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} | 13 | 2019-06-24T00:40:12Z | 2019-06-25T16:47:57Z | 2019-06-25T16:47:46Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
def reform(df,error_only=True):
melted=df.melt(id_vars=['station','day','source','index'])
splited = melted['index'].str.split('_', expand=True)
melted['type']=splited[0]
reformed=None
melted['uid']= melted['station']+'_'+ melted['type']+';'+melted['source']
for day,daydf in melted.groupby(['day']):
pivotdf=daydf.pivot(index='uid',columns='variable',values='value')
pivotdf.columns = [c +'_{}'.format(day) for c in pivotdf.columns if c !='uid' ]
pivotdf=pivotdf.reset_index()
pivotdf['key']=pivotdf['uid'].str.split(';',expand=True)[0].astype(str)
if reformed is None:
reformed=pivotdf
else:
print(reformed.dtypes,pivotdf.dtypes)
print(reformed.tail(4))
print(pivotdf.tail(4))
reformed = reformed.join(pivotdf, on ='key')
print(pivotdf.dtypes)
return(concat(reformed,axis=1))
```
#### Problem description
I'm using pandas 0.24.2, installed with conda.
The issue is occuring at the join step. pandas throws an error despite the fact that both column keys are the same. I obtain the following error `ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat`
This seems to stem from the `_maybe_coerce_merge_keys method`.
This is probably due to the fact that the station column is a string/object that is composed of numbers. I would assume that the current checking relies on the first few character to infer dtype and that the shown dtype is not used, as both columns are always indicated as object during the print.
Here is the dtype print before the join
```python
uid object
25%_0 float64
50%_0 float64
75%_0 float64
count_0 float64
max_0 float64
mean_0 float64
min_0 float64
std_0 float64
key object
dtype: object
uid object
25%_1 float64
50%_1 float64
75%_1 float64
count_1 float64
max_1 float64
mean_1 float64
min_1 float64
std_1 float64
key object
dtype: object
```
The key columns is composed of data like `123456_level`. I have also just done a test reversing the order of the string components to obtain 'level_123456'. But this did not change a thing.
#### Expected Output
I would expect that the columns are identified properly as object/string and that no dtype error is thrown during join/merge.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 22 Model 48 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.15
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 5 | 2019-06-24T02:59:45Z | 2019-11-05T22:11:14Z | 2019-11-05T22:11:14Z | MEMBER | null | Apparently tests.extension.tests_numpy patches PandasArray._typ so as to break all `isinstance(obj, ABCPandasArray)` checks. Learning this in the process of troubleshooting failing tests is really frustrating. This is a code smell and should be removed. | {
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Instead of patching existing EAs, this introduces ReshapeableArray which just wraps other EAs, and implements reshape methods. EAs that _do_ natively support 2D can set a `_allows_2d = True` and avoid being wrapped.
In the process of getting this passing, found a handful of new issues/bugs. Will try to push fixes for those independently. | {
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================================== FAILURES ===================================
__________________ TestReaders.test_usecols_int[xlrd-.xlsx] ___________________
self = <pandas.tests.io.excel.test_readers.TestReaders object at 0x00000268B4801B70>
read_ext = '.xlsx'
df_ref = A B C
index
2000-01-03 0.980269 3.685731 -0.36...0-01-07 -0.487094 0.571455 -1.611639
2000-01-10 0.836649 0.246462 0.588543
2000-01-11 -0.157161 1.340307 1.195778
def test_usecols_int(self, read_ext, df_ref):
df_ref = df_ref.reindex(columns=["A", "B", "C"])
# usecols as int
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
with ignore_xlrd_time_clock_warning():
df1 = pd.read_excel("test1" + read_ext, "Sheet1",
> index_col=0, usecols=3)
..\excel\test_readers.py:60:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x00000268B549AFD0>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('PendingDeprecationWarning', PendingDeprecationWarning("This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead."), 'C:\\Users\\simon\\Anaconda3\\envs\\pandas-dev\\lib\\site-packages\\xlrd\\xlsx.py', 266), ('PendingDeprecationWarning', PendingDeprecationWarning("This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead."), 'C:\\Users\\simon\\Anaconda3\\envs\\pandas-dev\\lib\\site-packages\\xlrd\\xlsx.py', 312), ('PendingDeprecationWarning', PendingDeprecationWarning("This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead."), 'C:\\Users\\simon\\Anaconda3\\envs\\pandas-dev\\lib\\site-packages\\xlrd\\xlsx.py', 266)].
C:\Users\simon\Anaconda3\envs\pandas-dev\lib\contextlib.py:119: AssertionError
```
```
INSTALLED VERSIONS
------------------
commit : cf74b0272af2e13e5b9ce40c8bf42df750ddc560
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 0.25.0.dev0+786.gcf74b0272
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 40.6.3
Cython : 0.29.10
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
matplotlib : 3.1.0
numexpr : 2.6.9
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
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} | 20 | 2019-06-24T13:23:19Z | 2020-01-04T19:07:05Z | 2020-01-04T19:07:05Z | NONE | null | The input column name in pandas.dataframe.query() contains special characters.
I saw the change in 0.25, but still have . \ / 等问题
And main problem is that I can't restore these characters after converting them to "_" , which is a very serious problem.
I have an idea
```python
input： pandas.dataframe
Intermediate processing：
`x2E`(Hex)-> like pandas`x2E`dataframe(Add backslashes after processing the data) ->pandas\x2Edataframe
output pandas\x2Edataframe
``` | {
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} | 2 | 2019-06-24T14:14:47Z | 2019-07-08T01:36:05Z | 2019-07-08T01:36:05Z | MEMBER | null | MyPy recently released a new semantic analyzer in v0.710:
https://mypy-lang.blogspot.com/2019/06/mypy-0710-released-new-semantic-analyzer.html
This will likely be the default in the next mypy release so would be good to get a head start on support. This currently causes a few errors in our code base:
```sh
$ mypy --new-semantic-analyzer pandas
pandas/core/arrays/interval.py:574: error: Name 'dtype' already defined on line 129
pandas/core/indexes/timedeltas.py:308: error: Name '_box_func' already defined on line 173
pandas/io/json/__init__.py:5: error: Name 'json' is not defined
pandas/io/json/__init__.py:5: error: Name 'normalize' is not defined
pandas/io/json/__init__.py:5: error: Name 'table_schema' is not defined
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27019 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27019/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27019/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27019/events | https://github.com/pandas-dev/pandas/issues/27019 | 459,923,425 | MDU6SXNzdWU0NTk5MjM0MjU= | 27,019 | DOC: Add glossary to the documentation | {
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- label (see #27005)
- list-like (see #21784)
scikit-learn added a glossary to their documentation (https://scikit-learn.org/stable/glossary.html), and I think this would also be useful for pandas. Not sure the exact terminology to start with, but feel free to comment here with your ideas, and we can work on this once we have a first list. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27020 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27020/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27020/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27020/events | https://github.com/pandas-dev/pandas/issues/27020 | 459,975,207 | MDU6SXNzdWU0NTk5NzUyMDc= | 27,020 | iloc doesn't error when boolean mask is too long | {
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} | [] | closed | false | null | [] | null | 1 | 2019-06-24T16:04:38Z | 2019-06-24T16:29:01Z | 2019-06-24T16:29:01Z | NONE | null | `iloc` doesn't error when passing a boolean mask that is too long (as long as the values are False). The same is true for Series.
```python
import pandas as pd
d = pd.DataFrame([1, 2])
d.iloc[[True, True, False]] # does not error even though index is too long
d.iloc[[True, True, True]] # error as expected
```
Sorry, not sure if this is an expected behaviour, but I was quite surprised at first.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.1.4-arch1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.6.2
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.10
numpy: 1.16.2
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 3.0.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27021 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27021/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27021/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27021/events | https://github.com/pandas-dev/pandas/issues/27021 | 459,991,061 | MDU6SXNzdWU0NTk5OTEwNjE= | 27,021 | column name dedupe when already deduped returns "column_name.1.1" | {
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"... | open | false | null | [] | null | 0 | 2019-06-24T16:41:59Z | 2021-07-10T05:27:27Z | null | NONE | null | This isn't necessarily a bug, just something I think could be handled slightly cleaner. The code:
```python
from io import StringIO
csv = StringIO('a,a.1,a')
pd.read_csv(csv)
```
returns
```
a a.1 a.1.1
```
where I think it could return
```
a a.1 a.2
```
I encountered this when working with data that included the columns ["Column name", "COLUMN NAME", "COLUMN NAME"]. I work in multiple steps (i.e., separating each task into a separate script that takes the output from the previous task—works for auditability), so after the first step the columns became ["Column name", "COLUMN NAME", "COLUMN NAME.1"]. Down the pipeline, I renamed all columns to use all caps for consistency, but I ended up with ["COLUMN NAME.1.1", "COLUMN NAME", "COLUMN NAME.1"]. Not a huge problem, but a little bit annoying nonetheless.
I'm going to dig in and see if I can implement this later this week in a pull request—just filing it now so that I don't forget. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27022 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27022/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27022/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27022/events | https://github.com/pandas-dev/pandas/issues/27022 | 460,024,468 | MDU6SXNzdWU0NjAwMjQ0Njg= | 27,022 | Unexpected ValueError when using a json reader to read file from disk using chunksize | {
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"descript... | closed | false | null | [] | null | 11 | 2019-06-24T18:06:31Z | 2020-05-08T21:05:05Z | 2020-05-08T21:05:04Z | NONE | null | Sent to pandas-dev@python.org on June 23 (vlb@c....com); posted here as recommended by Marc Garcia.
