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TYP: annotations
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| 2020-01-18T16:11:48Z
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MEMBER
| null |
Several of the Index subclasses are difficult to annotate, so I want to get the easy-ish parts to make it easier to focus on the pain points.
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Unclear DataFrame.info() range summary info for DatetimeIndex
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| 2020-02-05T08:03:37Z
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NONE
| null |
Within [DataFrame.info](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html), the description of the [DatetimeIndex](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html) of unsorted indexes might not be clear enough to users, because just showing the first and last element and not the *"min date" to "max date"* values.
Please check the following sample:
```python
import pandas as pd
dfs = []
for day in [30, 1, 2, 5]:
idx = pd.date_range(f'2020-01-{day} 00:00:00', periods=1000, freq='s')
dfs.append(pd.DataFrame({'count': range(len(idx)), 'date': idx}))
df = pd.concat(dfs)
df.set_index(['date'], inplace=True)
df
df.info()
df.index.min()
df.index.max()
```
Reated output is the following:
```
>>> df
count
date
2020-01-30 00:00:00 0
2020-01-30 00:00:01 1
2020-01-30 00:00:02 2
2020-01-30 00:00:03 3
2020-01-30 00:00:04 4
... ...
2020-01-05 00:16:35 995
2020-01-05 00:16:36 996
2020-01-05 00:16:37 997
2020-01-05 00:16:38 998
2020-01-05 00:16:39 999
[4000 rows x 1 columns]
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4000 entries, 2020-01-30 00:00:00 to 2020-01-05 00:16:39
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 count 4000 non-null int64
dtypes: int64(1)
memory usage: 62.5 KB
>>> df.index.min()
Timestamp('2020-01-01 00:00:00')
>>> df.index.max()
Timestamp('2020-01-30 00:16:39')
```
I would expect `2020-01-01 00:00:00 to 2020-01-30 00:16:39` (min and max range values for DatetimeIndex) and not `2020-01-30 00:00:00 to 2020-01-05 00:16:39` (first and last element).
In case, PR https://github.com/pandas-dev/pandas/pull/31069 fixes the issue.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : 5.3.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.3
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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REF: IntervalIndex.get_value partial slicing
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| 3
| 2020-01-18T03:46:12Z
| 2020-05-09T20:22:04Z
| 2020-05-09T20:22:04Z
|
MEMBER
| null |
In trying to refactor IntervalIndex.get_loc/get_value to more closely follow the other implementations, I stumbled on a behavior in a test case that surprised me:
```
idx = IntervalIndex.from_tuples([(1, 5), (3, 7)])
s = Series(range(len(idx)), index=idx)
key = [4]
>>> idx.get_loc(key) # <-- behaves like all the other get_locs in requiring scalar
KeyError: [4]
>>> idx.get_value(s, key) # <-- surprising
(1, 5] 0
(3, 7] 1
dtype: int64
```
All of the other FooIndex.get_value methods raise InvalidIndexError on non-scalar.
AFAICT this is behaving sort of like a slicing operation, since 4 is in the interior of both of these intervals. @jschendel is that correct? If so, we should try to refactor IntervalIndex.get_value to explicitly dispatch for this case, kind of like DatetimeIndex._get_string_slice
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ERR: better error message on unparseable datetimes
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CONTRIBUTOR
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- [ ] closes #10720
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NONE
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Backport PR #30977: JSON Date Handling 1.0 Regressions
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NONE
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Backport PR #31097: DOC: Replace ggpy with plotnine in ecosystem
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CLN/BUG: Float64Index.get_loc
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MEMBER
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- clean up unnecessary try/except
- the "bug" part is that ATM we incorrectly raise KeyError instead of TypeError on non-hashable.
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Backport PR #30553 on branch 1.0.x (TST/BUG: fix incorrectly-passing Exception in test_html)
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NONE
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Backport PR #30553: TST/BUG: fix incorrectly-passing Exception in test_html
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REF/CLN: Index.get_value wrapping incorrectly
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MEMBER
| null |
Index.get_value isn't wrapping non-scalar results correctly, as a result of which we have an ugly kludge in `Series.__getitem__` to do that wrapping. This fixes that.
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TYP: DataFrame.(index|columns) and Series.index
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| 2
| 2020-01-18T18:23:05Z
| 2020-01-24T20:20:14Z
| 2020-01-24T01:19:03Z
|
CONTRIBUTOR
| null |
Makes mypy discover the ``.index`` and ``.columns`` attributes and that they're ``Index`` (sub-)classes.
This is done by manually adding the ``properties.AxisProperty`` to DataFrame/Series instead of doing it programatically, as mypy doesn't like adding attributes programatically very much.
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Docs for `DataFrame.plot.scatter` say that `c` can be of type `int`, yet it's not explained what that means and passing one raises an error
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| 2020-01-18T19:53:39Z
| 2020-01-19T08:19:14Z
| 2020-01-19T08:19:14Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(
[[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1], [6.4, 3.2, 1], [5.9, 3.0, 2]],
columns=["length", "width", "species"],
)
ax1 = df.plot.scatter(x="length", y="width", c=128)
```
returns
```
IndexError: index 128 is out of bounds for axis 0 with size 3
```
#### Problem description
The docstring reads
> c : str, int or array_like, optional
> The color of each point. Possible values are:
>
> - A single color string referred to by name, RGB or RGBA code,
> for instance 'red' or '#a98d19'.
>
> - A sequence of color strings referred to by name, RGB or RGBA
> code, which will be used for each point's color recursively. For
> instance ['green','yellow'] all points will be filled in green or
> yellow, alternatively.
>
> - A column name or position whose values will be used to color the
> marker points according to a colormap.
but it doesn't say what passing a `c` of type `int` does.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 00c9b2df2dd5680eb13605f599e38a0f5950aeef
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.26.0.dev0+1837.g00c9b2df2
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.14
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : 2.2.2
blosc : 1.8.3
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.0.1
s3fs : 0.2.1
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
tabulate : 0.8.6
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.45.1
</details>
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REF: require PeriodArray in PeriodIndex._simple_new
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REF: share code between Int64Index and UInt64Index
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TST: Implement external error raised helper function.
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| 2020-02-14T09:05:38Z
| 2020-02-07T18:45:13Z
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MEMBER
| null |
- [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Opened PR for what @gfyoung wrote [here](https://github.com/pandas-dev/pandas/issues/30999#issuecomment-575426086)
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ENH: Implement CSV reading for BooleanArray
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| 2
| 2020-01-19T03:01:14Z
| 2020-01-23T20:56:01Z
| 2020-01-23T20:56:01Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
In 1.0.0 rc0, `pd.read_csv` returns this error when reading NA with specifying dtype of 'boolean'
```python
# import pandas as pd
# import io
>>> txt = """
X1,X2,X3
1,a,True
2,b,False
NA,NA,NA
"""
>>> df1 = pd.read_csv(io.StringIO(txt), dtype={'X3': 'boolean'}) # `pd.BooleanDtype()` also fails
raceback (most recent call last):
File "pandas/_libs/parsers.pyx", line 1191, in pandas._libs.parsers.TextReader._convert_with_dtype
File "/usr/local/lib/python3.7/site-packages/pandas/core/arrays/base.py", line 232, in _from_sequence_of_strings
raise AbstractMethodError(cls)
pandas.errors.AbstractMethodError: This method must be defined in the concrete class type
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 676, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 454, in _read
data = parser.read(nrows)
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1133, in read
ret = self._engine.read(nrows)
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 2037, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 859, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 951, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 1083, in pandas._libs.parsers.TextReader._convert_column_data
File "pandas/_libs/parsers.pyx", line 1114, in pandas._libs.parsers.TextReader._convert_tokens
File "pandas/_libs/parsers.pyx", line 1194, in pandas._libs.parsers.TextReader._convert_with_dtype
NotImplementedError: Extension Array: <class 'pandas.core.arrays.boolean.BooleanArray'> must implement _from_sequence_of_strings in order to be used in parser methods
```
while "Int64" and "string" with NA can be correctly recognized.
```python
>>> df2 = pd.read_csv(io.StringIO(txt), dtype={'X1': 'Int64', 'X2': 'string'})
>>> df2
X1 X2 X3
0 1 a True
1 2 b False
2 <NA> <NA> NaN
>>> df2.dtypes
X1 Int64
X2 string
X3 object
dtype: object
```
#### Problem description
NA literal for boolean is not parsed by `pd.read_csv`
#### Expected Output
```python
df3 = pd.read_csv(io.StringIO(txt), dtype={'X3': 'boolean'})
>>> df3
X1 X2 X3
0 1.0 a True
1 2.0 b False
2 NaN NaN <NA>
>>> df3.dtypes
X1 float64
X2 object
X3 boolean
dtype: object
# and
df4 = pd.read_csv(io.StringIO(txt), dtype={'X1': 'Int64', 'X2': 'string', 'X3': 'boolean'})
>>> df4
X1 X2 X3
0 1 a True
1 2 b False
2 <NA> <NA> <NA>
>>> df4.dtypes
X1 Int64
X2 string
X3 boolean
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.184-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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
pytest : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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TST/REF: Parameteratized tests
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| 2020-01-20T22:25:40Z
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/31068
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| 2020-01-19T16:50:53Z
| 2020-01-20T16:24:29Z
| 2020-01-20T15:32:58Z
|
MEMBER
| null |
Also engine.get_value is being called incorrectly in DTI/TDI, which this fixes.
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MDU6SXNzdWU1NTE5NTQ5OTg=
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Docs mention non-existent doc/source/api.rst
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| 2020-01-19T17:30:13Z
| 2020-01-25T12:17:55Z
| 2020-01-25T12:17:54Z
|
MEMBER
| null |
E.g. in doc/source/development/contributing.rst it says:
> * Our API documentation in ``doc/source/api.rst`` houses the auto-generated
> documentation from the docstrings. For classes, there are a few subtleties
> around controlling which methods and attributes have pages auto-generated.
>
There's a few other mentions of it elsewhere
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COMPAT: Return NotImplemented for subclassing
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| 14
| 2020-01-19T17:48:06Z
| 2020-01-28T21:14:13Z
| 2020-01-28T20:57:19Z
|
CONTRIBUTOR
| null |
This changes index ops to check the *type* of the argument in index
ops, rather than just the dtype. This lets index subclasses take control
of binary ops when they know better what the result should be.
Closes https://github.com/pandas-dev/pandas/issues/31109
cc @jbrockmendel, I've fixed this as close to the root of the problem as I could, but perhaps this case should be checked elsewhere? As written currently, it only affects add & sub between an Index and a datelike index, but perhaps it should be done more generally?
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TYP: Index get_indexer_foo methods
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TST:Disallow bare pytest raises issue 30999
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| 2020-01-19T18:57:46Z
| 2020-01-25T21:06:46Z
| 2020-01-23T08:18:57Z
|
CONTRIBUTOR
| null |
- [x] refers #30999
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This pull request is to add appropriate match arguments to bare pytest.raises listed in #30999 [Comment](https://github.com/pandas-dev/pandas/issues/30999#issuecomment-574339599)
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MDExOlB1bGxSZXF1ZXN0MzY0NTc0Nzg3
| 31,139
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BUG: in DataFrame.count not returning subclassed data types.
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[] | null | 2
| 2020-01-19T20:20:12Z
| 2020-01-21T13:13:42Z
| 2020-01-21T13:12:35Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Once #30945 is merged these tests will pass.
I also, didn't know if a PR without first submitting an issue was appropriate, but to cut down on workload I decided to give it a try. If I'm mistaken please let me know.
