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https://api.github.com/repos/pandas-dev/pandas/issues/27213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27213/events | https://github.com/pandas-dev/pandas/issues/27213 | 463,850,166 | MDU6SXNzdWU0NjM4NTAxNjY= | 27,213 | Incomplete copying with df.column.values | {
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} | 1 | 2019-07-03T16:37:11Z | 2019-07-03T19:48:33Z | 2019-07-03T19:48:23Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
my_test_df = pd.DataFrame(pd.Series([1,2,3,4,5]))
my_test_df.columns = ['a']
print(my_test_df)
c = my_test_df.a.values
c = c*5
print("after c")
print(my_test_df)
print(c)
e = my_test_df.a.values
for i in range(0, len(c)):
e[i] = e[i] * 5
print("after e")
print(my_test_df)
print(e)
def test_loop(a):
b = a.values
for i in range(0, len(a)):
b[i] = b[i] * 5
return b
d = test_loop(my_test_df.a)
print(my_test_df)
print(d)
```
outputs:
```
a
0 1
1 2
2 3
3 4
4 5
after c
a
0 1
1 2
2 3
3 4
4 5
[ 5 10 15 20 25]
after e
a
0 5
1 10
2 15
3 20
4 25
[ 5 10 15 20 25]
a
0 25
1 50
2 75
3 100
4 125
[ 25 50 75 100 125]
```
#### Problem description
When creating an object with the df.column.values, when the new object is acted on, it is expected to operate independently than the values it is assigned upon. Throughout the code, my_test_df.a is expected to remain constant.
#### Expected Output
```
a
0 1
1 2
2 3
3 4
4 5
```
every time my_test_df is printed
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 21 Model 2 Stepping 0, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.8.3 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27214/events | https://github.com/pandas-dev/pandas/issues/27214 | 463,850,803 | MDU6SXNzdWU0NjM4NTA4MDM= | 27,214 | pd.get_dummies different default return type than .str.get_dummies | {
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} | 6 | 2019-07-03T16:38:54Z | 2021-07-10T17:48:26Z | null | NONE | null | Let's consider this dataframe with both integer and string values in two columns
```python
import pandas as pd
df = pd.DataFrame({'col1': [0,1,1,0,1], 'col2':list('aabbc')})
```
When I want to use ```str.get_dummies``` on one column and want -1 instead of 1, I can do:
```python
print (-df.col2.str.get_dummies())
a b c
0 -1 0 0
1 -1 0 0
2 0 -1 0
3 0 -1 0
4 0 0 -1
```
which is correct, but at first I tried with ```pd.get_dummies``` and I got a value of 255 instead of -1:
```python
print (-pd.get_dummies(df.col2)) #or print (-pd.get_dummies(df[['col2']])) same result
a b c
0 255 0 0
1 255 0 0
2 0 255 0
3 0 255 0
4 0 0 255
```
If I used only the integer column ```col1```, I get the same behavior, but if I use both columns at once, then I get the right result:
```python
print (-pd.get_dummies(df))
col1 col2_a col2_b col2_c
0 0 -1 0 0
1 -1 -1 0 0
2 -1 0 -1 0
3 0 0 -1 0
4 -1 0 0 -1
```
As explained in the [doc](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html) , if you don't specify the ```columns``` parameter, then _all the columns with object or category dtype will be converted_, ```col1``` is then ignored as supposed. But if I pass both columns in the parameter, I get 255 instead of -1 again:
```python
print (-pd.get_dummies(df, columns = ['col1','col2']))
col1_0 col1_1 col2_a col2_b col2_c
0 255 0 255 0 0
1 0 255 255 0 0
2 0 255 0 255 0
3 255 0 0 255 0
4 0 255 0 0 255
```
I found this with ```pd.__version__ == "0.24.2"```.
Note: if you do multiply by ```-1``` instead of just the sign ```-```, then ```print (-1*pd.get_dummies(df.col2))``` gives the expected result
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27215 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27215/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27215/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27215/events | https://github.com/pandas-dev/pandas/issues/27215 | 463,877,439 | MDU6SXNzdWU0NjM4Nzc0Mzk= | 27,215 | Cannot hash table with index containing mixed types including non utf-8 bytes strings | {
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"description": "String extension data ty... | open | false | null | [] | null | 1 | 2019-07-03T17:42:48Z | 2019-12-01T17:35:42Z | null | NONE | null | #### Example
```
import pandas
from pandas.util import hash_pandas_object
hash_pandas_object(pandas.DataFrame({'a': [1,2]}, index=[1, b'\xff1']), encoding='latin1')
```
UnicodeDecodeError: 'ascii' codec can't decode byte 0xff in position 0: ordinal not in range(128)
#### Problem description
This is pretty niche :)
If a table passed to `hash_pandas_object` has an index with mixed types, then this branch is followed:
https://github.com/pandas-dev/pandas/blob/4e185fcaedfe75050a3aa4e9fa175f9579825388/pandas/core/util/hashing.py#L297-L298
which calls: `vals.astype(str)`, (I'm assuming so that the values can be converted to useful python objects) where vals is a numpy array.
As shown here, this does not work if the array contains ascii-compatible byte values:
```
>>> np.array([b'\xff']).astype(str)
UnicodeDecodeError: 'ascii' codec can't decode byte 0xff in position 0: ordinal not in range(128)
```
There is an encoding argument that can be passed to `hash_pandas_object` but this is not used when converting the values to str.
#### Expected Output
```
1 XXXXXXXXXXXXXXXXXXXXXX
b'11' XXXXXXXXXXXXXXXXXXXXXX
dtype: uint64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.125-linuxkit
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.0.0
pip: 19.1.1
setuptools: 40.8.0
Cython: 0.29.11
numpy: 1.18.0.dev0+13de4d8
scipy: 1.1.0
pyarrow: 0.13.0
xarray: None
IPython: 7.5.0
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 7 | 2019-07-03T20:47:01Z | 2019-07-09T21:00:12Z | 2019-07-09T21:00:12Z | CONTRIBUTOR | null | ERROR: type should be string, got "https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=13969 (off of a PR)\r\n\r\nshows quite a number of warnings of the form; we should either suppress or remove the benchmark.\r\n\r\n ```\r\n24.97%] ··· io.msgpack.MSGPack.time_read_msgpack 25.2±0ms\r\n[ 24.97%] ···· /home/vsts/work/1/s/asv_bench/benchmarks/io/msgpack.py:18: FutureWarning: to_msgpack is deprecated and will be removed in a future version.\r\n It is recommended to use pyarrow for on-the-wire transmission of pandas objects.\r\n self.df.to_msgpack(self.fname)\r\n /home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/timeit.py:6: FutureWarning: The read_msgpack is deprecated and will be removed in a future version.\r\n It is recommended to use pyarrow for on-the-wire transmission of pandas objects.\r\n times. See also Tim Peters' introduction to the Algorithms chapter in\r\n```\r\n\r\n```\r\n 6.83%] ···· For parameters: 0\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:329: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[50:1000, 20:50] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:330: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[2000:3000] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:331: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[:, 60:70] = np.nan\r\n \r\n For parameters: 1\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:329: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[50:1000, 20:50] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:330: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[2000:3000] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:331: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[:, 60:70] = np.nan\r\n\r\n[ 6.89%] ··· frame_methods.Count.time_count_level_multi ok\r\n[ 6.89%] ··· ====== ==========\r\n axis \r\n ------ ----------\r\n 0 78.6±0ms \r\n 1 114±0ms \r\n ====== ==========\r\n\r\n[ 6.89%] ···· For parameters: 0\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:329: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[50:1000, 20:50] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:330: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[2000:3000] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:331: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[:, 60:70] = np.nan\r\n \r\n For parameters: 1\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:329: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[50:1000, 20:50] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:330: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[2000:3000] = np.nan\r\n /home/vsts/work/1/s/asv_bench/benchmarks/frame_methods.py:331: FutureWarning: \r\n .ix is deprecated. Please use\r\n .loc for label based indexing or\r\n .iloc for positional indexing\r\n \r\n See the documentation here:\r\n http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\r\n self.df.ix[:, 60:70] = np.nan\r\n\r\n[ 6.96%] ··· frame_methods.Describe.time_dataframe_describe 133±0ms\r\n[ 7.02%] ··· frame_methods.Describe.time_series_describe 44.2±0ms\r\n```" | {
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} | 4 | 2019-07-03T20:57:07Z | 2021-07-10T17:49:57Z | null | CONTRIBUTOR | null | 47ffcd674 changes how we handle sorting EAs. We've made `nargsort` compatible with extension arrays. It now calls `ExtensionArray._values_for_argsort` and then applies pandas `nargsort` sorting algorithm to the values.