#### Code Sample, a copy-pastable example if possible
```python
path = 'file://localhost/Users/vlb/Learn/DSC_Intro/'
filename = path + 'yelp_dataset/review_test.json'
# read the entire file -- this works
reviews = pd.read_json(filename, lines=True)
reviews.info()
# create a reader to read in chunks -- this part seems to work
review_reader = pd.read_json(StringIO(filename), lines=True, chunksize=1)
type(review_reader)
# But trying to read from the reader throws an error
# ValueError: Unexpected character found when decoding 'false'
for chunk in review_reader:
print(chunk)
```
#### Data Samples
Either or both of the following records can be used
```python
{"review_id":"rEITo90tpyKmEfNDp3Ou3A","user_id":"6Fz_nus_OG4gar721OKgZA","business_id":"6lj2BJ4tJeu7db5asGHQ4w","stars":5.0,"useful":0,"funny":0,"cool":0,"text":"We've been a huge Slim's fan since they opened one up in Texas about two years ago when we used to live there. This place never disappoints. They even have great salads and grilled chicken. Plus they have fresh brewed sweet tea, it's the best!","date":"2017-05-26 01:23:19"}
```
```python
{"review_id":"Amo5gZBvCuPc_tZNpHwtsA","user_id":"DzZ7piLBF-WsJxqosfJgtA","business_id":"qx6WhZ42eDKmBchZDax4dQ","stars":5.0,"useful":1,"funny":0,"cool":0,"text":"Our family LOVES the food here. Quick, friendly, delicious, and a great restaurant to take kids to. 5 stars!","date":"2017-03-27 01:14:37"}
```
#### Problem description
#### Problem description
I am working a tutorial that uses a [JSON data file from Yelp](https://www.yelp.com/dataset/documentation/main). The file is huge, so it needs to be read in chunks.
I get an unexpected error: ValueError: Unexpected character found when decoding 'false'
For testing purposes, I have reduced the dataset to a much smaller file with only 3 lines. I can reproduce the error with that file as well as with a file containing only one (any one) of the three lines.
Note that if I simply read in the entire (test) data set in one go, that works. It's only when I create a reader and try to review the chunks that I get the error.
#### Expected Output
No errors. A chunk should print.
If there is an error, it should be less opaque than "Unexpected character found when decoding 'false'".
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
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} | 5 | 2019-06-24T20:40:25Z | 2019-06-28T22:56:22Z | 2019-06-28T22:56:22Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'a': [1,2], 'b': [2,3]})
df_coldiff = pd.DataFrame({'a': [1,2], 'ZZZ': [2,3]})
pd.testing.assert_frame_equal(df, df_coldiff, obj='myobj')
# raises:
# AssertionError: myobj.columns are different
#
# myobj.columns values are different (50.0 %)
# [left]: Index(['a', 'b'], dtype='object')
# [right]: Index(['a', 'ZZZ'], dtype='object')
df_valuediff = pd.DataFrame({'a': [1,2], 'b': [2,999]})
pd.testing.assert_frame_equal(df, df_valuediff, obj='myobj')
# raises:
# AssertionError: DataFrame.iloc[:, 1] are different
#
# DataFrame.iloc[:, 1] values are different (50.0 %)
# [left]: [2, 3]
# [right]: [2, 999]
```
#### Problem description
In the first example where the column names differ, the 'myobj' label is used in the assertion message.
In the second example where the values differ, the 'myobj' label does not appear.
It looks to me like these strings should be formatted with `obj` instead of hardcoded with `DataFrame`?
https://github.com/pandas-dev/pandas/blob/master/pandas/util/testing.py#L1252
https://github.com/pandas-dev/pandas/blob/master/pandas/util/testing.py#L1267
#### Expected Output
I expected the assertion message for the second example to be:
```
# AssertionError: myobj.iloc[:, 1] are different
#
# myobj.iloc[:, 1] values are different (50.0 %)
# [left]: [2, 3]
# [right]: [2, 999]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
pandas: 0.24.2
pytest: 4.4.1
pip: 19.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.7
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-06-24T21:17:44Z | 2019-06-27T21:26:33Z | 2019-06-27T20:41:02Z | MEMBER | null | Would close #26995 if I hadn't just updated that to reflect the fact that several other pandas-internal EAs don't handle the `deep` kwarg correctly.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 11 | 2019-06-24T22:36:27Z | 2020-03-30T02:29:34Z | 2020-03-30T02:29:33Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(index=pd.date_range(start='2019', periods=3, freq='H', tz='America/Los_Angeles'), data={'A': [1, 2, 3]})
df.query('A > 1')
```
#### Problem description
The above code produces a `FutureWarning`:
```
/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:407: FutureWarning: The default of the 'keep_tz' keyword will change to True in a future release. You can set 'keep_tz=True' to obtain the future behaviour and silence this warning.
s = level_values.to_series()
/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:415: FutureWarning: The default of the 'keep_tz' keyword will change to True in a future release. You can set 'keep_tz=True' to obtain the future behaviour and silence this warning.
dindex = axis_index.to_series()
```
#### Expected Output
I believe the internal pandas code (`generic.py`) needs to handle the warning by passing an appropriate keyword (`keep_tz=True`).
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
pandas: 0.24.2
pytest: 4.0.0
pip: 19.1.1
setuptools: 40.4.3
Cython: 0.29.10
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.1
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.8.2 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: 0.9.0
pandas_datareader: None
gcsfs: None
</details>
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} | 23 | 2019-06-24T23:22:15Z | 2019-11-16T21:54:12Z | 2019-11-16T21:54:02Z | CONTRIBUTOR | null | - [X] closes #26988
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
The replace functions used in the code path that caused the bug report appeared to densify the series. This PR adds a replace function to `CategoricalBlock` that takes advantage of the categorical data type when possible | {
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} | 3 | 2019-06-25T00:10:23Z | 2019-06-27T21:36:28Z | 2019-06-27T21:33:08Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Bug identified in #27015. Much less-kludgy patch for NDFrame.rank. | {
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} | 6 | 2019-06-25T01:03:52Z | 2019-06-29T07:52:24Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
# Create example dataframe
In [2]: df = pd.DataFrame({'a':[1,2,3],'b':[4,5,8]})
In [3]: df
Out[3]:
a b
0 1 4
1 2 5
2 3 8
# Add 0 to end of series
In [4]: df.a.append(pd.Series([0]), ignore_index=True)
Out[4]:
0 1
1 2
2 3
3 0
dtype: int64
# Add 0 to end of series and put replace in original dataframe
In [5]: df.a = df.a.append(pd.Series([0]), ignore_index=True)
# replacement fails without an error
In [6]: df
Out[6]:
a b
0 1 4
1 2 5
2 3 8
```
#### Problem description
The current failure is really annoying (as all silent failures are) because I have no idea A) why it doesn't work and B) how I can fix the issue.
What I expect: An error message saying something regarding why the operation did not succeed.
