=proxies, headers=headers)
first_concurrent_text = first_concurrent.text
first_concurrent_all_table = pd.read_html(first_concurrent_text)
first_concurrent_first_table = first_concurrent_all_table[0]
```
#### Problem description
The website has a table on it. Sometiems when I run the code it finds the table other times it does not. This is not an issue with syntax of code, rather I think its about the read_html function. I went over the module, stackoverflow, etc, and did not find a solution to my problem.
PS - I have the newest version of pandas.
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REF/TST: misplaced DataFrame.join tests
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TYP: annotations for internals, set_axis
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ENH: infer freq in timedelta_range
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MEMBER
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Step in the direction of #31195.
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DOC: Minor typo fixes for code style guide
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MDU6SXNzdWU1NzM0MTcwMzc=
| 32,380
|
Deprecate groupby() squeeze option
|
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| 10
| 2020-02-29T21:46:36Z
| 2020-10-01T03:46:52Z
| 2020-05-22T23:49:12Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
print(pd.DataFrame([{
'A': 1,
'B': 1,
}, {
'A': 2,
'B': 2,
}, {
'A': 2,
'B': 3,
}]).groupby('A', squeeze=True).count())
```
#### Problem description
I expected `.groupby(squeeze=True)` to, well, squeeze, and `count()` to return a Series. Instead `squeeze=True` doesn't seem to do anything, and `count()` returns a DataFrame.
A workaround is to write `.groupby('A').count().squeeze()`, which does work.
#### Expected Output
```
A
1 1
2 2
Name: B, dtype: int64
```
#### Actual output
```
B
A
1 1
2 2
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-4-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.1
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 44.0.0
Cython : None
pytest : 4.6.9
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.6.9
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NzM0Mjk1MDE=
| 32,381
|
Inconsistent behavior for 2D Groupby with Categorical MultiIndex raises TypeError with observed=False
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| 2020-02-29T23:21:46Z
| 2020-03-11T14:06:00Z
| 2020-03-11T14:05:59Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
x = np.random.normal(size=1000)
y = np.random.normal(size=1000)
z = pd.Series(np.full_like(x, 1))
xc = pd.cut(x, 10)
yc = pd.cut(y, 10)
cut = pd.DataFrame({"x": xc, "y": yc})
mi = pd.MultiIndex.from_frame(cut)
z.index = mi
```
#### Problem description
When I run the following, I get an expected result.
```python
>>> z.groupby(["x", "y"], observed=True).count()
x y
(-3.945, -3.223] (0.795, 1.448] 1
(-3.223, -2.509] (0.142, 0.795] 1
(0.795, 1.448] 1
(-2.509, -1.795] (-2.47, -1.817] 1
(-1.817, -1.164] 4
..
(1.775, 2.489] (2.754, 3.407] 1
(2.489, 3.203] (-0.511, 0.142] 1
(0.795, 1.448] 1
(1.448, 2.101] 1
(2.101, 2.754] 1
Length: 65, dtype: int64
```
However, if I want to preserve the categories that are not observed as in either of the two following, I get a `TypeError` that appears to be due to the inclusion of empty groups (mixing of NaNs and Categoricals?)
```python
z.groupby(["x", "y"]).count()
```
```python
z.groupby(["x", "y"], observed=False).count()
```
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-153-f64fa4f78c55> in <module>
----> 1 z.groupby(["x", "y"]).count()
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/groupby/generic.py in count(self)
797 dtype="int64",
798 )
--> 799 return self._reindex_output(result, fill_value=0)
800
801 def _apply_to_column_groupbys(self, func):
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _reindex_output(self, output, fill_value)
2488 levels_list = [ping.group_index for ping in groupings]
2489 index, _ = MultiIndex.from_product(
-> 2490 levels_list, names=self.grouper.names
2491 ).sortlevel()
2492
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/indexes/multi.py in from_product(cls, iterables, sortorder, names)
551
552 codes = cartesian_product(codes)
--> 553 return MultiIndex(levels, codes, sortorder=sortorder, names=names)
554
555 @classmethod
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/indexes/multi.py in __new__(cls, levels, codes, sortorder, names, dtype, copy, name, verify_integrity, _set_identity)
278
279 if verify_integrity:
--> 280 new_codes = result._verify_integrity()
281 result._codes = new_codes
282
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _verify_integrity(self, codes, levels)
366
367 codes = [
--> 368 self._validate_codes(level, code) for level, code in zip(levels, codes)
369 ]
370 new_codes = FrozenList(codes)
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/indexes/multi.py in <listcomp>(.0)
366
367 codes = [
--> 368 self._validate_codes(level, code) for level, code in zip(levels, codes)
369 ]
370 new_codes = FrozenList(codes)
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _validate_codes(self, level, code)
302 to a level with missing values (NaN, NaT, None).
303 """
--> 304 null_mask = isna(level)
305 if np.any(null_mask):
306 code = np.where(null_mask[code], -1, code)
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in isna(obj)
124 Name: 1, dtype: bool
125 """
--> 126 return _isna(obj)
127
128
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _isna_old(obj)
181 return False
182 elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
--> 183 return _isna_ndarraylike_old(obj)
184 elif isinstance(obj, ABCGeneric):
185 return obj._constructor(obj._data.isna(func=_isna_old))
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _isna_ndarraylike_old(obj)
281 else:
282 result = np.empty(shape, dtype=bool)
--> 283 vec = libmissing.isnaobj_old(values.ravel())
284 result[:] = vec.reshape(shape)
285
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got Categorical)
```
#### Expected Output
The expected output should preserve non-observed groups and return a result.
```python
x y
(-3.945, -3.223] (0.795, 1.448] 1
(-3.223, -2.509] (0.142, 0.795] 1
(0.795, 1.448] 1
(-2.509, -1.795] (-2.47, -1.817] 1
(-1.817, -1.164] 4
..
