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} | 2 | "2021-05-22T19:19:05Z" | "2021-05-24T18:30:47Z" | "2021-05-24T18:18:31Z" | MEMBER | null | This turns out to be a blocker for sharing arrays.integers.safe_cast with maybe_cast_to_integer_array, which in turn is a blocker for #34460 | {
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**Initial data stored in following format as dtype: object:**
17:00:00
**After using following code, data is returned with year added and desired dtype: datetime**
```
df['$Time'] = pd.to_datetime(df['$Time'], exact=True, format='%H:%M:%S', errors='coerce')
0 1900-01-01 17:00:00
1 1900-01-01 17:00:01
2 1900-01-01 17:00:02
3 1900-01-01 17:00:03
4 1900-01-01 17:00:04
Name: $Time, dtype: datetime64[ns]
```
**If Im using now dt.time to remove date and keep only hours, I'm getting dtype: object back... Is this a bug? What should be correct logic to keep %H:%M:%S and dtype: datetime?**
```
df['$Time'] = df['$Time'].dt.time
0 17:00:00
1 17:00:01
2 17:00:02
3 17:00:03
4 17:00:04
Name: $Time, dtype: object
```
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
I've a simple "ods" file (Test01.ods) with the below data in "sheet1" :-
[![enter image description here][2]][2]
also I saved it as "xlsx" (Test01.xlsx) so I've two files contains exactly the same data.
Now when I try to read them using Pandas "read_excel" with the below code
[![enter image description here][3]][3]
the "xslx" file is showing the line break char "\n" while the "ods" file does not.
[![enter image description here][4]][4]
Any idea why? and how to force "odf engine" to output the "\n" to the dataframe?
Thanks in advance
[1]: https://i.stack.imgur.com/K2PYO.png
[2]: https://i.stack.imgur.com/1lLUk.png
[3]: https://i.stack.imgur.com/QCRT5.png
[4]: https://i.stack.imgur.com/NUV5x.png
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] | open | false | null | [] | null | 5 | "2021-05-23T02:58:55Z" | "2021-08-21T01:33:54Z" | null | MEMBER | null | `Index._concat` (used by `Index.append`) is thin wrapper around `concat_compat`. It is overriden by CategoricalIndex so that CategoricalDtype is retained more often than it is in `concat_compat`. We should make these match.
If we just rip `CategoricalIndex._concat`, we break 6 tests, all of which boil down to:
```
def test_append_category_objects(self, ci):
# with objects
result = ci.append(Index(["c", "a"]))
expected = CategoricalIndex(list("aabbcaca"), categories=ci.categories)
> tm.assert_index_equal(result, expected, exact=True)
```
If we go the other way and change `concat_compat`, we break 6 different tests, all of which involve all-empty arrays or arrays that can be losslessly cast to the Categorical's dtype, e.g (edited for legibility)
```
def test_concat_empty_series_dtype_category_with_array(self):
# GH#18515
left = Series(np.array([]), dtype="category")
right = Series(dtype="float64")
result = concat([left, right])
> assert result.dtype == "float64"
def test_concat_categorical_coercion(self):
# GH 13524
# category + not-category => not-category
s1 = Series([1, 2, np.nan], dtype="category")
s2 = Series([2, 1, 2])
exp = Series([1, 2, np.nan, 2, 1, 2], dtype="object")
> tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
E AssertionError: Attributes of Series are different
E
E Attribute "dtype" are different
E [left]: CategoricalDtype(categories=[1, 2], ordered=False)
E [right]: object
```
Changing concat_compat results in much more convenient behavior, but it is textbook "values-dependent behavior" that in general we want to avoid (cc @jorisvandenbossche) | {
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] | open | false | null | [] | null | 2 | "2021-05-23T12:10:59Z" | "2021-05-27T08:05:12Z" | null | NONE | null | #### Documentation problem
[pandas.merge_asof()](https://pandas.pydata.org/docs/reference/api/pandas.merge_asof.html) states regarding `left_on`parameter:
_left_on : label
Field name to join on in left DataFrame._
#### Suggested fix for documentation
I tried to see if like `merge_ordered` a vector the size of the left DataFrame would not be accepted, and it is. I would suggest to complete the parameter description similarly to what is stated in [`merge_ordered()`](https://pandas.pydata.org/pandas-docs/version/1.2.4/reference/api/pandas.merge_ordered.html)
_left_on : label or list, or array-like
Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns._
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] | null | 6 | "2021-05-23T12:53:17Z" | "2021-05-26T03:45:10Z" | "2021-05-26T03:45:04Z" | NONE | null | Hi All,
I was wondering if you have any guidelines as to when it is possible to just go for df.column instead of df['column'], as I prefer the former for code styling, but I recall it gave me some issues before. I'm not sure I understand the specific difference between one and the other, is there any?
For now, what I do is only use ['column'] when declaring a new column and then always go for .column.
Thank you,
Iván. | {
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- I have confirmed this bug exists on the latest version of pandas.
#### Code Sample, a copy-pastable example
```python
import multiprocessing
from functools import partial
import time
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
tqdm.pandas(desc="Progress bar")
def some_heavy_row_function(row, mult):
a = row.sum()
b = a*mult
time.sleep(1)
return (a,b)
def fake_func_1(df, **kwargs):
# Apply some heavy row wise function
x_cols = ['A','B']
x = df.progress_apply(some_heavy_row_function, mult=kwargs['mult'], axis=1)
df[x_cols] = pd.DataFrame(data=x.tolist(), columns=x_cols, index=df.index) # v1
# add some colums
for c,v in kwargs.items():
df[c] = v
return df
def fake_func_2(df, **kwargs):
# Apply some heavy row wise function
x_cols = ['A','B']
x = df.progress_apply(some_heavy_row_function, mult=kwargs['mult'], axis=1)
df[x_cols] = pd.DataFrame.from_records(data=x, columns=x_cols, index=df.index). # v2
# add some colums
for c,v in kwargs.items():
df[c] = v
return df
def create_fake_df(n_rows):
return pd.DataFrame(np.random.rand(n_rows, 3), columns=['foo','bar','baz'])
def parallelize_dataframe(n_cores, df, func, **kwargs):
with multiprocessing.Pool(processes=n_cores) as pool:
df_splited = np.array_split(df, n_cores)
df_processed = pool.map(partial(func, **kwargs), df_splited)
df = pd.concat(df_processed)
return df
if __name__ == '__main__':
# Create an example data frame
df = create_fake_df(n_rows = 20)
print(df.head())
# "Fixed" function with three cores
n_cores = 3
df_1 = parallelize_dataframe(n_cores, df, fake_func_1, mult=10, x=1)
print(df_1.head())
# bug function with one core works
n_cores = 1
df_2 = parallelize_dataframe(n_cores, df, fake_func_2, mult=10, x=1)
print(df_2.head())
# bug function with >1 core crashes
n_cores = 3
df_3 = parallelize_dataframe(n_cores, df, fake_func_2, mult=10, x=1)
print(df_3.head())
```
#### Problem description
I don't know why it works when I do the small workaround in function 1 (which should be roughly what from record is doing) or why from_records works with 1 core but not with multiple cores. Maybe this is obvious to some. To me it looks like a bug. Thats why I am reporting it. Also this may help others trying similar things. This error occurs under python 3.6 and 3.9; and I tested pandas versions 1.0.2 and 1.2.4 respectively.