#### Output of ``pd.show_versions()``
<details>
```python
In [7]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 3.6.4
pip: 19.0.3
setuptools: 39.2.0
Cython: 0.28.4
numpy: 1.13.3
scipy: 1.1.0
pyarrow: None
xarray: 0.11.3
IPython: 6.5.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.3
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
#### So why are you even trying to do this anyways...?
Basically I want to verify that a part of my code is working right. The easiest way to do that is to simply replace the output data that exists with a simpler form. In this case, I add an extra data point to the independent variable series and then use that as an input on a function:
```python
df.a = # Add a zero point
df.b = df.a*5 #or some other really simple function like that
```
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} | 4 | 2019-06-25T01:54:10Z | 2019-06-26T11:53:31Z | 2019-06-26T11:53:31Z | NONE | null | I'm trying to pickle a dataframe with to_pickle and read_pickle, and when reading I get this error
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in try_read(path, encoding)
165 warnings.simplefilter("ignore", Warning)
--> 166 return read_wrapper(lambda f: pkl.load(f))
167 except Exception: # noqa: E722
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in read_wrapper(func)
145 compression=compression,
--> 146 is_text=False)
147 try:
~/miniconda3/lib/python3.7/site-packages/pandas/io/common.py in _get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text)
412 msg = 'Unrecognized compression type: {}'.format(compression)
--> 413 raise ValueError(msg)
414
ValueError: Unrecognized compression type: infer
```
When adding "compression=None" as an argument to both to_pickle and read_pickle, I get this error instead
```
TypeError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in try_read(path, encoding)
165 warnings.simplefilter("ignore", Warning)
--> 166 return read_wrapper(lambda f: pkl.load(f))
167 except Exception: # noqa: E722
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in read_wrapper(func)
147 try:
--> 148 return func(f)
149 finally:
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in (f)
165 warnings.simplefilter("ignore", Warning)
--> 166 return read_wrapper(lambda f: pkl.load(f))
167 except Exception: # noqa: E722
TypeError: file must have 'read' and 'readline' attributes
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in try_read(path, encoding)
172 return read_wrapper(
--> 173 lambda f: pc.load(f, encoding=encoding, compat=False))
174 # compat pickle
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in read_wrapper(func)
147 try:
--> 148 return func(f)
149 finally:
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in (f)
172 return read_wrapper(
--> 173 lambda f: pc.load(f, encoding=encoding, compat=False))
174 # compat pickle
~/miniconda3/lib/python3.7/site-packages/pandas/compat/pickle_compat.py in load(fh, encoding, compat, is_verbose)
219 try:
--> 220 fh.seek(0)
221 if encoding is not None:
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in getattr(self, name)
5066 return self[name]
-> 5067 return object.getattribute(self, name)
5068
AttributeError: 'DataFrame' object has no attribute 'seek'
```
Is this a bug? | {
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} | [] | closed | false | null | [] | null | 0 | 2019-06-25T01:54:18Z | 2019-06-25T01:54:48Z | 2019-06-25T01:54:48Z | NONE | null | I'm trying to pickle a dataframe with to_pickle and read_pickle, and when reading I get this error
```---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in try_read(path, encoding)
165 warnings.simplefilter("ignore", Warning)
--> 166 return read_wrapper(lambda f: pkl.load(f))
167 except Exception: # noqa: E722
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in read_wrapper(func)
145 compression=compression,
--> 146 is_text=False)
147 try:
~/miniconda3/lib/python3.7/site-packages/pandas/io/common.py in _get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text)
412 msg = 'Unrecognized compression type: {}'.format(compression)
--> 413 raise ValueError(msg)
414
ValueError: Unrecognized compression type: infer```
When adding "compression=None" as an argument to both to_pickle and read_pickle, I get this error instead
```
TypeError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in try_read(path, encoding)
165 warnings.simplefilter("ignore", Warning)
--> 166 return read_wrapper(lambda f: pkl.load(f))
167 except Exception: # noqa: E722
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in read_wrapper(func)
147 try:
--> 148 return func(f)
149 finally:
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in (f)
165 warnings.simplefilter("ignore", Warning)
--> 166 return read_wrapper(lambda f: pkl.load(f))
167 except Exception: # noqa: E722
TypeError: file must have 'read' and 'readline' attributes
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in try_read(path, encoding)
172 return read_wrapper(
--> 173 lambda f: pc.load(f, encoding=encoding, compat=False))
174 # compat pickle
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in read_wrapper(func)
147 try:
--> 148 return func(f)
149 finally:
~/miniconda3/lib/python3.7/site-packages/pandas/io/pickle.py in (f)
172 return read_wrapper(
--> 173 lambda f: pc.load(f, encoding=encoding, compat=False))
174 # compat pickle
~/miniconda3/lib/python3.7/site-packages/pandas/compat/pickle_compat.py in load(fh, encoding, compat, is_verbose)
219 try:
--> 220 fh.seek(0)
221 if encoding is not None:
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in getattr(self, name)
5066 return self[name]
-> 5067 return object.getattribute(self, name)
5068
AttributeError: 'DataFrame' object has no attribute 'seek'
```
Is this a bug? | {
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} | 3 | 2019-06-25T03:03:33Z | 2019-06-27T16:37:37Z | 2019-06-27T16:37:36Z | CONTRIBUTOR | null | This doesn't look might much of a perf gain, but in practice the gc collect can happen at odd times, so this change removes this uncertainty (and is much simpler code)
```
(pandas) bash-3.2$ asv continuous --bench chained_indexing master gc --config asv_bench/asv.conf.json
· Creating environments
· Discovering benchmarks
· Running 2 total benchmarks (2 commits * 1 environments * 1 benchmarks)
[ 0.00%] · For pandas commit d94146cf <master> (round 1/2):
[ 0.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...........
[ 0.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 25.00%] ··· Running (indexing.ChainIndexing.time_chained_indexing--).
[ 25.00%] · For pandas commit ab6cb31d <gc> (round 1/2):
[ 25.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 25.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 50.00%] ··· Running (indexing.ChainIndexing.time_chained_indexing--).
[ 50.00%] · For pandas commit ab6cb31d <gc> (round 2/2):
[ 50.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 75.00%] ··· indexing.ChainIndexing.time_chained_indexing ok
[ 75.00%] ··· ====== ==========
mode
------ ----------
None 72.6±5ms
warn 67.2±4ms
====== ==========
[ 75.00%] · For pandas commit d94146cf <master> (round 2/2):
[ 75.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 75.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[100.00%] ··· indexing.ChainIndexing.time_chained_indexing ok
[100.00%] ··· ====== ==========
mode
------ ----------
None 69.4±4ms
warn 79.5±4ms
====== ==========
BENCHMARKS NOT SIGNIFICANTLY CHANGED.
``` | {
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} | 3 | 2019-06-25T04:02:12Z | 2019-06-25T14:11:19Z | 2019-06-25T12:19:45Z | MEMBER | null | This came up in troubleshooting a different branch, putting it up to trim the diff there.
Also a couple of small misc cleanups. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27034/events | https://github.com/pandas-dev/pandas/pull/27034 | 460,378,285 | MDExOlB1bGxSZXF1ZXN0MjkxNTE5NDAz | 27,034 | API/REGR: Convert to float for index union | {
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} | 6 | 2019-06-25T11:52:28Z | 2019-06-27T17:24:58Z | 2019-06-27T17:24:54Z | CONTRIBUTOR | null | This restores the 0.24.x behavior of Index.union(other) between
Float and (U)Int indexes. These are again floating dtype.
left | right | output of left.union(right)
----- | ----- | ------
int |float | float64
int |uint | object
float | uint | float64
https://github.com/pandas-dev/pandas/issues/26778#issuecomment-505108465
Closes https://github.com/pandas-dev/pandas/issues/26778
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} | 3 | 2019-06-25T13:14:27Z | 2020-05-13T02:50:01Z | null | MEMBER | null | From a flaky test in geopandas, I observed the following behaviour:
```
In [1]: pd.__version__
Out[1]: '0.25.0.dev0+791.gf0919f272'
In [2]: from shapely.geometry import Point
In [3]: a = np.array([Point(1, 1), Point(1, 1)], dtype=object)
In [4]: pd.Series(a).duplicated()
Out[4]:
0 False
1 True
dtype: bool
In [6]: print(pd.Series(a).duplicated())
...: print(pd.Series(a).duplicated())
0 False
1 True
dtype: bool
0 False
1 False
dtype: bool
```
So you see that sometimes it works, sometimes it does not work.
I am also not fully sure how the object hashtable works (assuming `duplicated` uses the hashtable), as the shapely Point objects are not hashable:
```
In [9]: pd.Series(a).unique()
...
TypeError: unhashable type: 'Point'
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... | open | false | null | [] | null | 13 | 2019-06-25T13:28:29Z | 2019-08-26T20:31:35Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from pandas.plotting import deregister_matplotlib_converters
deregister_matplotlib_converters()
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts.plot()
```
```pytb
~/sandbox/pandas/pandas/plotting/_core.py in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
808 colormap=colormap, table=table, yerr=yerr,
809 xerr=xerr, label=label, secondary_y=secondary_y,
--> 810 **kwds)
811 __call__.__doc__ = plot_series.__doc__
812
~/sandbox/pandas/pandas/plotting/_core.py in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
766 yerr=yerr, xerr=xerr,
767 label=label, secondary_y=secondary_y,
--> 768 **kwds)
769
770
~/sandbox/pandas/pandas/plotting/_core.py in _plot(data, x, y, subplots, ax, kind, **kwds)
714 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
715
--> 716 plot_obj.generate()
717 plot_obj.draw()
718 return plot_obj.result
~/sandbox/pandas/pandas/plotting/_matplotlib/core.py in generate(self)
214 self._compute_plot_data()
215 self._setup_subplots()
--> 216 self._make_plot()
217 self._add_table()
218 self._make_legend()
~/sandbox/pandas/pandas/plotting/_matplotlib/core.py in _make_plot(self)
987 stacking_id=stacking_id,
988 is_errorbar=is_errorbar,
--> 989 **kwds)
990 self._add_legend_handle(newlines[0], label, index=i)
991
~/sandbox/pandas/pandas/plotting/_matplotlib/core.py in _ts_plot(cls, ax, x, data, style, **kwds)
1025 ax._plot_data.append((data, cls._kind, kwds))
1026
-> 1027 lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
1028 # set date formatter, locators and rescale limits
1029 format_dateaxis(ax, ax.freq, data.index)
~/sandbox/pandas/pandas/plotting/_matplotlib/core.py in _plot(cls, ax, x, y, style, column_num, stacking_id, **kwds)
1002 cls._initialize_stacker(ax, stacking_id, len(y))
1003 y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
-> 1004 lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
1005 cls._update_stacker(ax, stacking_id, y)
1006 return lines
~/sandbox/pandas/pandas/plotting/_matplotlib/core.py in _plot(cls, ax, x, y, style, is_errorbar, **kwds)
581 else:
582 args = (x, y)
--> 583 return ax.plot(*args, **kwds)
584
585 def _get_index_name(self):
~/Envs/pandas-dev/lib/python3.7/site-packages/matplotlib/axes/_axes.py in plot(self, scalex, scaley, data, *args, **kwargs)
1666 lines = [*self._get_lines(*args, data=data, **kwargs)]
1667 for line in lines:
-> 1668 self.add_line(line)
1669 self.autoscale_view(scalex=scalex, scaley=scaley)
1670 return lines
~/Envs/pandas-dev/lib/python3.7/site-packages/matplotlib/axes/_base.py in add_line(self, line)
1898 line.set_clip_path(self.patch)
1899
-> 1900 self._update_line_limits(line)
1901 if not line.get_label():
1902 line.set_label('_line%d' % len(self.lines))
~/Envs/pandas-dev/lib/python3.7/site-packages/matplotlib/axes/_base.py in _update_line_limits(self, line)