As for the test there are four logical branches in df.count() that needed to be address within the test:
1. Non-homogeneous data types (this is the partion of the test that is dependent on #30945 as it uses .sum()).
```
df = tm.SubclassedDataFrame(
{
"Person": ["John", "Myla", "Lewis", "John", "Myla"],
"Age": [24.0, np.nan, 21.0, 33, 26],
"Single": [False, True, True, True, False],
}
)
result = df.count()
assert isinstance(result, tm.SubclassedSeries)
```
2. Homogeneous data.
```
df = tm.SubclassedDataFrame({"A": [1, 0, 3], "B": [0, 5, 6], "C": [7, 8, 0]})
result = df.count()
assert isinstance(result, tm.SubclassedSeries)
```
3. MultiIndex with level kwarg.
```
df = tm.SubclassedDataFrame(
[[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]],
index=MultiIndex.from_tuples(
list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
),
columns=MultiIndex.from_tuples(
list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
),
)
result = df.count(level=1)
assert isinstance(result, tm.SubclassedDataFrame)
```
4. Force length of axis to be 0.
```
df = tm.SubclassedDataFrame()
result = df.count()
assert isinstance(result, tm.SubclassedSeries)
```
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MDU6SXNzdWU1NTE5ODU1MTk=
| 31,140
|
Feature Request: Add offset as head() second parameter
|
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[] | null | 3
| 2020-01-19T21:05:16Z
| 2020-01-20T20:16:29Z
| 2020-01-20T20:16:29Z
|
NONE
| null |
It would be very cool and intuitive if we could do somethig like this:
```python
df.head(10, 100)
```
#### Problem description
There are a lot of situations where you need to look through rows and rows of data and currently the alternatives are:
1. play with pd.options to display more rows
2. use indexing and slicing
3. output dataframe to excel
Adding `offset` would definitely save a lot of time and effort since it would take only a second to use. It is even easier than <strong>SQL</strong>'s `LIMIT` `OFFSET` approach
#### Docs
```python
DataFrame.head(self, n=5, offset=0)
Parameters:
n : int, default 5. Number of rows to select.
offset: int, default 0. Skips the offset rows before selecting rows.
```
#### Expected Output
```python
>>> df = pd.DataFrame({'animal':['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
>>> df.head(3, 2)
animal
2 falcon
3 lion
4 monkey
```
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MDU6SXNzdWU1NTE5OTA0MjM=
| 31,141
|
show dtype when using df.head()
|
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[] | null | 1
| 2020-01-19T21:43:30Z
| 2020-01-19T22:00:56Z
| 2020-01-19T22:00:56Z
|
NONE
| null |
I have a class that requires R, and I realized R will show the data type right below the column names when listing the first n-th rows. I was wondering if this is a feature in Pandas, like when I type `df.head()`, there will be dtype right below the column names.
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MDU6SXNzdWU1NTE5OTA2Nzg=
| 31,142
|
Series.combine with fill_value gives unexpected results
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| 7
| 2020-01-19T21:44:52Z
| 2020-04-24T14:48:52Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
a = pd.Series([1, 2, 3], index=["a", "b", "c"])
b = pd.Series([10, 20, 30], index=[0, "e", "f"])
c = b.combine(a, lambda x,y: x+y, fill_value=0)
print(c)
```
#### Problem description
This gives the output:
0 11
a 1
b 2
c 3
e 20
f 30
dtype: int64
#### Expected Output
0 10
a 1
b 2
c 3
e 20
f 30
dtype: int64
With higher numbers in the index it gives expected output i.e.:
```python
a = pd.Series([1, 2, 3], index=["a", "b", "c"])
b = pd.Series([10, 20, 30], index=[4, "e", "f"])
c = b.combine(a, lambda x,y: x+y, fill_value=0)
print(c)
```
4 10
a 1
b 2
c 3
e 20
f 30
dtype: int64
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.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 : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.12
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
</details>
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REF: share searchsorted between DTI/TDI/PI, insert between DTI/TDI
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| 1
| 2020-01-20T01:20:32Z
| 2020-01-20T21:24:34Z
| 2020-01-20T16:39:55Z
|
MEMBER
| null |
Made possible following #30950, takes the place of #30853.
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MDExOlB1bGxSZXF1ZXN0MzY0NjkxMzUw
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Check for pyarrow not feather before pyarrow tests
|
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| 6
| 2020-01-20T07:51:15Z
| 2020-01-24T03:54:17Z
| 2020-01-24T03:54:11Z
|
CONTRIBUTOR
| null |
read_feather/to_feather now use pyarrow.feather, not top-level (feather-format) feather, but some of their tests were still looking for top-level feather.
Two of them that deliberately cause a file not found error were also looking for the wrong form of error message. (Probably nobody noticed because the above was skipping them.)
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| 552,129,084
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MDExOlB1bGxSZXF1ZXN0MzY0NjkzOTUx
| 31,145
|
Use https for links where available
|
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| 5
| 2020-01-20T07:57:35Z
| 2020-01-24T03:55:13Z
| 2020-01-24T03:55:09Z
|
CONTRIBUTOR
| null |
As some of our links are suggestions to install add-on software, insecure links potentially allow an attacker to replace this with their malware.
During the process of writing this, I also found and fixed some broken or semi-broken (e.g. redirected to the top level instead of the page we want) links.
The following (already) broken links remain:
http://nipunbatra.github.io/2015/06/timeseries/ + others from this site
http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/CUJ/1992/9210/ross/ross.htm
http://mysite.verizon.net/aesir_research/date/jdalg2.htm
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MDExOlB1bGxSZXF1ZXN0MzY0Njk3MjA5
| 31,146
|
Remove possibly illegal test data
|
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| 5
| 2020-01-20T08:05:33Z
| 2020-05-26T09:39:30Z
| 2020-01-21T10:18:39Z
|
CONTRIBUTOR
| null |
Much of the test_html data looks like it was saved from real web pages, and not all of them look like the kind that are under a free software license. (I couldn't actually check because only one of the three identifies the source site, and that site raises a security warning.)
This removes the questionable data, and adds tests that try to do something equivalent on known-free data.
If I am mistaken and these pages are under a free license, please instead document that.
This data was added in:
https://github.com/pandas-dev/pandas/commit/1d573b4421d5ff16fd68ea0cc6c63f5f1c7ebcac computer_sales_page (HP), for MultiIndex header and empty string handling in headers (should give empty not NaN)
https://github.com/pandas-dev/pandas/commit/e22fe1b25d1ff9d5c880f6c35bc91dfab7aa0b84 nyse_wsj, for thousands separator
https://github.com/pandas-dev/pandas/commit/92aa2774ca9aa89d0a2cca011707b80db995df92 macau, for MultiIndex header and thousands separator
|
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| 552,134,221
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MDExOlB1bGxSZXF1ZXN0MzY0Njk4MjMx
| 31,147
|
Use Python 3 shebangs
|
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| 1
| 2020-01-20T08:07:45Z
| 2020-01-20T15:16:46Z
| 2020-01-20T15:16:42Z
|
CONTRIBUTOR
| null |
Plain 'python' may either not exist at all, or be Python 2.
|
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MDExOlB1bGxSZXF1ZXN0MzY0Nzc0MzA1
| 31,148
|
DOC: replace long table of contents on home page with main links
|
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| 14
| 2020-01-20T11:01:43Z
| 2020-01-27T19:53:54Z
| 2020-01-27T19:43:56Z
|
MEMBER
| null |
This is a proposal to remove the long table of contents on the documentation home page and replace it with a highlight of the main parts of the documentation.
Reasons: I don't think the long table of contents resulted in a very attractive landing page of the docs. And further, it was also a "manual" table of contents which would therefore easily get out of date. And with the new content, we can also at the same time give some explanation on the organization of the docs.
So would like to get feedback on the general idea. The wording in the different boxes can certainly still be improved. And we could also make similar boxes to point to eg the installation guide or for the different "getting help" links.
It's using some bootstrap elements ("cards") that can be used now since the sphinx theme is based on bootstrap (and all credit to @stijnvanhoey for the html/css part, I re-used some content of what he worked on for the getting started guides, a PR for that is coming shortly). Screenshot of how it currently looks below, and a live version can be checked here: http://jorisvandenbossche.github.io/example-pandas-docs/html-doc-home/
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MDU6SXNzdWU1NTIyNzc5NDQ=
| 31,149
|
Lint issue with new version of flake8-comprehensions plugin
|
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| 2020-01-20T12:44:58Z
| 2020-01-23T00:49:55Z
| 2020-01-23T00:49:55Z
|
MEMBER
| null |
[flake8-comprehensions](https://pypi.org/project/flake8-comprehensions/) have released a new version (3.2.1), and now when running ```./ci/code_checks.sh lint``` I get this output:
```
flake8 --version
3.7.9 (flake8-comprehensions: 3.2.1, mccabe: 0.6.1, pycodestyle: 2.5.0, pyflakes: 2.1.1) CPython 3.8.1 on Linux
Linting .py code
./pandas/tests/io/parser/test_network.py:208:21: C412 Unnecessary set comprehension - 'in' can take a generator.
Linting .py code DONE
```
(feel free to close this issue if this is happening because I have python version 3.8.1)
<details>
INSTALLED VERSIONS
------------------
commit : 7c94949dc89c62cae1bc647acd87266d6c3a0468
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.13.a-1-hardened
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0+107.g7c94949dc
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.1.0
Cython : 0.29.14
pytest : 5.3.3
hypothesis : 5.2.0
sphinx : 2.3.1
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.3
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzY0ODIxOTU4
| 31,150
|
API: generalized check_array_indexer for validating array-like getitem indexers
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| 22
| 2020-01-20T13:00:52Z
| 2020-01-29T14:46:57Z
| 2020-01-29T12:04:58Z
|
MEMBER
| null |
Closes https://github.com/pandas-dev/pandas/issues/30738. Also fixes the performance issue for other arrays from https://github.com/pandas-dev/pandas/issues/30744, and related to https://github.com/pandas-dev/pandas/pull/30308#issuecomment-571146305
This generalizes the `check_bool_array_indexer` helper method that we added for 1.0.0 to not be specific to boolean arrays, but any array-like input, and ensures that the output is a proper numpy array that can be used to index into numpy arrays.
I think such a more general "check" is useful, to avoid that all (external+internal) EAs need to do what the test EAs are already doing (checking for integer arrays as well) at https://github.com/pandas-dev/pandas/blob/master/pandas/tests/extension/decimal/array.py#L118-L126, and also to fix the performance issue in a general way (as was now only done for Categorical in https://github.com/pandas-dev/pandas/pull/30747/files)
If we agree on the general idea, I still need to clean up this PR (eg remove the existing `check_bool_array_indexer`, update the extending docs, etc)
cc @TomAugspurger
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| 5
| 2020-01-20T13:12:43Z
| 2021-11-12T16:45:29Z
| 2020-01-20T13:27:18Z
|
NONE
| null |
There is a ImportError raised, if you have a file named bottleneck.py in your current working directory.
In a python 3.7.5 console you get:
```python
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/__init__.py", line 55, in <module>
from pandas.core.api import (
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/core/api.py", line 5, in <module>
from pandas.core.arrays.integer import (
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/core/arrays/__init__.py", line 7, in <module>
from .categorical import Categorical # noqa: F401
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 54, in <module>
from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/core/base.py", line 36, in <module>
import pandas.core.nanops as nanops
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/core/nanops.py", line 38, in <module>
bn = import_optional_dependency("bottleneck", raise_on_missing=False, on_version="warn")
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/compat/_optional.py", line 99, in import_optional_dependency
version = _get_version(module)
File "/home/jalea/.local/lib/python3.7/site-packages/pandas/compat/_optional.py", line 48, in _get_version
raise ImportError("Can't determine version for {}".format(module.__name__))
ImportError: Can't determine version for bottleneck
```
If i delete the empty bottleneck.py in the directory, everything works fine. I you want to test it, just use `touch bottleneck.py` and open a interactive python console and you will get the error.
The problem also arises in tensorflow and keras. If there is a file named `bottleneck.py` you can't import these two, because they depend on pandas. This problem arised also in a keras [issue](https://github.com/keras-team/keras/issues/6602), but it didn't get looked into it.
Possible fix could be to get another `try: … except: …` block around line 97 of [pandas/compat/_optional.py](https://github.com/pandas-dev/pandas/blob/3f57ae7967776a8cd22d16e45a3476e63f08cbe4/pandas/compat/_optional.py) if `on_version == "warn"`.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : de_DE.UTF-8
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.2
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.1.0
Cython : 0.29.10
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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Backport PR #31095 on branch 1.0.x (DOC: Restore ExtensionIndex.dropna.__doc__)
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| 0
| 2020-01-20T13:35:47Z
| 2020-01-20T15:24:46Z
| 2020-01-20T15:24:46Z
|
NONE
| null |
Backport PR #31095: DOC: Restore ExtensionIndex.dropna.__doc__
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Cannot iterate over result of a group by multiple columns if one contains NaN
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| 2020-01-20T14:41:23Z
| 2020-01-20T21:45:44Z
| 2020-01-20T21:45:44Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
df = pd.DataFrame({"a": [1, 1, None, None], "b": [2,3,4,5]})
r = df.groupby(["a","b"])
print(len(r))
for _, i in r:
print(i)
```
#### Problem description
Hi,
when trying to perform a group by over multiples columns and if a column contains a Nan, the composite key is ignored. We can still access to the lines by iterating over the groups property of the generic.DataFrameGroupBy by using iloc but it is unwieldy.