(1.775, 2.489] (2.754, 3.407] 1
(2.489, 3.203] (-0.511, 0.142] 1
(0.795, 1.448] 1
(1.448, 2.101] 1
(2.101, 2.754] 1
Length: 65, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.3.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 : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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problem
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NONE
| null |
import pandas as pd
json_file = 'sample_data'
list(pd.read_json(json_file, lines=True))
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-ee639448d145> in <module>
1 import pandas as pd
2 json_file = 'sample_data'
----> 3 list(pd.read_json(json_file, lines=True))
c:\users\nimal\appdata\local\programs\python\python36\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
212 else:
213 kwargs[new_arg_name] = new_arg_value
--> 214 return func(*args, **kwargs)
215
216 return cast(F, wrapper)
c:\users\nimal\appdata\local\programs\python\python36\lib\site-packages\pandas\io\json\_json.py in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)
606 return json_reader
607
--> 608 result = json_reader.read()
609 if should_close:
610 filepath_or_buffer.close()
c:\users\nimal\appdata\local\programs\python\python36\lib\site-packages\pandas\io\json\_json.py in read(self)
727 elif self.lines:
728 data = ensure_str(self.data)
--> 729 obj = self._get_object_parser(self._combine_lines(data.split("\n")))
730 else:
731 obj = self._get_object_parser(self.data)
c:\users\nimal\appdata\local\programs\python\python36\lib\site-packages\pandas\io\json\_json.py in _get_object_parser(self, json)
751 obj = None
752 if typ == "frame":
--> 753 obj = FrameParser(json, **kwargs).parse()
754
755 if typ == "series" or obj is None:
c:\users\nimal\appdata\local\programs\python\python36\lib\site-packages\pandas\io\json\_json.py in parse(self)
855
856 else:
--> 857 self._parse_no_numpy()
858
859 if self.obj is None:
c:\users\nimal\appdata\local\programs\python\python36\lib\site-packages\pandas\io\json\_json.py in _parse_no_numpy(self)
1087 if orient == "columns":
1088 self.obj = DataFrame(
-> 1089 loads(json, precise_float=self.precise_float), dtype=None
1090 )
1091 elif orient == "split":
and my file looks like
{"score_hidden":false,"name":"t1_cnas8zv","link_id":"t3_2qyr1a","body":"Most of us have some family members like this. *Most* of my family is like this. ","downs":0,"created_utc":"1420070400","score":14,"author":"YoungModern","distinguished":null,"id":"cnas8zv","archived":false,"parent_id":"t3_2qyr1a","subreddit":"exmormon","author_flair_css_class":null,"author_flair_text":null,"gilded":0,"retrieved_on":1425124282,"ups":14,"controversiality":0,"subreddit_id":"t5_2r0gj","edited":false}
{"distinguished":null,"id":"cnas8zw","archived":false,"author":"RedCoatsForever","score":3,"created_utc":"1420070400","downs":0,"body":"But Mill's career was way better. Bentham is like, the Joseph Smith to Mill's Brigham Young.","link_id":"t3_2qv6c6","name":"t1_cnas8zw","score_hidden":false,"controversiality":0,"subreddit_id":"t5_2s4gt","edited":false,"retrieved_on":1425124282,"ups":3,"author_flair_css_class":"on","gilded":0,"author_flair_text":"Ontario","subreddit":"CanadaPolitics","parent_id":"t1_cnas2b6"}
{"score_hidden":false,"link_id":"t3_2qxefp","name":"t1_cnas8zx","created_utc":"1420070400","downs":0,"body":"Mine uses a strait razor, and as much as i love the clippers i love the razor so much more. Then he follows it up with a warm towel. \nI think i might go get a hair cut this week.","distinguished":null,"id":"cnas8zx","archived":false,"author":"vhisic","score":1,"subreddit":"AdviceAnimals","parent_id":"t3_2qxefp","retrieved_on":1425124282,"ups":1,"author_flair_css_class":null,"author_flair_text":null,"gilded":0,"controversiality":0,"subreddit_id":"t5_2s7tt","edited":false}
{"parent_id":"t3_2qys4x","subreddit":"AdviceAnimals","gilded":0,"author_flair_text":null,"author_flair_css_class":null,"retrieved_on":1425124282,"ups":1,"subreddit_id":"t5_2s7tt","edited":false,"controversiality":0,"score_hidden":false,"name":"t1_cnas8zy","link_id":"t3_2qys4x","body":"[deleted]","created_utc":"1420070400","downs":0,"score":1,"author":"[deleted]","archived":false,"distinguished":null,"id":"cnas8zy"}
{"body":"Very fast, thank you!","created_utc":"1420070400","downs":0,"author":"Mastersimpson","score":2,"id":"cnas8zz","distinguished":null,"archived":false,"score_hidden":false,"name":"t1_cnas8zz","link_id":"t3_2qm5bi","author_flair_css_class":null,"gilded":0,"author_flair_text":null,"retrieved_on":1425124282,"ups":2,"controversiality":0,"edited":false,"subreddit_id":"t5_2y51u","parent_id":"t3_2qm5bi","subreddit":"freedonuts"}
{"author":"BigGupp1","score":6,"archived":false,"id":"cnas900","distinguished":null,"body":"The guy is a professional, and very good at what he does. I highly doubt he misses often, if at all.","created_utc":"1420070400","downs":0,"name":"t1_cnas900","link_id":"t3_2qxe1p","score_hidden":false,"edited":false,"subreddit_id":"t5_2qh61","controversiality":0,"author_flair_text":null,"gilded":0,"author_flair_css_class":null,"retrieved_on":1425124282,"ups":6,"parent_id":"t1_cnaqg2t","subreddit":"WTF"}
{"score_hidden":false,"link_id":"t3_2qxlvm","name":"t1_cnas901","created_utc":"1420070400","downs":0,"body":"This is a great question, and I want to thank you for asking it. However, I don't have any answers. I'm interested in learning more myself. ","archived":false,"id":"cnas901","distinguished":null,"score":1,"author":"PeglegGecko","subreddit":"needadvice","parent_id":"t3_2qxlvm","ups":1,"retrieved_on":1425124282,"author_flair_text":null,"gilded":0,"author_flair_css_class":null,"subreddit_id":"t5_2r367","edited":false,"controversiality":0}
{"controversiality":0,"edited":false,"subreddit_id":"t5_2t9x3","ups":1,"retrieved_on":1425124282,"author_flair_css_class":null,"author_flair_text":null,"gilded":0,"subreddit":"summonerschool","parent_id":"t1_cnaiy0s","id":"cnas902","distinguished":null,"archived":false,"author":"politevelociraptor","score":1,"created_utc":"1420070400","downs":0,"body":"Is the IE-Shiv-Ghostblade-Zerks-LW-BT Still not the main graves build? 60% crit I think massive Attack speed and decent AD plus Pen?","link_id":"t3_2qx3kt","name":"t1_cnas902","score_hidden":false}
{"author_flair_css_class":"BartkowskiMatt","gilded":0,"author_flair_text":"LA Kings fan","ups":1,"retrieved_on":1425124282,"controversiality":0,"edited":false,"subreddit_id":"t5_2rmt9","parent_id":"t1_cnas8bm","subreddit":"BostonBruins","body":":D.","created_utc":"1420070400","downs":0,"score":1,"author":"slicked9778","distinguished":null,"id":"cnas903","archived":false,"score_hidden":false,"name":"t1_cnas903","link_id":"t3_2qyizj"}
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{"controversiality":0,"edited":false,"subreddit_id":"t5_32vyu","ups":1,"retrieved_on":1425124282,"author_flair_css_class":null,"author_flair_text":null,"gilded":0,"subreddit":"GCXRep","parent_id":"t1_cnajnze","id":"cnas90b","distinguished":null,"archived":false,"score":1,"author":"[deleted]","downs":0,"created_utc":"1420070401","body":"[deleted]","link_id":"t3_2qxljk","name":"t1_cnas90b","score_hidden":false}