This may have broken the (implied) ability for ExtensionArray's to "bring their own" sorting algorithm. For example, you can imagine a GPU-backed EA that has a very fast sorting algorithm available. While `ExtensionArray.argsort` can use that custom sorting algo, pandas *may* not. Anywhere that uses `nargsort(obj)` will now essentially do `nargsort(thing._values_for_argsort())` for EAs. | {
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} | 0 | 2019-07-03T22:01:47Z | 2019-07-04T00:41:48Z | 2019-07-04T00:41:48Z | MEMBER | null | An `is_empty` attribute would be useful, as it is often necessary to special case empty intervals when dealing with `Interval` methods such as `overlaps`, `contains`, etc. (xref #26893)
For a scalar `Interval` this should return a boolean:
```python
In [2]: pd.Interval(0, 0, closed='neither').is_empty
Out[2]: True
In [3]: pd.Interval(0, 1, closed='neither').is_empty
Out[3]: False
```
For an `IntervalIndex` or `IntervalArray` this should return a boolean ndarray positionally indicating if a given `Interval` is empty:
```python
In [4]: pd.IntervalIndex.from_tuples([(0, 0), (0, 1)]).is_empty
Out[4]: array([ True, False])
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} | 7 | 2019-07-03T22:03:41Z | 2020-05-09T19:50:47Z | 2020-05-09T19:50:47Z | NONE | null | #### Code Sample
```python
from pandas.io.json import json_normalize
data = [{'state': 'Florida',
'shortname': 'FL',
'info': {
'governor': 'Rick Scott'
},
'counties': [{'name': 'Dade', 'population': 12345},
{'name': 'Broward', 'population': 40000},
{'name': 'Palm Beach', 'population': 60000}]},
{'state': 'Ohio',
'shortname': 'OH',
'info': {
'governor': 'John Kasich'
},
'counties': [{'name': 'Summit', 'population': 1234},
{'name': 'Cuyahoga', 'population': 1337}]}]
print(json_normalize(data, ['counties', 'name'], ['state', 'shortname', ['info', 'governor']], errors='ignore'))
```
Returns
```
0 state shortname info.governor
0 D Florida FL NaN
1 a Florida FL NaN
2 d Florida FL NaN
3 e Florida FL NaN
4 B Florida FL NaN
5 r Florida FL NaN
6 o Florida FL NaN
7 w Florida FL NaN
8 a Florida FL NaN
9 r Florida FL NaN
10 d Florida FL NaN
11 P Florida FL NaN
12 a Florida FL NaN
13 l Florida FL NaN
14 m Florida FL NaN
15 Florida FL NaN
16 B Florida FL NaN
17 e Florida FL NaN
18 a Florida FL NaN
```
#### Problem description
Running json_normalize on a nested record_path with a nested meta argument gives an error that says it cannot find info.governer.
This is inconsistent because running it again, without the nested meta argument, successfully returns the data:
```python
# using the same data from before
print(json_normalize(data, ['counties', 'name'], ['state', 'shortname', 'info'], errors='ignore'))
```
```
0 state shortname info
0 D Florida FL {'governor': 'Rick Scott'}
1 a Florida FL {'governor': 'Rick Scott'}
2 d Florida FL {'governor': 'Rick Scott'}
3 e Florida FL {'governor': 'Rick Scott'}
4 B Florida FL {'governor': 'Rick Scott'}
5 r Florida FL {'governor': 'Rick Scott'}
6 o Florida FL {'governor': 'Rick Scott'}
7 w Florida FL {'governor': 'Rick Scott'}
```
Similarly, using a non-nested record path also works (in fact, this is the exact sample example that can be found in the [json_normalize pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.json.json_normalize.html)).
```python
# using the same data from before
print(json_normalize(data, 'counties', ['state', 'shortname', ['info', 'governor']]))
```
```
name population state shortname info.governor
0 Dade 12345 Florida FL Rick Scott
1 Broward 40000 Florida FL Rick Scott
2 Palm Beach 60000 Florida FL Rick Scott
3 Summit 1234 Ohio OH John Kasich
4 Cuyahoga 1337 Ohio OH John Kasich
```
The result is that it is much more difficult to access nested meta data.
May be related to: https://github.com/pandas-dev/pandas/issues/21537
#### Expected Output
```
0 state shortname info.governor
0 D Florida FL Rick Scott
1 a Florida FL Rick Scott
2 d Florida FL Rick Scott
3 e Florida FL Rick Scott
4 B Florida FL Rick Scott
5 r Florida FL Rick Scott
6 o Florida FL Rick Scott
7 w Florida FL Rick Scott
8 a Florida FL Rick Scott
9 r Florida FL Rick Scott
10 d Florida FL Rick Scott
11 P Florida FL Rick Scott
12 a Florida FL Rick Scott
13 l Florida FL Rick Scott
14 m Florida FL Rick Scott
15 Florida FL Rick Scott
16 B Florida FL Rick Scott
17 e Florida FL Rick Scott
18 a Florida FL Rick Scott
```
Note that `['counties', 'name']` is an arbitrary list of strings to use as a record path, and that this example is contrived (who really needs a table comprised of each letter of a string?). However, many real scenarios can be constructed that require this sort of nested record_path extraction along with nested meta path extraction.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.125-linuxkit
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.15.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-07-03T22:08:32Z | 2019-07-04T20:47:02Z | 2019-07-04T00:41:49Z | MEMBER | null | - [X] closes #27219
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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} | 2 | 2019-07-03T23:45:46Z | 2020-06-24T15:13:58Z | 2020-06-24T15:13:58Z | NONE | null | Ni!
This is a follow up to #2311 which has been closed after read support was implemented in #25427 .
People whose work flow involve outputting ODS files would benefit from write support.
.~´ | {
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} | 6 | 2019-07-04T01:05:12Z | 2019-07-06T02:04:23Z | 2019-07-05T16:24:09Z | MEMBER | null | Trying to make it clearer exactly what the failure modes being caught are. | {
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"default"... | open | false | null | [] | null | 2 | 2019-07-04T01:23:11Z | 2019-07-16T16:14:41Z | null | MEMBER | null | At the sprint there was some discussion of optimization and python call stacks. One place where we do many tiny calls is in `is_foo_dtype` checks
```
In [3]: arr = np.arange(10**5)
In [4]: %timeit is_float_dtype(arr)
1.23 µs ± 28.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [5]: %timeit is_float_dtype(arr.dtype)
678 ns ± 11 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [6]: %timeit arr.dtype.kind == 'f'
71.6 ns ± 1.87 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
```
~17x difference. Part of this is because `is_foo_dtype` will take either `arr` or `arr.dtype`. The potential savings stack up in places where we do many of these dtype checks on the same arguments. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27225 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27225/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27225/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27225/events | https://github.com/pandas-dev/pandas/issues/27225 | 464,169,489 | MDU6SXNzdWU0NjQxNjk0ODk= | 27,225 | The release notes link for v0.25.0rc0 release announcement is broken | {
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] | closed | false | null | [] | null | 2 | 2019-07-04T09:41:10Z | 2019-07-04T12:42:37Z | 2019-07-04T12:42:37Z | NONE | null | **Title**: The "release notes" link for v0.25.0rc0 release announcement is broken
**Version**: v0.25.0rc0 pre-release
**Steps to reproduce**:
1. Open your browser,
2. Navigate to pandas releases page (https://github.com/pandas-dev/pandas/releases),
3. Click on the "release notes" link for the v0.25.0rc0 pre-release.
**Actual results**: 404 Not Found (https://pandas.pydata.org/pandas-docs/version/0.25/whatsnew/v0.25.0.html)
**Expected results**: The release notes for the v0.25.0rc0 should be shown.
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} | 1 | 2019-07-04T12:39:10Z | 2019-07-08T01:36:42Z | 2019-07-08T01:36:42Z | MEMBER | null | After https://github.com/pandas-dev/pandas/pull/27076, we should update the contributing guide on how to use black (and pre-commit hook).
We should also put somewhere a short guideline on how to "update" your PR to resolve conflicts. | {
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} | 0 | 2019-07-04T12:47:22Z | 2019-07-15T20:11:14Z | 2019-07-15T20:11:14Z | CONTRIBUTOR | null | https://travis-ci.org/MacPython/pandas-wheels/jobs/554071675
Whoops (that's the RC).
Skipping that test for the RC. Will push a fix and re-enable push a fix. | {
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"color": "006b7... | closed | false | null | [] | null | 19 | 2019-07-04T12:47:50Z | 2019-10-11T21:59:27Z | 2019-10-11T21:59:27Z | CONTRIBUTOR | null |
<img width="454" alt="Screen Shot 2019-07-04 at 1 45 05 PM" src="https://user-images.githubusercontent.com/306380/60667707-13579000-9e62-11e9-88e6-ea1153057152.png">
- [x] closes #8829
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-07-04T14:35:15Z | 2019-07-10T16:27:11Z | 2019-07-10T16:23:06Z | MEMBER | null | A couple things going on here.
- Take simple, low-dependency, not-used-in-indexing.py functions out of indexing.py and put them in indexers.py.
- A couple of runtime-defined functions in internals get moved here too.
- Avoids some runtime imports (i.e. simplify dependency structure)
- Fixes some incorrect private names
- Adds typing
- Simplify some indexing.py methods
- Avoid duplicated validation calls
- Add typing
- De-duplicate _get_slice_axis method
- Move code outside of try/except blocks to clarify what the failure modes are.
Medium-term goals in indexing.py
- Isolate internals access
- refactor _setitem_with_indexer (over 300 lines) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27230 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27230/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27230/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27230/events | https://github.com/pandas-dev/pandas/issues/27230 | 464,317,082 | MDU6SXNzdWU0NjQzMTcwODI= | 27,230 | Dataframe loses level name after concat, only if columns types are str | {
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] | closed | false | null | [] | null | 2 | 2019-07-04T15:17:49Z | 2019-07-04T15:21:41Z | 2019-07-04T15:21:33Z | CONTRIBUTOR | null | #### Code Sample
```python
a = pd.DataFrame([1], index=[1], columns=['1'])
a.columns.names = ['n']
b = pd.DataFrame([1, 1], index=['1', '2'], columns=[1]).T
b.columns.names = ['n']
df1 = pd.concat([a, b])
a = pd.DataFrame([1], index=[1], columns=[1])
a.columns.names = ['n']
b = pd.DataFrame([1, 1], index=[1, 2], columns=[1]).T
b.columns.names = ['n']
df2 = pd.concat([a, b])
```
#### Problem description
When concatenating two dataframes with the same column level name (`'n'` in the case above), it is expected to find the same level name after the concatenation, i.e. on `df1`. This is not the case. If the columns are not `str`, as in `df2`, the behaviour is as expected. If this is not the expected behaviour, the two results should at least agree.