```python
for i in gb.groups:
print(df.iloc[gb.groups.get(i)])
```
It seems unexpected that the len of the result is different from the number of results got when iterating.
#### Expected Output
4
a b
0 1.0 2
a b
1 None 3
a b
1 None 4
a b
1 None 5
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
None
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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REF: move 'describe' functions to new file
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| 1
| 2020-01-20T14:55:32Z
| 2021-06-26T02:08:13Z
| null |
CONTRIBUTOR
| null |
Opening an issue about a refactor of the code for the `describe` method in Series and DataFrames as requested here: https://github.com/pandas-dev/pandas/pull/30209
Idea was to move all the helper functions (according to type) into a new file `pandas/core/algos/describe.py`
And to redo the tests on `describe` methods by dtype using parametrize.
---------
Seeing as the current organization of the files in the master branch does not follow this arrangement and would imply some perhaps bug changes, I'd prefer not to work on this myself.
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BUG: nonexistent Timestamp pre-summer/winter DST w/dateutil timezone
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| 5
| 2020-01-20T15:23:12Z
| 2020-01-24T16:31:54Z
| 2020-01-24T03:33:00Z
|
MEMBER
| null |
- [X] closes #31043
- [X] tests added 1 / passed 1
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
This implements rounding down to microseconds into Timedelta.total_seconds(). Lack of this rounding led to `dateutil.tz.tzinfo.utcoffset` and `dst` breaking less than 128 nanoseconds before winter/summer DST switch. This happened due to an unintended cast to float in `Timedelta.total_seconds()` compounded with `Timedelta.value` being np.int64 type which doesn't support long arithmetic. The loss of precision led to rounding up, which meant that `total_seconds` since epoch time implied DST time while the `Timedelta.value` hasn't yet reached DST.
Details and code examples below.
<details>
This was quite a journey, but I found out what's going on. Let's say we have a `dateutil.tz.tzinfo` object named `du_tz` and want to find out the DST-aware UTC offset.
1. We call `du_tz.utcoffset(dt)` which calls `du_tz._find_ttinfo(dt)` which calls `du_tz._resolve_ambiguous_time(dt)` to find the index of the last transition time that it uses to return the correct offset.
2. `du_tz._resolve_ambiguous_time(dt)` calls `du_tz._find_last_transition(dt)` which calls the `_datetime_to_timestamp(dt)` dateutil function.
3. This is what this function does:
```
def _datetime_to_timestamp(dt):
"""
Convert a :class:`datetime.datetime` object to an epoch timestamp in
seconds since January 1, 1970, ignoring the time zone.
"""
return (dt.replace(tzinfo=None) - EPOCH).total_seconds()
```
The problem is dateutil's reliance on `Timedelta.total_seconds` which casts to float:
```
def total_seconds(self):
"""
Total duration of timedelta in seconds (to ns precision).
"""
return self.value / 1e9
```
Demonstration:
```
IN:
import datetime
import pandas as pd
epoch = 1552211999999999872
ts = pd.Timestamp(epoch, tz='dateutil/US/Pacific')
EPOCH = datetime.datetime.utcfromtimestamp(0)
delta = ts.replace(tzinfo=None) - EPOCH
print(delta.value)
OUT:
1552183199999999872
IN:
print(delta.total_seconds())
OUT:
1552183200.0
IN:
print(ts.tz.dst(ts))
OUT:
datetime.timedelta(seconds=3600)
```
The same thing happens with a `pytz` timezone, only pytz relies on something else to check for DST transitions:
```
IN:
import datetime
import pandas as pd
epoch = 1552211999999999872
ts = pd.Timestamp(epoch, tz='US/Pacific')
EPOCH = datetime.datetime.utcfromtimestamp(0)
delta = ts.replace(tzinfo=None) - EPOCH
print(delta.value)
OUT:
1552183199999999872
IN:
print(delta.total_seconds())
OUT:
1552183200.0
IN:
print(ts.tz.dst(ts))
OUT:
datetime.timedelta(0)
```
So `timedelta` value is okay, but `timedelta.total_seconds` hits the precision limit of floats, and this leads to rounding and an incorrect DST offset.
</details>
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| 552,376,337
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MDExOlB1bGxSZXF1ZXN0MzY0ODk2NTM1
| 31,156
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DOC: Update of the 'getting started' pages in the sphinx section of the documentation
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| 9
| 2020-01-20T15:42:12Z
| 2020-02-17T18:54:40Z
| 2020-02-17T11:49:43Z
|
CONTRIBUTOR
| null |
- [ ] closes #26831
This PR provides an update of the getting started pages of the Pandas documentation, following from the discussion in #26831 and the [proposal](https://docs.google.com/document/d/1Rc_eql5KLrdf0c582KyWfs2ADVNxbJy4jfosnqdrVak/edit#heading=h.mqz2f6gbl3sd). The update went together with the creation of the new theme and tries to integrate as much as possible with the new layout (using bootstrap elements). This PR focuses on the getting started section and adds new sections to the documentation:
- An update of the 'getting started' intro page of the sphinx documentation to provide new users some more guidance on the 'first steps' with Pandas: Directions for installation, a short intro on main Pandas features each linking to a dedicated introduction tutorial (see next point), specific info targeted to people with another background and reference to more tutorials.
- A set of tutorials which introduce some key features of the Pandas package. Each tutorial is setup as a series of 'tasks/questions' so users get a first idea of the type of problems they can solve with Pandas for that topic. All tutorials provide a set of [returning elements](https://stijnvanhoey.github.io/pandas-getting-started-tutorials/tutorial-elements/). Both the intros as well as the tutorials itself try to guide people as much as possible to related sections of the user guide.
- A set of schemas to illustrate certain concepts, with according to the new theme. These schemas can be used in other locations in the user guide as well.
The aim is to move the [dsintro](https://pandas.io/docs/getting_started/dsintro.html) and the [basics](https://pandas.io/docs/getting_started/basics.html) to the general user guide, as both are too extensive for a getting started section. The 10 intro tutorials try to provide an alternative that is more fit to people starting with Pandas.
There is still some work to do (typo's in the tutorials, additional schemas...), but input is certainly welcome and appreciated, I'll continue improving it. See https://stijnvanhoey.github.io/example-pandas-docs/getting-started/getting_started/index.html for a live preview.
Note: (1) a separate PR is prepared for the general sphinx intro page with @jorisvandenbossche, see #31148 (2) some of the layout issues are more general and related to the [new sphinx theme](https://github.com/pandas-dev/pydata-bootstrap-sphinx-theme) and will be tackled there (e.g. spacing around titles and subtitles)
Some pictures:
- The intro to the 10 tutorials:
- the coming from section :
- example of 'remember' section in each tutorial:
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REGR: outer merge resulting in missing datetime values converts to int
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| 2020-01-22T19:36:22Z
| 2020-01-22T19:36:21Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(
{
"x": [0, 1],
"date": pd.to_datetime(["2020-01-05T12:00", "2020-01-05T13:00"]),
}
)
df2 = pd.DataFrame({"x": [0, 2], "y": [0, 1]})
merged = pd.merge(df, df2, on="x", how="outer")
merged.info()
print(merged)
```
> ```
> <class 'pandas.core.frame.DataFrame'>
> Int64Index: 3 entries, 0 to 2
> Data columns (total 3 columns):
> # Column Non-Null Count Dtype
> --- ------ -------------- -----
> 0 x 3 non-null int64
> 1 date 3 non-null object
> 2 y 2 non-null float64
> dtypes: float64(1), int64(1), object(1)
> memory usage: 96.0+ bytes
> ```
> ```
> x date y
> 0 0 1578225600000000000 0.0
> 1 1 1578229200000000000 NaN
> 2 2 -9223372036854775808 1.0
> ```
```python
merged["date"].map(type)
```
> ```
> 0 <class 'int'>
> 1 <class 'int'>
> 2 <class 'int'>
> Name: date, dtype: object
> ```
```python
print(pd.merge(df, df2, on="x", how="inner"))
```
> ```
> x date y
> 0 0 2020-01-05 12:00:00 0
> ```
#### Problem description
Merging 2 DataFrames which result in missing Datetime values silently converts the column to integers with an `object` dtype, instead of keeping `datetime64[ns]` as dtype, which was the behaviour in pandas 0.25.3, and the behaviour I would expect.
#### Expected Output
```python
merged = pd.merge(df, df2, on="x", how="outer")
merged.info()
print(merged)
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 0 to 2
Data columns (total 3 columns):
x 3 non-null int64
date 2 non-null datetime64[ns]
y 2 non-null float64
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 96.0 bytes
x date y
0 0 2020-01-05 12:00:00 0.0
1 1 2020-01-05 13:00:00 NaN
2 2 NaT 1.0
```python
merged["date"].map(type)
```
> ```
> 0 <class 'pandas._libs.tslibs.timestamps.Timesta...
> 1 <class 'pandas._libs.tslibs.timestamps.Timesta...
> 2 <class 'pandas._libs.tslibs.nattype.NaTType'>
> Name: date, dtype: object
> ```
This is the output from Python 0.25.3
Whether the `y`-column should be converted to float or `Int64` is open for debate, but not the issue at hand here.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 8.1
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0rc0
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.1.0.post20200119
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NTI0NTE2OTI=
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BUG? Intended bebavior? MI levels and codes ordered in lexsorted in from_*
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| 2020-01-20T18:27:06Z
| 2021-07-27T05:04:50Z
| null |
MEMBER
| null |
I find it a bit weird when working on a PR with MI issue and I am not sure if it is intended or a bug?and feel surprised they show different results
If creating MI e.g.
```python
>>> mi = pd.MultiIndex(levels=[['apple', 'baz', 'bar'], ['one', 'two', 'three']],
codes=[[0, 1, 2], [0, 1, 2]])
>>> mi
MultiIndex([('apple', 'one'),
( 'baz', 'two'),
( 'bar', 'three')])
>>> mi.levels
>>> FrozenList([['apple', 'baz', 'bar'], ['one', 'two', 'three']])
>>> mi.codes
>>> FrozenList([[0, 1, 2], [0, 1, 2]])
```
And i think this is correct behavior
However, if creating MI using `from_*`, then:
```python
>>> mi = pd.MultiIndex.from_tuples([['apple', 'one'], ['baz','two'], ['bar','three']])
# this MI looks same as the one above
>>> mi
MultiIndex([('apple', 'one'),
( 'baz', 'two'),
( 'bar', 'three')])
>>> mi.levels
>>> FrozenList([['apple', 'bar', 'baz'], ['one', 'three', 'two']])
>>> mi.codes
>>> FrozenList([[0, 2, 1], [0, 2, 1]])
```
Then checking codes and levels, they seem to be lexsorted already. I am not sure if this is intended? If so, maybe good to put some documentations describing it? since we do have `sortorder` option, honestly i am not sure how this argument works: changing the `sortorder` does not seem to change any output?
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ENH: Implement _from_sequence_of_strings for BooleanArray
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| 2020-01-20T18:30:01Z
| 2020-01-23T21:56:21Z
| 2020-01-23T20:56:01Z
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MEMBER
| null |
- [x] closes #31131
- [x] tests added / passed
- [x] passes `black pandas`
- [x] whatsnew entry
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MyPy doesn't recognize scalar arithmetic for pandas classes
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| 2020-09-30T15:27:55Z
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MEMBER
| null |
Something like:
```python
1 + pd.Series([1])
```
will yield `error: Unsupported operand types for + ("int" and "Series")` I assume because our arithmetic ops are added at runtime and not statically. Would be nice to support this somehow though
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| 1
| 2020-01-20T20:14:22Z
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MEMBER
| null |
- [x] closes #14600
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- [x] closes #12948
- [x] closes #15058
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: corner cases in DTI.get_value, Float64Index.get_value
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MEMBER
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Series lookups are affected for the Float64Index case.