#### Output
| | foo | bar | baz |
|---|------------|------------|-------------|
| 0 | 0.730996 | 0.206051 | 0.038810 |
| 1 | 0.369668 | 0.024069 | 0.383633 |
| 2 | 0.769936 | 0.758607 | 0.568493 |
| 3 | 0.304604 | 0.213722 | 0.612195 |
| 4 | 0.674473 | 0.646581 | 0.322751 |
Progress bar: 100%|████████████████████████████████| 6/6 [00:06<00:00, 1.00s/it]
Progress bar: 100%|████████████████████████████████| 7/7 [00:07<00:00, 1.00s/it]
Progress bar: 100%|████████████████████████████████| 7/7 [00:07<00:00, 1.00s/it]
| | foo | bar | baz | A | B | mult | x |
|---|------------|------------|-------------|------------|-------------|--------|----|
| 0 | 0.730996 | 0.206051 | 0.038810 | 0.975858 | 9.758577 | 10 | 1 |
| 1 | 0.369668 | 0.024069 | 0.383633 | 0.777371 | 7.773708 | 10 | 1 |
| 2 | 0.769936 | 0.758607 | 0.568493 | 2.097036 | 20.970361 | 10 | 1 |
| 3 | 0.304604 | 0.213722 | 0.612195 | 1.130520 | 11.305202 | 10 | 1 |
| 4 | 0.674473 | 0.646581 | 0.322751 | 1.643804 | 16.438041 | 10 | 1 |
Progress bar: 100%|████████████████████████████████| 20/20 [00:20<00:00, 1.00s/it]
| | foo | bar | baz | A | B | mult | x |
|---|------------|------------|-------------|------------|-------------|--------|----|
| 0 | 0.730996 | 0.206051 | 0.038810 | 0.975858 | 9.758577 | 10 | 1 |
| 1 | 0.369668 | 0.024069 | 0.383633 | 0.777371 | 7.773708 | 10 | 1 |
| 2 | 0.769936 | 0.758607 | 0.568493 | 2.097036 | 20.970361 | 10 | 1 |
| 3 | 0.304604 | 0.213722 | 0.612195 | 1.130520 | 11.305202 | 10 | 1 |
| 4 | 0.674473 | 0.646581 | 0.322751 | 1.643804 | 16.438041 | 10 | 1 |
Progress bar: 100%|████████████████████████████████| 6/6 [00:06<00:00, 1.00s/it]
Progress bar: 100%|████████████████████████████████| 7/7 [00:07<00:00, 1.00s/it]
Progress bar: 100%|████████████████████████████████| 7/7 [00:07<00:00, 1.00s/it]
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\...\conda\conda\envs\py3_9\lib\site-packages\pandas\core\indexes\range.py", line 351, in get_loc
return self._range.index(new_key)
ValueError: 0 is not in range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\...\conda\conda\envs\py3_9\lib\multiprocessing\pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "C:\...\conda\conda\envs\py3_9\lib\multiprocessing\pool.py", line 48, in mapstar
return list(map(*args))
File "paralell2.py", line 34, in fake_func_2
df[x_cols] = pd.DataFrame.from_records(data=x, columns=x_cols, index=df.index)
File "C:\...\conda\conda\envs\py3_9\lib\site-packages\pandas\core\frame.py", line 1855, in from_records
arrays, arr_columns = to_arrays(data, columns, coerce_float=coerce_float)
File "C:\...\conda\conda\envs\py3_9\lib\site-packages\pandas\core\internals\construction.py", line 527, in to_arrays
if isinstance(data[0], (list, tuple)):
File "C:\...\conda\conda\envs\py3_9\lib\site-packages\pandas\core\series.py", line 853, in __getitem__
return self._get_value(key)
File "C:\...\conda\conda\envs\py3_9\lib\site-packages\pandas\core\series.py", line 961, in _get_value
loc = self.index.get_loc(label)
File "C:\...\conda\conda\envs\py3_9\lib\site-packages\pandas\core\indexes\range.py", line 353, in get_loc
raise KeyError(key) from err
KeyError: 0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 0 | "2021-05-23T15:37:43Z" | "2021-08-21T01:34:29Z" | null | NONE | null | #### Is your feature request related to a problem?
`left_on` and `right_on` parameters of [`merge_ordered`](https://pandas.pydata.org/docs/reference/api/pandas.merge_ordered.html) accept vectors of the same length than the DataFrame they relate to.
Although not stated in the documentation, `left_on` parameter of [`merge_asof`](https://pandas.pydata.org/docs/reference/api/pandas.merge_asof.html) also accept vector of the same length than the left DataFrame.
But not `right_on`. This would allow not having to recreate a DataFrame with a new column for the keys in case it is not already part of the right DataFrame.
#### Describe the solution you'd like
`right_on` to accept vector as well.
#### API breaking implications
`right_on` will be able to accept vector.
#### Describe alternatives you've considered
As a workaround, I will create a new DataFrame with a new column to embed the keys.
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- [] I have confirmed this bug exists on the latest master of pandas
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
CI seems to be failing in some tests. I did an update to a PR #41594 which ONLY had a doc change and merge to latest master, and now it is failing in CI.
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I just stumbled across this problem, where I had a date field with some '--' values which needed to be coerced to NaT. Problem is that while using that, pd.to_datetime ended up using different date formats across the records, exchanging month with date apparently arbitrarily. I think I solved this by declaring the format explicitly, but I'm worried about any other related issues that I may oversee.
Any thoughts?
Thank you,
Iván. | {
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} | 15 | "2021-05-23T18:54:45Z" | "2021-07-30T22:15:03Z" | "2021-07-30T22:15:03Z" | NONE | null | #### Is your feature request related to a problem?
when sort_values ascending parameter is set to a string (df.sort_values(col_name, ascending='False') it does not error although it's meaningless, this could be quite confusing.
#### Describe the solution you'd like
Raise an error if ascending is equal to values that are not boolean
#### API breaking implications
Error raising
#### Describe alternatives you've considered
Warning is also good, but I think error makes more sense
#### Additional context
for an hour I was wondering why ascending='False'
is not working
```python
isinstance('False', bool) :)
```
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} | 1 | "2021-05-23T19:37:14Z" | "2021-06-04T18:11:35Z" | "2021-06-04T12:51:37Z" | CONTRIBUTOR | null | - [x] closes #40712 also linking #11700
- [x] tests added / passed
Adds an option: `styler.max.elements` with a default value of 2**18 = 262144.
```
>>> pd.options.styler.max.elements = 2**18
>>> df = pd.DataFrame(np.random.rand(10000,100))
>>> %timeit df.style.render()
4.11 s ± 30.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Even though this is a *'large'* dataframe it is reduced to the max elements and still renders in reasonable time (4s plus about 5s for a browser to render the date).
If the number of data elements exceeds the max then the right side, or bottom, or both of the DataFrame will be trimmed (starting with the largest axis and recursively reducing), leaving '...' filler indicators.
All reduction takes place before index or data looping so it avoids any crashes.
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} | 2 | "2021-05-23T21:14:00Z" | "2021-05-24T13:44:07Z" | "2021-05-24T13:44:07Z" | NONE | null | - [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
Y0=array([[0.-20.j , 0. +0.j ],
[0. +0.j , 0.-12.j ]])
Y0df=pd.DataFrame(Y0)
print(Y0df.to_latex(header=False,index=False,decimal=','), file=open('Y0.tex', 'w'))
```
#### Problem description
The contence of Y0.tex is shown below with only the real part having the correct decimal symbol.
\begin{tabular}{rr}
\toprule
0,000000-20.000000j & 0,000000+0.000000j \\
0,000000+0.000000j & 0,000000-12.000000j \\
\bottomrule
\end{tabular}
#### Expected Output
\begin{tabular}{rr}
\toprule
0,000000-20,000000j & 0,000000+0,000000j \\
0,000000+0.000000j & 0,000000-12,000000j \\
\bottomrule
\end{tabular}
#### Desired Output
#instead of using 'decimal' an 'use_numprint=True' would be nicer to allow for custom customisation within LaTeX, which would ideally produce the following output
%uncomment the following line if not already defined
%\newcommand{\np}[2][]{\numprint[#1]{#2}}
\begin{tabular}{rr}
\toprule
\np{0.000000}-\np{20.000000}j & \np{0.000000}+\np{0.000000}j \\
\np{0.000000}+\np{0.000000}j & \np{0.000000}-\np{12.000000}j \\
\bottomrule
\end{tabular}
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_South Africa.1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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- [ ] tests added / passed
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df Value
0 0.45
1 0.95
df.Value.round(1) doesn't give the correct answer. It should be 0.5 and 1.0
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} | 1 | "2021-05-24T14:42:08Z" | "2021-05-24T15:34:49Z" | "2021-05-24T15:34:49Z" | NONE | null | I wish I could use pandas to do unit conversion between miles/kilometres, inches/centimetres, feet/metres, etc, in a single line of code. I was thinking of something that someone could use like this:
~~~python
my_series_in_kilometres = my_series_in_miles.convert_units(from="mi", to="km")
my_series_in_feet = my_series_in_metres.convert_units(from="m", to="ft")
~~~
I think this feature could potentially ease the lives of people who have to do a lot of data munging with `pandas`. I don't know if this already exists (`pandas` is so vast!) but after having searched on Google and having found nothing, I decided to open an issue. I am aware that you can do this with simple arithmetic operations, but perhaps it'd be nice to have a built-in method that saves you the looking up in conversion tables. Thank you!
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} | 6 | "2021-05-24T15:32:39Z" | "2021-06-09T21:57:09Z" | "2021-06-08T14:12:08Z" | MEMBER | null | ```
>>> pd.__version__
'1.3.0.dev0+1695.g55e58542db'
>>>
>>> s = pd.Series([0, 0])
>>> s
0 0
1 0
dtype: int64
>>>
>>> s2 = pd.Series([42])
>>> s2
0 42
dtype: int64
>>>
>>> s.loc[0] = s2
>>> s
0 42.0
1 0.0
dtype: float64
>>>
```
Does not need a release note, this issue is only on master as on 1.2.4 this raises `ValueError: No axis named 1 for object type Series` which was fixed in #39358
draft, since if we merge and backport #40555 first, we can also remove the patch in this PR. | {
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} | 8 | "2021-05-24T15:44:59Z" | "2021-05-26T01:45:13Z" | "2021-05-26T01:45:12Z" | NONE | null | fixes #41629
Hi @LukeTonin, how does this look?
let me know if this needs any changes!
Thanks
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While using the datetime library, when we extract month from a given datetime datatype. There should be a way to sort those values in ascending order.
tried:
1. sorted()
2. sort_values
Neither of them throws a error nor gives the desired output.
Although found a solution on [StackOverflow](https://stackoverflow.com/questions/60624571/sort-list-of-month-name-strings-in-ascending-order) but I assume, this is used widely in time series problems and could come in handy!