{"subreddit":"Games","parent_id":"t1_cnas5ud","retrieved_on":1425124282,"ups":1,"author_flair_css_class":null,"gilded":0,"author_flair_text":null,"controversiality":0,"subreddit_id":"t5_2qhwp","edited":false,"score_hidden":false,"link_id":"t3_2qxl90","name":"t1_cnas90c","downs":0,"created_utc":"1420070401","body":"If you enjoy deep, 100 hour RPGs, then definitely worth the money.","distinguished":null,"id":"cnas90c","archived":false,"score":1,"author":"Crodface"}
{"score_hidden":false,"name":"t1_cnas90d","link_id":"t3_2qy6z1","body":"[deleted]","downs":0,"created_utc":"1420070401","author":"[deleted]","score":1,"archived":false,"distinguished":null,"id":"cnas90d","parent_id":"t1_cnaqtxv","subreddit":"BBW","gilded":0,"author_flair_text":null,"author_flair_css_class":null,"ups":1,"retrieved_on":1425124282,"subreddit_id":"t5_2qrqw","edited":false,"controversiality":0}
{"subreddit":"knives","parent_id":"t1_cnaqs9i","ups":1,"retrieved_on":1425124282,"gilded":0,"author_flair_text":null,"author_flair_css_class":null,"edited":false,"subreddit_id":"t5_2qzyn","controversiality":0,"score_hidden":false,"link_id":"t3_2qy4jn","name":"t1_cnas90e","downs":0,"created_utc":"1420070401","body":"Haha awesome man I got it from my grandpa this Christmas as well.","archived":false,"distinguished":null,"id":"cnas90e","score":1,"author":"MadagascarDifficulty"}
{"id":"cnas90f","distinguished":null,"archived":false,"author":"FreeSoul789","score":3,"created_utc":"1420070401","downs":0,"body":"I completely agree. I've spent that long staring at it sober..","link_id":"t3_2qy171","name":"t1_cnas90f","score_hidden":false,"controversiality":0,"subreddit_id":"t5_2qhvj","edited":false,"retrieved_on":1425124282,"ups":3,"author_flair_css_class":null,"gilded":0,"author_flair_text":null,"subreddit":"LSD","parent_id":"t1_cnaqrew"}
{"score_hidden":false,"link_id":"t3_2qy21w","name":"t1_cnas90g","created_utc":"1420070401","downs":0,"body":">>If a woman wants to give up a child for adoption with a named father, she is unable to do so without the father's consent.\n\n>That is easy enough to do. Simply don't name the father.\n\nWomen are *only* not financially responsible *if* there is no named father. If the father isn't named, he's not on the hook for child support either, so *it doesn't matter*.\n\nYou're deliberately removing context and responding to sentences that go together to form an overall argument separately in a way that does not make sense. At this point you're being intentionally misleading to twist this into what you want it to be instead of actually participating in the spirit of the argument. ","archived":false,"id":"cnas90g","distinguished":null,"author":"dewprisms","score":17,"subreddit":"changemyview","parent_id":"t1_cnarzyw","retrieved_on":1425124282,"ups":17,"author_flair_text":"3\u2206","gilded":0,"author_flair_css_class":" points","edited":false,"subreddit_id":"t5_2w2s8","controversiality":0}
{"score_hidden":false,"link_id":"t3_2qxty0","name":"t1_cnas90h","created_utc":"1420070401","downs":0,"body":"I haven't. Still trying to get someone to commit. \n\nWhere are you located?","distinguished":null,"id":"cnas90h","archived":false,"score":1,"author":"TimDisaster","subreddit":"beertrade","parent_id":"t1_cnarxyo","retrieved_on":1425124282,"ups":1,"author_flair_css_class":"repLevel4","gilded":0,"author_flair_text":null,"controversiality":0,"subreddit_id":"t5_2rgco","edited":false}
{"parent_id":"t3_2qy7at","subreddit":"funny","gilded":0,"author_flair_text":null,"author_flair_css_class":null,"retrieved_on":1425124282,"ups":1,"subreddit_id":"t5_2qh33","edited":false,"controversiality":0,"score_hidden":false,"name":"t1_cnas90i","link_id":"t3_2qy7at","body":"Wheredugit?","downs":0,"created_utc":"1420070401","score":1,"author":"deephaven","archived":false,"distinguished":null,"id":"cnas90i"}
{"created_utc":"1420070401","downs":0,"body":"It's a religion that doesn't have a set in stone creed. so I'm assuming there's a few more.","archived":false,"distinguished":null,"id":"cnas90j","score":2,"author":"Vamking12","score_hidden":false,"link_id":"t3_2qy44e","name":"t1_cnas90j","retrieved_on":1425124282,"ups":2,"author_flair_text":"Pumpkin Pump/Pimp/Pie","gilded":0,"author_flair_css_class":"","edited":false,"subreddit_id":"t5_2vizz","controversiality":0,"subreddit":"TumblrInAction","parent_id":"t1_cnan3hv"}
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{"created_utc":"1420070401","downs":0,"body":"Roofers, the only people on a job site more savage than the rock people.","archived":false,"id":"cnas90l","distinguished":null,"score":2,"author":"Movepeck","score_hidden":false,"link_id":"t3_2qy8r4","name":"t1_cnas90l","retrieved_on":1425124282,"ups":2,"author_flair_text":null,"gilded":0,"author_flair_css_class":null,"subreddit_id":"t5_2qh1i","edited":false,"controversiality":0,"subreddit":"AskReddit","parent_id":"t1_cnaqrf6"}
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{"score":1,"author":"CarpeAeonem","distinguished":null,"id":"cnas90o","archived":false,"body":"Agreed! You should get it while it's on sale. ","created_utc":"1420070401","downs":0,"name":"t1_cnas90o","link_id":"t3_2q8qx6","score_hidden":false,"controversiality":0,"subreddit_id":"t5_2vs7z","edited":false,"author_flair_css_class":"stagsilhouette","author_flair_text":"","gilded":0,"retrieved_on":1425124282,"ups":1,"parent_id":"t1_cnas1x6","subreddit":"HannibalTV"}
{"controversiality":0,"edited":false,"subreddit_id":"t5_2qh0u","ups":1,"retrieved_on":1425124282,"author_flair_css_class":null,"gilded":0,"author_flair_text":null,"subreddit":"pics","parent_id":"t3_2qxggo","id":"cnas90p","distinguished":null,"archived":false,"score":1,"author":"asdjfweaiv","created_utc":"1420070401","downs":0,"body":"Tumblr is leaking again.","link_id":"t3_2qxggo","name":"t1_cnas90p","score_hidden":false}
{"archived":false,"id":"cnas90q","distinguished":null,"score":0,"author":"uncannylizard","created_utc":"1420070401","downs":0,"body":"The vast majority of countries in the world recognize the state of Palestine btw, in addition to the state of Israel.","link_id":"t3_2qxiaz","name":"t1_cnas90q","score_hidden":false,"edited":false,"subreddit_id":"t5_2qh13","controversiality":0,"ups":0,"retrieved_on":1425124282,"gilded":0,"author_flair_text":null,"author_flair_css_class":null,"subreddit":"worldnews","parent_id":"t1_cnahbvw"}
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{"score_hidden":false,"link_id":"t3_2qy0eu","name":"t1_cnas90s","created_utc":"1420070402","downs":0,"body":"Thank you, I certainly am!","distinguished":null,"id":"cnas90s","archived":false,"author":"gingerguitarx92x","score":2,"subreddit":"guitarpedals","parent_id":"t1_cnaq8ts","retrieved_on":1425124282,"ups":2,"author_flair_css_class":null,"gilded":0,"author_flair_text":null,"controversiality":0,"subreddit_id":"t5_2r0bp","edited":false}
{"name":"t1_cnas90t","link_id":"t3_2qxm8n","score_hidden":false,"score":1,"author":"billj457","distinguished":null,"id":"cnas90t","archived":false,"body":"Chappelle: https://www.youtube.com/watch?v=4B93BoC9ylg","created_utc":"1420070402","downs":0,"parent_id":"t1_cnaiag9","subreddit":"BMW","controversiality":0,"edited":false,"subreddit_id":"t5_2qn3a","author_flair_css_class":"","author_flair_text":"2009 E90 335xi 6MT","gilded":0,"ups":1,"retrieved_on":1425124282}