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Backport PR #31133 on branch 1.0.x (BUG: Break reference from grouping level to MI)
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NONE
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Backport PR #31133: BUG: Break reference from grouping level to MI
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TST: Replaced try-catch blocks with pytest.raises
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- [x] tests added / passed
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Backport PR #29836 on branch 1.0.x (ENH: XLSB support)
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NONE
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Backport PR #29836: ENH: XLSB support
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DOC: fix DataFrame.plot docs
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| 3
| 2020-01-21T00:27:07Z
| 2020-01-26T15:55:17Z
| 2020-01-23T12:35:16Z
|
MEMBER
| null |
- [x] fixes #29489
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzY1MDQ5OTkx
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|
REF: simplify index.pyx
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| 1
| 2020-01-21T00:51:02Z
| 2020-01-24T04:27:23Z
| 2020-01-24T04:02:48Z
|
MEMBER
| null |
simplification of is_definitely_invalid_key is made possible by the recent change to make EA non-hashable.
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MDExOlB1bGxSZXF1ZXN0MzY1MDU4NDE0
| 31,169
|
REF: require scalar in IntervalIndex.get_loc, get_value
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| 1
| 2020-01-21T01:38:58Z
| 2020-01-24T00:54:01Z
| 2020-01-24T00:52:00Z
|
MEMBER
| null |
cc @jschendel the casting of tuple->Interval on L819-820 seems sketchy, can you confirm if thats appropriate?
I'm also curious about the restriction on slice steps in _convert_slice_indexer, would be nice to have a comment there about why that is.
xref #31117.
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MDU6SXNzdWU1NTI1ODgzMzM=
| 31,170
|
pandas read_csv() doesn't work with StreamingBody object where python engine is required
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| 2021-07-27T05:05:54Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import boto3
>>> import s3fs
>>> import pandas as pd
>>>
>>> s3 = boto3.client("s3")
>>> response = s3.get_object(Bucket="df-raw-869771", Key="csv/customer-01.csv")
>>>
>>> pd.read_csv(response["Body"])
customer_id|store_id|first_name|last_name|email|address_id|activebool|dw_insert_date|dw_update_date|active
0 9|2|MARGARET|MOORE|MARGARET.MOORE@sakilacustom...
1 13|2|KAREN|JACKSON|KAREN.JACKSON@sakilacustome...
2 17|1|DONNA|THOMPSON|DONNA.THOMPSON@sakilacusto...
3 21|1|MICHELLE|CLARK|MICHELLE.CLARK@sakilacusto...
4 25|1|DEBORAH|WALKER|DEBORAH.WALKER@sakilacusto...
... ...
1188 587|1|SERGIO|STANFIELD|SERGIO.STANFIELD@sakila...
1189 591|1|KENT|ARSENAULT|KENT.ARSENAULT@sakilacust...
1190 595|1|TERRENCE|GUNDERSON|TERRENCE.GUNDERSON@sa...
1191 599|2|AUSTIN|CINTRON|AUSTIN.CINTRON@sakilacust...
1192 4|2|BARBARA|JONES|BARBARA@sakilacustomer.org|8...
[1193 rows x 1 columns]
```
if with `skipfooter` argument added
```python
>>> import boto3
>>> import s3fs
>>> import pandas as pd
>>>
>>> s3 = boto3.client("s3")
>>> response = s3.get_object(Bucket="df-raw-869771", Key="csv/customer-01.csv")
>>>
>>> pd.read_csv(response["Body"], skipfooter=1)
__main__:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.
99 117 115 116 111 109 101 114 95 105 100 124 115.1 116.1 111.1 ... 114.28 103.11 124.112 53.6 55.4 124.113 65.51 99.26 116.33 105.31 118.13 101.35 124.114 49.70 51.21
0 45 49 49 45 50 48 49 57 124 124 49 10 53 55 124 ... 69 83 84 124 69 68 78 65 46 87 69 83 84 64 115
1 97 107 105 108 97 99 117 115 116 111 109 101 114 46 111 ... 76 68 73 78 69 46 80 69 82 75 73 78 83 64 115
2 97 107 105 108 97 99 117 115 116 111 109 101 114 46 111 ... 73 76 76 73 65 77 83 79 78 64 115 97 107 105 108
3 97 99 117 115 116 111 109 101 114 46 111 114 103 124 50 ... 114 103 124 50 55 48 124 65 99 116 105 118 101 124 49
4 51 45 49 49 45 50 48 49 57 124 124 49 10 50 54 ... 115 97 107 105 108 97 99 117 115 116 111 109 101 114 46
.. .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
84 99 116 105 118 101 124 49 51 45 49 49 45 50 48 49 ... 51 45 49 49 45 50 48 49 57 124 124 49 10 51 56
85 51 124 49 124 77 65 82 84 73 78 124 66 65 76 69 ... 51 53 124 50 124 82 73 67 75 89 124 83 72 69 76
86 66 89 124 82 73 67 75 89 46 83 72 69 76 66 89 ... 88 84 69 82 124 72 69 67 84 79 82 46 80 79 73
87 78 68 69 88 84 69 82 64 115 97 107 105 108 97 99 ... 103 124 53 52 53 124 65 99 116 105 118 101 124 49 51
88 45 49 49 45 50 48 49 57 124 124 49 10 53 52 51 ... 116 111 109 101 114 46 111 114 103 124 53 57 55 124 65
[89 rows x 1024 columns]
```
#### Problem description
I am encountering a problem while using pandas read_csv(). Data is being read in from s3 as StreamingBody object and noticed it worked only when c engine parser is used. (e.g. skipfooter only works with python engine parser).
When a S3 URL is given to read_csv(), there aren't any issues at all
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0MzY1MDY3MDEz
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TST: Add more regression tests for fixed issues
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| 0
| 2020-01-21T02:29:18Z
| 2020-01-21T18:26:52Z
| 2020-01-21T18:26:47Z
|
MEMBER
| null |
- [x] closes #16894
- [x] closes #16916
- [x] closes #16410
- [x] closes #15454
- [x] closes #16699
- [x] closes #16231
- [x] closes #15150
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0MzY1MDY5ODM5
| 31,172
|
BUG: inconsistency between PeriodIndex.get_value vs get_loc
|
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| 2
| 2020-01-21T02:44:42Z
| 2020-01-25T16:34:19Z
| 2020-01-25T16:07:16Z
|
MEMBER
| null |
ATM we have inconsistent treatment for indexing with strings where the string has a higher resolution than the PeriodIndex. Luckily only one test checks for the inconsistent `PeriodIndex.__contains__` behavior, so fixing it isn't _too_ invasive.
```
pi = pd.period_range("2017-09-01", freq="D", periods=3)
ser = pd.Series(range(5, 8), index=pi)
key = "2017-09-01 00:00:01"
# master
>>> key in pi
True
>>> pi.get_loc(key)
0
>>> pi.get_value(ser, key)
KeyError: '2017-09-01 00:00:01'
# PR
>>> key in pi
False
>>> pi.get_loc(key)
KeyError: '2017-09-01 00:00:01'
>>> pi.get_value(ser, key)
KeyError: '2017-09-01 00:00:01'
```
Note: the diff is fairly big because we're mirroring the code in `get_value`. Once this is fixed, we'll have get_value dispatch to get_loc and remove basically the same amount of code as we're adding here.
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MDU6SXNzdWU1NTI3MDMxMjA=
| 31,173
|
ValueError: can not merge DataFrame with instance of type
|
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| 2020-01-21T08:42:27Z
| 2020-04-25T12:00:13Z
| 2020-04-25T12:00:13Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df_debtor = pd.merge(debtor_marital,debtor_gender, left_on = "IDENTITY", right_on = "IDENTITY", how = "inner")
```
#### Problem description
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in
----> 1 df_debtor = pd.merge(debtor_marital,debtor_gender, left_on = "IDENTITY", right_on = "IDENTITY", how = "inner")
~/opt/anaconda3/lib/python3.7/site-packages/modin/pandas/general.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
75 if not isinstance(left, DataFrame):
76 raise ValueError(
---> 77 "can not merge DataFrame with instance of type {}".format(type(right))
78 )
79
### ValueError: can not merge DataFrame with instance of type
|
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| 552,850,817
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MDU6SXNzdWU1NTI4NTA4MTc=
| 31,174
|
Pip 20 ruins builds.
|
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| 2020-01-21T12:36:57Z
| 2020-01-21T12:49:45Z
| 2020-01-21T12:49:44Z
|
CONTRIBUTOR
| null |
#### Problem description:
During testing, it was noticed that setting up the environment with version 20 pip fails.
New release was several hours ago.
#### Output
```
[Updating pip]
Collecting pip
Downloading https://files.pythonhosted.org/packages/60/65/16487a7c4e0f95bb3fc89c2e377be331fd496b7a9b08fd3077de7f3ae2cf/pip-20.0-py2.py3-none-any.whl (1.5MB)
Requirement already up-to-date: wheel in /home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages (0.33.6)
Requirement already up-to-date: setuptools in /home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages (45.1.0.post20200119)
Installing collected packages: pip
Found existing installation: pip 19.3.1
Uninstalling pip-19.3.1:
Successfully uninstalled pip-19.3.1
Successfully installed pip-20.0
[Install pandas]
Traceback (most recent call last):
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/__main__.py", line 19, in <module>
sys.exit(_main())
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/_internal/cli/main.py", line 73, in main
command = create_command(cmd_name, isolated=("--isolated" in cmd_args))
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/_internal/commands/__init__.py", line 96, in create_command
module = importlib.import_module(module_path)
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/_internal/commands/install.py", line 24, in <module>
from pip._internal.cli.req_command import RequirementCommand
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/_internal/cli/req_command.py", line 20, in <module>
from pip._internal.operations.prepare import RequirementPreparer
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/_internal/operations/prepare.py", line 16, in <module>
from pip._internal.distributions import (
File "/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/_internal/distributions/__init__.py", line 1, in <module>
from pip._internal.distributions.source import SourceDistribution
ImportError: cannot import name 'SourceDistribution' from 'pip._internal.distributions.source' (/home/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pip/_internal/distributions/source/__init__.py)
##[error]Process completed with exit code 1.
```
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| 552,871,702
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MDU6SXNzdWU1NTI4NzE3MDI=
| 31,175
|
test_timestamp:test_class_ops_pytz can fail due to bad luck of timing of calls
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| 1
| 2020-01-21T13:18:43Z
| 2020-01-23T23:10:17Z
| 2020-01-23T23:10:17Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
I had a test fail in CI and the output looks like this:
```
______________________ TestTimestamp.test_class_ops_pytz _______________________
[gw1] linux -- Python 3.6.6 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.scalar.timestamp.test_timestamp.TestTimestamp object at 0x7fb560287320>
def test_class_ops_pytz(self):
def compare(x, y):
assert int(Timestamp(x).value / 1e9) == int(Timestamp(y).value / 1e9)
compare(Timestamp.now(), datetime.now())
compare(Timestamp.now("UTC"), datetime.now(timezone("UTC")))
compare(Timestamp.utcnow(), datetime.utcnow())
> compare(Timestamp.today(), datetime.today())
pandas/tests/scalar/timestamp/test_timestamp.py:759:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
x = Timestamp('2020-01-21 03:19:15.998086')
y = datetime.datetime(2020, 1, 21, 3, 19, 16, 5328)
def compare(x, y):
> assert int(Timestamp(x).value / 1e9) == int(Timestamp(y).value / 1e9)
E AssertionError: assert 1579576755 == 1579576756
E + where 1579576755 = int((1579576755998086000 / 1000000000.0))
E + where 1579576755998086000 = Timestamp('2020-01-21 03:19:15.998086').value
E + where Timestamp('2020-01-21 03:19:15.998086') = Timestamp(Timestamp('2020-01-21 03:19:15.998086'))
E + and 1579576756 = int((1579576756005328000 / 1000000000.0))
E + where 1579576756005328000 = Timestamp('2020-01-21 03:19:16.005328').value
E + where Timestamp('2020-01-21 03:19:16.005328') = Timestamp(datetime.datetime(2020, 1, 21, 3, 19, 16, 5328))
```
#### Problem description
So due to "bad luck", the call to `Timestamp.today()` was just before the next second that hit when `datetime.now()` was called, making the comparison fail. I think the comparison should be:
```
int((Timestamp(x).value - Timestamp(y).value)/1.0e9) == 0
```
which would handle this round off error better.