Thank you | {
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} | 0 | "2021-05-24T18:59:56Z" | "2021-05-26T01:46:37Z" | "2021-05-26T01:46:37Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
def sum_last(values, index, n):
return values[-n:].sum()
df = pd.DataFrame({'id': [0, 0, 1, 1], 'x': [1, 1, 1, 1]})
grouped_x = df.groupby('id')['x']
grouped_x.transform(sum_last, 1, engine='numba')
# 0 1.0
# 1 1.0
# 2 1.0
# 3 1.0
# Name: x, dtype: float64
```
```python
grouped_x.transform(sum_last, 2, engine='numba')
# 0 1.0
# 1 1.0
# 2 1.0
# 3 1.0
# Name: x, dtype: float64
```
#### Problem description
When using `groupby.transform` with `engine='numba'` calling the same function with different arguments gives the same results as the first time it was called. I believe this is because the function is being cached with its arguments.
https://github.com/pandas-dev/pandas/blob/85f48783dcd1ba04997e5b55fdb68162b1be652b/pandas/core/groupby/groupby.py#L1133-L1135
https://github.com/pandas-dev/pandas/blob/85f48783dcd1ba04997e5b55fdb68162b1be652b/pandas/core/groupby/groupby.py#L1140-L1142
#### Expected Output
```python
grouped_x.transform(sum_last, 2, engine='numba')
# 0 2.0
# 1 2.0
# 2 2.0
# 3 2.0
# Name: x, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-7614-generic
Version : #15~1618626693~20.10~ecb25cd-Ubuntu SMP Thu Apr 22 16:00:45 UTC
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : 4.0.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 2021.04.0
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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A lot of the audit trail for the features to get this to work are documented in the issue.
## For Reviewers
These are the key changes to note in **DataFrame.to_latex tests**
- The expected output remains as close as possible to the original, altering the arg input in `to_latex` to demonstrate the new combinations needed.
- No tests have been removed, and all pass with often minor alterations to the test formatting .
- `hrules=True` is added to all tests to get `\toprule \midrule \bottomrule`. This is a new option and the default in Styler is not to have these.
- `\cline` (for multirow) has gone. This feature is not implemented in Styler.
- `col_space` is gone, meaning there is not a nice console alignment of text. Of course the LaTeX rendering is unaffected by this, and when styling LaTeX commands are added this needs to be removed anyway.
- There are some nice new features added here, but all currently exists from Styler anyway, and that is where testing resides.
## Follow-Ups
- [ ] Make jinja2 a **hard dependency** (#43423) (this PR actually contains the jinja2 PR to help with tests passing)
- [ ] Deprecate all `pandas.options.display.latex` documenting the replacement `styler.latex` options instead.
- [ ] Remove all redundant code for python LatexFormatter.
- [ ] Reimplement cline? Im not a huge fan of this and I think it (maybe?) quite complicated to implement
## Revised Docs
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- [x] acceptance of the `jinja2` requirement for LaTeX output, since `Styler` requires `jinja2`: this is blocker.
- [x] make Styler functional for all DataFrames (i.e. including non-unique columns, indexes) (#41269).
- [x] enhance for `longtable` keyword or other environments (#41866).
- [x] enhance for `caption` to allow short and long captions, and allow top/bottom caption placement. (#41659)
- [x] enhance with ability to style indexes/column headers with styles (e.g bold rows). (#41993)
- [x] enhance with an escape option for LaTeX characters. (#41619)
- [ ] <s>enhance `.hide_columns` with `.show_columns` alternative (optional)</s> not necessary
- [x] enhance `.hide_columns` and `.hide_index` to have dual function to hide the entire array, or specific items (#41266)
- [x] make styler reactive to separate sparse columns and index separately (#41512)
- [x] acceptance of removal of specific kwargs and all formatting keyword args (see below).
- [ ] validation for ``column_format`` to pass tests or just rely on docs/duck typing?
- [x] validation for ``caption`` to pass tests (#43368) or just remove the validation in favour of duck typing?
- [x] ability to format index with a formatter #43101
- [x] separate `siunitx` for encapsulating headers in curly braces only when necessary #43397
- [x] index names removal #43346
- [x] Optionally use multirow / multicol or just blank out cells. #43369 #43382
- [x] fix bug: label doesnt render if a caption is not given (table and longtable) #43399
### Removed Keyword Args
`col_space`: **removed** since `Styler.to_latex()` does not necessarily produce display values of equal length, (this is a console display only arg)
`sparsify`: **replaced** by alternative kwargs `sparse_index` and `sparse_columns`
`multicolumn`, `multicolumn_format`, `multirow`: **replaced** by alternative kwargs `multirow_align` `multicol_align`
`formatters` `float_format` : **replaced** by the superior `df.style.format(...)` args
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 10 | "2021-05-24T22:19:48Z" | "2021-07-06T22:33:05Z" | "2021-07-06T21:32:53Z" | CONTRIBUTOR | null | Whats new:
Update BS script tags in template
Replace jumbotron with utilities
Replace font-weight-bold with fw-bold
Replace ml-auto with ms-auto
Replace float-right with float-end
Replace data-target with data-bs-target
Replace data-toggle with data-bs-toggle
Add CSS rules for <a>
- [x] closes #41457
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 16 | "2021-05-25T05:18:11Z" | "2021-05-29T08:21:48Z" | "2021-05-27T18:18:15Z" | CONTRIBUTOR | null | - [ ] xref #41485
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-05-25T06:47:54Z" | "2021-05-25T15:00:11Z" | "2021-05-25T14:46:23Z" | CONTRIBUTOR | null | follow-on from adding `Styler.to_latex`.
This is basic addition that primarily points to the core method docs (which have detailed descriptions and examples)
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} | 1 | "2021-05-25T07:58:00Z" | "2021-06-04T18:10:35Z" | "2021-06-04T14:40:12Z" | CONTRIBUTOR | null | completes an item on the list for `DataFrame.to_latex` deprecation #41649
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- [ X] I have confirmed this bug exists on the latest version of pandas.
- [] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
iris_data = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
iris = iris_data.set_index(['species', 'petal_width'])
iris1 = iris[:100]
iris2 = iris[100:]
iris2.index = iris2.index.reorder_levels([1,0])
pd.concat([iris1, iris2])
```
#### Problem description
When concatenating on a named MultiIndex, expected behavior is that corresponding named levels are joined even if the order of levels is different. Instead, the levels are only joined in order. This occurs even if `levels=` is passed explicitly
#### Expected Output
Same as `iris`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.101-0504101-generic
Version : #202102261049 SMP Fri Feb 26 11:30:58 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-05-25T10:22:55Z" | "2021-05-25T13:33:18Z" | "2021-05-25T12:43:05Z" | CONTRIBUTOR | null | Add `text_gradient` to Styler user guide after merge.
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} | 1 | "2021-05-25T14:55:41Z" | "2021-05-31T15:12:52Z" | "2021-05-31T14:50:51Z" | MEMBER | null | - [ ] xref #33401
- [x] tests added / passed
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I think this is the last deprecation needed for the constructor/astype consistency. | {
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} | 1 | "2021-05-25T16:06:25Z" | "2021-05-27T10:18:44Z" | "2021-05-27T01:52:24Z" | MEMBER | null | perf neutral refactor, another step towards #41372
after this...
can use pyarrow native functions
for expand case use _wrap_result and _str_map (without a perf impact) | {
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# What is new:
Now defaultdict can be provides as dtype.
The implementation allows moving to supporting Iterable as dtype
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- [ ] tests added / passed
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hopefully numpy will provide an alternative that makes this unnecessary | {
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] | closed | false | null | [] | null | 1 | "2021-05-25T19:43:37Z" | "2021-05-26T16:27:54Z" | "2021-05-26T16:27:54Z" | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
[https://stackoverflow.com/questions/67693185/pandas-dataframe-agg-does-not-work-with-np-std](https://stackoverflow.com/questions/67693185/pandas-dataframe-agg-does-not-work-with-np-std)
---
#### Question about pandas
I have a dataframe (containing sensor data) with the columns like ['ax', 'ay', 'az', 'gx', 'gy', 'gz']. I want to aggregate some columns with some functions. I set this goal through a dict variable like
`features_dict = {'ax': ['mean', 'std'], 'ay': ['max', 'mean', 'std', 'median']}`
in which each column will be aggregated with a different function set.
I implement that by the following codes
```
features_dict = {'ax': ['mean', 'std'], 'ay': ['max', 'mean', 'std', 'median']}
for key in features_dict:
df_agg = df.agg({key: features_dict[key]})
```
The returning result only has results for other functions except for **std**, and when I eliminate other functions except for std, or setting the `features_dict = {'ax': ['std]}`, it raises the error **ValueError("no results")**.