{"subreddit":"movies","parent_id":"t3_2qyjda","controversiality":0,"subreddit_id":"t5_2qh3s","edited":false,"retrieved_on":1425124282,"ups":1,"author_flair_css_class":null,"author_flair_text":null,"gilded":0,"link_id":"t3_2qyjda","name":"t1_cnas90u","score_hidden":false,"distinguished":null,"id":"cnas90u","archived":false,"author":"kylionsfan","score":1,"created_utc":"1420070402","downs":0,"body":"Goonies"}
{"link_id":"t3_2e7eti","name":"t1_cnas90v","score_hidden":false,"archived":false,"id":"cnas90v","distinguished":null,"score":1,"author":"[deleted]","created_utc":"1420070402","downs":0,"body":"[deleted]","subreddit":"Games","parent_id":"t1_cnark1x","edited":false,"subreddit_id":"t5_2qhwp","controversiality":0,"retrieved_on":1425124282,"ups":1,"gilded":0,"author_flair_text":null,"author_flair_css_class":null}
{"subreddit_id":"t5_2stl8","edited":false,"controversiality":0,"ups":1,"retrieved_on":1425124282,"author_flair_text":"Developer of the /r/SRGG","gilded":0,"author_flair_css_class":"ao-kuang","subreddit":"Smite","parent_id":"t3_2qynia","archived":false,"distinguished":null,"id":"cnas90x","author":"MrRangerLP","score":1,"created_utc":"1420070402","downs":0,"body":"Finish my Masters grind for 1v1 Joust","link_id":"t3_2qynia","name":"t1_cnas90x","score_hidden":false}
{"subreddit":"BabyBumps","parent_id":"t3_2qxxgp","retrieved_on":1425124282,"ups":2,"gilded":0,"author_flair_text":null,"author_flair_css_class":null,"edited":false,"subreddit_id":"t5_2s7cl","controversiality":0,"score_hidden":false,"link_id":"t3_2qxxgp","name":"t1_cnas90y","created_utc":"1420070402","downs":0,"body":"I thought you wanted to name your baby Primrose. I was like, \"hmm, I don't hate it.\"","archived":false,"distinguished":null,"id":"cnas90y","author":"-purple-is-a-fruit-","score":2}
{"subreddit_id":"t5_2z5u0","edited":false,"controversiality":0,"ups":1,"retrieved_on":1425124282,"gilded":0,"author_flair_text":"Dirt | Sergei | Simetra | Sledge","author_flair_css_class":"","subreddit":"randomsuperpowers","parent_id":"t1_cnas75o","archived":false,"distinguished":null,"id":"cnas90z","author":"Mace55555","score":1,"downs":0,"created_utc":"1420070402","body":"[Slightly stronger than an RPG.]","link_id":"t3_2qs1ie","name":"t1_cnas90z","score_hidden":false}
{"subreddit":"funny","parent_id":"t3_2qxtg0","ups":1,"retrieved_on":1425124282,"gilded":0,"author_flair_text":null,"author_flair_css_class":null,"subreddit_id":"t5_2qh33","edited":false,"controversiality":0,"score_hidden":false,"link_id":"t3_2qxtg0","name":"t1_cnas910","created_utc":"1420070402","downs":0,"body":"[deleted]","archived":false,"distinguished":null,"id":"cnas910","score":1,"author":"[deleted]"}
{"author_flair_text":null,"gilded":0,"author_flair_css_class":null,"retrieved_on":1425124282,"ups":1,"edited":false,"subreddit_id":"t5_2qh1i","controversiality":0,"parent_id":"t3_2qys1t","subreddit":"AskReddit","body":"I don't know 100% why, but, for me, I delete comments if I was wrong about something. Like, if I misread a question and answered what I misread or I misinterpreted something. I'm not going to delete it just because I got 299\u2640489242049t4bit43\u2665 downvotes. ","created_utc":"1420070402","downs":0,"score":1,"author":"[deleted]","archived":false,"distinguished":null,"id":"cnas911","score_hidden":false,"name":"t1_cnas911","link_id":"t3_2qys1t"}
{"parent_id":"t1_cnajcvn","subreddit":"devils","author_flair_text":"#11 - Pain Train Gionta","gilded":0,"author_flair_css_class":"Home11","ups":3,"retrieved_on":1425124282,"subreddit_id":"t5_2roo0","edited":false,"controversiality":0,"score_hidden":false,"name":"t1_cnas912","link_id":"t3_2qxx2l","body":"I'll try to find a good pair at my NYE party. Will report back. ","downs":0,"created_utc":"1420070402","author":"GreasyBastard_","score":3,"archived":false,"distinguished":null,"id":"cnas912"}
{"gilded":0,"author_flair_text":"RCUS, yo","author_flair_css_class":"","retrieved_on":1425124282,"ups":2,"subreddit_id":"t5_2riuy","edited":false,"controversiality":0,"parent_id":"t3_2qyqhj","subreddit":"Reformed","body":"I love it. Okay, based upon point (6), is it right for a Christian to say, \"it is not me doing the sin, but sin in me\" [Romans 7:17]? Or is the passage saying something quite different than what Ryle understands it to mean?","created_utc":"1420070402","downs":0,"author":"BSMason","score":2,"archived":false,"id":"cnas913","distinguished":null,"score_hidden":false,"name":"t1_cnas913","link_id":"t3_2qyqhj"}
{"created_utc":"1420070402","downs":0,"body":"I like this idea, though just to confirm though technically if 6 bosses dropped loot you needed but lost, does that mean you get 24 counter points?\n\nIf you lose even using counters, does that mean its lost for the duration of the raid? Like\n\nKargath drops something you need, lose loot, get +4 on roll, butcher drops weapon, you still lose, does the counter apply or would you get +8 for the next boss as you still lost?","distinguished":null,"id":"cnas914","archived":false,"author":"Deftest366","score":1,"score_hidden":false,"link_id":"t3_2qylyq","name":"t1_cnas914","ups":1,"retrieved_on":1425124282,"author_flair_css_class":"horde-paladin","author_flair_text":"","gilded":0,"controversiality":0,"subreddit_id":"t5_2qio8","edited":false,"subreddit":"wow","parent_id":"t3_2qylyq"}
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{"parent_id":"t3_2qx5c4","subreddit":"Android","edited":false,"subreddit_id":"t5_2qlqh","controversiality":0,"gilded":0,"author_flair_text":"Nexus 5 CM12","author_flair_css_class":"userGray","ups":0,"retrieved_on":1425124282,"name":"t1_cnas916","link_id":"t3_2qx5c4","score_hidden":false,"author":"Tropiux","score":0,"archived":false,"id":"cnas916","distinguished":null,"body":"I just wish Google dropped support for Android < 4.0 already. ","created_utc":"1420070402","downs":0}
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{"parent_id":"t1_cnarr0q","subreddit":"falcons","subreddit_id":"t5_2ql17","edited":false,"controversiality":0,"author_flair_text":null,"gilded":0,"author_flair_css_class":null,"retrieved_on":1425124282,"ups":3,"name":"t1_cnas918","link_id":"t3_2qy1f3","score_hidden":false,"author":"ATL84","score":3,"archived":false,"id":"cnas918","distinguished":null,"body":"Haha guilty. But I've heard some people say they weren't thrilled about guys like McDaniels and Del Rio as well. Won't pass judgement until I see how this all plays out","created_utc":"1420070402","downs":0}
{"subreddit_id":"t5_2ty3s","edited":false,"controversiality":0,"ups":1,"retrieved_on":1425124282,"gilded":0,"author_flair_text":null,"author_flair_css_class":null,"subreddit":"dayz","parent_id":"t3_2qylol","archived":false,"distinguished":null,"id":"cnas919","score":1,"author":"Arnasx","created_utc":"1420070403","downs":0,"body":"Unfortunately at this time of point you can have the best CPU, GPU available to buy and you still would not get 30 FPS in Cities. The game is only optimized for specific CPU's and GPU's that the developers use.","link_id":"t3_2qylol","name":"t1_cnas919","score_hidden":false}