#### Expected Output
No failure
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
C:\Anaconda3\envs\pandas-dev\lib\site-packages\fastparquet\dataframe.py:5: Futur
eWarning: pandas.core.index is deprecated and will be removed in a future versio
n. The public classes are available in the top-level namespace.
from pandas.core.index import CategoricalIndex, RangeIndex, Index, MultiIndex
INSTALLED VERSIONS
------------------
commit : 8e4dfff71a0547c821e67477b65c7b1930df588e
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0rc0+156.g8e4dfff71
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.57.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.46.0
>>>
</details>
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Parquet file written with Dask does not load using Pandas
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[] | null | 4
| 2020-01-21T14:28:33Z
| 2020-03-11T14:18:36Z
| 2020-03-11T14:18:35Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
import dask.dataframe as dd
from dask.diagnostics import ProgressBar
from dask.distributed import Client
client = Client('192.168.1.33:8786', processes=True)
path = "D:\\Downloads\\Wikipedia\\Yago\\"
names = ['dummy1', 'class1', 'statement', 'class2', 'value']
dtypes = {'dummy1': 'category', 'class1': 'category', 'statement': 'category', 'class2': np.str, 'value': 'category'}
cols = ['class1', 'statement', 'class2', 'value']
df = dd.read_csv(path + "yagoGeonamesOnlyData.tsv", sep="\t", skiprows=1, header=None, names=names, dtype=dtypes, usecols=cols, blocksize=1e7)
df.to_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow', compression='BROTLI', write_index=False) # Can't specify npartitions.
```
#### Problem description
The above written file cannot be loaded using Pandas:
`df = pd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow')`
but does load with Dask:
`df = dd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow', index=False)`
The pandas version gives the following error:
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in
1 #df = dd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow', index=False)
----> 2 df = pd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow')
~\AppData\Roaming\Python\Python37\site-packages\pandas\io\parquet.py in read_parquet(path, engine, columns, **kwargs)
294
295 impl = get_engine(engine)
--> 296 return impl.read(path, columns=columns, **kwargs)
~\AppData\Roaming\Python\Python37\site-packages\pandas\io\parquet.py in read(self, path, columns, **kwargs)
123 kwargs["use_pandas_metadata"] = True
124 result = self.api.parquet.read_table(
--> 125 path, columns=columns, **kwargs
126 ).to_pandas()
127 if should_close:
E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\pyarrow\parquet.py in read_table(source, columns, use_threads, metadata, use_pandas_metadata, memory_map, read_dictionary, filesystem, filters, buffer_size)
1272 read_dictionary=read_dictionary,
1273 buffer_size=buffer_size,
-> 1274 filesystem=filesystem, filters=filters)
1275 else:
1276 pf = ParquetFile(source, metadata=metadata,
E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\pyarrow\parquet.py in __init__(self, path_or_paths, filesystem, schema, metadata, split_row_groups, validate_schema, filters, metadata_nthreads, read_dictionary, memory_map, buffer_size)
1058
1059 if validate_schema:
-> 1060 self.validate_schemas()
1061
1062 def equals(self, other):
E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\pyarrow\parquet.py in validate_schemas(self)
1111 '{1!s}\n\nvs\n\n{2!s}'
1112 .format(piece, file_schema,
-> 1113 dataset_schema))
1114
1115 def read(self, columns=None, use_threads=True, use_pandas_metadata=False):
ValueError: Schema in D:\Downloads\Wikipedia\Yago\yagoGeonamesOnlyData\part.439.parquet was different.
class1: dictionary
statement: dictionary
class2: string
value: null
metadata
--------
{b'ARROW:schema': b'/////zAEAAAUAAAAAAAAAAAACgAOAAYABQAIAAoAAAAAAQMAEAAAAAAA'
b'CgAMAAAABAAIAAoAAADUAgAABAAAAAEAAAAMAAAACAAMAAQACAAIAAAA'
b'CAAAABAAAAAGAAAAcGFuZGFzAACeAgAAeyJpbmRleF9jb2x1bW5zIjog'
b'W10sICJjb2x1bW5faW5kZXhlcyI6IFtdLCAiY29sdW1ucyI6IFt7Im5h'
b'bWUiOiAiY2xhc3MxIiwgImZpZWxkX25hbWUiOiAiY2xhc3MxIiwgInBh'
b'bmRhc190eXBlIjogImNhdGVnb3JpY2FsIiwgIm51bXB5X3R5cGUiOiAi'
b'aW50MzIiLCAibWV0YWRhdGEiOiB7Im51bV9jYXRlZ29yaWVzIjogMTEz'
b'MjY3LCAib3JkZXJlZCI6IGZhbHNlfX0sIHsibmFtZSI6ICJzdGF0ZW1l'
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b'cGUiOiAiY2F0ZWdvcmljYWwiLCAibnVtcHlfdHlwZSI6ICJpbnQ4Iiwg'
b'Im1ldGFkYXRhIjogeyJudW1fY2F0ZWdvcmllcyI6IDEsICJvcmRlcmVk'
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b'OAAAABAAAAAoAAAACAAIAAAABAAIAAAADAAAAAgADAAIAAcACAAAAAAA'
b'AAEgAAAAAAAAAAQABAAEAAAABgAAAGNsYXNzMQAA',
b'pandas': b'{"index_columns": [], "column_indexes": [], "columns": [{"name":'
b' "class1", "field_name": "class1", "pandas_type": "categorical",'
b' "numpy_type": "int32", "metadata": {"num_categories": 113267, "'
b'ordered": false}}, {"name": "statement", "field_name": "statemen'
b't", "pandas_type": "categorical", "numpy_type": "int8", "metadat'
b'a": {"num_categories": 1, "ordered": false}}, {"name": "class2",'
b' "field_name": "class2", "pandas_type": "unicode", "numpy_type":'
b' "object", "metadata": null}, {"name": "value", "field_name": "v'
b'alue", "pandas_type": "empty", "numpy_type": "object", "metadata'
b'": null}], "creator": {"library": "pyarrow", "version": "0.15.1"'
b'}, "pandas_version": "0.25.3"}'}
vs
class1: dictionary
statement: dictionary
class2: string
value: string
metadata
--------
{b'ARROW:schema': b'/////zAEAAAUAAAAAAAAAAAACgAOAAYABQAIAAoAAAAAAQMAEAAAAAAA'
b'CgAMAAAABAAIAAoAAADUAgAABAAAAAEAAAAMAAAACAAMAAQACAAIAAAA'
b'CAAAABAAAAAGAAAAcGFuZGFzAACfAgAAeyJpbmRleF9jb2x1bW5zIjog'
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b'AADY////AAABBRQAAAAMAAAABAAAAAAAAAAU////BQAAAHZhbHVlAAAA'
b'EAAUAAgABgAHAAwAAAAQABAAAAAAAAEFFAAAAAwAAAAEAAAAAAAAAEz/'
b'//8GAAAAY2xhc3MyAACs////AAABBTwAAAA0AAAAEAAAACgAAAAIABAA'
b'CAAEAAgAAAAMAAAAAQAAAAAAAACk////AAAAAQgAAAAAAAAAnP///wkA'
b'AABzdGF0ZW1lbnQAAAAQABgACAAGAAcADAAQABQAEAAAAAAAAQVAAAAA'
b'OAAAABAAAAAoAAAACAAIAAAABAAIAAAADAAAAAgADAAIAAcACAAAAAAA'
b'AAEQAAAAAAAAAAQABAAEAAAABgAAAGNsYXNzMQAA',
b'pandas': b'{"index_columns": [], "column_indexes": [], "columns": [{"name":'
b' "class1", "field_name": "class1", "pandas_type": "categorical",'
b' "numpy_type": "int16", "metadata": {"num_categories": 16573, "o'
b'rdered": false}}, {"name": "statement", "field_name": "statement'
b'", "pandas_type": "categorical", "numpy_type": "int8", "metadata'
b'": {"num_categories": 4, "ordered": false}}, {"name": "class2", '
b'"field_name": "class2", "pandas_type": "unicode", "numpy_type": '
b'"object", "metadata": null}, {"name": "value", "field_name": "va'
b'lue", "pandas_type": "unicode", "numpy_type": "object", "metadat'
b'a": null}], "creator": {"library": "pyarrow", "version": "0.15.1'
b'"}, "pandas_version": "0.25.3"}'}
```
#### Expected Output
For the file to be loaded without error.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\xarray\core\merge.py:10: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)
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 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.16.4
pytz : 2018.6
dateutil : 2.7.5
pip : 19.3.1
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.3.1
hypothesis : 3.82.1
sphinx : 2.2.1
blosc : 1.6.1
feather : 0.4.0
xlsxwriter : 1.1.2
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.0.3
numexpr : 2.6.8
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : 3.4.4
xarray : 0.11.3
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.1.2
</details>
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MDU6SXNzdWU1NTI5NDU5OTM=
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Setting multiindex with columns that contain list as element raises TypeError
|
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| 2020-01-21T15:22:20Z
| 2020-09-01T22:59:48Z
| 2020-09-01T22:59:48Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'A': ['x1','x2','x3', 'x4'], 'B':[['v1','v2'],['v3','v4'],['v5','v6'],['v7','v8']], 'C':[['c1','c2'],['c3','c4','cc'],['c5','c6'],['c7','c8']],'D':[['d1','d2'],['d3','d4'],['d5','d6'],['d7','d8']], 'E':[['e1','e2'],['e3','e4'],['e5','e6'],['e7','e8']]})
df
| A | B | C | D | E
-- | -- | -- | -- | -- | --
x1 | [v1, v2] | [c1, c2] | [d1, d2] | [e1, e2]
x2 | [v3, v4] | [c3, c4, cc] | [d3, d4] | [e3, e4]
x3 | [v5, v6] | [c5, c6] | [d5, d6] | [e5, e6]
x4 | [v7, v8] | [c7, c8] | [d7, d8] | [e7, e8]
```
#### Problem description
I was trying to set index with column A and column C (multiindex) in order to explode columns B,D,E only. (Column C should be excluded for explosion for one of its elements has different size)
Below code should do the job:
```
df.set_index(['A','C']).apply(pd.Series.explode).reset_index()
```
But pandas refuse to set multiindex with columns that contains list as their element, raising TypeError.
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\arrays\categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
383 try:
--> 384 codes, categories = factorize(values, sort=True)
385 except TypeError:
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
207 kwargs[new_arg_name] = new_arg_value
--> 208 return func(*args, **kwargs)
209
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
671 labels, uniques = _factorize_array(
--> 672 values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value
673 )
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\algorithms.py in _factorize_array(values, na_sentinel, size_hint, na_value)
507 uniques, labels = table.factorize(
--> 508 values, na_sentinel=na_sentinel, na_value=na_value
509 )
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.factorize()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique()
TypeError: unhashable type: 'list'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-32-c3b81bee4c46> in <module>
1 # setting index with columns that contains list values : it causes error
----> 2 df.set_index(['A','C']) # error because C has list values in it.
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\frame.py in set_index(self, keys, drop, append, inplace, verify_integrity)
4459 )
4460
-> 4461 index = ensure_index_from_sequences(arrays, names)
4462
4463 if verify_integrity and not index.is_unique:
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\indexes\base.py in ensure_index_from_sequences(sequences, names)
5702 return Index(sequences[0], name=names)
5703 else:
-> 5704 return MultiIndex.from_arrays(sequences, names=names)
5705
5706
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\indexes\multi.py in from_arrays(cls, arrays, sortorder, names)
418 from pandas.core.arrays.categorical import _factorize_from_iterables
419
--> 420 codes, levels = _factorize_from_iterables(arrays)
421 if names is None:
422 names = [getattr(arr, "name", None) for arr in arrays]
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\arrays\categorical.py in _factorize_from_iterables(iterables)
2814 # For consistency, it should return a list of 2 lists.
2815 return [[], []]
-> 2816 return map(list, zip(*(_factorize_from_iterable(it) for it in iterables)))
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\arrays\categorical.py in <genexpr>(.0)
2814 # For consistency, it should return a list of 2 lists.