#### Expected Output
```
df1.columns.name == df2.columns.name
```
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.17.0rc1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-07-04T16:42:46Z | 2019-07-05T13:32:04Z | 2019-07-05T13:32:04Z | CONTRIBUTOR | null | {
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} | 2 | 2019-07-04T17:58:06Z | 2019-07-04T18:26:29Z | 2019-07-04T18:26:22Z | NONE | null | Reading the [contributing documentation section on pep8](https://github.com/pandas-dev/pandas/blob/master/doc/source/development/contributing.rst#python-pep8), I noticed the acceptable line-length for Python code is given as 79 characters as [specified in pep8](https://www.python.org/dev/peps/pep-0008/#maximum-line-length).
> we restrict line-length to 79 characters to promote readability
However, looking at setup.cfg, I see that the maximum line length is [specified as 88 characters](https://github.com/pandas-dev/pandas/blob/master/setup.cfg#L15) and flake8 is set up to ignore line length errors.
```
[flake8]
max-line-length = 88
ignore =
E501, # longer line length
```
Is the contributing documentation out-of-date, or am I making an incorrect interpretation of `setup.cfg`? | {
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} | 2 | 2019-07-04T18:14:49Z | 2019-07-08T12:06:55Z | 2019-07-08T01:36:42Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/27226
Starting this by copying some content from dask (https://github.com/dask/dask/pull/4983)
Also related to https://github.com/pandas-dev/pandas/issues/23616 (recommending pre-commit hooks)
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https://api.github.com/repos/pandas-dev/pandas/issues/27234 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27234/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27234/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27234/events | https://github.com/pandas-dev/pandas/issues/27234 | 464,399,948 | MDU6SXNzdWU0NjQzOTk5NDg= | 27,234 | Preserve data types in pandas.DataFrame.mean() | {
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"color": "006... | closed | false | null | [] | null | 2 | 2019-07-04T21:40:19Z | 2019-11-12T20:35:17Z | 2019-11-12T20:35:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame([[1,1+1j,True],[1,0,False],[1,1j,True]])
df.mean()
```
#### Received output
```
0 (1+0j)
1 (0.3333333333333333+0.6666666666666666j)
2 (0.6666666666666666+0j)
dtype: complex128
```
#### Expected output
```
0 1
1 (0.3333333333333333+0.6666666666666666j)
2 0.6666666666666666
dtype: object
```
#### Problem description
When `mean()` is applied column-wise it should handle data types column-wise.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.15.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27235/events | https://github.com/pandas-dev/pandas/pull/27235 | 464,402,720 | MDExOlB1bGxSZXF1ZXN0Mjk0Njg5ODk1 | 27,235 | Adding fastpath and name parameters to docstring for Series class and others. | {
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] | closed | false | null | [] | null | 6 | 2019-07-04T22:01:02Z | 2019-08-16T14:42:31Z | 2019-07-09T12:59:53Z | CONTRIBUTOR | null | - [x] closes #27178
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I modified the docstring for the Series class to include the "fastpath" and "name" parameters. I searched the code base, and also modified the docstrings for the following classes in a similar way:
Categorial
CategoricalIndex
Grouping
Index
MultiIndex
PeriodIndex
RangeIndex
It appears that fastpath is definitely used internally, and is not deprecated. There were no other examples of fastpath being documented in the parameters section of a docstring, so I went with this convention:
fastpath : bool, default False
Internal use only | {
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} | 6 | 2019-07-04T22:17:37Z | 2019-07-05T15:23:22Z | 2019-07-05T15:21:47Z | MEMBER | null | _Almost_ gets to a point where Series and DataFrame ops can share the na_op definition.
AFAICT this is the only non-test usage of libalgos.arrmap_object. | {
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} | 118 | 2019-07-04T23:04:26Z | 2020-04-27T18:03:29Z | 2020-04-27T16:09:26Z | CONTRIBUTOR | null | - [x] closes #3942
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Added a key parameter to the DataFrame/Series.sort_values and sort_index functions matching Python sorted semantics for allowing custom sorting orders. Address open issue https://github.com/pandas-dev/pandas/issues/3942.
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} | 0 | 2019-07-05T00:08:55Z | 2019-07-05T17:57:50Z | 2019-07-05T14:28:36Z | MEMBER | null | In working on #27130 I'm finding we have a number of kludges where we check for operator names instead of operator identity because we haven't imported ops.radd, ops.rsub, etc. This is in general because of circularity-avoidance. So this PR breaks off those function definitions into roperator.py in a new core.ops directory.
In a follow-up, parts of core.missing devoted to handling division by zero will belong in core/ops/ | {
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} | 3 | 2019-07-05T01:25:34Z | 2019-07-06T22:02:24Z | 2019-07-06T21:45:47Z | MEMBER | null | - [x] closes #26987
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Takes over from #27130 by implementing dispatch_fill_zeros. This is unfortunately similar to dispatch_missing, and eventually the two functions will be merged. ATM I'm trying to do exactly that in the branch for 27130, having lots of trouble pop up with sparse and IntegerArray. | {
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"descript... | closed | false | null | [] | null | 1 | 2019-07-05T03:29:10Z | 2019-07-05T16:43:02Z | 2019-07-05T16:42:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
na_values = pandas.io.common._NA_VALUES.difference({'null','NULL'})
excelbook = pandas.read_excel(configpath,sheet_name=sheetIndex,index_col=False,na_values=na_values)
```
#### Problem description
there's a word 'null' in my excel file.but it become a NaN when pandas read it out.
then i cut 'null' from my own na_values and used it at pandas.read_excel,but it seems like it is invalid in read_excel because it still changed 'null' to NaN in result.
Dose it is a bug or i used it at a wrong way?
my environment:
python version:3.7.3
pandas version:0.24.2
system:windows7
Received output:
0 ......
1 ......
2 ......
3 ......
4 [1 5 '25' nan 10000 100000 25 1000]
Expected output:
0 ......
1 ......
2 ......
3 ......
4 [1 5 '25' 'null' 10000 100000 25 1000]
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} | 2 | 2019-07-05T03:33:45Z | 2021-07-10T17:53:19Z | null | CONTRIBUTOR | null | Sub issue of #23843
#### Code Sample, a copy-pastable example if possible
```python
data = [{"CreatedBy":{"Name": "User001"},
"Lookup":{"TextField":"Some text"},
"Image":{"a":"b"}}]
json_normalize(data).to_json(orient="records")
```
#### Current Output
```
[{"CreatedBy.Name":"User001",
"Image.a":"b",
"Lookup.TextField":"Some text"}]
```
#### Problem description
A usecase where I don't want to flatten they dict under the key `Image`
#### Expected Output
```
[{"CreatedBy.Name":"User001",
"Image:{a":"b}",
"Lookup.TextField":"Some text"]
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 39.1.0
Cython: None
numpy: 1.15.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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} | 68 | 2019-07-05T04:47:17Z | 2019-10-17T18:44:56Z | 2019-10-16T14:40:59Z | CONTRIBUTOR | null | - [x] progress towards #27138
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
In #27138 I proposed doing some changes to ``MultiIndex``, so that the index type can have its data collected in ``_data`` as type ``List[Categorical]``,+ adding ``MultiIndex.arrays`` in order to access each full level as zero-copy ``Categorical``.
This is the first part of that proposal, and drops setting the names on the ``levels[x].name`` attribute and instead sets the names on the ``MultiIndex._names`` attribute.
This PR is a minorly backward-breaking change (so would be good to get into 0.25), while the followup will not break anything. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27243 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27243/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27243/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27243/events | https://github.com/pandas-dev/pandas/pull/27243 | 464,497,374 | MDExOlB1bGxSZXF1ZXN0Mjk0NzYyMzcw | 27,243 | BUG: merge_asof with multiple by columns with tz | {
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} | 1 | 2019-07-05T07:14:18Z | 2020-03-14T02:08:52Z | 2019-07-05T14:29:35Z | MEMBER | null | - [x] closes #26649
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27244 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27244/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27244/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27244/events | https://github.com/pandas-dev/pandas/issues/27244 | 464,521,190 | MDU6SXNzdWU0NjQ1MjExOTA= | 27,244 | TypeError: no default __reduce__ due to non-trivial __cinit__ | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-05T08:19:16Z | 2019-07-05T11:02:28Z | 2019-07-05T11:02:27Z | NONE | null | While I was saving large dataframe I got this error!
the dataframe contains two string columns and one has lists of words.
```python
1840679it [1:16:28, 401.19it/s]
Traceback (most recent call last):
File ".../main.py", line 15, in <module>
data.load_data()
File ".../data.py", line 118, in load_data
self.tweets.to_pickle(join(self.path, 'tweets_0'))
File "C:\Python35\lib\site-packages\pandas\core\generic.py", line 2593, in to_pickle
protocol=protocol)
File "C:\Python35\lib\site-packages\pandas\io\pickle.py", line 77, in to_pickle
f.write(pkl.dumps(obj, protocol=protocol))
File "stringsource", line 2, in spacy.tokens.span.Span.__reduce_cython__
TypeError: no default __reduce__ due to non-trivial __cinit__
```
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} | 1 | 2019-07-05T08:27:48Z | 2019-07-09T21:29:23Z | 2019-07-09T21:29:23Z | CONTRIBUTOR | null | - [x ] closes #25815
- [x ] tests added / passed
- [x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-07-05T10:08:28Z | 2019-07-06T06:15:26Z | 2019-07-05T22:08:36Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
import numpy as np
# first example: non-overlapping intervals
cols = pd.IntervalIndex.from_tuples([(1, 2), (2, 3), (3, 4)])
data = pd.DataFrame(np.random.rand(3, 3), columns=cols)
print(data[data.columns[0]])
# 0 0.722574
# 1 0.344239
# 2 0.015408
# Name: (1, 2], dtype: float64
# second example: overlapping intervals
cols = pd.IntervalIndex.from_tuples([(1, 3), (2, 4), (3, 5)])
data = pd.DataFrame(np.random.rand(3, 3), columns=cols)
print(data[data.columns[0]])
# (2, 4] (1, 3]
# 0 0.772658 0.901796
# 1 0.482587 0.870468
# 2 0.215840 0.190018
```
#### Problem description
We use `pandas` `DataFrame`s to store spectra of energetic particles measured on the Moon. The DataFrame contains the counts of particles detected, where the index is then usually the time when the spectrum was acquired, and the columns are different bins for particle energy. Due to the properties of the instrument, these bins can overlap for certain species of particles - e.g. a particle measured in the first bin can have an energy between 1 and 3, while a particle measured in the second bin can have energies between 2 and 4 in the example above.