{"body":"Have You Tried Turning It Off And On Again?","downs":0,"created_utc":"1420070403","author":"A_french_chinese_man","score":1,"id":"cnas91a","distinguished":null,"archived":false,"score_hidden":false,"name":"t1_cnas91a","link_id":"t3_2qxfa5","author_flair_css_class":null,"gilded":0,"author_flair_text":null,"retrieved_on":1425124282,"ups":1,"controversiality":0,"edited":false,"subreddit_id":"t5_2qh1i","parent_id":"t3_2qxfa5","subreddit":"AskReddit"}
{"retrieved_on":1425124282,"ups":1,"author_flair_css_class":null,"gilded":0,"author_flair_text":null,"controversiality":0,"edited":1420071816,"subreddit_id":"t5_2qh1i","subreddit":"AskReddit","parent_id":"t3_2qykcw","downs":0,"created_utc":"1420070403","body":"My Great to the power of 6 ..or 7 grandfather came over here from Germany around 1776. On the ship ride over his mother dies. \n\nTwo weeks off the boat his father dies. He was only around 9 years old. \n\nHe then was sold as an \"indigent servant\" he earned his independence by fighting in the revolutionary war. \n\nHe moved from ohio to near Pittsburgh, lived to be 101 years old. ","id":"cnas91b","distinguished":null,"archived":false,"author":"TomHanksDied","score":1,"score_hidden":false,"link_id":"t3_2qykcw","name":"t1_cnas91b"}
{"edited":false,"subreddit_id":"t5_2qhr3","controversiality":0,"retrieved_on":1425124282,"ups":1,"gilded":0,"author_flair_text":"2001 Pontiac Firebird","author_flair_css_class":"","subreddit":"Autos","parent_id":"t3_2qjwg1","archived":false,"id":"cnas91c","distinguished":null,"author":"blazefalcon","score":1,"created_utc":"1420070403","downs":0,"body":"Dodge Rampage, pretty much any pre-80s Japanese cars, original VW Scirocco, Porsche 928, clean old Beetles, any Nissan Z series but that's more understandable. \n\nOh, and FJ Cruisers. They're just neat. ","link_id":"t3_2qjwg1","name":"t1_cnas91c","score_hidden":false}
{"author_flair_text":"Gustav1985","gilded":0,"author_flair_css_class":"cmdr","ups":1,"retrieved_on":1425124282,"edited":false,"subreddit_id":"t5_2vi60","controversiality":0,"parent_id":"t3_2qynaq","subreddit":"EliteDangerous","body":"I had one to kill pirates in an unclaimed system so when I was there all I had to do wall drop in to USS and if I wasn't attacked I would scan the ships there and if they came up with no sub faction I shot them add it counted. ","created_utc":"1420070403","downs":0,"score":1,"author":"Gustav55","archived":false,"id":"cnas91d","distinguished":null,"score_hidden":false,"name":"t1_cnas91d","link_id":"t3_2qynaq"}
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pd.read_json() leads to a segmentation fault
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| 2020-04-12T21:50:14Z
| 2020-04-12T21:50:14Z
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NONE
| null |
#### Code Sample
The following code leads to either a segmentation fault or a bus error:
```python
def load_dataframe(name):
...
df_json = db.dataframes.find_one({"_id": name})["df"]
df = pd.read_json(df_json)
return df
```
But the following workaround fixes it:
```python
def load_dataframe(name):
...
df_json = db.dataframes.find_one({"_id": name})["df"]
df = pd.DataFrame().from_dict(json.loads(df_json))
return df
```
#### Problem description
I am storing several jsonified dataframes in MongoDB. Trying to go straight from the json to the dataframe leads to either a segmentation fault or a bus error. I am storying several dataframes, but confusingly it only happens with one dataframe and not the others. The problem dataframe is identical in nature to other dataframes (same column names and data types). Unfortunately, it's not shareable but happy to answer any questions I can.
#### Output of ``pd.show_versions()``
Note: I am using the latest pandas, 1.0.1. I downgraded to pandas 0.25.3 and had the same issue.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.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 : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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`DataFrame.groupby.apply` keeps group-by column in values
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| 2020-03-01T14:58:47Z
| 2020-03-01T14:58:47Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> df = pd.DataFrame({'d': [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], 'v': [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})
>>> df.groupby('d').sum()
v
d
1.0 6.0
2.0 15.0
>>> df.groupby('d').apply(sum)
d v
d
1.0 3.0 6.0
2.0 6.0 15.0
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : e6ead5587fce2df844bbe568b63a150c47954fdd
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-88-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.26.0.dev0+2391.ge6ead5587.dirty
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.5.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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BUG: Rolling functions doesn't work on decreasing variable index
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| 1
| 2020-03-01T15:48:28Z
| 2020-03-11T01:51:04Z
| 2020-03-11T01:51:04Z
|
CONTRIBUTOR
| null |
#### Code Sample
```python
def test_rolling_on_decreasing_index(self):
# GH-19248
index = [
Timestamp("20190101 09:00:00"),
Timestamp("20190101 09:00:02"),
Timestamp("20190101 09:00:03"),
Timestamp("20190101 09:00:05"),
Timestamp("20190101 09:00:06"),
]
df = DataFrame({"column": [3, 4, 4, 2, 1]}, index=reversed(index))
result = df.rolling("2s").min()
expected = DataFrame(
{"column": [3.0, 4.0, 4.0, 2.0, 1.0]}, index=reversed(index)
)
# actual incorrect output is [3, 3, 3, 2, 1]
tm.assert_frame_equal(result, expected)
```
#### Problem description
Rolling window operations on variable decreasing index don't work even after https://github.com/pandas-dev/pandas/pull/28297. The reason for this is that the `calculate_variable_window_bounds` function doesn't work with decreasing index. Tested with latest master (~version 1.0.1).
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MDExOlB1bGxSZXF1ZXN0MzgyMDYwNzM3
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|
BUG: Fix rolling functions with variable windows on decreasing index
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| 6
| 2020-03-01T15:52:32Z
| 2020-03-11T01:54:20Z
| 2020-03-11T01:51:04Z
|
CONTRIBUTOR
| null |
- [x] Closes https://github.com/pandas-dev/pandas/issues/32385
- [x] tests updated & passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
Plotting Int64 columns with nulled integers (NAType) fails #32073
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| 2020-03-03T10:48:21Z
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|
CONTRIBUTOR
| null |
- [x] closes #32073
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzgyMDg0Mjg3
| 32,388
|
Create CITATION.md
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| 2020-03-01T19:29:10Z
| 2020-03-31T17:39:44Z
| 2020-03-31T17:39:44Z
|
CONTRIBUTOR
| null |
Addresses #24036 by adding a `CITATION.md` file to the repository.