1920 Figures out the data limit of the given line, updating self.dataLim.
1921 """
-> 1922 path = line.get_path()
1923 if path.vertices.size == 0:
1924 return
~/Envs/pandas-dev/lib/python3.7/site-packages/matplotlib/lines.py in get_path(self)
1025 """
1026 if self._invalidy or self._invalidx:
-> 1027 self.recache()
1028 return self._path
1029
~/Envs/pandas-dev/lib/python3.7/site-packages/matplotlib/lines.py in recache(self, always)
668 if always or self._invalidx:
669 xconv = self.convert_xunits(self._xorig)
--> 670 x = _to_unmasked_float_array(xconv).ravel()
671 else:
672 x = self._x
~/Envs/pandas-dev/lib/python3.7/site-packages/matplotlib/cbook/__init__.py in _to_unmasked_float_array(x)
1388 return np.ma.asarray(x, float).filled(np.nan)
1389 else:
-> 1390 return np.asarray(x, float)
1391
1392
~/Envs/pandas-dev/lib/python3.7/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
536
537 """
--> 538 return array(a, dtype, copy=False, order=order)
539
540
TypeError: float() argument must be a string or a number, not 'Period'
```
#### Problem description
Above code is the first time series plotting example in the docs. It throws a type error as follows
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-06-25T14:18:15Z | 2019-06-25T17:59:44Z | 2019-06-25T16:13:20Z | MEMBER | null | Along with some non-reachable branches in Block.quantile. This in turn lets us get rid of Block._na_value. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27038/events | https://github.com/pandas-dev/pandas/issues/27038 | 460,455,656 | MDU6SXNzdWU0NjA0NTU2NTY= | 27,038 | Building pandas complains missng algos.c | {
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Want to create a pull-request to solve [another Issue I filed last week](https://github.com/pandas-dev/pandas/issues/26944). Am trying to follow the [pandas-dev instructions for creating a development environment](https://dev.pandas.io/development/contributing.html#working-with-the-code).
I have installed VisualStudio2019 with the `Python development workload` and the `Python native development tools` options. I can launch Visual Studio from the Start Menu.
I am using a fresh conda virtualenv which does not have pandas installed. I then forked and cloned the pandas repository. I am working in a python `3.7.3` installation
The next step fails
```python
(pandafix) PS C:\Users\kanugu\code\pfix\pandas-soilstack> python .\setup.py build_ext --inplace -j 4
running build_ext
pandas._libs.algos: -> [['pandas\\_libs/algos.c']]
Traceback (most recent call last):
File ".\setup.py", line 787, in <module>
**setuptools_kwargs)
File "C:\Users\kanugu\.conda\envs\pandafix\lib\site-packages\setuptools\__init__.py", line 145, in setup
return distutils.core.setup(**attrs)
File "C:\Users\kanugu\.conda\envs\pandafix\lib\distutils\core.py", line 148, in setup
dist.run_commands()
File "C:\Users\kanugu\.conda\envs\pandafix\lib\distutils\dist.py", line 966, in run_commands
self.run_command(cmd)
File "C:\Users\kanugu\.conda\envs\pandafix\lib\distutils\dist.py", line 985, in run_command
cmd_obj.run()
File "C:\Users\kanugu\.conda\envs\pandafix\lib\distutils\command\build_ext.py", line 340, in run
self.build_extensions()
File ".\setup.py", line 377, in build_extensions
self.check_cython_extensions(self.extensions)
File ".\setup.py", line 374, in check_cython_extensions
""".format(src=src))
Exception: Cython-generated file 'pandas\_libs/algos.c' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
```
#### Problem description
I am not sure where the problem originates from. Some issue with the source? Cannot find VisualStudio? Something else?
#### Output of ``pd.show_versions()``
Am using `python 3.7.3`
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} | 3 | 2019-06-25T17:03:55Z | 2019-06-26T12:18:59Z | 2019-06-26T12:18:55Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/25593 | {
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} | [] | closed | false | null | [] | null | 0 | 2019-06-25T17:45:13Z | 2019-06-25T17:47:54Z | 2019-06-25T17:47:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 1 | 2019-06-25T18:00:40Z | 2019-06-25T18:21:26Z | 2019-06-25T18:21:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
date = pd.to_datetime(20191230, format="%Y%m%d")
print(date.weekofyear)
```
#### Problem description
[this should explain **why** the current behavior is a problem and why the expected output is a better solution.]
I'm doing a project in which I want to print the dates of a particular week of the month .
This code is printing 1 as weekofyear when we are giving date '2019-12-30' or '2019-12-31',
but it should print 53 as output, as these dates lie in the 53rd week of the year.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
53
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.6.2
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.0
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.4
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-06-25T19:56:50Z | 2019-06-27T18:16:28Z | 2019-06-27T18:16:28Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
# Your code here
NCOLS = 3
NROWS = 50
data = {"col{}".format(int((i - NCOLS / 2) % NCOLS + 1)): np.random.randint(
-100, 100, size=(NROWS)
)
for i in range(NCOLS)
}
df = pd.dataframe(data)
df.fillna(0, limit=-5)
```
#### Problem description
I am currently on pandas 0.24.2 and running the above code does not throw an error even though the limit is less than or equal to zero. This error does not arise when the `data` is made up of floats instead of ints (replace `np.random.randint` with `np.random.uniform`).
#### Expected Output
`ValueError: Limit must be greater than 0`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.7
numpy: 1.15.0
scipy: 1.2.1
pyarrow: 0.14.0.RAY
xarray: 0.12.0
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 6 | 2019-06-25T20:59:23Z | 2020-04-05T17:36:42Z | 2019-06-26T15:56:35Z | MEMBER | null | Also some unrecheable try_cast code. | {
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} | 10 | 2019-06-25T22:21:11Z | 2019-07-11T15:46:40Z | 2019-07-01T16:41:06Z | CONTRIBUTOR | null | - [x] closes #12537
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2019-06-26T10:20:26Z | 2019-06-26T10:28:08Z | 2019-06-26T10:27:03Z | CONTRIBUTOR | null | For df.to_json() the speech marks should be escaped. Currently, the json is not valid. This was already reported as an issue here https://github.com/pandas-dev/pandas/issues/15288, but most likely incorrectly marked as duplicate with something else.
```
idx = pd.MultiIndex.from_product([['Zara', 'LV', 'Roots'],
['Orders', 'GMV', 'AOV']],
names=['Brand', 'Metric'])
col = ['Yesterday', 'Yesterday-1', 'Yesterday-7', 'Thirty day average']
df = pd.DataFrame('-', idx, col)
df.to_json()
'{"Yesterday":{"["Zara","Orders"]":"-","["Zara","GMV"]":"-","["Zara","AOV"]":"-","["LV","Orders"]":"-","["LV","GMV"]":"-","["LV","AOV"]":"-","["Roots","Orders"]":"-","["Roots","GMV"]":"-","["Roots","AOV"]":"-"},"Yesterday-1":{"["Zara","Orders"]":"-","["Zara","GMV"]":"-","["Zara","AOV"]":"-","["LV","Orders"]":"-","["LV","GMV"]":"-","["LV","AOV"]":"-","["Roots","Orders"]":"-","["Roots","GMV"]":"-","["Roots","AOV"]":"-"},"Yesterday-7":{"["Zara","Orders"]":"-","["Zara","GMV"]":"-","["Zara","AOV"]":"-","["LV","Orders"]":"-","["LV","GMV"]":"-","["LV","AOV"]":"-","["Roots","Orders"]":"-","["Roots","GMV"]":"-","["Roots","AOV"]":"-"},"Thirty day average":{"["Zara","Orders"]":"-","["Zara","GMV"]":"-","["Zara","AOV"]":"-","["LV","Orders"]":"-","["LV","GMV"]":"-","["LV","AOV"]":"-","["Roots","Orders"]":"-","["Roots","GMV"]":"-","["Roots","AOV"]":"-"}}'
```
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] | closed | false | null | [] | null | 12 | 2019-06-26T10:32:00Z | 2019-09-05T17:07:37Z | 2019-09-05T17:07:37Z | CONTRIBUTOR | null | - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The original implementation of checking whether the json contains nested structure is wrong as it will return `True` even for a non nested json (e.g. `[{'x' : 1}]`). The changes ensures that `nested_to_record` is not called unnecessarily | {
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} | 2 | 2019-06-26T10:47:17Z | 2019-06-26T11:52:55Z | 2019-06-26T11:52:01Z | NONE | null | ```python
import pandas as pd
df = pd.DataFrame({'a': range(0, 10), 'b': range(0, 10), 'c': [False, True]*5},
index=pd.to_datetime(['01:00', '01:01', '01:02', '01:03', '01:04',
'01:31', '01:32', '01:33', '01:34', '01:35']))
df.resample('15min').agg({'a': 'sum', 'b': 'mean', 'c': 'first'})
# Out[5]:
# a b c
# 2019-06-26 01:00:00 10 2.0 0.0
# 2019-06-26 01:15:00 0 NaN NaN
# 2019-06-26 01:30:00 35 7.0 1.0
```
####
One problem I think is the creation of the empty group (01:15), but if `sum` would produce `nan` as does `mean` and `first` it would be easily solved with `dropna` after, but `sum` produces zeros. It's obviously more annoying if the column contains zeros before resampling.