When we want to access one of the columns of the spectrum by its name (which is needed to apply some corrections which are different for each energy bin), we can use `data[data.columns[0]]` or iterate over the columns in a for loop, such as `for bin in data: do_something(data[bin])`. However, in the case where the intervals are overlapping, this does not seem to work correctly, as seen above. Contrary to what is stated [in the docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#basics), the selection does not return just a single columns as a `Series`, but a new `DataFrame` containing the column we want, but also all columns that overlap with it.
I see why this might be useful in some cases, but it's unexpected and not really documented as far as I saw. With this behavior, single columns cannot be selected based on their names anymore. Instead, I can only select them based on their index using `.iloc[:, i]`.
#### Expected Output
```python
# (1, 3]
# 0 0.901796
# 1 0.870468
# 2 0.190018
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.0-5-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.24.2
pytest: 4.3.0
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: 1.8.4
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 13 | 2019-07-05T10:44:36Z | 2019-07-17T11:53:39Z | 2019-07-17T11:53:32Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Picked up version 0.25.0rc0 last night:
https://files.pythonhosted.org/packages/19/b6/124914598447928889c6fe08a3e40ff791e1c66c7f6e3234374eefe38359/pandas-0.25.0rc0-cp35-cp35m-win_amd64.whl#sha256=ef9976c835d4a3f1de54b0d176711d27952b48cdf752b37fe60e69395fe35b9b
```
>>>from pandas import DataFrame
...
TypeError: descriptor '__subclasses__' of 'type' object needs an argument
```
Full traceback:
```
File "c:\my_project\test_runner.py", line 6, in <module>
from pandas import DataFrame
File "c:\my_project\venv\lib\site-packages\pandas-0.25.0rc0-py3.5-win-amd64.egg\pandas\__init__.py", line 55, in <module>
from pandas.core.api import (
File "c:\my_project\venv\lib\site-packages\pandas-0.25.0rc0-py3.5-win-amd64.egg\pandas\core\api.py", line 5, in <module>
from pandas.core.arrays.integer import (
File "c:\my_project\venv\lib\site-packages\pandas-0.25.0rc0-py3.5-win-amd64.egg\pandas\core\arrays\__init__.py", line 1, in <module>
from .array_ import array # noqa: F401
File "c:\my_project\venv\lib\site-packages\pandas-0.25.0rc0-py3.5-win-amd64.egg\pandas\core\arrays\array_.py", line 7, in <module>
from pandas.core.dtypes.common import (
File "c:\my_project\venv\lib\site-packages\pandas-0.25.0rc0-py3.5-win-amd64.egg\pandas\core\dtypes\common.py", line 11, in <module>
from pandas.core.dtypes.dtypes import (
File "c:\my_project\venv\lib\site-packages\pandas-0.25.0rc0-py3.5-win-amd64.egg\pandas\core\dtypes\dtypes.py", line 53, in <module>
class Registry:
File "c:\my_project\venv\lib\site-packages\pandas-0.25.0rc0-py3.5-win-amd64.egg\pandas\core\dtypes\dtypes.py", line 84, in Registry
self, dtype: Union[Type[ExtensionDtype], str]
File "C:\Users\my_user\AppData\Local\Programs\Python\Python35\Lib\typing.py", line 552, in __getitem__
dict(self.__dict__), parameters, _root=True)
File "C:\Users\my_user\AppData\Local\Programs\Python\Python35\Lib\typing.py", line 512, in __new__
for t2 in all_params - {t1} if not isinstance(t2, TypeVar)):
File "C:\Users\my_user\AppData\Local\Programs\Python\Python35\Lib\typing.py", line 512, in <genexpr>
for t2 in all_params - {t1} if not isinstance(t2, TypeVar)):
File "C:\Users\my_user\AppData\Local\Programs\Python\Python35\Lib\typing.py", line 1077, in __subclasscheck__
if super().__subclasscheck__(cls):
File "c:\my_project\venv\lib\abc.py", line 225, in __subclasscheck__
for scls in cls.__subclasses__():
TypeError: descriptor '__subclasses__' of 'type' object needs an argument
```
#### Expected Output
No error
Currently works on pandas==0.24.2
#### Output of ``pd.show_versions()``
https://files.pythonhosted.org/packages/19/b6/124914598447928889c6fe08a3e40ff791e1c66c7f6e3234374eefe38359/pandas-0.25.0rc0-cp35-cp35m-win_amd64.whl#sha256=ef9976c835d4a3f1de54b0d176711d27952b48cdf752b37fe60e69395fe35b9b | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27248 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27248/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27248/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27248/events | https://github.com/pandas-dev/pandas/issues/27248 | 464,608,224 | MDU6SXNzdWU0NjQ2MDgyMjQ= | 27,248 | DataFrame.__setitem__ with MultiIndex fails when expanding with new key | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 2 | 2019-07-05T12:00:44Z | 2021-07-10T17:54:31Z | null | NONE | null | ```python
import pandas as pd
df = pd.DataFrame({'ID': [1, 2, 3, 4],
'DT': [2018, 2018, 2017, 2018],
'F1': [0, 1, 0, 0],
'F2': [0, 0, 1, 0] })
df.loc[5]= [5, 2019, 1, 0] # this works
indexed= df.set_index(['ID', 'DT'], inplace=False)
indexed.loc[(2, 2018)] # this also works
indexed.loc[(1, 2019)]= [3, 4] # this fails
```
#### Problem description
I know there is a workaround for this by adding , : but I feel the behavior is not intuitive as it is implemented. I think the arguments to __getitem__ and __setitem__ should be treated consistently and also an assignment over __setitem__ should work the same way for simple indexes (with one level) and for multilevel indexes.
A formatted description of this can be viewed on stackoverflow here:
[Stackoverflow description](https://stackoverflow.com/questions/56901975/unexpected-behavior-of-loc-on-multilevel-indexed-dataframes
)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.24.1
pytest: 3.3.2
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.4
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | [] | closed | false | null | [] | null | 2 | 2019-07-05T12:27:33Z | 2019-07-05T12:39:00Z | 2019-07-05T12:39:00Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
s = pd.Series(['apple', '1.0', '2', -3])
# both code work
pd.to_numeric(s, errors='abc')
pd.to_numeric(s, errors='coerce')
```
#### Problem description
If there is a typo or any value assigned to `errors` argument other than `raise` nor `ignore`, it will be considered as `coerce`.
I track down to the source code, it turns out pandas doesn't check valid values for this argument.
#### Expected Output
An error should be raised.
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} | 4 | 2019-07-05T12:28:32Z | 2019-07-09T20:57:37Z | 2019-07-09T20:57:37Z | NONE | null | #### Code Sample
```python
X = pd.DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=[1, 2, 3, 1, 2, 3], C=[1, 2, 3, 4, 5, 6], D=list("abcdef")))
X = X.set_index(["A", "B", "C"])
X.loc[(1, 1, 2), :]
```
#### Problem description
This correctly throws a `KeyError` but the message is incorrect, instead of showing `"KeyError: (1, 1, 2)"` it shows `"KeyError: 42"`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.16-200.fc28.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 0.19.1
pyarrow: 0.13.0
xarray: None
IPython: 7.1.1
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 0 | 2019-07-05T14:32:21Z | 2019-07-23T21:14:53Z | 2019-07-23T21:14:53Z | MEMBER | null | Discussed in #27236, after that there will be only one non-test usage of libalgos.arrmap_* and it looks like it may be feasible to just use np.vectorize. Check if this is correct and if there is a perf impact. | {
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} | 16 | 2019-07-05T14:54:31Z | 2019-07-17T11:52:36Z | 2019-07-17T11:52:36Z | NONE | null | I'm using this code to load an Excel file.
```python
df = pandas.read_excel(
"data.xlsx",
sheet_name="sheet1",
usecols=[0, 1],
header=None,
names=["foo", "bar"]
)
print(df.head())
```
The Excel file has the cells `A7`=`1`, `A8`=`2`, `A9`=`3`, everything else is empty.
With pandas 0.24.2 I get this:
```
foo bar
0 1 NaN
1 2 NaN
2 3 NaN
```
With pandas 0.25.0rc0 I get:
```
Traceback (most recent call last):
File "tester.py", line 8, in <module>
names=["foo", "bar"]
File "/home/me/.env/lib/python3.7/site-packages/pandas/util/_decorators.py", line 196, in wrapper
return func(*args, **kwargs)
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 877, in parse
**kwds
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 507, in parse
**kwds
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/parsers.py", line 2218, in TextParser
return TextFileReader(*args, **kwds)
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/parsers.py", line 895, in __init__
self._make_engine(self.engine)
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/parsers.py", line 1147, in _make_engine
self._engine = klass(self.f, **self.options)
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/parsers.py", line 2305, in __init__
) = self._infer_columns()
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/parsers.py", line 2712, in _infer_columns
_validate_usecols_names(self.usecols, range(ncols))
File "/home/me/.env/lib/python3.7/site-packages/pandas/io/parsers.py", line 1255, in _validate_usecols_names
"columns expected but not found: {missing}".format(missing=missing)
ValueError: Usecols do not match columns, columns expected but not found: [1]
```
The problem happens because the `bar` column does not contain any data. As soon as I put a value into it, both versions do the same thing.