I copied the citation instructions from here: https://pandas.io/about/citing.html
and added a section for the published software on Zenodo. Following [this recommendation](https://github.com/sherpa/sherpa/pull/634#issuecomment-553668211), I did this for the latest released version (v1.0.1) with a note for the user to go fetch the citation from Zenodo for the version they are actually using.
Tagging @TomAugspurger and @jreback since you were active in the linked issue.
I still recommend to update the citation request on the pandas website directly (https://pandas.io/about/citing.html), as well as on the Scipy website (https://www.scipy.org/citing.html#pandas), on https://pandas.pydata.org/ and maybe provide a `<package>.__citation__` variable as suggested [here](https://github.com/pandas-dev/pandas/issues/24036#issuecomment-585672595).
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DOC: Reorganize Getting Started documentation pages
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| 2020-03-02T07:24:43Z
| 2020-03-11T22:22:19Z
| 2020-03-11T22:15:22Z
|
MEMBER
| null |
- [x] closes #32308
Issue Related:
- `getting_started/index.html` removes `10 minutes to pandas`, `Essential basic functionality` and `Intro to data structures` from the toctree and moves them as links in `user_guide/index.html`
- `getting_started/tutorials.html` now only references community tutorials
- the pandas cheatsheet is now referenced at the bottom `Tutorial` section of `getting_started/index.html`
Misc:
- Lowercases `Pandas` to `pandas`
- Some rephrasing
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MDU6SXNzdWU1NzM4MTM5MDU=
| 32,390
|
hdf5 loading performance issue for non-sorted data in pandas 1.0.1
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[] | null | 4
| 2020-03-02T08:53:47Z
| 2020-03-05T17:15:45Z
| null |
NONE
| null |
#### Problem Description
compared to the legacy pandas 0.2x.x, the most recent 1.0.1 version doesnt perform well in HDF5 file loading unless the conditioned column was sorted beforehand
```python
import os
import numpy as np
import pandas as pd
row = 6000
col = 2
start_date = '20000101'
freq = 'B'
ind = pd.date_range(start_date,periods=row,freq=freq)
a = pd.DataFrame(np.random.rand(row*col).reshape((row,-1)),index=ind)
a = a.reset_index(drop=False)
a.columns = ['date','x','y']
a = pd.concat([a]*2000)
a.sort_values('y').to_hdf(os.path.join(os.path.expanduser('~'),'hdf_test.h5'),key='df',data_columns=True,format='table')
a.sort_values('date').to_hdf(os.path.join(os.path.expanduser('~'),'hdf_test_sorted.h5'),key='df',data_columns=True,format='table')
# significantly slower in pandas 1.0.1 (this is not an issue in pandas 0.2x.x)
store = pd.HDFStore(os.path.join(os.path.expanduser('~'),'hdf_test.h5'),mode='r')
result = store.select('df',where="{0}>=20150101 & {0}<=20160730".format('date'),)
store.close()
# as fast as pandas 0.2x.x if data was sorted on the 'term' column
store = pd.HDFStore(os.path.join(os.path.expanduser('~'),'hdf_test_sorted.h5'),mode='r')
result = store.select('df',where="{0}>=20150101 & {0}<=20160730".format('date'),)
store.close()
```
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> Not sure this is even supported by gcsfs - I don't see `encoding` as an available option there:
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| 2020-03-02T09:25:27Z
| 2020-03-02T09:24:34Z
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NONE
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> Not sure this is even supported by gcsfs - I don't see `encoding` as an available option there:
>
> https://github.com/dask/gcsfs/blob/523eb65b3e7feb05f9c10ce84523d1058716fecf/gcsfs/core.py#L1133
>
> Might need to start upstream if wanted to make this possible
Hi, @WillAyd
In the current gcsf master `GCSFileSystem.open()` has been removed and `fsspec.AbstractFileSystem.open()` has works instead:
https://github.com/intake/filesystem_spec/blob/4c66e096d32dafe264e2d6707992ee6935685944/fsspec/spec.py#L717
where applying of passed `encoding` for the text reading\writing is now implemented:
```
if "b" not in mode:
mode = mode.replace("t", "") + "b"
text_kwargs = {
k: kwargs.pop(k)
for k in ["encoding", "errors", "newline"]
if k in kwargs
}
return io.TextIOWrapper(
self.open(path, mode, block_size, **kwargs), **text_kwargs
)
```
[Note also this issue from gcsfs](https://github.com/dask/gcsfs/issues/142)
So, it looks that in `pandas` ignoring of `encoding` parameter happens because in the `pandas.io.gcs.get_filepath_or_buffer` the mode = 'rb' is passed to call of `GCSFileSystem.open(filepath_or_buffer, mode)`
Tracing back to the moment of the first actual setting the `mode` parameter we have stop on this line:
`pandas.io.common.py`
```
def get_filepath_or_buffer(
filepath_or_buffer, encoding=None, compression=None, mode=None
)
```
It is so, because in the call of `get_filepath_or_buffer()` performed from here
https://github.com/pandas-dev/pandas/blob/29d6b0232aab9576afa896ff5bab0b994760495a/pandas/io/parsers.py#L430
we do not pass value of `mode` and default `mode=None` works.
As I could suggest for read_csv() we need pass `mode=r` and for to_csv() we need pass `mode=w` in the call of `get_filepath_or_buffer()`. But I'm not sure where it's better to implement this change.
_Originally posted by @EgorBEremeev in https://github.com/pandas-dev/pandas/issues/26124#issuecomment-593121783_
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MDU6SXNzdWU1NzM4Mzk3ODg=
| 32,392
|
read_csv from Google Cloud Storage ignores encoding
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| 2020-09-03T03:06:19Z
| 2020-09-03T03:06:19Z
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NONE
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#### Code Sample, a copy-pastable example if possible
```python
dataframe = pd.read_csv('gs://mybucket/my_file', encoding = 'cp1251')
```
#### Problem description
Reading csv files which have encoding other than utf-8, like cp1251, from the Google Cloud Storage fails with error:
> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc4 in position 0: invalid continuation byte
the stacktrace from the Google Cloud Function environment:
> Traceback (most recent call last):
> File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 383, in run_background_function
> _function_handler.invoke_user_function(event_object)
> File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 217, in invoke_user_function
> return call_user_function(request_or_event)
> File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 214, in call_user_function
> event_context.Context(**request_or_event.context))
> File "/user_code/main.py", line 60, in load_csv_to_bq
> na_filter=False)
> File "/env/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 676, in parser_f
> return _read(filepath_or_buffer, kwds)
> File "/env/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 448, in _read
> parser = TextFileReader(fp_or_buf, **kwds)
> File "/env/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 880, in __init__
> self._make_engine(self.engine)
> File "/env/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1114, in _make_engine
> self._engine = CParserWrapper(self.f, **self.options)
> File "/env/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1891, in __init__
> self._reader = parsers.TextReader(src, **kwds)
> File "pandas/_libs/parsers.pyx", line 529, in pandas._libs.parsers.TextReader.__cinit__
> File "pandas/_libs/parsers.pyx", line 748, in pandas._libs.parsers.TextReader._get_header
> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc4 in position 0: invalid continuation byte
It looks that in `pandas` ignoring of `encoding` parameter happens, because in the `pandas.io.gcs.get_filepath_or_buffer` the `mode = 'rb'` is passed to call of `GCSFileSystem.open(filepath_or_buffer, mode)`
Tracing back to the moment of the first actual setting the `mode` parameter we have stop on this line:
`pandas.io.common.py`
```
def get_filepath_or_buffer(
filepath_or_buffer, encoding=None, compression=None, mode=None
)
```
, because in the call of `get_filepath_or_buffer()` performed from here
https://github.com/pandas-dev/pandas/blob/29d6b0232aab9576afa896ff5bab0b994760495a/pandas/io/parsers.py#L430-L432
we do not pass value of `mode` and default `mode=None` works.