Is there a simple way to include a parameter in resample or aggregate to avoid the empty group creation in this case?
As a small note, why does `first` converts `bool` to `int`? I'd think it should keep the output as `bool`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-06-26T12:41:28Z | 2020-01-16T00:34:52Z | 2019-06-27T02:49:02Z | MEMBER | null | This was put in place as we were fixing up old annotations but isn't required at this point, so can safely remove and stick with mypy defaults (which is more strict)
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https://api.github.com/repos/pandas-dev/pandas/issues/27049 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27049/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27049/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27049/events | https://github.com/pandas-dev/pandas/issues/27049 | 460,954,014 | MDU6SXNzdWU0NjA5NTQwMTQ= | 27,049 | Inconsistent behavior when boolean mask has inconsistent size with a Series | {
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} | [] | closed | false | null | [] | null | 2 | 2019-06-26T12:54:42Z | 2019-06-26T13:10:55Z | 2019-06-26T13:10:55Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
series = pd.Series(np.arange(3))
print(series.iloc[[True, False, True, False]])
print(series.iloc[[True, False, True, True]])
```
```pytb
0 0
2 2
dtype: int64
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py in _getbool_axis(self, key, axis)
1517 try:
-> 1518 return self.obj._take(inds, axis=axis)
1519 except Exception as detail:
~/miniconda3/lib/python3.7/site-packages/pandas/core/series.py in _take(self, indices, axis, is_copy)
3925 indices = ensure_platform_int(indices)
-> 3926 new_index = self.index.take(indices)
3927
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
798 raise ValueError(msg.format(self.__class__.__name__))
--> 799 taken = self.values.take(indices)
800 return self._shallow_copy(taken)
IndexError: index 3 is out of bounds for size 3
During handling of the above exception, another exception occurred:
IndexError Traceback (most recent call last)
<ipython-input-1-f9cf8f154f3b> in <module>
4 series = pd.Series(np.arange(3))
5 print(series.iloc[[True, False, True, False]])
----> 6 print(series.iloc[[True, False, True, True]])
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1498
1499 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1500 return self._getitem_axis(maybe_callable, axis=axis)
1501
1502 def _is_scalar_access(self, key):
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
2215 if com.is_bool_indexer(key):
2216 self._validate_key(key, axis)
-> 2217 return self._getbool_axis(key, axis=axis)
2218
2219 # a list of integers
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py in _getbool_axis(self, key, axis)
1518 return self.obj._take(inds, axis=axis)
1519 except Exception as detail:
-> 1520 raise self._exception(detail)
1521
1522 def _get_slice_axis(self, slice_obj, axis=None):
IndexError: index 3 is out of bounds for size 3
```
#### Problem description
Giving a boolean mask of a different size than a Series, filtering will work when boolean values out of bounds will be False while it wil error with the proper IndexError when of these values will True.
It seems a surprising results, a little bit inconsistent. I would have expect an error in all cases due to the inconsistent size of the boolean mask.
#### Expected Output
Raise an error with inconsistent size of the boolean mask and the Series to filter.
#### Output of ``pd.show_versions()``
<details>
```
/home/lemaitre/miniconda3/lib/python3.7/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: 0.11.1
xarray: None
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.3.0
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/27050 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27050/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27050/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27050/events | https://github.com/pandas-dev/pandas/pull/27050 | 460,976,377 | MDExOlB1bGxSZXF1ZXN0MjkxOTkzNjc1 | 27,050 | Contributing Guide for Type Hints | {
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} | 10 | 2019-06-26T13:37:58Z | 2019-08-25T16:06:33Z | 2019-08-25T16:05:00Z | MEMBER | null | Want to get this documented as we've been picking up momentum with type hints
@toobaz @TomAugspurger @vaibhavhrt @gwrome @makbigc | {
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"description": "read_excel, to_excel"... | closed | false | null | [] | null | 4 | 2019-06-26T14:38:09Z | 2019-08-13T09:41:59Z | 2019-08-13T09:41:59Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I have recently worked on a project where I needed to insert pandas dataframes into an existing Excel workbook (.xlsx) file.
I wrote the following function:
```python
def append_df_to_excel(filename, df, sheet_name='Sheet1', startcol='A', startrow=1, **to_excel_kwargs):
"""
Append a DataFrame [df] to existing Excel file [filename]
into [sheet_name] Sheet.
If [filename] doesn't exist, then this function will create it.
Parameters:
filename : File path or existing ExcelWriter
(Example: '/path/to/file.xlsx')
df : dataframe to save to workbook
sheet_name : Name of sheet which will contain DataFrame.
(default: 'Sheet1')
startrow : upper left cell row to dump data frame - Excel row (starting from 1) not Python row (starting from 0)
startcol : upper left cell column to dump data frame - Excel column (character)
to_excel_kwargs : arguments which will be passed to `DataFrame.to_excel()`
[can be dictionary]
Returns: None
"""
# ignore [engine] parameter if it was passed
if 'engine' in to_excel_kwargs:
to_excel_kwargs.pop('engine')
writer = pd.ExcelWriter(filename, engine='openpyxl')
try:
# try to open an existing workbook
writer.book = load_workbook(filename)
# copy existing sheets
writer.sheets = {ws.title:ws for ws in writer.book.worksheets}
except FileNotFoundError:
# file does not exist yet, we will create it
pass
# Convert Excel rows into Python rows
if startrow > 0:
startrow = startrow - 1
# Convert Excel columns into Python columns
startcol = col2num(startcol) - 1
# write out the new sheet
if 'header' in to_excel_kwargs:
header = to_excel_kwargs['header']
del(to_excel_kwargs['header'])
else:
header = False
df.to_excel(writer, sheet_name, startrow=startrow, startcol=startcol, header=header, index=False, **to_excel_kwargs)
writer.save()
```
Could this be optional behaviour of df.to_excel()
#### Problem description
It is sometimes necessary to fill data into templates provided in excel without overwriting other data or formulae in the excel workbook. Being able to do so using df.to_excel (and then developing the functions to make this relatively efficient if you have to write to many places in the same file) seems a natural and useful extension.
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... | open | false | null | [] | null | 1 | 2019-06-26T14:41:11Z | 2021-07-10T05:31:05Z | null | NONE | null | ```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
n=3
df1 = pd.DataFrame(data=np.random.rand(10, n))
df2 = pd.DataFrame(data=np.random.rand(10, n) * 2)
# Using Pandas plot method
fig, axes = plt.subplots(ncols=n, nrows=2, sharey='row')
for ax, d in zip(axes, [df1, df2]):
for i in range(n):
d.iloc[:, i].plot(ax=ax[i])
ax[i].tick_params(labelright=True)
fig.tight_layout()
# Using Matplotlib plot method
fig, axes = plt.subplots(ncols=3, nrows=2, sharey='row')
for ax, d in zip(axes, [df1, df2]):
for i in range(n):
ax[i].plot(d.iloc[:, i])
ax[i].tick_params(labelright=True)
fig.tight_layout()
```
#### Problem description
When using subplots in matplotlib, and the keyword `sharey`, the behaviour of `tick_params` changes and becomes inconsistent.
This seems to be a pandas related issue, as the code above will show how the pandas plot method is not behaving.
#### Expected Output
#### Output of pandas 0.24.0
<details>
Versions used
python: 3.6.8.final.0
pandas: 0.24.0
matplotlib: 3.1.0
</details>
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"de... | closed | false | null | [] | null | 1 | 2019-06-26T15:13:01Z | 2019-06-27T03:42:13Z | 2019-06-27T03:42:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df0=pd.DataFrame({'index':['a','b'],'foo':[1,2]}).set_index('index')
df1=pd.DataFrame({'index':['b','c'],'bar':[3,4]}).set_index('index')
pd.concat([df0,df1],axis=1)
```
#### Problem description
pandas.concat removes the name of the index column when concatenating dataframes with string typed index column, giving as a result:
| |foo|bar|
|---|---|---|
|a|1.0|NaN|
|b|2.0|3.0|
|c|NaN|4.0|
#### Expected Output
|index|foo|bar|
|---|---|---|
|a|1.0|NaN|
|b|2.0|3.0|
|c|NaN|4.0|
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.5.1
pip: 18.0
setuptools: 41.0.1
Cython: 0.28.2
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 3.0.3
openpyxl: 2.5.3
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: 0.9.2
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 1 | 2019-06-26T15:37:59Z | 2019-11-06T17:51:05Z | 2019-11-06T17:51:05Z | MEMBER | null | In our continuous integration we validate that all the examples in the documentation follow PEP-8, and are valid code that can run.
Currently, we're skipping the essential basic functionality page, because the examples showing the `pipe` functionality can't be run, because the variables it uses are not defined. The example can be seen here:
https://github.com/pandas-dev/pandas/blame/master/doc/source/getting_started/basics.rst#L765
What we should do is to replace this example, by a real-world example, where the data is previously created. This way users will be able to run the code, and also will understand better a real-world case where `.pipe()` can be used.
A great resource for understanding `pipe` and to get inspiration for an example is this blog post: https://tomaugspurger.github.io/method-chaining
What needs to be done here is:
- Add a **minimalistic** DataFrame with somehow real-world data.