I'm using Python 3.7.3 in Ubuntu 19.04. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27253 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27253/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27253/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27253/events | https://github.com/pandas-dev/pandas/issues/27253 | 464,783,747 | MDU6SXNzdWU0NjQ3ODM3NDc= | 27,253 | BUG: _table_mod is 'NoneType' when it should not be | {
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] | closed | false | null | [] | null | 3 | 2019-07-05T22:00:09Z | 2019-07-21T00:37:16Z | 2019-07-21T00:37:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import supereeg as se
brain_object = se.load('example_data')
brain_object.save('example.bo')
```
#### Problem description
The package I work on, [SuperEEG](https://github.com/ContextLab/supereeg), uses [deepdish](https://github.com/uchicago-cs/deepdish) for HDF5 loading and saving. Deepdish includes Pandas as a dependency, and when attempting to save an object, pandas throws the following error: `AttributeError: 'NoneType' object has no attribute 'exceptions'`
However, with pandas 0.23.4, the object saves to disk without issue.
#### Expected Output
File saves to disk
#### Full stacktrace
<details>
Traceback (most recent call last):
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\pandas\io\pytables.py", line 1160, in get_node
return self._handle.get_node(self.root, key)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\tables\file.py", line 1644, in get_node
node = where._v_file._get_node(nodepath)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\tables\file.py", line 1599, in _get_node
node = self._node_manager.get_node(nodepath)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\tables\file.py", line 437, in get_node
node = self.node_factory(key)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\tables\group.py", line 1181, in _g_load_child
node_type = self._g_check_has_child(childname)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\tables\group.py", line 398, in _g_check_has_child
% (self._v_pathname, name))
tables.exceptions.NoSuchNodeError: group ``/`` does not have a child named ``//locs``
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\supereeg\brain.py", line 638, in save
dd.io.save(fname, bo, compression=compression)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\deepdish\io\hdf5io.py", line 584, in save
filters=filters, idtable=idtable)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\deepdish\io\hdf5io.py", line 253, in _save_level
store.put(group._v_pathname + '/' + name, level)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\pandas\io\pytables.py", line 889, in put
self._write_to_group(key, value, append=append, **kwargs)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\pandas\io\pytables.py", line 1367, in _write_to_group
group = self.get_node(key)
File "C:\Users\Tudor\Anaconda3\envs\supereeg_env\lib\site-packages\pandas\io\pytables.py", line 1161, in get_node
except _table_mod.exceptions.NoSuchNodeError:
AttributeError: 'NoneType' object has no attribute 'exceptions'
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2efb60717bda9fc64344c5f6647d58564930808e
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.0rc0+0.g2efb60717.dirty
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.11
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 : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27254 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27254/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27254/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27254/events | https://github.com/pandas-dev/pandas/pull/27254 | 464,784,278 | MDExOlB1bGxSZXF1ZXN0Mjk0OTkwNjc0 | 27,254 | REF: rename _data-->_mgr | {
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"de... | closed | false | null | [] | null | 2 | 2019-07-05T22:03:16Z | 2019-07-07T21:51:51Z | 2019-07-07T21:51:38Z | MEMBER | null | At the sprint there was discussion of trimming down the exposed surface of internals. Among the things that makes this difficult is identifying exactly what that surface is. This makes it feasiable to grep for all accesses by using the unambiguous `._mgr` instead of the overloaded `._data`. | {
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] | closed | false | null | [] | null | 1 | 2019-07-05T22:10:45Z | 2019-07-21T00:36:52Z | 2019-07-21T00:36:51Z | NONE | null | - [x] closes #27253
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Currently in get_node() it is not guaranteed that _table_mod is not none, and this breaks some packages that depend on Pandas, forcing use of older versions of Pandas. I added `_tables()` to fix this issue
see #27253 for more details | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27256 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27256/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27256/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27256/events | https://github.com/pandas-dev/pandas/pull/27256 | 464,805,646 | MDExOlB1bGxSZXF1ZXN0Mjk1MDA2NTY4 | 27,256 | BUG: Enable changing dtype of column where column names are not unique | {
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"color": "e1... | closed | false | null | [] | null | 3 | 2019-07-06T01:32:37Z | 2019-10-06T22:42:58Z | 2019-10-06T22:42:57Z | CONTRIBUTOR | null | - [ ] closes #22035, partially #24798
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-07-06T01:51:02Z | 2019-07-06T21:03:18Z | 2019-07-06T17:43:37Z | MEMBER | null | - Move arithmetic-centric functions from core.missing to core.ops.missing.
- module-level docstring giving the motivation for these functions
- tiny optimization:
```
if op in [operator.floordiv, operator.truediv, getattr(operator, "div", None)]:
result = mask_zero_div_zero(left, right, result)
```
becomes
```
if op is operator.floordiv:
result = mask_zero_div_zero(left, right, result)
```
because in py3 numpy behaves the way we want for truediv. | {
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} | 3 | 2019-07-06T02:03:20Z | 2020-01-04T20:14:39Z | 2019-07-08T01:15:43Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/27223#issuecomment-508828776
The change in that PR broke a test in geopandas. We are using some indexing internals in geopandas that were relying on an InvalidIndexError being catched there (and the PR changed it to only TypeError and KeyError.
A small reproducible example without needing geopandas (but it is dummy code, not doing something useful):
```
from pandas.core.indexing import _NDFrameIndexer
class _CoordinateIndexer(_NDFrameIndexer):
def _getitem_tuple(self, tup):
obj = self.obj
xs, ys = tup
return obj[xs][ys]
pd.Series._create_indexer('cx', _CoordinateIndexer)
s = pd.Series(range(5))
s.cx[:, :]
```
The below passes on 0.24.2, but gives an error now on master.
The actual code in geopandas is:
https://github.com/geopandas/geopandas/blob/7c66c93f0f76de53615c02810e817ef57fb6a8de/geopandas/base.py#L734-L761
I know this is quite internal code of pandas that is being used here, so we should think about making this cleaner, but short term it would be nice to fix this. | {
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} | 3 | 2019-07-06T02:11:11Z | 2019-07-08T12:23:00Z | 2019-07-08T01:15:44Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/27258 | {
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} | 1 | 2019-07-06T03:26:49Z | 2019-07-06T21:02:40Z | 2019-07-06T18:12:12Z | MEMBER | null | also clean up some unfortunate formatting left by black.
I _think_ we can also get rid of the transpose kwarg in putmask, separate pass. | {
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} | 5 | 2019-07-06T07:47:59Z | 2019-08-26T14:22:31Z | 2019-08-26T14:22:31Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
idx = pd.date_range('2000', '2005', freq='d', closed='left')
datas = pd.DataFrame({'Color': [ 'green' if x> 1 else 'red' for x in np.random.randn(len(idx))],
'Measure': np.random.randn(len(idx)), 'Year': idx.year},
index=idx.date)
datas.query('Measure > 0').groupby(['Color','Year']).size().unstack()
```
blows-up with
````
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-6c03ccdf9365> in <module>
6 'Measure': np.random.randn(len(idx)), 'Year': idx.year},
7 index=idx.date)
----> 8 datas.query('Measure > 0').groupby(['Color','Year']).size().unstack()
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\frame.py in query(self, expr, inplace, **kwargs)
3190 kwargs["level"] = kwargs.pop("level", 0) + 1
3191 kwargs["target"] = None
-> 3192 res = self.eval(expr, **kwargs)
3193
3194 try:
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\frame.py in eval(self, expr, inplace, **kwargs)
3306 kwargs["target"] = self
3307 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
-> 3308 return _eval(expr, inplace=inplace, **kwargs)
3309
3310 def select_dtypes(self, include=None, exclude=None):
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
320 )
321
--> 322 parsed_expr = Expr(expr, engine=engine, parser=parser, env=env, truediv=truediv)
323
324 # construct the engine and evaluate the parsed expression
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in __init__(self, expr, engine, parser, env, truediv, level)
825 self.env.scope["truediv"] = truediv
826 self._visitor = _parsers[parser](self.env, self.engine, self.parser)
--> 827 self.terms = self.parse()
828
829 @property
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in parse(self)
842 def parse(self):
843 """Parse an expression"""
--> 844 return self._visitor.visit(self.expr)
845
846 @property
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit_Module(self, node, **kwargs)
445 raise SyntaxError("only a single expression is allowed")
446 expr = node.body[0]
--> 447 return self.visit(expr, **kwargs)
448
449 def visit_Expr(self, node, **kwargs):
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit_Expr(self, node, **kwargs)
448
449 def visit_Expr(self, node, **kwargs):
--> 450 return self.visit(node.value, **kwargs)
451
452 def _rewrite_membership_op(self, node, left, right):
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit_Compare(self, node, **kwargs)
742 op = self.translate_In(ops[0])
743 binop = ast.BinOp(op=op, left=node.left, right=comps[0])
--> 744 return self.visit(binop)
745
746 # recursive case: we have a chained comparison, a CMP b CMP c, etc.
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit_BinOp(self, node, **kwargs)
561
562 def visit_BinOp(self, node, **kwargs):
--> 563 op, op_class, left, right = self._maybe_transform_eq_ne(node)
564 left, right = self._maybe_downcast_constants(left, right)
565 return self._maybe_evaluate_binop(op, op_class, left, right)
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in _maybe_transform_eq_ne(self, node, left, right)
482 left = self.visit(node.left, side="left")
483 if right is None:
--> 484 right = self.visit(node.right, side="right")
485 op, op_class, left, right = self._rewrite_membership_op(node, left, right)
486 return op, op_class, left, right
C:\WinP\bd38\bu\WPy32-3800b2\python-3.8.0b2\lib\site-packages\pandas\core\computation\expr.py in visit(self, node, **kwargs)
438
439 method = "visit_" + node.__class__.__name__
--> 440 visitor = getattr(self, method)
441 return visitor(node, **kwargs)
442
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_Constant'
```` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27262/events | https://github.com/pandas-dev/pandas/pull/27262 | 464,861,830 | MDExOlB1bGxSZXF1ZXN0Mjk1MDQ0MTQ2 | 27,262 | Enhancement include or exclude keys in json normalize | {
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] | closed | false | null | [] | null | 26 | 2019-07-06T14:57:57Z | 2019-12-17T17:45:56Z | 2019-12-17T17:45:55Z | CONTRIBUTOR | null | - [x] closes #27241
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27263/events | https://github.com/pandas-dev/pandas/issues/27263 | 464,865,600 | MDU6SXNzdWU0NjQ4NjU2MDA= | 27,263 | pandas 0.24.2 problem on python 3.4.8: 'colletions' has no attribute 'abc' in pandas/combat/__init__.py line 139. | {
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} | [] | closed | false | null | [] | null | 4 | 2019-07-06T15:42:19Z | 2019-07-08T13:11:10Z | 2019-07-06T15:56:12Z | NONE | null | #### Code Sample
```python
import pandas as pd
path = "./demo.dat"
data = pd.read_table(path,header=None,names=["id","label2","text"],sep='\s+')
```
#### Problem description
I install pandas 0.24.2 in Python 3.4.8. When I just import pandas, there is a bug. I open the `/usr/lib64/python3.4/site-packages/pandas/compat/__init__.py` and find something wrong.