The error logs is like this:
```
Traceback (most recent call last):
File "C:\prediction_framework_django\predictions\predictor.py", line 112, in pre_aggregated_unseen_data
agg_df = dataframe.agg({axis: [np.std]})
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\frame.py", line 7578, in aggregate
result, how = self._aggregate(func, axis, *args, **kwargs)
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\frame.py", line 7609, in _aggregate
return aggregate(self, arg, *args, **kwargs)
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\aggregation.py", line 582, in aggregate
return agg_dict_like(obj, arg, _axis), True
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\aggregation.py", line 768, in agg_dict_like
results = {key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()}
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\aggregation.py", line 768, in <dictcomp>
results = {key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()}
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\series.py", line 3974, in aggregate
result, how = aggregate(self, func, *args, **kwargs)
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\aggregation.py", line 586, in aggregate
return agg_list_like(obj, arg, _axis=_axis), None
File "C:\prediction_framework_django\env\lib\site-packages\pandas\core\aggregation.py", line 672, in agg_list_like
raise ValueError("no results")
ValueError: no results
```
I don't think that comes from the data in the data frame itself as it worked with other similar functions, so can someone give me the hint?
Thank you!
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https://api.github.com/repos/pandas-dev/pandas/issues/41667 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41667/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41667/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41667/events | https://github.com/pandas-dev/pandas/pull/41667 | 901,405,205 | MDExOlB1bGxSZXF1ZXN0NjUyODY2NDMx | 41,667 | ENH: add abiility for json.dumps to parse and add timezones | {
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} | [] | closed | false | null | [] | null | 2 | "2021-05-25T21:42:40Z" | "2021-05-26T15:42:30Z" | "2021-05-26T15:42:27Z" | NONE | null | - [x] closes #12997
- [x] tests modofied
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Ok, so this is a doozy AND it's not done, but it can be merged as is.
What do we do:
We add timezones to json output!
Before:
```
ts = pd.Timestamp('2020-10-5 4:00:05', tz='US/Pacific')
print(pd.io.json.dumps(ts, iso_dates=True))
print(ts.isoformat())
```
would provide
`"2020-10-05T11:00:05.000Z"`
and
`"2020-10-05T04:00:05.000-07:00"`
respectively.
Now they both produce
``"2020-10-05T04:00:05.000-07:00"``
So what do I mean by not complete?
This still doesn't work for arrays that have datetime values. This only works for single datetime values.
So:
`pd.DataFrame({'foo': range(5)}, index=pd.date_range('2020-10-12', freq='H', periods=5, tz='US/Eastern')).to_json(date_format='iso')`
will still produce:
`'{"foo":{"2020-10-12T04:00:00.000Z":0,"2020-10-12T05:00:00.000Z":1,"2020-10-12T06:00:00.000Z":2,"2020-10-12T07:00:00.000Z":3,"2020-10-12T08:00:00.000Z":4}}'`
and not
`'{"foo":{"2020-10-12T00:00:00.000-04:00":0,"2020-10-12T01:00:00.000-04:00":1,"2020-10-12T02:00:00.000-04:00":2,"2020-10-12T03:00:00.000-04:00":3,"2020-10-12T04:00:00.000-04:00":4}}'`
Working on it currently. Open to recommendations! | {
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} | 5 | "2021-05-25T22:11:50Z" | "2021-05-27T18:41:10Z" | "2021-05-27T18:41:06Z" | CONTRIBUTOR | null | - [ ] closes #35603
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This test checks if a Series cast to object still shows data in float format.
Ran `pytest pandas/tests/series/test_constructors.py`
Output:
```
================================================= test session starts =================================================
platform win32 -- Python 3.8.10, pytest-6.2.4, py-1.10.0, pluggy-0.13.1
rootdir: C:\Users\mdhsi\pandas-michael, configfile: pyproject.toml
plugins: hypothesis-6.12.0, asyncio-0.14.0, cov-2.11.1, forked-1.3.0, instafail-0.4.1, xdist-2.2.1
collected 301 items
pandas\tests\series\test_constructors.py .......................................................................................................................................................................................................x.....................................................................................................
--------------------------- generated xml file: C:\Users\mdhsi\pandas-michael\test-data.xml ---------------------------
================================================ slowest 30 durations =================================================
0.25s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_empty_constructor[<lambda>-True0]
0.13s call pandas/tests/series/test_constructors.py::TestSeriesConstructorIndexCoercion::test_series_constructor_datetimelike_index_coercion
0.09s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['dateutil/US/Pacific'-True]
0.04s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_dtype_datetime64_10
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_empty[OrderedDict]
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_empty[dict]
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_cant_cast_datetimelike[PeriodIndex]
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_cant_cast_datetimelike[DatetimeIndex]
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_cant_cast_datetimelike[TimedeltaIndex]
0.02s setup pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_datetimelike_scalar_to_string_dtype[string]
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_pass_none
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_categorical_with_coercion
0.02s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_dtype_timedelta64
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[pytz.FixedOffset(300)-False]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['Asia/Tokyo'-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[pytz.FixedOffset(300)-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[tzlocal()-False]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['dateutil/Asia/Singapore'-False]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[datetime.timezone(datetime.timedelta(seconds=3600))-False]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[tzutc()-False]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['+01:15'-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['UTC'-False]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['UTC-02:15'-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[tzlocal()-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[pytz.FixedOffset(-300)-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['dateutil/Asia/Singapore'-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['UTC'-True]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive['-02:15'-False]
0.01s call pandas/tests/series/test_constructors.py::TestSeriesConstructors::test_constructor_data_aware_dtype_naive[pytz.FixedOffset(-300)-False]
=========================================== 300 passed, 1 xfailed in 2.22s ============================================
``` | {
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] | open | false | null | [] | null | 2 | "2021-05-25T22:49:34Z" | "2021-08-21T01:39:38Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series(["a", "a", "b"]).astype("category")
s.map({"a": (1, 2), "b": (3, 4)})
```
#### Problem description
```python-traceback
NotImplementedError: isna is not defined for MultiIndex
```
<details><summary>Full traceback</summary>
```python-traceback
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-209-1bb58119eb75> in <module>
1 import pandas as pd
2 s = pd.Series(["a", "a", "b"]).astype("category")
----> 3 s.map({"a": (1, 2), "b": (3, 4)})
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/series.py in map(self, arg, na_action)
3907 dtype: object
3908 """
-> 3909 new_values = super()._map_values(arg, na_action=na_action)
3910 return self._constructor(new_values, index=self.index).__finalize__(
3911 self, method="map"
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/base.py in _map_values(self, mapper, na_action)
901 # "Union[ExtensionArray, Any]" has no attribute "map"
902 # [union-attr]
--> 903 return self._values.map(mapper) # type: ignore[union-attr]
904
905 values = self._values
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/arrays/categorical.py in map(self, mapper)
1196 new_categories = self.categories.map(mapper)
1197 try:
-> 1198 return self.from_codes(
1199 self._codes.copy(), categories=new_categories, ordered=self.ordered
1200 )
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/arrays/categorical.py in from_codes(cls, codes, categories, ordered, dtype)
567 Categories (2, object): ['a' < 'b']
568 """
--> 569 dtype = CategoricalDtype._from_values_or_dtype(
570 categories=categories, ordered=ordered, dtype=dtype
571 )
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/dtypes/dtypes.py in _from_values_or_dtype(cls, values, categories, ordered, dtype)
271 # Note: This could potentially have categories=None and
272 # ordered=None.
--> 273 dtype = CategoricalDtype(categories, ordered)
274
275 return dtype
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/dtypes/dtypes.py in __init__(self, categories, ordered)
158
159 def __init__(self, categories=None, ordered: Ordered = False):
--> 160 self._finalize(categories, ordered, fastpath=False)
161
162 @classmethod
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/dtypes/dtypes.py in _finalize(self, categories, ordered, fastpath)
312
313 if categories is not None:
--> 314 categories = self.validate_categories(categories, fastpath=fastpath)
315
316 self._categories = categories
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/dtypes/dtypes.py in validate_categories(categories, fastpath)
505 if not fastpath:
506
--> 507 if categories.hasnans:
508 raise ValueError("Categorical categories cannot be null")
509
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/indexes/base.py in hasnans(self)
2193 """
2194 if self._can_hold_na:
-> 2195 return bool(self._isnan.any())
2196 else:
2197 return False
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/indexes/base.py in _isnan(self)
2172 """
2173 if self._can_hold_na:
-> 2174 return isna(self)
2175 else:
2176 # shouldn't reach to this condition by checking hasnans beforehand
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/dtypes/missing.py in isna(obj)
125 Name: 1, dtype: bool
126 """
--> 127 return _isna(obj)
128
129
~/miniconda3/envs/seaborn-py38-latest/lib/python3.8/site-packages/pandas/core/dtypes/missing.py in _isna(obj, inf_as_na)
154 # hack (for now) because MI registers as ndarray
155 elif isinstance(obj, ABCMultiIndex):
--> 156 raise NotImplementedError("isna is not defined for MultiIndex")
157 elif isinstance(obj, type):
158 return False
NotImplementedError: isna is not defined for MultiIndex
```
</details>
#### Expected Output
With an object-typed Series:
```python
import pandas as pd
s = pd.Series(["a", "a", "b"])
s.map({"a": (1, 2), "b": (3, 4)})
```
```
0 (1, 2)
1 (1, 2)
2 (3, 4)
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Apr 12 20:57:45 PDT 2021; root:xnu-6153.141.28.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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} | 12 | "2021-05-25T23:22:58Z" | "2021-06-26T07:47:43Z" | "2021-05-28T15:58:08Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/41671 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41671/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41671/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41671/events | https://github.com/pandas-dev/pandas/issues/41671 | 901,566,089 | MDU6SXNzdWU5MDE1NjYwODk= | 41,671 | QST: pandas.loc["x", :] (slicing) automatically converts int to float : is this issue addressed somewhere? | {
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] | closed | false | null | [] | null | 1 | "2021-05-26T00:47:14Z" | "2021-08-21T01:42:37Z" | "2021-08-21T01:42:37Z" | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions. yes, and I can not find any resolution or explanations other then confirming the issue is there. refer to https://stackoverflow.com/questions/55455010/how-to-stop-pandas-dataframe-from-converting-int-to-float-for-no-reason
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
please look into the following link for the code which explains the issue as well as reproducible codes.
https://stackoverflow.com/questions/55455010/how-to-stop-pandas-dataframe-from-converting-int-to-float-for-no-reason
```python
# Your code here, if applicable
```
I am looking for confirmation on whether this is a bug, if it is a feature, point me to appropriate documentation.