- Create couple of **one-liner** functions that can be used with pipe and the dataset created, in a meaningful way.
- Make sure that the example is not overcomplicated, and that the data and the functions are so simple that the reader can focus all their attention to understanding `pipe` and method chaining.
- Start validating the code in the file (i.e. remove this line: https://github.com/pandas-dev/pandas/blob/master/setup.cfg#L49) | {
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"description": "Continuous Integratio... | open | false | null | [] | null | 2 | 2019-06-26T15:52:26Z | 2020-01-10T14:11:00Z | null | MEMBER | null | Our script `scripts/validate_docstrings.py` validates that docstrings in our code follow certain standards (the right parameters are documented, the docstring contain examples, capitalization and punctuation...).
There is currently one case when the validation is incorrect. See this (hypothetical) example:
```python
class Series:
ndim = 1
```
If we execute `./scripts/validate_docstrings.py pandas.Series.ndim`, we'd expect the script to detect that `ndim` doesn't have a docstring. But instead, the script is validating the docstring of the type `int` (the doc of the scalar `1`).
This is happening in practice for `offset`, and is preventing us to validate some error types to the CI. See: https://github.com/pandas-dev/pandas/issues/26982#issuecomment-505499869
We should detect this case in the script, and not validate the docstrings of attributes. | {
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] | closed | false | null | [] | null | 2 | 2019-06-26T16:00:52Z | 2019-09-19T17:18:38Z | 2019-09-19T17:18:37Z | NONE | null | On PyPI, narrow-unicode wheels are not provided. This causes [my](https://github.com/scikit-hep/decaylanguage) Azure pipelines that uses Pandas to take 8 minutes on the Python 2.7 versions instead of 45 seconds for all other Python 3 versions. The manylinux container does [have both](https://github.com/pypa/manylinux) "cp27mu" and "cp27m" versions - and Azure is using the narrow-unicode one. Numpy does provide this narrow-unicode wheel, so it's only Pandas doing the build.
Could narrow unicode builds be provided as well? It would be a file like `*cp27-cp27m-manylinux1_x86_64.whl` in addition to `cp27-cp27mu-manylinux1_x86_64.whl `. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27057 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27057/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27057/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27057/events | https://github.com/pandas-dev/pandas/issues/27057 | 461,059,512 | MDU6SXNzdWU0NjEwNTk1MTI= | 27,057 | rolling(offset) treats duplicated Timestamps inconsistently between closed=`left`/`right`; | {
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} | [] | closed | false | null | [] | null | 0 | 2019-06-26T16:06:14Z | 2019-08-02T15:32:05Z | 2019-08-02T15:32:05Z | NONE | null | On second thought, ok. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27058 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27058/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27058/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27058/events | https://github.com/pandas-dev/pandas/issues/27058 | 461,067,249 | MDU6SXNzdWU0NjEwNjcyNDk= | 27,058 | ENH: add Series.set_index | {
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**update**: use `set_axis`.
```python
import pandas as pd
dates=[
"2001-01-01 23:50",
"2001-01-02 14:00",
"2001-01-03 00:08"]
ser=pd.Series(range(3),pd.DatetimeIndex(dates))
```
sometimes when writing a sequence of operation, you want to replace the index of the object you're working on. Say, quantizing the dates. I can't find the .method way of doing
``
ser.index= expr
``
So that I can't write something like
```
(ser
.replace_index(ser.index.floor("D")) # < this doesn't exist
.rolling("5d")
.sum()
```
Things that don't solve this:
- `reindex` is aligning, so if your new index permutes the indexes, it isn't suitable.
- `set_index` <del>takes a column name, not an expression</del>, and isn't available on series. **update** works for frames, though you must provide an arraylike, not a listlike.
- `reindex_like` expects a PandasObject instead of a listlike, and is aligning
- `reset_index` does not take a values argument
```
In [1]: [_ for _ in dir(ser) if 'index' in _ and not _.startswith("_")]
Out [1]:
['first_valid_index',
'index',
'last_valid_index',
'reindex',
'reindex_axis',
'reindex_like',
'reset_index',
'sort_index']
``` | {
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} | 14 | 2019-06-26T17:43:02Z | 2020-01-02T01:45:40Z | 2020-01-01T15:58:48Z | NONE | null | I've searched the documentation and old issues and I can't find anything on the reason for this unusual choice. The original historic PR was #2922.
One user raised the question before in #14900 and aggressively shut down by jeff without an answer.
It has caused bugs in my code in the past, and I've seen it do the same for other people,
It is the cause of subtle bugs seen in the [wild](https://gist.github.com/betatim/c59039682d92fab89859358e8c585313), https://github.com/pandas-dev/pandas/issues/26959#issuecomment-504456480. It's inconsistency with python slice conventions is confusing to newbies who ask on [SO](https://stackoverflow.com/questions/55187559/why-is-loc-slicing-in-pandas-inclusive-of-stop-contrary-to-typical-python-slic) but no explanation is given.
Every [pandas tutorial](https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c) has to mention this special case:
```
.loc includes the last value with slice notation. In other data containers such as Python lists, the last value is excluded.
```
users often need to slice something with `closed='left'` behavior, and try [to add it](https://github.com/pandas-dev/pandas/issues/12398) in similar situations where it isn't available.
This behavior is fully documented. That's not the problem. For example,
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html says
```
Note that contrary to usual python slices, both the start and the stop are
included, when present in the index! See "Slicing with labels".).
```
The "Slicing with labels" section documents the behavior, but gives no reason why the choice to break with python conventions was taken.
Another user who asked this question was given a workaround which is much more cumbersome compared to the convenience of `.loc` in https://github.com/pandas-dev/pandas/issues/16571#issuecomment-305599270.
He points to [DataFrame.between_time](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.between_time.html), which has kwds for requesting this behavior, but infuriatingly, accepts only times and not datetimes.
A few related cookbook entries were added
https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html#dataframes
which explains
```
There are 2 explicit slicing methods, with a third general case
Positional-oriented (Python slicing style : exclusive of end)
Label-oriented (Non-Python slicing style : inclusive of end)
```
this again documents how `loc` works, but again offers no reason why pandas originally broke with python conventions.
In every case I've seen someone ask "why is label-based slicing right-inclusive?" the answer has always been "because it's label based, not position based", which doesn't really explain anything.
The same issue exists with string based partial time slicing such like `df.loc[:"2018"]`,
which will include rows with `2018-01-01` prefix.
So, over the years several people have found this undesirable and/or have tried to find out why, but with an hour's worth of gooling and reading, I can't find an explanation ever have been given.
I'm not saying there's no good reason, I understand that indexing can get complicated, and mixed cases are tricky, etc'. But I want to understand why this was necessary or desirable, and why making `.loc` pythonic was unfeasible.
I'll be opening a very small POC PR for discussion in a few minutes, which adds a new indexer called `locs`. <del>It is far from complete, but it seems to do exactly what I want for the single-index case, in what I've tried so far.</del> it passes all the equivalent tests loc does.
To summarize:
- it's been asked before, but not answered.
- the behavior is documented, but is surprising and never explained.
- It's not immediately obvious why it's impossible to implement the pythonic version.
So I ask, why is `.loc` right-inclusive?
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"color": "AD... | closed | false | null | [] | null | 9 | 2019-06-26T17:46:53Z | 2019-08-08T18:49:43Z | 2019-07-31T14:23:52Z | NONE | null | **Note**: this began as a POC for an additional indexer `loc_left`, which has now been updated to become the low-level API used for providing the `NDFrame.slice` functionality.
WIP/POC for #27059. In short, `loc` was designed with unpythonic right-inclusive semantics, and for dubious considerations. It would be an improvement to treat slicing as taking a `closed` argument, as many pieces of pandas functionality do, such as `Rolling`, `Interval`.
- Adds a `loc_left` indexer to complements `loc`. This could be made private.
- Adds an `NDFrame.slice` method is discussed in #27059 which mimics python's `slice` signature, with the addition of a `closed` keyword. The default is 'left' to match pythonic convention.
- The indexer reuses the loc machinery with very few changes, and passes a test suite which augments the existing tests for loc.
- [X] implement and test `loc_left[]`
- [X] implement and test `slice(closed=left/both)`
- [ ] implement and test `slice(closed=right/neither)`
**Note**: #27209 by @simonjayhawkins enhances `IndexSlice` with a `closed` argument instead of introducing a new indexer. `slice` could be adapted to make use of that if desired.