I download the source file from [https://pypi.org/project/pandas/#files](https://files.pythonhosted.org/packages/b2/4c/b6f966ac91c5670ba4ef0b0b5613b5379e3c7abdfad4e7b89a87d73bae13/pandas-0.24.2.tar.gz) and check again. The code are same.
Finally, I modify the `/usr/lib64/python3.4/site-packages/pandas/compat/__init__.py` and delete the `abc`. The code run well.
**I don't know why. Is there something wrong?** | {
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"description": "Extendin... | open | false | null | [] | null | 2 | 2019-07-06T17:31:09Z | 2021-07-10T17:54:59Z | null | MEMBER | null | For the `factorize`, `unique`, `groupby` hashtable-based functionalities, we included a `_values_for_factorize()` / `factorize()` method on the ExtensionArray. So for those methods, it is working nicely. However, for some of the other hashtable-based methods such as `duplicated()` or `drop_duplicates`, this machinery is not used and the EA is still coerced to a numpy array before passing to the algos code.
Small illustration that this is the fact by patching the IntegerArray to print when being coerced to a numpy array:
```patch
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -364,6 +364,7 @@ class IntegerArray(ExtensionArray, ExtensionOpsMixin):
the array interface, return my values
We return an object array here to preserve our scalar values
"""
+ print("getting coerced to an array")
return self._coerce_to_ndarray()
```
```
In [2]: s = pd.Series([1, 2, 1, 2, None], dtype='Int64')
In [3]: s
Out[3]: getting coerced to an array
0 1
1 2
2 1
3 2
4 NaN
dtype: Int64
In [4]: s.duplicated()
getting coerced to an array
getting coerced to an array
Out[4]:
0 False
1 False
2 True
3 True
4 False
dtype: bool
In [5]: s.unique()
Out[5]:
<IntegerArray>
[1, 2, NaN]
Length: 3, dtype: Int64
``` | {
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} | 32 | 2019-07-06T19:28:11Z | 2019-08-26T20:53:51Z | 2019-07-18T10:51:50Z | CONTRIBUTOR | null | replaces #24366
closes #16538
closes #10511
Sometimes a values column is presented with list-like values on one row.
Instead we may want to split each individual value onto its own row,
keeping the same mapping to the other key columns. While it's possible
to chain together existing pandas operations (in fact that's exactly
what this implementation is) to do this, the sequence of operations
is not obvious. By contrast this is available as a built-in operation
in say Spark and is a fairly common use case
provides a nice inversion here
```
In [31]: s = pd.DataFrame({'a': pd.date_range('20190101', periods=3, tz='UTC')}).apply(lambda x: x.array, axis=1)
In [32]: s
Out[32]:
0 [2019-01-01 00:00:00+00:00]
1 [2019-01-02 00:00:00+00:00]
2 [2019-01-03 00:00:00+00:00]
dtype: object
In [33]: s.iloc[0]
Out[33]:
<DatetimeArray>
['2019-01-01 00:00:00+00:00']
Length: 1, dtype: datetime64[ns, UTC]
In [34]: s.explode()
Out[34]:
0 2019-01-01 00:00:00+00:00
1 2019-01-02 00:00:00+00:00
2 2019-01-03 00:00:00+00:00
dtype: datetime64[ns, UTC]
```
```
In [4]: s = pd.Series([np.arange(np.random.randint(100)) for _ in range(100000)])
In [5]: s
Out[5]:
0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
1 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
2 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
3 [0, 1, 2, 3, 4]
4 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
...
99995 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
99996 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
99997 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
99998 [0, 1, 2, 3]
99999 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
Length: 100000, dtype: object
In [6]: s.explode()
Out[6]:
0 0
0 1
0 2
0 3
0 4
..
99999 43
99999 44
99999 45
99999 46
99999 47
Length: 4950205, dtype: object
In [7]: %timeit s.explode()
584 ms ± 17.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Dataframe exploding
```
In [1]: df = pd.DataFrame(
...: [[11, range(5), 10], [22, range(3), 20]], columns=["A", "B", "C"]
...: ).set_index("C")
...: df
Out[1]:
A B
C
10 11 (0, 1, 2, 3, 4)
20 22 (0, 1, 2)
In [2]: df.explode(['B'])
Out[2]:
A B
C
10 11 0
10 11 1
10 11 2
10 11 3
10 11 4
20 22 0
20 22 1
20 22 2
```
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} | 1 | 2019-07-06T21:19:13Z | 2019-07-08T01:22:34Z | 2019-07-08T01:14:26Z | MEMBER | null | We have some semi-circular dispatch logic between non-EA Series and pd.Index ops. This avoids that circularity by implementing the relevant logic directly in the Series implementation. After this I'm going to start migrating the Index implementations to use the Series implementation, after which we will have One True Implementation (also have to migrate RangeIndex and IntegerArray implementations) | {
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} | 1 | 2019-07-07T02:50:30Z | 2019-07-07T14:55:43Z | 2019-07-07T14:13:40Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 5 | 2019-07-07T03:55:18Z | 2019-07-11T04:00:28Z | 2019-07-11T04:00:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### 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>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 3 | 2019-07-07T04:23:09Z | 2019-07-10T00:03:41Z | 2019-07-09T21:54:26Z | MEMBER | null | Motivated by a tedious troubleshooting process in a different branch.
I'm pretty sure we can take this even further, saving it for another pass since the diff here is already pretty big. | {
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} | [] | closed | false | null | [] | null | 3 | 2019-07-07T11:02:49Z | 2019-07-08T13:34:50Z | 2019-07-07T11:26:19Z | NONE | null | A lot of time we want to split dataframe into train and test by time and make sure it's stratified by a key. I suggest **get_train_test_stratified_by_time_and_col** which does this.
**stratify_split_col_name**: The column name which has to be split in train and test in a stratified way.
**time_sort_col_name**: The column name used for sorting rows by time.
**test_size**: Test size
**ascending:** For sorting time_sort_col_name by ascending or descending.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-07-07T12:21:43Z | 2019-07-07T15:58:13Z | 2019-07-07T15:58:12Z | CONTRIBUTOR | null | Closes #23616
The trickiest part is probably ensuring that the configuration works on as many machines as possible. Can people try this out locally and let me know if / how it works? | {
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} | 11 | 2019-07-07T12:42:45Z | 2019-07-15T20:11:24Z | 2019-07-15T20:11:14Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/19694
Closes https://github.com/pandas-dev/pandas/issues/27227 | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Weighted std could be implemented but there are also other functions weighted `Window` is lacking. | {
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} | 1 | 2019-07-07T15:48:32Z | 2019-07-08T06:47:54Z | 2019-07-08T01:13:33Z | MEMBER | null | …ds::test_slice
it looks like this was probably added for debugging. This is not necessary with parametrisation. | {
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} | 4 | 2019-07-07T16:19:02Z | 2019-07-08T13:46:13Z | 2019-07-08T12:43:11Z | MEMBER | null | Discovered while trying to reconcile Series vs Index arithmetic behavior.
A couple of other small misc changes ported from other branches, will be noted inline. | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
- Contribution Guide
- fix incorrect paths to doc folder
- read_json()
- add period to end of sentences
- conform parameter types to project standard
- add newlines for readability and consistency
- for convert_dates parameter, fix typos and malformed description and add list as accepted type
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} | 1 | 2019-07-07T23:13:17Z | 2019-07-08T01:07:32Z | 2019-07-08T01:07:17Z | CONTRIBUTOR | null | ```python
import pandas as pd
from pandas._libs import lib
test = pd.date_range("20110101", freq="s", periods=10).values
lib.infer_dtype(test, skipna = False)
print('Trying to infer datetime dtype:')
lib.infer_dtype(test.astype('O'), skipna = False)
lib.infer_dtype(test.tolist(), skipna = False)
lib.infer_datetimelike_array(test.astype('O'))
lib.infer_datetimelike_array(test.tolist())
lib.maybe_convert_objects(test.astype('O')).dtype
```
```python
'datetime64'
Trying to infer datetime dtype:
'integer'
'integer'
'mixed'
'mixed'
dtype('int64')
```
I'm not sure that all of these should produce 'datetime' but any of should. | {
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} | 7 | 2019-07-08T01:47:56Z | 2019-07-31T12:31:00Z | 2019-07-31T12:31:00Z | CONTRIBUTOR | null | xref #26272
xref #27267
As the first step of moving towards integer-na dtypes as the primary integer type, we need to teach ``infer_dtype`` that ``integer-na`` is a valid inferred type, right now
```
In [1]: from pandas.api.types import infer_dtype
In [3]: infer_dtype([2, 3,4], skipna=False)
Out[3]: 'integer'
In [4]: infer_dtype([2, 3, 4, np.nan], skipna=False)
Out[4]: 'mixed-integer-float'
In [5]: infer_dtype([2, 3, 4.2, np.nan], skipna=False)
Out[5]: 'mixed-integer-float'
```
[4] could return 'integer-na' to indicate that we might want to infer ``Int64`` dtype and is distinct from the inferred type of [5] which must become ``float64``.