Thanks in advance!
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] | open | false | null | [] | null | 2 | "2021-05-26T03:54:22Z" | "2021-08-21T01:43:12Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
tdf2 = pd.DataFrame([], columns=['lang', 'name'])
print(type(tdf2.agg({'name': lambda y: y.values})))
tdf2 = pd.DataFrame([['a', 'boof']], columns=['lang', 'name'])
print(type(tdf2.agg({'name': lambda y: y.values})))
```
#### Problem description
This seems very similar to the description of #16621, and may be the same bug; check there for some history, which points to an original fix in #2476. You can also look at [this StackExchange question](https://stackoverflow.com/questions/67697606/pandas-dataframe-agg-produces-multiindex-after-selection-with-missing-categories/) if you want to see how I nearly pulled my hair out.
The first call of `.agg()` above, on the empty DataFrame, produces a DataFrame result. The second call of `.agg()` produces a Series instead.
This has unfortunate implications when used with `groupby()`:
```python
tdf2 = (
pd.DataFrame([['a', 'boof'], ['a', 'toop']], columns=['lang', 'name'])
.astype({'lang': pd.CategoricalDtype(['a', 'b'])})
)
print(tdf2.groupby('lang').apply(lambda x: x.agg({'name': lambda y: ', '.join(y.values)})))
```
...produces:
| lang | | 0 | name |
| -- | -- | -- | -- |
| **a** | **name** | boof, toop | NaN |
...instead of:
| lang | name |
| -- | -- |
| **a** | boof, toop |
#### Expected Output
I'm assuming that the call with data produces a series because the `agg()` is single-valued, therefore the top code should probably produce two Series:
```
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
```
#### Actual output
```
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.series.Series'>
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Apr 12 20:57:45 PDT 2021; root:xnu-6153.141.28.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.7
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
```
</details>
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] | closed | false | null | [] | null | 4 | "2021-05-26T04:19:49Z" | "2021-06-21T16:10:44Z" | "2021-06-21T16:10:44Z" | MEMBER | null | ### Problem
Currently calculating the the windowing aggregation of several pandas objects requires concatenating them together and returning the entire result
```
pd.concat([df1, df2, df3, ...], axis=1).rolling(10).mean()
```
By holding the latest state of a windowing aggregation, it is possible to calculate the above more efficiently (and flexibly) in an online fashion, e.g.
```
roll = df1.rolling(10)
roll.mean()
roll.mean(update=df2)
roll.mean(update=df3)
```
Aiming to eventually target:
```
rolling/expanding/ewm
groupby.rolling/groupby.expanding/groupby.ewm
```
As windowing operations are adapting `numba` this would be a `numba` only feature.
### Important API features
1. Independent state sharing (e.g. an update provided to rolling mean should not affect the state of rolling variance)
2. Independent numba engine configuration (e.g. a user should be able to have rolling mean call `nogil=True` while also having rolling variance call `nogil=False`)
### Proposed API options (example with `ExponentialMovingWindow`)
1. `ExponentialMovingWindow` automatically hold state of an aggregation call, and an `update` keyword exists on the aggregation methods to pass in updated data
```
In [1]: s = pd.Series([0, 1, 2, np.nan, 4])
In [2]: ewm = s.head(1).ewm(com=0.5)
# Prime the first resulting state
In [3]: ewm.mean(engine="numba")
Out[3]:
0 0.0
dtype: float64
In [4]: ewm.mean(engine="numba", update=s.iloc[1:4])
Out[4]:
1 0.750000
2 1.615385
3 1.615385
dtype: float64
In [5]: ewm.mean(engine="numba", update=s.tail(1))
Out[5]:
1 3.670213
dtype: float64
# Clear the latest ewm state
In [6]: ewm.reset_update()
# Re-prime the first resulting state
In [7]: ewm.mean(engine="numba")
Out[7]:
0 0.0
dtype: float64
In [8]: ewm.mean(engine="numba", update=s.tail(4))
Out[8]:
1 0.750000
2 1.615385
3 1.615385
4 3.670213
dtype: float64
```
2. A new `online` method in `ewm` that makes a new `OnlineExponentialMovingWindow` object. The aggregations methods on this object then support an `update` keyword.
```
In [6]: s = pd.Series([0, 1, 2, np.nan, 4])
# This will raise if numba is not installed
In [7]: ewm_online = s.head(1).ewm(com=0.5).online()
In [8]: ewm_online
Out[8]: OnlineExponentialMovingWindow [com=0.5,min_periods=1,adjust=True,ignore_na=False,axis=0]
# Prime the first resulting state
In [10]: ewm_online.mean()
Out[10]:
0 0.0
dtype: float64
In [11]: ewm_online.mean(update=s.iloc[1:4])
Out[11]:
1 0.750000
2 1.615385
3 1.615385
dtype: float64
# Clear the latest ewm state
In [12]: ewm_online.reset()
# Re-prime the first resulting state
In [13]: ewm_online.mean()
Out[13]:
0 0.0
dtype: float64
In [14]: ewm_online.mean(update=s.tail(4))
Out[14]:
1 0.750000
2 1.615385
3 1.615385
4 3.670213
dtype: float64
```
### Considered API Options
1. Create a new object per windowing method + aggregation, but this would lead to too many individual classes
2. Use [streamz](https://github.com/python-streamz/streamz) as an optional dependency for online calculations. However, streamz has a full API workflow adopted for this operation which may be difficult to reuse. Also, we would ideally want this to start as a numba accelerated operation cc @mrocklin
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} | 1 | "2021-05-26T05:22:05Z" | "2021-05-26T18:33:02Z" | "2021-05-26T17:12:42Z" | MEMBER | null | - [x] closes #16710
- [x] closes #16719
- [x] closes #16867
- [x] closes #17091
- [x] closes #17286
- [x] closes #18549
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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] | open | false | null | [] | null | 3 | "2021-05-26T09:39:27Z" | "2021-08-21T01:43:40Z" | null | NONE | null | #### Is your feature request related to a problem?
I often use Groupby combined with the "level" parameter, to create groups for particular subsets of my multi-indexed df.
Yet, Those levels are again included in the created groups. I then have to drop them "manually" at the level of each group (as I will not need those, especially since they take a unique value at the level of a particular group).
#### Describe the solution you'd like
Would it be possible to add an option to pandas.DataFrame.Groupby API, to automatically remove grouping levels from the index of created groups?
Example of my current code:
```python
df = df.groupby(level=['contract','subcontract']).apply(
lambda grp: grp.droplevel(['contract','subcontract']).interpolate('slinear'))
```
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} | 5 | "2021-05-26T09:57:39Z" | "2021-10-17T20:55:07Z" | "2021-10-17T20:55:07Z" | NONE | null | #### Location of the documentation
* https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html
* https://pandas.pydata.org/docs/dev/reference/api/pandas.to_datetime.html?highlight=to_datetime
#### Documentation problem
The `to_datetime` method does something wildly different on strings or Series vs DataFrames. This caught me by surprise and I think it would help if the documentation reflected this more clearly.
#### Suggested fix for documentation
Something along the lines of:
For most datatypes given to the `to_datetime` method it creates datetime objects out of strings. If `to_datetime` is given a dataframe it does something quite different: it combines columns that contain year, month and day information _into_ datetime objects.
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} | 6 | "2021-05-26T10:03:14Z" | "2021-05-28T19:05:52Z" | "2021-05-28T19:05:48Z" | CONTRIBUTOR | null | - [ ✓ ] closes #39037
- [ ✓ ] tests added / passed
- [ ✓ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 3 | "2021-05-26T14:07:36Z" | "2021-07-04T21:44:01Z" | "2021-07-04T21:44:01Z" | MEMBER | null | from https://numpy.org/neps/nep-0029-deprecation_policy.html
> On Dec 26, 2021 drop support for Python 3.7 (initially released on Jun 27, 2018)
The next release after 1.3 can be scheduled for 6 months, and we can drop 3.7 support in the next release.