2815 return [[], []]
-> 2816 return map(list, zip(*(_factorize_from_iterable(it) for it in iterables)))
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\arrays\categorical.py in _factorize_from_iterable(values)
2786 # but only the resulting categories, the order of which is independent
2787 # from ordered. Set ordered to False as default. See GH #15457
-> 2788 cat = Categorical(values, ordered=False)
2789 categories = cat.categories
2790 codes = cat.codes
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\arrays\categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
384 codes, categories = factorize(values, sort=True)
385 except TypeError:
--> 386 codes, categories = factorize(values, sort=False)
387 if dtype._ordered:
388 # raise, as we don't have a sortable data structure and so
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
206 else:
207 kwargs[new_arg_name] = new_arg_value
--> 208 return func(*args, **kwargs)
209
210 return wrapper
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
670
671 labels, uniques = _factorize_array(
--> 672 values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value
673 )
674
~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\algorithms.py in _factorize_array(values, na_sentinel, size_hint, na_value)
506 table = hash_klass(size_hint or len(values))
507 uniques, labels = table.factorize(
--> 508 values, na_sentinel=na_sentinel, na_value=na_value
509 )
510
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.factorize()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique()
TypeError: unhashable type: 'list'
```
####
Setting index with a single column with lists works fine.
```
df.set_index('C')
A B D E
C
[c1, c2] x1 [v1, v2] [d1, d2] [e1, e2]
[c3, c4, cc] x2 [v3, v4] [d3, d4] [e3, e4]
[c5, c6] x3 [v5, v6] [d5, d6] [e5, e6]
[c7, c8] x4 [v7, v8] [d7, d8] [e7, e8]
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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MDU6SXNzdWU1NTMwMDUyNDI=
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|
ewm NaN handling is wrong : simple example
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[] | null | 19
| 2020-01-21T16:56:40Z
| 2020-10-05T01:13:14Z
| null |
NONE
| null |
Hello,
It seems that the handling of NaN values returns bad values on this simple case (I'm using pandas 0.25.3)
import numpy as np
import pandas as pd
x = np.array([1, np.NaN, 5])
alpha = 0.5
x_ewm = pd.Series(x).ewm(alpha=alpha, adjust=False).mean()
x_ewm_simple = alpha * x[2] + ((1 - alpha) **2) * x[0]
print(x_ewm[2])
print(x_ewm_simple)
3.6666666666666665
2.75
According to pandas documentation https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html
When ignore_na is False (default), weights are based on absolute positions. For example, the weights of x and y used in calculating the final weighted average of [x, None, y] are (1-alpha)**2 and 1 (if adjust is True), and (1-alpha)**2 and alpha (if adjust is False).
Can you explain how 3.6666666666666665 is computed? Thank you.
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CLN: core.internals
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MEMBER
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some annotations, remove an unused argument, privatize where possible, avoid a runtime/circular import
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BUG/CLN: use more-correct isinstance check
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| 4
| 2020-01-21T17:45:21Z
| 2020-01-24T04:39:30Z
| 2020-01-24T04:39:06Z
|
MEMBER
| null |
this is only kind of a bug, since the isinstance check still returns True on the `type(obj)`
|
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MDExOlB1bGxSZXF1ZXN0MzY1NDY5NDcy
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TYP: annotations in indexes
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| 2020-01-24T04:42:47Z
| 2020-01-24T03:28:41Z
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MDExOlB1bGxSZXF1ZXN0MzY1NDcyNjU1
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|
CLN: Make Series._values match Index._values
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| 12
| 2020-01-21T19:32:13Z
| 2020-01-28T17:11:52Z
| 2020-01-28T06:11:29Z
|
MEMBER
| null |
Discussed in #31037
- Note this is _not_ an alternative to that, as this should not be considered for 1.0.
- This does not implement any of the simplifications that it makes available.
- There is a decent chance that the check in core.apply can be changed so that we can still use the fastpath for these dtypes.
- The PandasDType edit is not central to the change, just included for perf comparison against #31037.
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MDExOlB1bGxSZXF1ZXN0MzY1NDg2NzY1
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BUG: Series/Frame invert dtypes
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| 4
| 2020-01-21T20:06:08Z
| 2020-02-01T14:53:28Z
| 2020-01-23T22:45:52Z
|
CONTRIBUTOR
| null |
Fixes NDFrame.__invert__ to preserve dtypes by applying things blockwise. Notably, this fixes `~Series[BooleanDtype]`.
This also provides a pattern for other unary ops, though I think that those are less pressing for 1.0, which should probably have a working `~Series[BooleanDtype]`
cc @jorisvandenbossche
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MDU6SXNzdWU1NTMxMjAzNDY=
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_BaseOffset's parameter `n` should be prevented from being 0
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| 2020-01-21T20:47:21Z
| 2020-09-27T21:49:14Z
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|
MEMBER
| null |
The docstring says
```
Require that `n` be a nonzero integer.
```
but the "nonzero" condition isn't enforced.
Enforcing it would prevent the undesired behaviour noticed in #29901 , whereby
```
cbh = pd.offsets.CustomBusinessHour(start='07:00', end='01:00')
time = pd.Timestamp('2019-03-06 13:00:00')
time + 0*cbh
```
was returning
```
2019-03-07 07:00:00
```
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DEPR: _BaseOffset's parameter `n` should be prevented from being 0
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|
MEMBER
| null |
- [ ] closes #31184
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1NTMxMjM1MTk=
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Adding and subtracting DateOffsets results in different behaviour
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| 2020-01-21T20:54:32Z
| 2020-06-28T20:26:05Z
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MEMBER
| null |
```
>>> import datetime as dt
>>> import pandas as pd
>>> dt.datetime(2019, 3, 6, 16, 0) + pd.offsets.BusinessHour()
Timestamp('2019-03-07 09:00:00')
>>> dt.datetime(2019, 3, 7, 10, 0) - pd.offsets.BusinessHour()
Timestamp('2019-03-06 17:00:00')
```
Would have expected the same answer from these two commands.
Originally noticed in #29901
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MDExOlB1bGxSZXF1ZXN0MzY1NTM5OTg2
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BUG: IntegerArray.astype(boolean)
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CONTRIBUTOR
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Closes https://github.com/pandas-dev/pandas/issues/31102
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REF: move misplaced tests
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MDU6SXNzdWU1NTMyMTU5MzE=
| 31,190
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ENH: assert_frame_equal can produce unhelpful output
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| 1
| 2020-01-21T23:43:55Z
| 2020-03-22T20:43:15Z
| 2020-03-22T20:43:08Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [10, 20, 30]}, index=[0, 1, 2])
df2 = pd.DataFrame({'a': [2, 1, 3], 'b': [20, 999, 30]}, index=[1, 0, 2])
print(df1)
print(df2)
pd.testing.assert_frame_equal(df1, df2, check_like=True)
```
Generates the output:
```
a b
0 1 10
1 2 20
2 3 30
a b
1 2 20
0 1 999
2 3 30
Traceback (most recent call last):
<removed stack trace>
raise AssertionError(msg)
AssertionError: DataFrame.iloc[:, 1] are different
DataFrame.iloc[:, 1] values are different (33.33333 %)
[left]: [20, 10, 30]
[right]: [20, 999, 30]
```
#### Problem description
When running assert_frame_equal with check_like=True the error message can be unhelpful if the column or index order differs (or worse, both).
#29218 has greatly helped the issue with column reordering, although it could be insufficient if check_names is false and the column names differ (but this is probably a small edge case).
However, row (index) reordering is still an issue. As the output above demonstrates, it is not obvious that the left column has been reordered. In most cases the user could probably work it out but it is not as clear as it could be.
#### Possible Solution
One solution would be to explicitly state the index values in the output. This would probably have to affect the output of assert_series_equal as well. This would be quite a significant change to the output though so it may be a sledgehammer to crack a nut.
```
DataFrame.iloc[:, 1] values are different (33.33333 %)
[index]: [1, 0, 2]
[left]: [20, 10, 30]
[right]: [20, 999, 30]
```
A side effect of including the index would be that you could avoid having to display the entire sequence for low numbers of differences in long sequences - although I'm not suggesting that be part of this change.
Is this worth doing and is the above a sensible solution?
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 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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MDExOlB1bGxSZXF1ZXN0MzY1NjE0NTQw
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Fixturize JSON tests
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| 13
| 2020-01-22T01:13:14Z
| 2020-03-19T09:33:04Z
| 2020-03-19T09:32:50Z
|
MEMBER
| null |
Ultimately looking to split this module up into something like:
- test_serializer.py
- test_deserializer.py
- test_roundtrip.py
- test_orient.py
TBD on those being the actual modules, but for now starting with some parametrization
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CLN: remove unused fixtures
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REF: combine IndexEngine test files
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| 1
| 2020-01-22T02:40:46Z
| 2020-01-24T04:42:30Z
| 2020-01-24T03:27:21Z
|
MEMBER
| null |
indexing tests are pretty scattered. One step at a time...
|
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MDExOlB1bGxSZXF1ZXN0MzY1NjM1NjM4
| 31,194
|
Make DTI.get_loc/get_value require tzawareness-compat for datetimes
|
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| 2021-11-08T16:37:39Z
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| null |
- [x] closes #30994
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzY1NjUxODU1
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WIP/TST/ENH: require freq match in assert_index_equal
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MEMBER
| null |
582 tests are failing locally, so there's some work yet to be done.
@mroeschke 3 of those 582 are in window tests. Can you take a look and suggest where an edit should go?
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TST: More regression tests
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MEMBER
| null |
- [x] closes #30986
- [x] closes #19020
- [x] closes #19974
- [x] closes #18265
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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dataframe memory usage increased after .loc[] or df[a:b]
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NONE
| null |
#### Code Sample
```python
import numpy
import pandas
def test1(freq):
"""
after using df.loc[], no matter how big/small the df is, the memory usage is
increased, almost doubled
"""
index = pandas.date_range('2019-01-01', '2019-01-02', freq=freq,
tz='UTC')
df = pandas.DataFrame(numpy.random.randn(len(index), 10), index=index,
columns=['ax', 'ay', 'az', 'qw', 'qx', 'qy', 'qz',
'wx', 'wy', 'wz'], dtype=numpy.float32)
idx = pandas.date_range(start='2019-01-01 12:00:00',
end='2019-01-01 13:00:00',
freq=freq, tz='UTC')
df.info()
df.loc[idx] = 0
df.info()
def test2(freq):
"""
after using df[], rough observation:
- df is less than around 50mb, the memory usage is increased
- df is greater than 50mb, the memory usage is NOT increased
"""
index = pandas.date_range('2019-01-01', '2019-01-02', freq=freq,
tz='UTC')
df = pandas.DataFrame(numpy.random.randn(len(index), 10), index=index,
columns=['ax', 'ay', 'az', 'qw', 'qx', 'qy', 'qz',
'wx', 'wy', 'wz'], dtype=numpy.float32)
df.info()
df[df.index.min(): df.index.max()]
df.info()
if __name__ == '__main__':
freqs = ['40ms', '80ms', '90ms', '150ms']
for freq in freqs:
print(f'\n=====freq is {freq} in loc test====\n')
test1(freq)
for freq in freqs:
print(f'\n=====freq is {freq} in slice test====\n')
test2(freq)
```
#### Problem description
The memory usage of a dataframe is increased somehow after .loc or df[a:b]
- after using df.loc[], no matter how big/small the df is, the memory usage is
increased, almost doubled
- after using df[], rough observation:
- df is less than around 50mb, the memory usage is increased
- df is greater than 50mb, the memory usage is NOT increased
as from the log of the above code:
```
=====freq is 40ms in loc test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2160001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 40L
Data columns (total 10 columns):
ax float32
ay float32
az float32
qw float32
qx float32
qy float32
qz float32
wx float32
wy float32
wz float32
dtypes: float32(10)
memory usage: 98.9 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2160001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 40L
Data columns (total 10 columns):
ax float32
ay float32
az float32
qw float32
qx float32
qy float32
qz float32
wx float32
wy float32
wz float32
dtypes: float32(10)
memory usage: 178.9 MB <------------ increased!