This will allow us to then support changing integer columns when we add nulls to Int64 rather than coerce to float64; this is pretty common in indexing setting operations.
Secondly we can then enable ``.to_numeric`` to infer to integer-na (or unsigned-na) and the corresponding dtypes (#26272).
Finally we could support coercion of ``object`` dtypes from integers and nulls to coerce to Int64 (#27267 for ``.explode()`` and ``.infer_objects()``
This issue itself only is a very minor user facing change (infer_dtype itself). | {
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} | 1 | 2019-07-08T04:22:33Z | 2019-07-10T00:05:27Z | 2019-07-09T21:13:28Z | MEMBER | null | - [x] closes #21729
- [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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} | 8 | 2019-07-08T08:20:09Z | 2021-07-10T17:58:33Z | 2021-07-10T17:58:33Z | NONE | null | #### Code Sample
```python
In [6]: pd.Timedelta(6, 'M')
Out[6]: Timedelta('182 days 14:54:36')
In [7]: pd.Timedelta('6M')
Out[7]: Timedelta('0 days 00:06:00')
In [8]: pd.Timedelta(6, 'W')
Out[8]: Timedelta('42 days 00:00:00')
In [9]: pd.Timedelta('6W')
Out[9]: Timedelta('42 days 00:00:00')
```
#### Problem description
Out[7] shoud return 182 days but 6 minutes | {
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] | closed | false | null | [] | null | 5 | 2019-07-08T09:13:58Z | 2020-11-14T10:16:32Z | 2020-11-14T10:16:32Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'Data': ['afoo abc: agsegsegs', 'b def: eafsegsg', 'c ghi:']})
df2 = df
df2.loc[:, 'foo2'] = df2.Data.fillna('').astype(str).str.extract(r'.*(\w{3}).*')
print df2['foo2']
```
#### Problem description
When printing df2['foo2'] I get:
0 NaN
1 NaN
2 NaN
If instead I change the line assigning to 'foo2' to:
```python
df2['foo2'] = df2.Data.fillna('').astype(str).str.extract(r'.*(\w{3}).*')
```
When printing df2['foo2'] I get:
0 egs
1 gsg
2 ghi
I would have expected df.loc[:, 'foo2'] and df['foo2'] assignments to behave the same way.
#### Output of ``pd.show_versions()``
<details>
pandas: 0.23.4
numpy: 1.15.4
python: 2.7.14.final.0
</details>
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} | 1 | 2019-07-08T12:44:49Z | 2019-07-08T13:31:04Z | 2019-07-08T13:31:04Z | MEMBER | null | xref #27259
cc @jbrockmendel | {
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} | 3 | 2019-07-08T13:41:32Z | 2019-07-16T14:35:00Z | 2019-07-16T14:34:59Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/27247
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} | 2 | 2019-07-08T15:16:23Z | 2019-07-10T04:29:56Z | 2019-07-10T04:29:52Z | MEMBER | null | A couple small things I saw reading over the whatsnew notes. | {
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} | 4 | 2019-07-08T15:32:51Z | 2019-07-08T16:52:39Z | 2019-07-08T16:41:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a':1}, index = [1])
print(df.append(pd.DataFrame(data={'a':0}, index=[0]), sort=False))
print(df.append(pd.DataFrame(data={'a':0}, index=[0]), sort=True))
```
#### Problem description
The sort kwarg in pd.DataFrame.append does not provide any sorting, regardless of the boolean value which it is assigned.
#### Expected Output
sort=True:
a
0 0
1 1
sort=False:
a
1 1
0 0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-07-08T16:10:49Z | 2019-07-10T09:39:35Z | 2019-07-09T20:57:38Z | MEMBER | null | - [x] closes #27250
- [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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] | closed | false | null | [] | null | 3 | 2019-07-08T18:29:41Z | 2019-09-18T13:42:37Z | 2019-09-18T13:42:37Z | NONE | null | It would be convenient to have `from_product` infer level names from inputs if at all possible.
### Current behavior
```python
>>> a = pd.Series([1, 2, 3], name='a')
>>> b = pd.Series(['a', 'b'], name='b')
>>> pd.MultiIndex.from_product([a, b])
MultiIndex(levels=[[1, 2, 3], ['a', 'b']],
codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])
```
### Current workaround
```python
>>> arrs = [a, b]
>>> pd.MultiIndex.from_product(ins, names=[el.name for el in arrs])
MultiIndex(levels=[[1, 2, 3], ['a', 'b']],
codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
names=['a', 'b'])
```
Obviously this would't make sense for lists or arrays being passed in, but in the case of a `Series`, it would be nice to have the name persisted.
---
From glancing at the source, it looks like this would be somewhat simple to implement, the naive approach might be something like:
```
from pandas.core.arrays.categorical import _factorize_from_iterables
from pandas.core.reshape.util import cartesian_product
if not is_list_like(iterables):
raise TypeError("Input must be a list / sequence of iterables.")
elif is_iterator(iterables):
iterables = list(iterables)
if names is None:
names = [el.name if hasattr(el, 'name') else None for el in iterables]
codes, levels = _factorize_from_iterables(iterables)
codes = cartesian_product(codes)
return MultiIndex(levels, codes, sortorder=sortorder, names=names)
``` | {
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The current version of numpy that pip will attempt to install is 1.17.0, which is no longer Python 2 compatible.
The setup.py requirements should include an upper limit for the numpy version to resolve this (i.e. `numpy >= 1.13.3,<1.17.0` | {
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} | 3 | 2019-07-08T19:34:09Z | 2019-07-11T15:45:57Z | 2019-07-09T20:49:30Z | CONTRIBUTOR | null | {
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} | 2 | 2019-07-08T19:58:49Z | 2019-07-10T16:39:22Z | 2019-07-10T16:39:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import os
import pandas as pd
s = pd.Series(["a", "b", "c", "a"], dtype="category")
s.astype('category')
FILEPATH = 'example.pickle'
s.to_pickle(FILEPATH)
s = pd.read_pickle(FILEPATH)
os.remove(FILEPATH)
s.astype('category')
```
Output
```python-traceback
Traceback (most recent call last):
File "mre.py", line 13, in <module>
s.astype('category')
File "/Users/roy/pandas/pandas/core/generic.py", line 5935, in astype
dtype=dtype, copy=copy, errors=errors, **kwargs
File "/Users/roy/pandas/pandas/core/internals/managers.py", line 581, in astype
return self.apply("astype", dtype=dtype, **kwargs)
File "/Users/roy/pandas/pandas/core/internals/managers.py", line 438, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/roy/pandas/pandas/core/internals/blocks.py", line 555, in astype
return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
File "/Users/roy/pandas/pandas/core/internals/blocks.py", line 606, in _astype
return self.make_block(self.values.astype(dtype, copy=copy))
File "/Users/roy/pandas/pandas/core/arrays/categorical.py", line 524, in astype
self = self.copy() if copy else self
File "/Users/roy/pandas/pandas/core/arrays/categorical.py", line 503, in copy
values=self._codes.copy(), dtype=self.dtype, fastpath=True
File "/Users/roy/pandas/pandas/core/arrays/categorical.py", line 353, in __init__
self._dtype = self._dtype.update_dtype(dtype)
File "/Users/roy/pandas/pandas/core/dtypes/dtypes.py", line 556, in update_dtype
new_ordered_from_sentinel = dtype._ordered_from_sentinel
AttributeError: 'CategoricalDtype' object has no attribute '_ordered_from_sentinel'
```
#### Problem description
Calling `Series.astype('category')` on a categorical series loaded using `pd.read_pickle` errors with pandas 0.25.0rc0. The example code ran without error using pandas 0.24.2
#### Expected Output
```
0 a
1 b
2 c
3 a
dtype: category
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c64c9cb44222a42f7b02d4d6007919cd0645f1be
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0rc0+23.gc64c9cb44
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
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 : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-07-08T21:08:15Z | 2019-07-10T00:04:59Z | 2019-07-09T20:48:31Z | MEMBER | null | Trying to whittle down try blocks in internals since many of them are unclear on the failure modes | {
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} | 3 | 2019-07-08T21:22:34Z | 2019-07-22T23:28:21Z | 2019-07-22T23:28:21Z | MEMBER | null | ```
dti = pd.date_range('2016-01-01', periods=3)
nat = np.timedelta64('NaT')
dta = dti._data.copy()
dta[0] = nat # <-- should raise TypeError, doesn't
ser = pd.Series(dti.copy())
ser[1] = nat # <-- should raise TypeError, doesn't
df = pd.DataFrame(dti.copy())
df.loc[2, 0] = nat # <-- should raise TypeError, doesn't
```
I haven't checked, but I expect the same thing happens in reverse for Timedelta, possible Period dtypes. | {
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} | 4 | 2019-07-08T21:51:04Z | 2019-07-10T16:26:24Z | 2019-07-10T16:20:05Z | MEMBER | null | `_can_hold_element` is not well-documented, and in local testing it does _not_ match my intution of "we could do `self.values[:] = element` without raising". But adding two assertions in `fillna` _does_ work in all the tests:
```
# fillna, but if we cannot coerce, then try again as an ObjectBlock
try:
# equivalent: self._try_coerce_args(value) would not raise
# Note: we only call try_coerce_args to let it raise
self._try_coerce_args(value)
assert self._can_hold_element(value)
except (TypeError, ValueError):
assert not self._can_hold_element(value)
```
(at least after patching the `DatetimeBlock` and `TimedeltaBlock` implementations) | {
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} | 1 | 2019-07-08T21:53:23Z | 2019-07-09T20:42:59Z | 2019-07-09T20:42:59Z | CONTRIBUTOR | null | I believe after `seen.object_ = 1` here:
https://github.com/pandas-dev/pandas/blob/c64c9cb44222a42f7b02d4d6007919cd0645f1be/pandas/_libs/lib.pyx#L1956-L1958
should go `break` as well.