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- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```sh
#16 409.4 Installing 'psycopg2-binary'
#16 409.5 $ ['/usr/local/bin/pip', 'install', '--verbose', '--upgrade', '--require-hashes', '--no-use-pep517', '--no-deps', '--exists-action=i', '-r', '/tmp/pipenv-lcvqdjrs-requirements/pipenv-h5kx5keb-requirement.txt', '-i', 'https://pypi.org/simple']
#16 411.6 Traceback (most recent call last):
#16 411.6 File "/usr/local/bin/pipenv", line 8, in <module>
#16 411.6 sys.exit(cli())
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/core.py", line 829, in __call__
#16 411.6 return self.main(*args, **kwargs)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/core.py", line 782, in main
#16 411.6 rv = self.invoke(ctx)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/core.py", line 1259, in invoke
#16 411.6 return _process_result(sub_ctx.command.invoke(sub_ctx))
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/core.py", line 1066, in invoke
#16 411.6 return ctx.invoke(self.callback, **ctx.params)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/core.py", line 610, in invoke
#16 411.6 return callback(*args, **kwargs)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/decorators.py", line 73, in new_func
#16 411.6 return ctx.invoke(f, obj, *args, **kwargs)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/core.py", line 610, in invoke
#16 411.6 return callback(*args, **kwargs)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/vendor/click/decorators.py", line 21, in new_func
#16 411.6 return f(get_current_context(), *args, **kwargs)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/cli/command.py", line 233, in install
#16 411.6 retcode = do_install(
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/core.py", line 2052, in do_install
#16 411.6 do_init(
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/core.py", line 1304, in do_init
#16 411.6 do_install_dependencies(
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/core.py", line 899, in do_install_dependencies
#16 411.6 batch_install(
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/core.py", line 796, in batch_install
#16 411.6 _cleanup_procs(procs, failed_deps_queue, retry=retry)
#16 411.6 File "/usr/local/lib/python3.9/site-packages/pipenv/core.py", line 703, in _cleanup_procs
#16 411.6 raise exceptions.InstallError(c.dep.name, extra=err_lines)
#16 411.6 pipenv.exceptions.InstallError: ERROR: Couldn't install package: pandas
#16 411.6 Package installation failed...
#16 411.7 /usr/local/lib/python3.9/subprocess.py:1052: ResourceWarning: subprocess 1716 is still running
#16 411.7 _warn("subprocess %s is still running" % self.pid,
#16 411.7 sys:1: ResourceWarning: unclosed file <_io.FileIO name=6 mode='wb' closefd=True>
#16 411.7 sys:1: ResourceWarning: unclosed file <_io.FileIO name=7 mode='rb' closefd=True>
------
executor failed running [/bin/sh -c pipenv install --system --deploy --dev --verbose]: exit code: 1
```
#### Problem description
running into this issue while building the docker image
OS: MAC OX X 11.4
Apple M1 [arm64]
python 3.9 | {
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] | open | false | null | [] | null | 2 | "2021-05-26T15:45:14Z" | "2021-08-21T01:48:08Z" | null | NONE | null | It works as expected when the dataframe w/ multi level column index have 5 or more columns. Like below.
```
arrays = [
["x", "x", "x", "x","x"],
["y", "y", "y", "y","y"],
["bar1", "bar2", "baz1", "baz2","bar3"],
["w", "w", "w", "w","w"],
["z", "z", "z", "z","z"],
]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=["1","2","3","4","5"])
df = pd.DataFrame(np.random.randn(3, 5), index=["A", "B", "C"], columns=index)
print("before group by")
print(df)
df = df.groupby(df.columns.names[0:-1], axis=1).min()
print("after group by - GOOD")
print(df)
```
Returns expected results
```
before group by
1 x
2 y
3 bar1 bar2 baz1 baz2 bar3
4 w w w w w
5 z z z z z
A 1.508435 0.079822 -1.052383 -1.622857 0.738883
B -1.842490 0.432695 -0.089462 -0.146393 1.146759
C -2.109186 2.211993 1.911678 -0.399462 -0.817357
after group by - GOOD
1 x
2 y
3 bar1 bar2 bar3 baz1 baz2
4 w w w w w
A 1.508435 0.079822 0.738883 -1.052383 -1.622857
B -1.842490 0.432695 1.146759 -0.089462 -0.146393
C -2.109186 2.211993 -0.817357 1.911678 -0.399462
```
**BUT** it does not work when the dataframe has 4 or less columns
```
arrays = [
["x", "x", "x", "x"],
["y", "y", "y", "y"],
["bar1", "bar2", "baz1", "baz2"],
["w", "w", "w", "w"],
["z", "z", "z", "z"],
]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=["1","2","3","4","5"])
df = pd.DataFrame(np.random.randn(3, 4), index=["A", "B", "C"], columns=index)
print("before group by")
print(df)
df = df.groupby(df.columns.names[0:-1], axis=1).min()
print("after group by - BAD")
print(df)
```
**Returns unexpected results where multi level column index is gone and is replaced by column names**
```
before group by
1 x
2 y
3 bar1 bar2 baz1 baz2
4 w w w w
5 z z z z
A 0.760272 1.128186 0.185643 0.238319
B 0.711782 0.535957 -0.633338 -1.435772
C -1.297180 -0.721576 0.380401 1.407220
after group by - BAD
1 2 3 4
A 0.760272 1.128186 0.185643 0.238319
B 0.711782 0.535957 -0.633338 -1.435772
C -1.297180 -0.721576 0.380401 1.407220
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} | 3 | "2021-05-26T17:14:09Z" | "2021-06-01T15:34:39Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Problem description
`openpyxl` has fairly high memory consumption by default. To address this for write-only operations, they recommend two things:
1. Install the `lxml` package, which is more optimized than the default xml parser.
2. Use the `write_only` mode on the workbook.
In `pandas/pandas/io/excel/_openpyxl.py` we have the [a comment](https://github.com/pandas-dev/pandas/blob/d662e9740a8cd738970cce97e341640b00cadcf8/pandas/io/excel/_openpyxl.py#L64) which states intent - `# Create workbook object with default optimized_write=True.`
However `openpyxl` depreciated the `optimized_write` option in [version 2.4](https://openpyxl.readthedocs.io/en/stable/changes.html?highlight=optimized_write%3DTrue.#a1-2016-04-11) and now recommends setting `write_only=True` on workbook creation. Pandas requires 2.6 so the result is that writes to excel use more memory than necessary. Switching to `write_only=True` has [other ramifications](https://openpyxl.readthedocs.io/en/stable/optimized.html#write-only-mode), like only being able to write out once, so would need to be tested.
#### Proposed solution
1. Print out a warning about `lxml` being missing if writing to excel in an environment that doesn't have it installed.
2. Use the `write_only` mode if possible.
#### other context
Running on a GCP cloud function, I had a `df = pd.DataFrame()` of size ~150mb. `df.to_excel()` without lxml used >2gb memory, crashing the function. With lxml it used ~1.4gb memory, returning successfully but getting too close. Adding in write-only should lower that by an order of magnitude to <100mb.
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} | 2 | "2021-05-26T19:01:28Z" | "2021-06-25T23:11:43Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import dataclasses
import pandas as pd
from typing import List
from collections import UserList
@dataclasses.dataclass(frozen=True)
class MyList(UserList):
data: List[float]
stuff = [MyList([1, 2, 3]), [4, 5, 6]]
df = pd.DataFrame(stuff)
print("No error!")
```
#### Problem description
I wrote code like this for pandas 1.0.5. When I upgraded to 1.2 my unittests started failing with `TypeError: asdict() should be called on dataclass instances`. PR https://github.com/pandas-dev/pandas/pull/27999 and issue https://github.com/pandas-dev/pandas/issues/21910 changed the behavior of the DataFrame constructor to treat `data` as all dataclasses if the first element is a dataclass. The behavior is documented but not quite what I'd like.
#### Expected Output
Admittedly a esoteric case, but I'm documenting my upgrade issuse. In this case I think checking for `is_list_like(data[0])` before `is_dataclass(data[0])` works better for me. Or maybe in hindsight from_dataclasses wasn't a bad idea.