Examples:
```text
In [6]: import pandas as pd
...: ix= pd.DatetimeIndex(["2001-01","2001-01","2001-02","2001-02","2001-03","2001-03"])
...: df=pd.DataFrame(dict(a=range(6)),index=ix)
...: df
Out[6]:
a
2001-01-01 0
2001-01-01 1
2001-02-01 2
2001-02-01 3
2001-03-01 4
2001-03-01 5
In [7]: df.slice("2001-03")
Out[7]:
a
2001-01-01 0
2001-01-01 1
2001-02-01 2
2001-02-01 3
In [8]: df.slice("2001-03", closed="right")
Out[8]:
a
2001-01-01 0
2001-01-01 1
2001-02-01 2
2001-02-01 3
2001-03-01 4
2001-03-01 5
In [9]: df.slice("2001-02", "2001-03")
Out[9]:
a
2001-02-01 2
2001-02-01 3
In [10]: df.slice("2001-02", "2001-03",closed="right")
Out[10]:
a
2001-02-01 2
2001-02-01 3
2001-03-01 4
2001-03-01 5
In [11]: df.slice("2001-01", "2001-03",step=2)
Out[11]:
a
2001-01-01 0
2001-02-01 2
In [12]: df.slice("2001-01", "2001-03",step=2, closed="right")
Out[12]:
a
2001-02-01 2
2001-03-01 4
``` | {
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> conda search pyreadstat -c conda-forge
Loading channels: done
# Name Version Build Channel
pyreadstat 0.2.1 py36h301d43c_0 conda-forge
pyreadstat 0.2.1 py37h301d43c_0 conda-forge
pyreadstat 0.2.5 py36h301d43c_0 conda-forge
pyreadstat 0.2.5 py37h301d43c_0 conda-forge
pyreadstat 0.2.6 py36h301d43c_0 conda-forge
pyreadstat 0.2.6 py37h301d43c_0 conda-forge
```
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Using the ``cxx-compile`` would be better because it will use the compiler from the virtual environment and it will not rely on the system anymore. That would be great to also avoid any kind of abi problems if someone uses some old compiler
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} | 1 | 2019-06-26T19:55:10Z | 2019-06-27T21:36:18Z | 2019-06-27T02:17:02Z | MEMBER | null | - [x] closes #26896
Depending on how flaky the coverage calculations are, we could consider adding `threshold` to the yaml to specify how far the coverage can drop while posting success.
https://docs.codecov.io/docs/commit-status
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] | closed | false | null | [] | null | 8 | 2019-06-27T03:00:05Z | 2019-10-31T22:42:38Z | 2019-10-31T22:42:38Z | MEMBER | null | Trying out to simply use the ExtensionBlock instead of the current DatetimeTZBlock for datetime tz data (with the exception of one override to have `.values` backwards compatible), to see what breaks. | {
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"color": "207de5"... | closed | false | null | [] | null | 10 | 2019-06-27T05:40:31Z | 2019-07-15T01:06:37Z | 2019-07-15T01:06:37Z | CONTRIBUTOR | null | - [x] closes #16918
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A new argument `fill_value` is added to allow users to supply default values for missing fields for each column. This enhancement also addresses the undesirable type conversion from `int` to `float` due to missing fields as raised in #16918 | {
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] | closed | false | null | [] | null | 4 | 2019-06-27T10:33:03Z | 2019-06-27T18:17:51Z | 2019-06-27T17:21:02Z | CONTRIBUTOR | null | fillna method was not throwing an ValueError with a dataframe full of
integers (no NAs) and limit keyword set to a negative number. Issue can
be seen in GH27042.
Begun with writing tests for checking the behaviour of fillna
and values of limit in test/frame/test_missing.py for both negative and
non-integer values of limit.
Then wrote new test file tests/util/test_validate_fillna_kwargs.py,
where the function would now also take limit as an argument and
perform necassary checks in there.
Made changes to util/_validators.py to correctly check values of limit
as was already done in core/internal/blocks.py
Updated calls to validate_fillna_kwargs in various files
to include the limit argument.
- [x ] closes #27042
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x ] whatsnew entry
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} | 11 | 2019-06-27T13:43:34Z | 2020-07-08T21:47:42Z | 2020-07-08T21:47:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df_test = pd.DataFrame()
df_test['A'] = pd.Series(np.arange(0,2), dtype='category').cat.set_categories(list(range(0,3)))
df_test['B'] = pd.Series(np.arange(10,12), dtype='category').cat.set_categories(list(range(10,13)))
print("Test DF:")
print(df_test)
print("\nThe following are as expected, unobserved categories have size = 0:")
print(df_test.groupby('A').size())
print(df_test.groupby('B').size())
print("\nThe following does not consider categories, I would expect 9 result lines here:")
print(df_test.groupby(['A','B']).size())
print("\nExpected:")
print(pd.DataFrame({'A':list(range(0,3))*3, 'B':list(range(10,13))*3, '':[1]*2+[0]*7 }).set_index(['A','B']))
```
#### Problem description
groupby([cols]) gives back a result for all categories if only one column that is categorical is provided (e.g. ['A']), but it only shows the observed combinations if multiple categorical columns are provided ['A', 'B'], regardless of the setting of observed. I would expect that I would get a result for all combinations of the categorical columns.
#### Expected Output
A result for all combinations of the categorical categories of the groupby columns. For the example above:
A B
0 10 1
1 11 1
2 12 0
0 10 0
1 11 0
2 12 0
0 10 0
1 11 0
2 12 0
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
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} | 24 | 2019-06-27T15:10:07Z | 2019-07-04T03:30:51Z | 2019-07-04T03:28:24Z | MEMBER | null | So we can see what it looks like | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27077 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27077/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27077/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27077/events | https://github.com/pandas-dev/pandas/pull/27077 | 461,606,972 | MDExOlB1bGxSZXF1ZXN0MjkyNDk2OTU3 | 27,077 | BUG/TST: fillna limit checks and tests | {
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} | 4 | 2019-06-27T15:45:40Z | 2019-06-27T18:21:17Z | 2019-06-27T18:16:28Z | CONTRIBUTOR | null | Reversed the order of returning and performing checks on `limit`
in fillna method in blocks.py so that an ValueError is raised
before returning.
Alternative to PR #27074
- [tick ] closes #27042
- [ ] tests added / passed
- [tick ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ tick] whatsnew entry
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} | 2 | 2019-06-27T15:57:25Z | 2021-01-27T00:15:44Z | 2021-01-27T00:15:44Z | CONTRIBUTOR | null | #27071 allows for the preservation dtype for filtering type of ops in groupby (first, last, min, max). This *should* work for all EA (though was fixing Categorical).
We should
- test all EA more generally for groupby filtering type ops & potentially other aggregations
- document what works / what doesn't work. | {
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} | 0 | 2019-06-27T16:52:28Z | 2019-06-27T18:36:43Z | 2019-06-27T18:33:20Z | MEMBER | null | - [x] closes #26976
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Remove unused maybe_to_sparse unrelated. | {
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} | 2 | 2019-06-27T16:53:52Z | 2019-08-13T16:15:37Z | 2019-08-13T16:15:37Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [2]: a = pd.SparseArray([0, 10, 1])
In [3]: np.maximum.reduce(a)
Out[3]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/Envs/pandas-dev/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/Envs/pandas-dev/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
400 if cls is not object \
401 and callable(cls.__dict__.get('__repr__')):
--> 402 return _repr_pprint(obj, self, cycle)
403
404 return _default_pprint(obj, self, cycle)
~/Envs/pandas-dev/lib/python3.7/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
695 """A pprint that just redirects to the normal repr function."""
696 # Find newlines and replace them with p.break_()
--> 697 output = repr(obj)
698 for idx,output_line in enumerate(output.splitlines()):
699 if idx:
~/sandbox/pandas/pandas/core/arrays/sparse.py in __repr__(self)
1815 def __repr__(self):
1816 return '{self}\nFill: {fill}\n{index}'.format(
-> 1817 self=printing.pprint_thing(self),
1818 fill=printing.pprint_thing(self.fill_value),
1819 index=printing.pprint_thing(self.sp_index))
~/sandbox/pandas/pandas/io/formats/printing.py in pprint_thing(thing, _nest_lvl, escape_chars, default_escapes, quote_strings, max_seq_items)
215 result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars,
216 quote_strings=quote_strings,
--> 217 max_seq_items=max_seq_items)
218 elif isinstance(thing, str) and quote_strings:
219 result = "'{thing}'".format(thing=as_escaped_unicode(thing))
~/sandbox/pandas/pandas/io/formats/printing.py in _pprint_seq(seq, _nest_lvl, max_seq_items, **kwds)
111 r = [pprint_thing(next(s),
112 _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
--> 113 for i in range(min(nitems, len(seq)))]
114 body = ", ".join(r)
115
~/sandbox/pandas/pandas/io/formats/printing.py in <listcomp>(.0)
111 r = [pprint_thing(next(s),
112 _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
--> 113 for i in range(min(nitems, len(seq)))]
114 body = ", ".join(r)
115
~/sandbox/pandas/pandas/core/arrays/base.py in __iter__(self)
283 # calls to ``__getitem__``, which may be slower than necessary.
284 for i in range(len(self)):
--> 285 yield self[i]
286
287 # ------------------------------------------------------------------------
~/sandbox/pandas/pandas/core/arrays/sparse.py in __getitem__(self, key)
1092
1093 if is_integer(key):
-> 1094 return self._get_val_at(key)
1095 elif isinstance(key, tuple):
1096 data_slice = self.to_dense()[key]
~/sandbox/pandas/pandas/core/arrays/sparse.py in _get_val_at(self, loc)
1135 return self.fill_value
1136 else:
-> 1137 return libindex.get_value_at(self.sp_values, sp_loc)
1138
1139 def take(self, indices, allow_fill=False, fill_value=None):
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got numpy.int64)
In [4]: result = np.maximum.reduce(a)
In [5]: type(result)
Out[5]: pandas.core.arrays.sparse.SparseArray
```
should be a scalar 10. | {
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} | 8 | 2019-06-27T17:04:14Z | 2020-05-09T07:57:33Z | 2020-05-09T07:57:17Z | CONTRIBUTOR | null | We don't have a good method of testing for equality between EA's
```
In [19]: from pandas.tests.extension.decimal.array import DecimalArray
In [20]: from decimal import Decimal
In [21]: x = DecimalArray([Decimal('1'),Decimal('Nan')])
In [22]: x == x
Out[22]: array([ True, False])
In [23]: x = pd.Series([1,np.nan], dtype='Int64').array
In [24]: x == x
Out[24]: array([ True, False])
```
These *happen* to work with Series now because the null checks are handled at a higher level.