Test:
```python
size = 10**7
arr = list(range(size))
arr[0] = pd.NaT
arr = np.array(arr)
```
```python
%timeit lib.maybe_convert_objects(arr, convert_datetime=0, convert_timedelta=0)
```
As now output is:
```python
1.84 s ± 14.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
With `break`:
```python
57.1 µs ± 887 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
And both cases produce same array:
```python
array([NaT, 1, 2, ..., 9999997, 9999998, 9999999], dtype=object)
``` | {
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} | 9 | 2019-07-08T22:34:58Z | 2019-07-09T20:43:12Z | 2019-07-09T20:42:59Z | CONTRIBUTOR | null | - [X] closes #27299
- [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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} | 4 | 2019-07-09T03:43:17Z | 2019-07-09T21:00:19Z | 2019-07-09T21:00:12Z | CONTRIBUTOR | null | - [x] closes #27217
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~~TODO: add tests for suppressed warnings.~~
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} | 9 | 2019-07-09T07:26:32Z | 2019-07-10T20:49:11Z | 2019-07-10T18:36:00Z | MEMBER | null | xref #19228, #26807
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`test_window.py` was getting fairly unwieldily. There a couple test classes that use a non-pytest-idomatic `Base` class, so those test classes are kept together for now. I split out the rest of the test classes into separate files as they made sense.
I think the classes in `test_window.py` need further untangling before a `conftest.py` file can be made for the new directory.
Open to additional quick win optimizations.
cc @jreback | {
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... | open | false | null | [] | null | 2 | 2019-07-09T10:49:55Z | 2020-04-02T04:50:36Z | null | NONE | null | Hi, I am new to github and this is my first post so sry for the layout.
#### Code
```python
import pandas as pd
date_parser = "%Y-%m-%d"
d = {'2019-07-09': [1, 2]}
df = pd.DataFrame(data=d)
df.columns = pd.to_datetime(df.columns, format=date_parser)
print(df)
df['second_column'] = df[df.columns]
print(df)
df.to_csv("test.csv", header=True, index=True, date_format=date_parser)
```
#### Problem description
I tried to export dataframe to csv using DataFrame to_csv() with column name as TimeStamp format, but date_format doesn't seem to work on column name, so I get 2019-07-09 00:00:00 instead of 2019-07-09 in my csv file.
By the way, I don't understand why before df['second_column'] = df[df.columns], my first column name format is 2019-07-09, and after this line is 2019-07-09 00:00:00.
#### output
```
2019-07-09
0 1
1 2
2019-07-09 00:00:00 second_column
0 1 1
1 2 2
```
#### test.csv
```
,2019-07-09 00:00:00,second_column
0,1,1
1,2,2
```
#### Expected test.csv
```
,2019-07-09,second_column
0,1,1
1,2,2
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0.24.2
pytest: None
pip: 9.0.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 5.5.0
sphinx: 1.6.7
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.6
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 2 | 2019-07-09T13:57:21Z | 2019-07-09T20:50:41Z | 2019-07-09T20:50:36Z | MEMBER | null | I think I made a small mistake in https://github.com/pandas-dev/pandas/pull/27076 in adding the E501 code (line length) to the ignore list, as setting the flake8 max line length to 88 should have been sufficient.
Black does not check or correct line length in comments or multiline strings (eg docstrings), so this still need to be catched by flake8 | {
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] | open | false | null | [] | null | 1 | 2019-07-09T15:16:57Z | 2019-07-10T19:02:55Z | null | CONTRIBUTOR | null | RE: PR #27280
Multiple docstring tests are failing because of pretty whitespace formatting of the expected output. Here is an example from `read_json()` in `pandas/io/json/_json.py:500`
```python
Encoding/decoding a Dataframe using ``'split'`` formatted JSON:
>>> df.to_json(orient='split')
'{"columns":["col 1","col 2"],
"index":["row 1","row 2"],
"data":[["a","b"],["c","d"]]}'
```
Running the validation script results in the following error:
```
Line 153, in pandas.read_json
Failed example:
df.to_json(orient='split')
Expected:
'{"columns":["col 1","col 2"],
"index":["row 1","row 2"],
"data":[["a","b"],["c","d"]]}'
Got:
'{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}'
```
There is another similarly failing example in this function's doctests. `Series.to_json` has similar failures as well.
The `+NORMALIZE_WHITESPACE` directive does not help and neither do ellipses. Removing the whitespace does cure the failures, but it looks nasty.
<details>
INSTALLED VERSIONS
------------------
commit : c64c9cb44222a42f7b02d4d6007919cd0645f1be
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0rc0+23.gc64c9cb44
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.11
pytest : 5.0.0
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 22 | 2019-07-09T17:51:59Z | 2019-08-02T14:53:44Z | 2019-07-17T11:46:54Z | NONE | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Related to #25915, #26587, #24859, #26113, #10056 (orderedicts), #11181/#11416 (list of namedtuple). #25911.
**Update**: #13304/#13309 Was merged three years ago so let's just make list of dicts act like list of OrderedDict (as pointed out by @jorisvandenbossche).
**Actual**
```python
In [63]: data= [
...: {'name': 'Joe', 'state': 'NY', 'age': 18},
...: {'name': 'Jane', 'state': 'KY', 'age': 19}
...: ]
...: pd.DataFrame(data)
Out[63]:
age name state
0 18 Joe NY
1 19 Jane KY
```
**Expected**
```
In [64]: pd.DataFrame(data)
Out[64]:
name state age
0 Joe NY 18
1 Jane KY 19
```
<del>
Four years ago, #10056 asked for the implied order of columns in a list of `OrderedDict` to be preserved by the `DataFrame` constructor. @thatneat [commented](https://github.com/pandas-dev/pandas/issues/10056#issuecomment-509383829) yesterday that with 3.7 guaranteed dict order, this should extend to dict-like in general. I think users have a reasonable expectation for this to work, and therefore that pandas should support it. @jreback [voted](https://github.com/pandas-dev/pandas/issues/10056#issuecomment-98812435) +0 on adding this (four years ago).
`namedtuple` has the convenient property of homogeneous keys and key-order which a list of dicts
doesn't have, dicts are allowed to omit keys, and the key order also may change from dict to dict.
Given that, I settled on a reasonable compromise that matches user expectations in practice:
1. Only look at the first dict in the list.
2. Only guarantee the column order of the keys which actually appear in it.
3. **Clarification** the order among columns not included in the first dict is undefined, except that they will appear after all the columns that do.
4. **Added** Changes apply to Python3.6+ only
In practice, I think the only case users actually care about is sensible behavior when passing a list of dicts which is homogeneous in terms of key and key-order, which this PR provides.
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} | 2 | 2019-07-09T18:08:34Z | 2019-07-09T19:16:19Z | 2019-07-09T19:16:09Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
test = pd.DataFrame([[np.nan, 2, np.nan],
[3, 4, np.nan],
[np.nan, np.nan, 30],
[np.nan, 3, 5]],
columns=list('ABC'))
# For multiple columns, `inplace=True` is not working.
test[['A','B']].fillna('Missing',inplace=True)
print(test)
# For a single column,` inplace=True` works.
test['C'].fillna('Missing',inplace=True)
print(test)
```
#### Problem description
``pandas.DataFrame.fillna`` with ``inplace=True`` is not working with multiple columns. It only works on a single column. I saw #12838 but this is still confusing.
#### Input Table
| A | B | C |
|----:|-----:|-----:|
| NaN | 2.0 | NaN |
| 3.0 | 4.0 | NaN |
| NaN | NaN | 30.0 |
| NaN | 3.0 | 5.0 |
#### Current Output [ Same as input. Nothing changes. ]
| A | B | C |
|----:|-----:|-----:|
| NaN | 2.0 | NaN |
| 3.0 | 4.0 | NaN |
| NaN | NaN | 30.0 |
| NaN | 3.0 | 5.0 |
#### Expected Output
```python
test[['A','B']].fillna('Missing',inplace=True)
```
| A | B | C |
|--------:|--------:|-----:|
| Missing | 2.0 | NaN |
| 3.0 | 4.0 | NaN |
| Missing | Missing | 30.0 |
| Missing | 3.0 | 5.0 |
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.1.1
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27311/events | https://github.com/pandas-dev/pandas/pull/27311 | 465,924,946 | MDExOlB1bGxSZXF1ZXN0Mjk1ODcwNjgz | 27,311 | BUG: Fix inserting of wrong-dtyped NaT, closes #27297 | {
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} | 10 | 2019-07-09T18:30:44Z | 2019-07-22T23:22:38Z | 2019-07-22T17:03:57Z | MEMBER | null | - [x] closes #27297
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Needs tests for PeriodDType case. The tests are probably not in the ideal place, how to move/parametrize them depends on what axis we want to sort them along (i.e. all Series indexing tests together or all timedelta-insertion tests together) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27312/events | https://github.com/pandas-dev/pandas/issues/27312 | 465,927,708 | MDU6SXNzdWU0NjU5Mjc3MDg= | 27,312 | Solving merge conflicts in whatsnew once and for all | {
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] | closed | false | null | [] | null | 9 | 2019-07-09T18:37:43Z | 2019-08-30T21:54:22Z | 2019-08-30T21:54:22Z | NONE | null | [towncrier](https://github.com/hawkowl/towncrier), as used by `pipenv`,
```
towncrier is a utility to produce useful, summarised news files for your project.
Rather than reading the Git history as some newer tools to produce it, or
having one single file which developers all write to, towncrier reads
“news fragments” which contain information useful to end users.
```
if the package itself is not option, you may still want to adpot its approach of assembling issuenum named fragment files together. For an example, see
https://github.com/pypa/pipenv/tree/master/news
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