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I ran into this when testing dask against pandas nightly builds. https://github.com/dask/dask/issues/7710
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
{"a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2]},
index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
)
df.a.groupby(df.b).sum()
```
#### Problem description
According to the series groupby docstring, I should be able to groupby series objects, but I get a `FutureWarning`
```python-traceback
/home/julia/conda/envs/dask-upstream/lib/python3.8/site-packages/pandas/core/indexes/base.py:3372: FutureWarning: Promotion of numbers and bools to strings is deprecated. In the future, code such as `np.concatenate((['string'], [0]))` will raise an error, while `np.asarray(['string', 0])` will return an array with `dtype=object`. To avoid the warning while retaining a string result use `dtype='U'` (or 'S'). To get an array of Python objects use `dtype=object`. (Warning added in NumPy 1.21)
return self._engine.get_loc(casted_key)
```
#### Expected Output
When I call `.tolist()` on the series before passing it as `by` I get the expected result:
```python
df.a.groupby(df.b.tolist()).sum()
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : e7c1ef71d80797cab0bffda8c47948261cffc139
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-72-generic
Version : #80~18.04.1-Ubuntu SMP Mon Apr 12 23:26:25 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1738.ge7c1ef71d8
numpy : 1.22.0.dev0+4.gb283e1632
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 46.1.3
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.05.0+16.g07df543
fastparquet : 0.6.3
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : 2021.05.0
scipy : 1.6.3
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : 0.18.2
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-05-26T20:38:04Z" | "2021-05-27T01:54:04Z" | "2021-05-27T01:47:23Z" | CONTRIBUTOR | null | - [x] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 8 | "2021-05-26T21:51:39Z" | "2021-07-07T02:39:48Z" | "2021-06-21T13:03:13Z" | MEMBER | null | - [x] closes #39417
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- [Zoom link](https://nyu.zoom.us/j/98201283174?pwd=d0VoUy9GTDRlVVZ0cCtTK0s4NnlBZz09)
- [Agenda/notes](https://docs.google.com/document/d/1JoiqTdK6gi5W1QiTWrqtTV-rNmIqU3xROgHY5iT4J5g/edit#heading=h.pd2rym64bmch)
---
For this session, thinking we try something a bit different: In [the workshop around the Diátaxis documentation framework](https://github.com/pandas-dev/pandas/issues/40603#issuecomment-830860731), @evildmp was explaining his highly incremental approach to documentation improvement. He:
1. Picks a small chunk of content
2. Asks things like:
- Is this in the right place?
- Is it still valuable?
- Is there anything that could be improved in it?
3. He moves/edits/deletes it
4. Repeat
(Hope I summed that up well!) I thought this might be interesting to try as a group; kind of like [mob programming](https://en.wikipedia.org/wiki/Mob_programming) but for docs. We'll see how it goes!
@pandas-dev/pandas-core @pandas-dev/pandas-triage @pandas-dev/pydata-sphinx-theme @pandas-dev/pandas-docs | {
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- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
I tried to install pandas in linux machine using pip3 and error raised as in the image uploaded
please look into this and help
```
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- [ ] ~~closes #xxxx~~
- [ ] tests ~~added /~~ passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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] | open | false | null | [] | null | 2 | "2021-05-27T14:46:43Z" | "2021-11-04T23:07:47Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import datetime as dt
import pandas as pd
from psycopg2.tz import FixedOffsetTimezone
# Simulate getting a timestamp from the DB
psycopg2_utc = FixedOffsetTimezone()
timestamp = dt.datetime.now(psycopg2_utc)
df = pd.DataFrame({'foo': [timestamp]})
df.to_parquet('test1.parquet')
try:
pd.read_parquet('test1.parquet')
except Exception as e:
print(f'First load failed: {e}')
# Fix by converting to pytz UTC
df['foo'] = df['foo'].dt.tz_convert('UTC')
df.to_parquet('test2.parquet')
pd.read_parquet('test2.parquet')
print('Second load succeeded')
```
#### Problem description
When a DataFrame that contains a datetime with a psycopg2 timezone object is stored in a Parquet file then loading the file raises a `pytz.exceptions.UnknownTimeZoneError`. Conversion to pytz UTC via `.dt.tz_convert` fixes the issue.
This is similar to but different from #25423 and #35997 as far as I understand.
See also [this StackOverflow discussion](https://stackoverflow.com/questions/66104233/pandas-read-parquet-pytz-exceptions-unknowntimezoneerror).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-73-generic
Version : #82~18.04.1-Ubuntu SMP Fri Apr 16 15:10:02 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.0
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 1 | "2021-05-27T17:51:59Z" | "2021-06-04T08:10:07Z" | "2021-06-03T23:28:33Z" | CONTRIBUTOR | null | follow up on recent `to_html` enhancement.
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### Blockers
- [x] Acceptance of the `jinja2` requirement for HTML output, since `Styler` requires `jinja2`.
- [x] Acceptance of removal/hard break of specific kwargs and all formatting keyword args (see below).
### Development
- [x] make `Styler.to_html` as performant or better than `DataFrame.to_html` (#39972) (#40425) (#41269)
- [x] make Styler functional for all DataFrames (i.e. including non-unique columns, indexes) (#41269).
- [x] enhance with ability to style indexes/column headers with styles (e.g bold rows) (#43149)
- [x] <s> enhance `.hide_columns` with `.show_columns` alternative (optional) </s> not necessary
- [x] add the sparsify kwargs (#41946).
- [x] add render trimming to prevent browser crash on large data (#41635)
- [x] decide whether to add or drop max_rows / max_cols / min_rows (#42972)
- [x] enhance `.hide_columns` and `.hide_index` to have dual function to hide the entire array, or specific items (#41266 #43771)
- [x] make styler reactive to separate sparse columns and index separately (#41512)
- [ ] add pandas options for `styler` for default output manipulation, using `_repr_html_` methods (#41395)
- [ ] add the ability to render a a table_id even if exclude_styles is True (for back compat)
- [x] add the ability to hide index names #43346
- [ ] <s>add the `class="dataframe"` attribute by default for google colab </s> (https://github.com/googlecolab/colabtools/issues/1687)
### Treatment of Keyword Args
Old Keyword | New Keyword | Reason
--- | --- | ---
`buf` | `buf` |
`encoding` | `encoding` |
`table_id` | `table_uuid` |
`classes` | `table_attributes` | extended utility
`sparsify` | `sparse_index`<br>`sparse_columns` | more control of individual components
`max_rows`<br>`min_rows`<br>`max_cols` | `max_rows`<br>`max_columns` | `min_rows` removed since it is console related
`bold_rows` | `bold_headers` | included to allows some back compatability
... | `doctype_html` | adds ability to form a full HTML document or just `style` and `table` HTML elements.
... | `exclude_styles` | adds ability to more closely replicate `DataFrame.to_html` by dropping extra features.
... | `caption` | adds ability to set a caption on Styler from the calling method.
`na_rep`<br>`formatters`<br>`float_format`<br>`decimal`<br>`escape` | *removed* | replaced by `Styler.format(...)` which has more control
`columns`<br>`header`<br>`index` | *removed* | replaced by `Styler.hide_index(...)` and `Styler.hide_columns(...)`
`border` | *removed* | deprecated HTML should be controlled with CSS
`col_space` | *removed* | should be controlled with CSS, e.g. 'min-width: 100px;'
`render_links` | *removed* | not useful. only renders links identified as `http[...]`, not as sub part of string, i.e. `other http[...] text`. if required should be added to `Styler.format(...)`
`notebook` | *removed* | This seems to only provide minor alignment changes that should be controlled with other CSS
`show_dimensions` | *removed* | This information is not needed in an HTML table of the DataFrame. `df.shape` provides the necessary workaround, and as a last resort `caption=f"dimension: {df.shape}"` displays the info.
`justify` | *removed* | since this is a styling arg which should be dealt with by other methods.
`index_names` | *removed* | assumed index names are removed from DataFrame if unwanted. As an alternative `hide_columns` could introduce a keyword argument to specifically hide the index names row.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/41696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41696/events | https://github.com/pandas-dev/pandas/issues/41696 | 904,548,354 | MDU6SXNzdWU5MDQ1NDgzNTQ= | 41,696 | Series idxmin() TypeError?Need change type to 'float' | {
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```
ltc:
调度报障工单号
DD182103227 5
..
DD082104265 5
Name: 供电所, Length: 848, dtype: Int64
```
However, when using:
` ltc.idxmin()`
i got an error:
` TypeError: argmin() takes 1 positional argument but 2 were given`
when using:
` ltc.astype('float').idxmin()`
i got the right answer:
'DD092104104'
But the api doesn't say that idxmin() can only use on float data.Why? | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 6 | "2021-05-28T04:12:15Z" | "2021-07-02T01:23:47Z" | "2021-07-02T01:23:08Z" | CONTRIBUTOR | null | - [x] closes #41448
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Issue #41448 relates specifically to our openpyxl engine, and is resolved by this commit. However, the bug also exists in our pyxlsb engine and cannot be resolved until the upstream https://github.com/willtrnr/pyxlsb/issues/33 is addressed. I propose closing #41448 and leaving the pyxlsb case as an xfail for now. Let me know if there's a better way to track this. | {
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} | 13 | "2021-05-28T12:59:07Z" | "2021-06-05T09:12:49Z" | "2021-06-05T09:12:39Z" | CONTRIBUTOR | null | - [x] xref #41485
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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] | open | false | null | [] | null | 4 | "2021-05-28T14:24:07Z" | "2021-06-17T09:26:28Z" | null | NONE | null | The following code fails:
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.normal(size=(100, 10)))
df.ewm(alpha=0.1).agg(lambda x: x)
```
Below the traceback
```python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-69-c018ca7d52b8> in <module>
----> 1 df.ewm(alpha=0.1).agg(lambda x: x)
c:\dev\projects\weedget\venv\lib\site-packages\pandas\core\window\ewm.py in aggregate(self, func, *args, **kwargs)
316 )
317 def aggregate(self, func, *args, **kwargs):
--> 318 return super().aggregate(func, *args, **kwargs)
319
320 agg = aggregate
c:\dev\projects\weedget\venv\lib\site-packages\pandas\core\window\rolling.py in aggregate(self, func, *args, **kwargs)
472 result, how = aggregate(self, func, *args, **kwargs)
473 if result is None:
--> 474 return self.apply(func, raw=False, args=args, kwargs=kwargs)
475 return result
476
c:\dev\projects\weedget\venv\lib\site-packages\pandas\core\window\rolling.py in __getattr__(self, attr)
206 return self[attr]
207
--> 208 raise AttributeError(
209 f"'{type(self).__name__}' object has no attribute '{attr}'"
210 )
AttributeError: 'ExponentialMovingWindow' object has no attribute 'apply'
```
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} | 7 | "2021-05-28T14:29:58Z" | "2021-05-31T18:44:41Z" | "2021-05-31T16:59:24Z" | MEMBER | null | - [ ] closes #41074
- [ ] tests added / passed
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- [ ] whatsnew entry
it appears that the clean-up in #35899 to simplify _reduce may have been a bit premature, have reinstated a few lines of the removed code for backport purposes.