=====freq is 80ms in loc test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1080001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 80L
Data columns (total 10 columns):
ax 1080001 non-null float32
ay 1080001 non-null float32
az 1080001 non-null float32
qw 1080001 non-null float32
qx 1080001 non-null float32
qy 1080001 non-null float32
qz 1080001 non-null float32
wx 1080001 non-null float32
wy 1080001 non-null float32
wz 1080001 non-null float32
dtypes: float32(10)
memory usage: 49.4 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1080001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 80L
Data columns (total 10 columns):
ax 1080001 non-null float32
ay 1080001 non-null float32
az 1080001 non-null float32
qw 1080001 non-null float32
qx 1080001 non-null float32
qy 1080001 non-null float32
qz 1080001 non-null float32
wx 1080001 non-null float32
wy 1080001 non-null float32
wz 1080001 non-null float32
dtypes: float32(10)
memory usage: 89.4 MB <------------ increased!
=====freq is 90ms in loc test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 960001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 90L
Data columns (total 10 columns):
ax 960001 non-null float32
ay 960001 non-null float32
az 960001 non-null float32
qw 960001 non-null float32
qx 960001 non-null float32
qy 960001 non-null float32
qz 960001 non-null float32
wx 960001 non-null float32
wy 960001 non-null float32
wz 960001 non-null float32
dtypes: float32(10)
memory usage: 43.9 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 960001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 90L
Data columns (total 10 columns):
ax 960001 non-null float32
ay 960001 non-null float32
az 960001 non-null float32
qw 960001 non-null float32
qx 960001 non-null float32
qy 960001 non-null float32
qz 960001 non-null float32
wx 960001 non-null float32
wy 960001 non-null float32
wz 960001 non-null float32
dtypes: float32(10)
memory usage: 83.9 MB . <------------ increased!
=====freq is 150ms in loc test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 576001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 150L
Data columns (total 10 columns):
ax 576001 non-null float32
ay 576001 non-null float32
az 576001 non-null float32
qw 576001 non-null float32
qx 576001 non-null float32
qy 576001 non-null float32
qz 576001 non-null float32
wx 576001 non-null float32
wy 576001 non-null float32
wz 576001 non-null float32
dtypes: float32(10)
memory usage: 26.4 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 576001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 150L
Data columns (total 10 columns):
ax 576001 non-null float32
ay 576001 non-null float32
az 576001 non-null float32
qw 576001 non-null float32
qx 576001 non-null float32
qy 576001 non-null float32
qz 576001 non-null float32
wx 576001 non-null float32
wy 576001 non-null float32
wz 576001 non-null float32
dtypes: float32(10)
memory usage: 46.4 MB <------------ increased!
=====freq is 40ms in slice test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2160001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 40L
Data columns (total 10 columns):
ax float32
ay float32
az float32
qw float32
qx float32
qy float32
qz float32
wx float32
wy float32
wz float32
dtypes: float32(10)
memory usage: 98.9 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2160001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 40L
Data columns (total 10 columns):
ax float32
ay float32
az float32
qw float32
qx float32
qy float32
qz float32
wx float32
wy float32
wz float32
dtypes: float32(10)
memory usage: 98.9 MB <------------ unchanged!
=====freq is 80ms in slice test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1080001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 80L
Data columns (total 10 columns):
ax 1080001 non-null float32
ay 1080001 non-null float32
az 1080001 non-null float32
qw 1080001 non-null float32
qx 1080001 non-null float32
qy 1080001 non-null float32
qz 1080001 non-null float32
wx 1080001 non-null float32
wy 1080001 non-null float32
wz 1080001 non-null float32
dtypes: float32(10)
memory usage: 49.4 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1080001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 80L
Data columns (total 10 columns):
ax 1080001 non-null float32
ay 1080001 non-null float32
az 1080001 non-null float32
qw 1080001 non-null float32
qx 1080001 non-null float32
qy 1080001 non-null float32
qz 1080001 non-null float32
wx 1080001 non-null float32
wy 1080001 non-null float32
wz 1080001 non-null float32
dtypes: float32(10)
memory usage: 49.4 MB <------------ unchanged!
=====freq is 90ms in slice test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 960001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 90L
Data columns (total 10 columns):
ax 960001 non-null float32
ay 960001 non-null float32
az 960001 non-null float32
qw 960001 non-null float32
qx 960001 non-null float32
qy 960001 non-null float32
qz 960001 non-null float32
wx 960001 non-null float32
wy 960001 non-null float32
wz 960001 non-null float32
dtypes: float32(10)
memory usage: 43.9 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 960001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 90L
Data columns (total 10 columns):
ax 960001 non-null float32
ay 960001 non-null float32
az 960001 non-null float32
qw 960001 non-null float32
qx 960001 non-null float32
qy 960001 non-null float32
qz 960001 non-null float32
wx 960001 non-null float32
wy 960001 non-null float32
wz 960001 non-null float32
dtypes: float32(10)
memory usage: 83.9 MB <------------ increased!
=====freq is 150ms in slice test====
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 576001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 150L
Data columns (total 10 columns):
ax 576001 non-null float32
ay 576001 non-null float32
az 576001 non-null float32
qw 576001 non-null float32
qx 576001 non-null float32
qy 576001 non-null float32
qz 576001 non-null float32
wx 576001 non-null float32
wy 576001 non-null float32
wz 576001 non-null float32
dtypes: float32(10)
memory usage: 26.4 MB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 576001 entries, 2019-01-01 00:00:00+00:00 to 2019-01-02 00:00:00+00:00
Freq: 150L
Data columns (total 10 columns):
ax 576001 non-null float32
ay 576001 non-null float32
az 576001 non-null float32
qw 576001 non-null float32
qx 576001 non-null float32
qy 576001 non-null float32
qz 576001 non-null float32
wx 576001 non-null float32
wy 576001 non-null float32
wz 576001 non-null float32
dtypes: float32(10)
memory usage: 46.4 MB <------------ increased!
```
#### Expected Output
The memory usage of dataframe shall be no change after .loc or slicing.
#### 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 : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_AU.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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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MDExOlB1bGxSZXF1ZXN0MzY1NzU2NzU2
| 31,198
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DOC Remove Python 2 specific comments from documentation
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| 2020-01-26T09:42:56Z
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CONTRIBUTOR
| null |
A few minor fixes to remove Python 2 specific comments from the documentation, since 1.0 does not support Python 2.
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Writing HDF5 with StringDtype columns fails with AttributeError: 'StringArray' object has no attribute 'size'
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| 2020-05-03T20:34:58Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas
df = pandas.read_csv("s3://noaa-global-hourly-pds/2019/72047299999.csv",
parse_dates=["DATE"],
dtype=dict.fromkeys(["STATION", "NAME", "REPORT_TYPE",
"QUALITY_CONTROL", "WND", "CIG", "VIS", "TMP", "DEW", "SLP", "AA1",
"AA2", "AY1", "AY2", "GF1", "MW1", "REM"], pandas.StringDtype()))
df.to_hdf("/tmp/fubar.h5", "root")
pandas.read_hdf("/tmp/fubar.h5", "root")
```
#### Problem description
The code fails to write the HDF5. It fails with an exception `AttributeError: 'StringArray' object has no attribute 'size'`:
```
Traceback (most recent call last):
File "mwe15.py", line 7, in <module>
df.to_hdf("/tmp/fubar.h5", "root")
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/generic.py", line 2504, in to_hdf
encoding=encoding,
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 282, in to_hdf
f(store)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 274, in <lambda>
encoding=encoding,
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 1042, in put
errors=errors,
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 1709, in _write_to_group
data_columns=data_columns,
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 3100, in write
self.write_array(f"block{i}_values", blk.values, items=blk_items)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 2906, in write_array
empty_array = value.size == 0
AttributeError: 'StringArray' object has no attribute 'size'
```
It does write a file `/tmp/fubar.h5`, but pandas cannot read it. Commenting out the writing bit and leaving only the reading bit results in:
```
Traceback (most recent call last):
File "mwe15.py", line 8, in <module>
pandas.read_hdf("/tmp/fubar.h5", "root")
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 428, in read_hdf
auto_close=auto_close,
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 814, in select
return it.get_result()
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 1829, in get_result
results = self.func(self.start, self.stop, where)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 798, in func
return s.read(start=_start, stop=_stop, where=_where, columns=columns)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 3068, in read
blk_items = self.read_index(f"block{i}_items")
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/io/pytables.py", line 2752, in read_index
variety = _ensure_decoded(getattr(self.attrs, f"{key}_variety"))
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/tables/attributeset.py", line 301, in __getattr__
"'%s'" % (name, self._v__nodepath))
AttributeError: Attribute 'block0_items_variety' does not exist in node: '/root'
Closing remaining open files:/tmp/fubar.h5...done
```
#### Expected Output
I expect a well-formed HDF5 file to be written, and the `pandas.read_hdf` command to return a `DataFrame` identical to the one written to disk just before.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.12.14-lp150.12.82-default
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.0rc0
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191101
Cython : 0.29.14
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.0
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : None
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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raise more explanatory error when failing initialising pd.Timestamp
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| 2
| 2020-01-22T10:54:37Z
| 2020-02-26T02:19:00Z
| 2020-02-26T02:19:00Z
|
NONE
| null |
It's not really an issue, just a suggestion. I noticed this behavior when initialising a timestamp:
```python
# this doesn't fail
pd.Timestamp('2020')
# this does
pd.Timestamp(year=2020)
```
I guess that the latter fails because of args/kwargs specification issues (when an integer is passed it is read as a posix timestamp in the first place), and I'm OK with it. However, the error it raises is:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-e2faf77534f3> in <module>
----> 1 pd.Timestamp(year=2020)
pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()
TypeError: an integer is required (got type NoneType)
```
This is a bit misleading for beginners - it doesn't say anything about **why** it failed, but just about **what** failed.
Is there a chance it can be changed to something more explicit in the long term? Something like "provide at least year, month, day"?
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MDU6SXNzdWU1NTM0NjQ5MTM=
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|
COMPAT: numpy object array coercion warnings
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| 2020-01-22T11:00:28Z
| 2020-01-22T16:20:33Z
| 2020-01-22T14:46:59Z
|
MEMBER
| null |
#### Problem description
Since at least a couple hours ago, the Numpy Dev pipeline fails 39 tests:
```
##[error]39 test(s) failed, 60964 test(s) collected.
```
I checked and all 39 are like this:
```
numpy.VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
```
Full output of one of the tests below.
<details>
```
True
>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True
"""
> return array(a, dtype, copy=False, order=order)
E numpy.VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
```
</details>
Thanks to @jreback for pointing out we've run into this before, but Numpy reverted the deprecation warning. The original issue was #30043
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IntervalIndex.map raises "TypeError: Unexpected keyword arguments {'closed'}" in pandas 1.0.0rc0
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| 1
| 2020-01-22T12:25:51Z
| 2020-01-23T14:16:00Z
| 2020-01-23T14:16:00Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
pd.interval_range(1,3,2).map(str)
```
> ```
> TypeError Traceback (most recent call last)
> <ipython-input-4-91424d1c664f> in <module>
> ----> 1 pd.interval_range(1,3,2).map(str)
>
> C:\Miniconda3\envs\pandas_new\lib\site-packages\pandas\core\indexes\base.py in map(self, mapper, na_action)
> 4546 attributes["dtype"] = self.dtype
> 4547
> -> 4548 return Index(new_values, **attributes)
> 4549
> 4550 def isin(self, values, level=None):
>
> C:\Miniconda3\envs\pandas_new\lib\site-packages\pandas\core\indexes\base.py in __new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs)
> 389 if new_dtype is not None:
> 390 return cls(
> --> 391 new_data, dtype=new_dtype, copy=False, name=name, **kwargs
> 392 )
> 393
>
> C:\Miniconda3\envs\pandas_new\lib\site-packages\pandas\core\indexes\base.py in __new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs)
> 393
> 394 if kwargs:
> --> 395 raise TypeError(f"Unexpected keyword arguments {repr(set(kwargs))}")
> 396 if subarr.ndim > 1:
> 397 # GH#13601, GH#20285, GH#27125
>
> TypeError: Unexpected keyword arguments {'closed'}
> ```
#### Problem description
When using map on an `IntervalIndex` raises a `TypeError` since testing on pandas 1.0.0rc0. In 0.25.1 it worked as expected.
As a workaround `pd.interval_range(1,3,2).astype(object).map(str)` works, but the casting to `object` should not be necessary.