But in the current gcsf master `GCSFileSystem.open()` has been removed and `fsspec.AbstractFileSystem.open()` [has works instead](https://github.com/intake/filesystem_spec/blob/4c66e096d32dafe264e2d6707992ee6935685944/fsspec/spec.py#L717):
where applying of passed `encoding` for the **text reading\writing** is now implemented:
```
if "b" not in mode:
mode = mode.replace("t", "") + "b"
text_kwargs = {
k: kwargs.pop(k)
for k in ["encoding", "errors", "newline"]
if k in kwargs
}
return io.TextIOWrapper(
self.open(path, mode, block_size, **kwargs), **text_kwargs
)
```
#### Expected Output
The encoding value passed into pd.read_csv() is applyied while reading from GCS, csv files are read.
As I could suggest for read_csv() we need pass `mode=r` and for to_csv() (see #26124) we need pass `mode=w` in the call of `get_filepath_or_buffer()`. But I'm not sure where in code it's better to implement this change.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : 0.6.0
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.13.1
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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DOC: change link to contributing guide in README.md
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xref https://github.com/pandas-dev/pandas/pull/32068#issuecomment-593362224
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pd.to_numeric(..., errors="coerce") failing silently when strings contain "uint64"
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| 4
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| 2020-03-15T00:38:58Z
| 2020-03-15T00:38:58Z
|
NONE
| null |
#### Problem description
When trying to coerce strings to numeric values using `to_numeric()`, the occurrence of the substring "uint64" (but not any other dtype-like substring it seems) leads to silent failure to coerce.
```python
strs = ["32", "64", "uint32", "float64", "sdnfonsdf uint32 knsdf", "sdnfonsdf uint64 knsdf", "uint64"]
print([pd.to_numeric(s, errors="coerce") for s in strs])
pd.to_numeric(pd.Series(["32", "64", "uint64"]), errors="coerce")
```
```bash
[32, 64, nan, nan, nan, 'sdnfonsdf uint64 knsdf', 'uint64']
0 32
1 64
2 uint64
dtype: object
```
#### Expected Output
```bash
[32, 64, nan, nan, nan, nan, nan]
0 32.0
1 64.0
2 NaN
dtype: float64
```
Seems to fail equally in 0.25.3 and 1.0...
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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MDU6SXNzdWU1NzQwNzc1MDc=
| 32,395
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assignment with datetime64[ns, UTC] raises TypeError
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| 2020-05-25T17:39:42Z
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|
NONE
| null |
#### Code Sample
```python
# Your code here
import datetime, pandas as pd
data = [[datetime.datetime(2020, 2, 28, 13, 51, 27, tzinfo=datetime.timezone.utc)],
[datetime.datetime(2020, 2, 28, 13, 51, 27, tzinfo=datetime.timezone.utc)],
[datetime.datetime(2020, 2, 28, 13, 51, 27, tzinfo=datetime.timezone.utc)],
[datetime.datetime(2020, 2, 28, 13, 51, 27, tzinfo=datetime.timezone.utc)]]
df = pd.DataFrame(data, columns=['cd']) # dtype: datetime64[ns, UTC]
df2 = pd.DataFrame(index=df.index)
df2.loc[:,'cd2'] = df['cd'] # no error
df2.loc[df.index,'cd3'] = df['cd'] # error
df2.loc[df.index,'cd2'] = df['cd'] # no error
```
#### Problem description
If you try to assign datetime values (with zone and indexes) to a column, it will raise TypeError: data type not understood.
No errors raise with index ':', or when the column already has the correct type. Note that this only happens if the datetime has zone information. With tzinfo=None, no errors occur.
#### Output of ``pd.show_versions()``
I noticed this bug in version 1.0.1
No errors occur in 0.25.3
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 20.0.1
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : 0.6.0
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.2.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
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xlwt : None
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Added figsize for autocorrelation_plot and lag_plot (#31650)
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| 8
| 2020-03-02T17:03:21Z
| 2020-07-17T11:24:05Z
| 2020-07-17T11:24:05Z
|
CONTRIBUTOR
| null |
- closes #31650
- Passed pandas.tests.plotting.test_misc.py
- Now creating new axes with "fig, ax = plt.subplots(1, 1, figsize=figsize)"
- Also changed plotting.lag_plot regarding the same issue
- Figure size is only changed if no axis parameter is given
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TST: add message check to pytest.raises (tests/arrays/test_boolean.py)
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CONTRIBUTOR
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- [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] Added in an error message
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CLN: Avoid unnecessary values_from_object
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MEMBER
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xref #27165.
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Backport PR #32104 on branch 1.0.x (BUG: Pickle NA objects)
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NONE
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Backport PR #32104: BUG: Pickle NA objects
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CLN: remove unused values from interpolate call
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HDFStore: Fix empty result of keys() method on non-pandas hdf5 file
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| 2020-03-02T23:26:12Z
| 2020-06-14T15:32:11Z
| 2020-06-14T15:32:10Z
|
CONTRIBUTOR
| null |
First try to get pandas style tables, if there are none, return the list of non-pandas (hdf5 native) tables in the file (if any)
- [x] closes #29916
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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MDU6SXNzdWU1NzQzMTc5Njc=
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BUG: overflow on pd.Timedelta(nanoseconds=) constructor
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| 0
| 2020-03-02T23:36:34Z
| 2020-03-04T14:22:36Z
| 2020-03-04T14:22:36Z
|
CONTRIBUTOR
| null |
```
In [1]: pd.__version__
Out[1]: '1.1.0.dev0+570.g9a02c3503
```
It appears we are overflowing when using nanoseconds= keyword for Timedelta. This works just fine when passing smaller values or passing as the first positional arg.