The longer term fix would probably be to support min_count in _mgr.reduce, https://github.com/pandas-dev/pandas/pull/40143#issuecomment-787945291 | {
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} | 0 | "2021-05-28T15:03:59Z" | "2021-05-31T19:12:18Z" | "2021-05-31T19:04:25Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] tests added / passed
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I'm suggesting adding this sentence after the above location to make the function name easier to understand:
The 'first' dataframe overrides the second one where both first.loc[index, col] and second.loc[index, col] are not nan, upon calling
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} | 1 | "2021-05-28T16:00:43Z" | "2021-05-31T18:10:51Z" | "2021-05-31T17:41:50Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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- [x] tests added / passed
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} | 1 | "2021-05-28T17:03:11Z" | "2021-06-01T16:05:15Z" | "2021-06-01T14:52:40Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
preliminary to de-duplicating some code in internals.construction | {
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} | 1 | "2021-05-28T18:07:21Z" | "2021-05-30T19:25:24Z" | "2021-05-30T19:25:04Z" | MEMBER | null | If I'm not missing anything, Travis is only used to test on ARM64, and it's not testing on GBQ, since it doesn't have its dependencies in the conda environment. So, I think all GBQ stuff for Travis can be removed.
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} | 1 | "2021-05-28T19:22:18Z" | "2021-05-31T16:19:09Z" | "2021-05-31T16:17:27Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
After this it is exclusively used in core.construction and core.internals.construction | {
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} | 3 | "2021-05-28T22:29:58Z" | "2021-05-29T19:36:48Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
**badly-formatted.csv**
```csv
lname fname birthday hometown
Deere Jane 2003-01-02 Philadelphia, PA
Doe John 2001-04-01 New York, NY
Jefferson Thomas 1743-04-13 Shadwell, VA
Lincoln Abraham 1809-02-12 Sinking Spring, KY
Washington George 1732-02-22 Pope's Creek, VA
```
(note extra tab characters)
**main.py**
```python
import pandas
pandas.read_csv("badly-formatted.csv", sep="\t", error_bad_lines=False, warn_bad_lines=True)
```
#### Problem description
When using `pandas.read_csv()` with the `error_bad_lines=False` argument (and the default `warn_bad_lines=True` arg), the output being printed to stderr appears to be `bytes` type that has been cast to `str`. This adds noise to the console output, and is making it difficult to properly capture stderr output from this constructor.
#### Expected stderr Output from MRE
```
Skipping line 3: expected 4 fields, saw 5
Skipping line 4: expected 4 fields, saw 5
Skipping line 6: expected 4 fields, saw 5
```
#### Actual stderr Output from MRE
```
b'Skipping line 3: expected 4 fields, saw 5\nSkipping line 4: expected 4 fields, saw 5\nSkipping line 6: expected 4 fields, saw 5\n'
```
#### Inconsistent line breaks with larger files
I have also seen line breaks appear inconsistently when reading in larger files. I haven't been able to isolate any causes of this inconsistency yet
#### Actual stderr output from large file
```
b'Skipping line 342635: expected 25 fields, saw 26\n'
b'Skipping line 392116: expected 25 fields, saw 26\n'
b'Skipping line 544651: expected 25 fields, saw 26\nSkipping line 553773: expected 25 fields, saw 26\n'
b'Skipping line 559046: expected 25 fields, saw 26\nSkipping line 559146: expected 25 fields, saw 26\nSkipping line 559148: expected 25 fields, saw 26\nSkipping line 559155: expected 25 fields, saw 26\n'
b'Skipping line 596525: expected 25 fields, saw 26\n'
b'Skipping line 634470: expected 25 fields, saw 26\n'
b'Skipping line 777743: expected 25 fields, saw 26\nSkipping line 777744: expected 25 fields, saw 26\nSkipping line 777745: expected 25 fields, saw 26\nSkipping line 777746: expected 25 fields, saw 26\nSkipping line 777747: expected 25 fields, saw 26\nSkipping line 777748: expected 25 fields, saw 26\nSkipping line 777749: expected 25 fields, saw 26\nSkipping line 777750: expected 25 fields, saw 26\nSkipping line 777831: expected 25 fields, saw 26\nSkipping line 777832: expected 25 fields, saw 26\nSkipping line 778297: expected 25 fields, saw 26\n'
```
#### Expected stderr output from large file
```
Skipping line 342635: expected 25 fields, saw 26
Skipping line 392116: expected 25 fields, saw 26
Skipping line 544651: expected 25 fields, saw 26
Skipping line 553773: expected 25 fields, saw 26
Skipping line 559046: expected 25 fields, saw 26
Skipping line 559146: expected 25 fields, saw 26
Skipping line 559148: expected 25 fields, saw 26
Skipping line 559155: expected 25 fields, saw 26
Skipping line 596525: expected 25 fields, saw 26
Skipping line 634470: expected 25 fields, saw 26
Skipping line 777743: expected 25 fields, saw 26
Skipping line 777744: expected 25 fields, saw 26
Skipping line 777745: expected 25 fields, saw 26
Skipping line 777746: expected 25 fields, saw 26
Skipping line 777747: expected 25 fields, saw 26
Skipping line 777748: expected 25 fields, saw 26
Skipping line 777749: expected 25 fields, saw 26
Skipping line 777750: expected 25 fields, saw 26
Skipping line 777831: expected 25 fields, saw 26
Skipping line 777832: expected 25 fields, saw 26
Skipping line 778297: expected 25 fields, saw 26
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Mar 8 22:11:48 PST 2021; root:xnu-4903.278.65~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 21.1.2
setuptools : 56.0.0
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 2.2.5
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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tests copied from #41701; i think this gets at the root problem cc @simonjayhawkins
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```
dataDir = r'D:\\'
files = glob(os.path.join(dataDir, '*.xlsx'))
print(files)
if os.path.isfile(files[0]):
print('ok')
df = pd.read_excel(files[0], engine='openpyxl')
```
which prints:
`['D:\\\\file_index_all.xlsx', 'D:\\\\file_index_all2.xlsx']`
`ok`
But I get the following error.
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-52-5dc0ef3ce47b> in <module>
6 if os.path.isfile(files[0]):
7 print('ok')
----> 8 df = pd.read_excel(files[0], engine='openpyxl')
c:\users\ranab\miniconda3\lib\site-packages\pandas\io\excel\_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
302
303 if not isinstance(io, ExcelFile):
--> 304 io = ExcelFile(io, engine=engine)
305 elif engine and engine != io.engine:
306 raise ValueError(
c:\users\ranab\miniconda3\lib\site-packages\pandas\io\excel\_base.py in __init__(self, io, engine)
819 self._io = stringify_path(io)
820
--> 821 self._reader = self._engines[engine](self._io)
822
823 def __fspath__(self):
c:\users\ranab\miniconda3\lib\site-packages\pandas\io\excel\_openpyxl.py in __init__(self, filepath_or_buffer)
482 Object to be parsed.
483 """
--> 484 import_optional_dependency("openpyxl")
485 super().__init__(filepath_or_buffer)
486
c:\users\ranab\miniconda3\lib\site-packages\pandas\compat\_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version)
90 except ImportError:
91 if raise_on_missing:
---> 92 raise ImportError(msg) from None
93 else:
94 return None
ImportError: Missing optional dependency 'openpyxl'. Use pip or conda to install openpyxl.
```
pandas version:
`pd.__version__`<br> `1.0.1`
Also one other thing I noticed despite having `openpyxl 3.0.7` I can't import it in **jupyter notebook** but in **pycharm** <br>
```
import openpyxl
print(openpyxl.__version__)
```
shows `3.0.7`
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- [ ✔] tests added / passed
- [✔ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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"description": "Changes that would be nice to have in the next release. These issues are not blocking. They will be pushed to the next release if no one has time to fix them.",
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"title": "Contributions Welcome",
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} | 4 | "2021-05-29T14:59:30Z" | "2021-08-15T21:08:08Z" | "2021-08-15T21:08:08Z" | MEMBER | null | We keep leaving this comment to each other and to contributors in review, so let's add a rule for it
PR should be made to https://github.com/pandas-dev/pandas-dev-flaker if anyone wants to give this a go | {
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