#### Expected Output
> ```
> Index(['(1, 2]', '(2, 3]'], dtype='object')
> ```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 8.1
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0rc0
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.1.0.post20200119
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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numpydev ragged array dtype warning
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| 10
| 2020-01-22T12:41:36Z
| 2020-01-22T17:48:55Z
| 2020-01-22T14:46:59Z
|
CONTRIBUTOR
| null |
Closes #31201
There are still two failures in the Categorical constructor I'm looking into. Not sure what's best yet.
```python
In [2]: pd.Categorical(['a', ('a', 'b')])
/Users/taugspurger/sandbox/pandas/pandas/core/dtypes/cast.py:1066: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
v = np.array(v, copy=False)
Out[2]:
[a, (a, b)]
Categories (2, object): [(a, b), a]
```
I think that perhaps the user should see this warning, but have an option to pass through a `dtype` to silence it... Not sure yet.
Tagged for backport to keep the CI passing on that branch.
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convert numeric column to dedicated `pd.StringDtype()`
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| 11
| 2020-01-22T13:01:30Z
| 2020-05-26T22:20:20Z
| 2020-05-26T22:20:20Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
pd.Series(range(5, 10), dtype="Int64").astype("string")
```
raises `TypeError: data type not understood`
while
```python
pd.Series(range(5, 10)).astype("string")
```
raises `ValueError: StringArray requires a sequence of strings or missing values.`
If you first do `astype(str)`:
```python
pd.Series(range(5, 10)).astype(str).astype("string")
```
and
```python
pd.Series(range(5, 10), dtype="Int64").astype(str).astype("string")
```
work as expected:
```
0 5
1 6
2 7
3 8
4 9
dtype: string
```
While `astype(object)` raises in both cases `ValueError: StringArray requires a sequence of strings or missing values.`
#### Problem description
I can understand the `ValueError`, since you don't feed strings to the `StringArray`. Best for me would be if the `astype("string")` converts it to strings, or if the `astype(str)` would return a `StringArray`, but in any case, I would expect both `pd.Series(range(5, 10), dtype="Int64").astype("string")` and `pd.Series(range(5, 10)).astype("string")` to raise the same error.
#### Expected Output
```
0 5
1 6
2 7
3 8
4 9
dtype: string
```
or
`ValueError: StringArray requires a sequence of strings or missing values.`
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
|
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MDU6SXNzdWU1NTM1NDM0NDM=
| 31,205
|
BUG: plotting regular tz-aware timeseries gives UserWarning
|
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| 1
| 2020-01-22T13:31:48Z
| 2020-02-03T07:36:36Z
| 2020-02-03T07:36:36Z
|
MEMBER
| null |
With this example of plotting a tz-aware, regular timeseries:
```
df = pd.DataFrame(np.random.randn(100, 3), index=pd.date_range("2012", freq='H', periods=100, tz='UTC'), columns=['a', 'b', 'c'])
df.plot()
```
gives a warning that the user shouldn't see I think (there is nothing to do about it):
```
/home/joris/scipy/pandas/pandas/core/arrays/datetimes.py:1099: UserWarning: Converting
to PeriodArray/Index representation will drop timezone information.
UserWarning,
```
When setting a filterwarning to error, you can see the warning is coming from a `to_period()` call in the matplotlib plotting backend included in pandas:
```
In [5]: warnings.simplefilter("error", UserWarning)
In [6]: df.plot()
---------------------------------------------------------------------------
UserWarning Traceback (most recent call last)
<ipython-input-6-848b80e64df8> in <module>
----> 1 df.plot()
~/scipy/pandas/pandas/plotting/_core.py in __call__(self, *args, **kwargs)
846 data.columns = label_name
847
--> 848 return plot_backend.plot(data, kind=kind, **kwargs)
849
850 def line(self, x=None, y=None, **kwargs):
~/scipy/pandas/pandas/plotting/_matplotlib/__init__.py in plot(data, kind, **kwargs)
59 kwargs["ax"] = getattr(ax, "left_ax", ax)
60 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 61 plot_obj.generate()
62 plot_obj.draw()
63 return plot_obj.result
~/scipy/pandas/pandas/plotting/_matplotlib/core.py in generate(self)
261 self._compute_plot_data()
262 self._setup_subplots()
--> 263 self._make_plot()
264 self._add_table()
265 self._make_legend()
~/scipy/pandas/pandas/plotting/_matplotlib/core.py in _make_plot(self)
1050 from pandas.plotting._matplotlib.timeseries import _maybe_convert_index
1051
-> 1052 data = _maybe_convert_index(self._get_ax(0), self.data)
1053
1054 x = data.index # dummy, not used
~/scipy/pandas/pandas/plotting/_matplotlib/timeseries.py in _maybe_convert_index(ax, data)
252
253 if isinstance(data.index, ABCDatetimeIndex):
--> 254 data = data.to_period(freq=freq)
255 elif isinstance(data.index, ABCPeriodIndex):
256 data.index = data.index.asfreq(freq=freq)
~/scipy/pandas/pandas/core/frame.py in to_period(self, freq, axis, copy)
8379 axis = self._get_axis_number(axis)
8380 if axis == 0:
-> 8381 new_data.set_axis(1, self.index.to_period(freq=freq))
8382 elif axis == 1:
8383 new_data.set_axis(0, self.columns.to_period(freq=freq))
~/scipy/pandas/pandas/core/accessor.py in f(self, *args, **kwargs)
97 def _create_delegator_method(name):
98 def f(self, *args, **kwargs):
---> 99 return self._delegate_method(name, *args, **kwargs)
100
101 f.__name__ = name
~/scipy/pandas/pandas/core/indexes/datetimelike.py in _delegate_method(self, name, *args, **kwargs)
986
987 def _delegate_method(self, name, *args, **kwargs):
--> 988 result = operator.methodcaller(name, *args, **kwargs)(self._data)
989 if name not in self._raw_methods:
990 result = Index(result, name=self.name)
~/scipy/pandas/pandas/core/arrays/datetimes.py in to_period(self, freq)
1097 "Converting to PeriodArray/Index representation "
1098 "will drop timezone information.",
-> 1099 UserWarning,
1100 )
1101
UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
```
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MDExOlB1bGxSZXF1ZXN0MzY1ODc4MDU0
| 31,206
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Backport PR #31017 on branch 1.0.x (REG: restore format_type attr)
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| 1
| 2020-01-22T14:17:27Z
| 2020-01-23T09:41:34Z
| 2020-01-22T21:08:28Z
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NONE
| null |
Backport PR #31017: REG: restore format_type attr
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BUG: no longer raise user warning when plotting tz aware time series
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| 2020-02-05T18:54:49Z
| 2020-02-03T07:36:36Z
|
MEMBER
| null |
- [x] closes #31205
- [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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MDU6SXNzdWU1NTM1OTQ5NjY=
| 31,208
|
dtypes convert to object on merge on 1.0.0rc0
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| 1
| 2020-01-22T14:56:15Z
| 2020-01-23T04:26:32Z
| 2020-01-23T04:26:32Z
|
NONE
| null |
#### dtypes convert to object on merge
Currently on 1.0.0rc0, when doing a left merge with datetime64[ns] on the right dataframe, if any rows from the left dataframe don't have a match on the right dataframe, then the result dataframe converts datetime to be object. If all items match, then it will remain as a datetime column. This previously maintained dtype in 0.25.3 and 0.24.2.
It seems to no longer maintain the dtype and populate values with NaT.
With 1.0.0rc0, after this I am able to convert to datetime column and it'll properly recognize as a NaT value.
#### Example with extra value in left dataframe
```python
df1 = pd.DataFrame({'x': {0: 'a', 1: 'b', 2:'c'}, 'y': {0: '1', 1: '2', 2:'4'}})
df2 = pd.DataFrame({'y': {0: '1', 1: '2', 2:'3'}, 'z': {0: '2018-05-01', 1: '2018-05-02', 2:'2018-05-03'}})
df2['z'] = df2['z'].astype('datetime64[ns]')
result = pd.merge(df1, df2, how='left', on='y')
```
#### Output
```python
# 0.24.2
result.dtypes
x object
y object
z datetime64[ns]
dtype: object
# 0.25.3
result.dtypes
x object
y object
z datetime64[ns]
dtype: object
# 1.0.0rc0
result.dtypes
x object
y object
z object
dtype: object
```
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MDExOlB1bGxSZXF1ZXN0MzY1OTA2Njc5
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Backport PR #31203 on branch 1.0.x (numpydev ragged array dtype warning)
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NONE
| null |
Backport PR #31203: numpydev ragged array dtype warning
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COMPAT: Argsort position matches NumPy
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| 9
| 2020-01-22T15:14:38Z
| 2020-01-28T06:43:30Z
| 2020-01-27T14:05:16Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/29884
Bit of a WIP, CI is probably going to fail here. One API question, do we want NaT to sort at the end, regardless of the NumPy version? Or should we match the behavior of the installed NumPy (NaT at the start for <1.18, at the end for >=1.18)?
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| 553,619,879
|
MDExOlB1bGxSZXF1ZXN0MzY1OTE5NTM0
| 31,211
|
BUG: Fixed upcast dtype for datetime64 in merge
|
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| 2
| 2020-01-22T15:34:12Z
| 2020-01-23T20:57:16Z
| 2020-01-23T04:26:33Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/31208
|
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| 553,628,061
|
MDU6SXNzdWU1NTM2MjgwNjE=
| 31,212
|
CI: Web and docs (pull_request) fails: can't rich intershipinx inventory
|
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| 2020-01-22T15:47:23Z
| 2020-01-22T20:49:54Z
| 2020-01-22T20:49:54Z
|
MEMBER
| null |
#### Problem description
Seems like currently CI / Web and docs (pull_request) fails for everyone with this error message:
```
WARNING: failed to reach any of the inventories with the following issues:
intersphinx inventory 'http://www.statsmodels.org/devel/objects.inv' not readable due to ValueError: unknown or unsupported inventory version: ValueError('invalid inventory header: ')
```
All my PRs had the `CI / Web and docs (pull_request)` check break without any changes to docs, and I checked PRs by other people: other people seem to have this problem.
#### Expected behaviour
Passing the test
|
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| 553,634,494
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MDExOlB1bGxSZXF1ZXN0MzY1OTMxNzA3
| 31,213
|
DOC: pin statsmodels inventory
|
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| 2
| 2020-01-22T15:57:30Z
| 2020-01-22T18:18:27Z
| 2020-01-22T18:18:22Z
|
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/31212
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MDU6SXNzdWU1NTM2NDQ1MTM=
| 31,214
|
min(axis = 1) propagates NAN when encountering missing timestamps
|
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[] | null | 1
| 2020-01-22T16:13:44Z
| 2020-01-22T16:27:44Z
| 2020-01-22T16:27:43Z
|
NONE
| null |
Hello there,
Consider this:
```
dftest = pd.DataFrame({'time1' : ['2019-01-01- 10:10:10.123456Z',
'2019-01-01- 10:10:10.123456Z'],
'time2' : ['ha',
'pandas!!!'],
'time3' :['here we go',
'again']})
dftest
Out[179]:
time1 time2 time3
0 2019-01-01- 10:10:10.123456Z ha here we go
1 2019-01-01- 10:10:10.123456Z pandas!!! again
```
I am trying to get the minimum timestamp between `time1`, `time2` and `time3`. Of course, in this example `time2` and `time3` are not timestamps so (after proper conversion to NaTs) I expect the minimum to be `time1` all the time.
Surprisingly, this fails entirely.
```
dftest['time1'] = pd.to_datetime(dftest.time1, errors = 'coerce')
dftest['time2'] = pd.to_datetime(dftest.time2, errors = 'coerce')
dftest['time3'] = pd.to_datetime(dftest.time3, errors = 'coerce')
dftest
Out[181]:
time1 time2 time3
0 2019-01-01 10:10:10.123456+00:00 NaT NaT
1 2019-01-01 10:10:10.123456+00:00 NaT NaT
```
And now:
```
dftest.loc[:, ['time1', 'time2', 'time3']].min(axis = 1)
Out[182]:
0 nan
1 nan
dtype: float64
```
This looks like a bug. It seems that the errors is coming from the timezone component. What do you think?
Thanks!
|
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