```
In [3]: pd.Timedelta(nanoseconds=1e10)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-d56d2f0a71ff> in <module>
----> 1 pd.Timedelta(nanoseconds=1e10)
~/pandas/pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.Timedelta.__new__()
ValueError: Could not convert object to NumPy timedelta
# ok if by default constructor
In [4]: pd.Timedelta(1e10)
Out[4]: Timedelta('0 days 00:00:10')
```
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TYP: internals
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| 1
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MEMBER
| null |
some of these are a real bear, in particular `SingleBlockManager.__init__`
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Add missing newline
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| 2
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|
CONTRIBUTOR
| null |
The line ">>> idx.isin([1, 4]) array([ True, False, False])" is was not rendered properly, because it was missing a newline above it.
https://pandas.pydata.org/docs/reference/api/pandas.Index.isin.html
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: remove unreachable branch in Index._union
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MDExOlB1bGxSZXF1ZXN0MzgyNzMxNzIz
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CLN: remove is_period_arraylike, is_datetime_arraylike
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MEMBER
| null |
There are better alternatives
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MemoryError, Python 32bit, Error when reading a large file
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[] | null | 1
| 2020-03-03T10:24:11Z
| 2021-07-29T06:17:04Z
| null |
NONE
| null |
#### Code Sample
```python
import pandas as pd
path = 'dataset.gz'
df = pd.read_csv(path, sep = ';', compression = 'gzip')
print(sys.getsizeof(df ) / 1024 / 1024, " Mb")
```
#### Code Output (Problem)
```
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
<ipython-input-13-b36dac541c6a> in <module>
2
3 path = 'dataset.gz'
----> 4 df = pd.read_csv(path, sep = ';', compression = 'gzip')
5 print(sys.getsizeof(df) / 1024 / 1024, " Mb")
~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
674 )
675
--> 676 return _read(filepath_or_buffer, kwds)
677
678 parser_f.__name__ = name
~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
452
453 try:
--> 454 data = parser.read(nrows)
455 finally:
456 parser.close()
~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1131 def read(self, nrows=None):
1132 nrows = _validate_integer("nrows", nrows)
-> 1133 ret = self._engine.read(nrows)
1134
1135 # May alter columns / col_dict
~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
2035 def read(self, nrows=None):
2036 try:
-> 2037 data = self._reader.read(nrows)
2038 except StopIteration:
2039 if self._first_chunk:
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype()
pandas\_libs\parsers.pyx in pandas._libs.parsers._try_int64()
MemoryError: Unable to allocate 1.00 MiB for an array with shape (131072,) and data type int64
```
#### Problem description
The pandas `read_csv` function got a memory error, this shouldn't happen (I have a 16 Gb RAM computer).
Meanwhile, I tried another python (conda) 3.7.4 which works fine but using pandas 0.25.1, it's currently my workaround.
#### Expected Output
From my workaround (Python 3.7.4, conda, pandas 0.25.1)
```
625.6165533065796 Mb
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
None
</details>
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MDExOlB1bGxSZXF1ZXN0MzgyODk4NDYz
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|
Properly handle missing attributes in query/eval strings
|
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| 9
| 2020-03-03T11:33:08Z
| 2020-04-10T19:31:01Z
| 2020-04-10T19:30:56Z
|
CONTRIBUTOR
| null |
Consider this script:
```python
import pandas as pd
pd.eval("pd.thing")
```
Currently it raises an error like this:
```
File "/home/alex/work/pandas/pandas/core/computation/expr.py", line 640, in visit_Attribute
raise ValueError(f"Invalid Attribute context {ctx.__name__}")
AttributeError: 'Load' object has no attribute '__name__'
```
Adding `__class__` to that line changes the error to the more sensible:
ValueError: Invalid Attribute context Load
Re-raising the original error gives what's really needed:
```
File "/home/alex/work/pandas/pandas/core/computation/expr.py", line 631, in visit_Attribute
v = getattr(resolved, attr)
File "/home/alex/work/pandas/pandas/__init__.py", line 260, in __getattr__
raise AttributeError(f"module 'pandas' has no attribute '{name}'")
AttributeError: module 'pandas' has no attribute 'thing'
```
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pandas1.0.1 has trouble with certain column names
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| 11
| 2020-03-03T12:37:59Z
| 2021-01-20T18:57:32Z
| 2021-01-20T18:57:32Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.read_json(file_path,lines=True)
TypeError: unhashable type: ‘dict’ . `
```
#### Problem description
Pandas1.0.1 with python3.7 hits the above for json containing dicts containing the key "last_status_change_at" and possibly others, while pandas0.25.3 with python3.7 does not. I haven't checked yet with python3.6.
#### Expected Output
no unhashableness
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS (pandas 1.0.1 which hits error)
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
INSTALLED VERSIONS (pandas0.25.3 which doesnt hit error)
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzgyOTUzNTg3
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BUG: fixes plotting with nullable integers (#32073)
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|
CONTRIBUTOR
| null |
- [x] closes #32073
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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Implement Index.__array_ufunc__
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| 3
| 2020-03-03T15:17:58Z
| 2020-09-17T19:39:55Z
| null |
CONTRIBUTOR
| null |
It'd be similar to `Series.__array_ufunc__`, (extract array, apply ufunc, box in index with name) with the exception that
1. No alignment when operating between multiple indexes
2. We'd need to decide whether Index or Series defers when calling ufuncs on inputs of mixed index and series. Check what binops does.
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DEPR: _ndarray_values vs to_numpy vs __array__
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MEMBER
| null |
Is there a clear explanation for when to use one of these instead of another?
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Object dtype gives ValueError in _maybe_cast_with_dtype
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| 2020-03-03T16:32:56Z
| 2021-07-29T06:18:06Z
| null |
NONE
| null |
####
```python
import pandas as pd
import numpy as np
frame = pd.DataFrame(index=pd.Index([1, np.nan]))
fixed_index = frame.index.where(frame.index.notna(), 'a' + frame.reset_index().index.astype(str))
Traceback (most recent call last):
File "<input>", line 4, in <module>
File "C:\Users\Miniconda3\envs\testbed_dtype\lib\site-packages\pandas\core\indexes\base.py", line 3791, in where
return self._shallow_copy_with_infer(values, dtype=dtype)
File "C:\Users\Miniconda3\envs\testbed_dtype\lib\site-packages\pandas\core\indexes\base.py", line 567, in _shallow_copy_with_infer
return Index(values, **attributes)
File "C:\Users\Miniconda3\envs\testbed_dtype\lib\site-packages\pandas\core\indexes\base.py", line 385, in __new__
data = _maybe_cast_with_dtype(data, dtype, copy)
File "C:\Users\Miniconda3\envs\testbed_dtype\lib\site-packages\pandas\core\indexes\base.py", line 5457, in _maybe_cast_with_dtype
data = data.astype(dtype)
ValueError: could not convert string to float: 'a1'
```
#### Problem description
Data is being cast to incorrect dtype based on inferred dtype and gives a ValueError.
Issue was not present in pandas==0.25.3
#### Expected Output
With pandas==0.25.3
fixed_index
Index([1.0, 'a1'], dtype='object')
#### Output of ``pd.show_versions()``
python : 3.7.6.final.0
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : 1.3.0
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NzQ3OTcxNzk=
| 32,414
|
EA: fillna should accept same type
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| 10
| 2020-03-03T16:41:33Z
| 2021-08-21T01:09:48Z
| null |
MEMBER
| null |
```
cat = pd.Categorical(["A", "B", None, "A"])
ser = pd.Series(cat).fillna("B")
>>> filled = cat.fillna(ser) # <-- works
>>> cat.fillna(filled)
TypeError: 'value' parameter must be a scalar, dict or Series, but you passed a Categorical
```
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RLS: 1.0.2
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|
MEMBER
| null |
@pandas-dev/pandas-core think end of this week?
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