```
In [26]: x.equals(x)
Out[26]: True
In [27]: x = pd.Series(DecimalArray([Decimal('1'),Decimal('Nan')]))
In [28]: x.equals(x)
Out[28]: True
```
we could provide a default implementation that should just work in the presence of NA.
```
def equals(self, other):
return ((self == other) | (self.isna() == other.isna())).all()
```
actually we should *also* implement a default ``__eq__`` | {
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} | 3 | 2019-06-27T17:12:05Z | 2019-06-27T21:26:08Z | 2019-06-27T21:23:03Z | MEMBER | null | @jreback @jbrockmendel
Recreated pickle file for 0.20.3 using Python 3.5.2 on Mac. Created msgpack file anew (previously 0.19 was last version tested)
Lots of follow ups can be done here including:
- Automatically generating one of these files as part of build process
- Decoupling script from test requirements (fixturization and break off)
Can also add more platforms / versions here as required but this should be a good starting point | {
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} | 5 | 2019-06-27T18:34:25Z | 2019-06-27T21:27:18Z | 2019-06-27T21:22:38Z | MEMBER | null | Not clear on what type of deprecation cycle we want here
- [x] closes #26995, closes #27024 | {
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} | 1 | 2019-06-27T21:26:19Z | 2021-05-13T09:30:04Z | 2019-06-29T02:37:45Z | CONTRIBUTOR | null | msgpack uses the internal format on the wire. this makes for *only* pandas producers and consumers, but with ``pyarrow`` support now fully handling this case (and better performing), it is time to deprecate msgpack. | {
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} | 9 | 2019-06-27T22:05:15Z | 2021-11-20T05:33:42Z | 2021-11-20T05:33:41Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/26628#issuecomment-500166805
It would be good to create:
`pandas/tests/tseries/offsets/test_business_offsets.py` for business offsets
`pandas/tests/tseries/offsets/test_date_offsets.py` for all offsets that subclass `DateOffset`
and converting the tests to pytest idioms. | {
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} | 0 | 2019-06-27T22:05:21Z | 2019-06-28T15:27:27Z | 2019-06-28T12:33:26Z | NONE | null | While working on the tests for #27060, I found there's no test coverage for `loc` slicing of PeriodIndex and TimedeltaIndex. Added test for string and label slicing.
Someday, it may be a good idea to refactor and parameterize by Index type. But not today. | {
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] | closed | false | null | [] | null | 8 | 2019-06-27T22:20:21Z | 2019-07-31T14:11:10Z | 2019-07-31T14:11:05Z | NONE | null | - [ ] tests
- [ ] whatsnew
- [ ] update [documentation](https://dev.pandas.io/user_guide/timeseries.html#offset-aliases)
This is missing tests, but I'm not sure I'm doing this properly (timezones, DST) so comments/advice welcome. If reviewers are inclined to merge a complete PR, I'll gladly add the missing tests.
I think the `+timedelta(days=1,nanoseconds=-1)` trick is safe, pytz database shows no dst transitions at midnight in the history of the world, though I encounter a trippy "non-existent time" Exception, which should merit an achievement badge in some other context.
```python
In [4]: import pandas as pd
...: index=pd.date_range("2011-12-31","2012-01-01",freq="12H")
...: index
Out[4]:
DatetimeIndex(['2011-12-31 00:00:00', '2011-12-31 12:00:00',
'2012-01-01 00:00:00'],
dtype='datetime64[ns]', freq='12H')
In [5]: index+pd.offsets.DayEnd()
Out[5]:
DatetimeIndex(['2011-12-31 23:59:59.999999999',
'2011-12-31 23:59:59.999999999',
'2012-01-01 23:59:59.999999999'],
dtype='datetime64[ns]', freq='12H')
In [6]: index-pd.offsets.DayEnd()
Out[6]:
DatetimeIndex(['2011-12-30 23:59:59.999999999',
'2011-12-30 23:59:59.999999999',
'2011-12-31 23:59:59.999999999'],
dtype='datetime64[ns]', freq='12H')
In [2]: index-pd.offsets.DayBegin()
Out[2]: DatetimeIndex(['2011-12-30', '2011-12-30', '2011-12-31'], dtype='datetime64[ns]', freq='12H')
In [3]: index+pd.offsets.DayBegin()
Out[3]: DatetimeIndex(['2012-01-01', '2012-01-01', '2012-01-02'], dtype='datetime64[ns]', freq='12H')
```
I needed `DayEnd` in #26959, when you want to compute rolling stat by day, and you want the window backward to always be from the end of the day, not whatever random time the last event that day occurred.
related #7049, insofar as users have a need for a wider range of aliases then supported. | {
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} | 2 | 2019-06-27T23:35:24Z | 2019-06-28T13:13:43Z | 2019-06-28T13:13:25Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Closing a `FIXME` note in the test suite. | {
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"description... | closed | false | null | [] | null | 9 | 2019-06-28T01:35:06Z | 2019-07-31T14:09:21Z | 2019-07-31T14:09:16Z | NONE | null | related #27087 (Add `DayBegin`, `DayEnd` offsets)
- [ ] closes #15303
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I ran into #15303
```python
In [4]: import pandas as pd
...: index=pd.date_range("2000-01-01","2000-01-02",freq="1H")
...: index.round("M")
ValueError: <MonthEnd> is a non-fixed frequency
```
This [comment](https://github.com/pandas-dev/pandas/issues/15303#issuecomment-277401817) argues that implementing `round()` doesn't make sense here, since with non-fixed offsets it might round some midpoints up and some down. That might be what the user actually wants, but it's a subtle point. I put the commented code in anyway, just as an option, and implemented dti.`floor`/`ceil`.
If there's intent-to-merge, I'll add the tests etc'.
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... | closed | false | null | [] | null | 5 | 2019-06-28T05:44:48Z | 2021-07-10T05:39:38Z | 2021-07-10T05:39:38Z | NONE | null | I believe using pandas dataframes to work with forecasts is a pretty standard use case. Therefore I am missing a convenient method to extend the dataframe in its length while shifting, instead of loosing values. Currently the only way to do this is something like:
```python
df_ext = pd.DataFrame(index=pd.date_range(df.index[-1], periods=8, closed='right'))
df2 = pd.concat([df, df_ext], axis=0, sort=True)
df2["forecast"] = df2["some column"].shift(7)
```
My proposal would be to add an extra argument to the shift function i.e. something along the lines:
```python
df["x"].shift(7, inplace=True, extend_index_freq='D')
``` | {
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Note added to the docs, warning users to be careful when adapting datetime indices generated by pandas. | {
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} | [] | closed | false | null | [] | null | 1 | 2019-06-28T13:46:16Z | 2019-07-08T15:48:07Z | 2019-07-08T15:48:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'person_id' :[1,2,3],'date1':
['12/31/2007','11/25/2009','10/06/2005'],'val1':
[2,4,6],'date2': ['12/31/2017','11/25/2019','10/06/2015'],'val2':[1,3,5],'date3':
['12/31/2027','11/25/2029','10/06/2025'],'val3':[7,9,11]})
pd.wide_to_long(df, stubnames=['date', 'val'], i='person_id',
j='grp').sort_index(level=0)
```
#### Problem description
winde_to_long retruns zero output when I change the column name format. The same code works when I have a different column name format. Please refer this post in SO for more details on list of column names that you can try. Replace the sample column names given in code with the column names in screenshot
https://stackoverflow.com/questions/56807104/wide-to-long-returns-empty-output-python-dataframe?noredirect=1#comment100169905_56807104
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 10 | 2019-06-28T14:44:47Z | 2019-07-04T00:44:31Z | 2019-07-03T21:59:48Z | MEMBER | null | Closes #27000
(didn't yet add tests, docs or whatsnew, but I think the implementation is already more or less complete)
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} | 2 | 2019-06-28T15:06:58Z | 2020-09-29T20:34:36Z | 2020-09-29T20:34:36Z | MEMBER | null | Follow up to this https://github.com/pandas-dev/pandas/pull/25092#issuecomment-506752805
Right now for readers we test directly setting `engine=` as a keyword argument, whereas in the writer tests we set the global option. I *think* we should consistently set the keyword argument and simply have a test on both reading / writing that setting the option will actually trigger that keyword argument to improve coverage and keep things simple | {
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} | 1 | 2019-06-28T16:10:16Z | 2020-01-16T00:34:53Z | 2019-06-28T18:32:33Z | MEMBER | null | Considered a "mistake" in the original PEP 484 using the implicit `Optional[]` on parameters with a default argument of None this PR enables a mypy flag that doesn't do that and checks types more strictly | {
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} | 2 | 2019-06-28T16:10:33Z | 2019-07-17T23:35:16Z | null | CONTRIBUTOR | null | we have a ``DatetimeIndex.snap``, this is duplicative of our more general ``.ceil()``, ``.floor()`` and ``.round()`` (and ``.to_period().to_timestamp()``); the former are also generally available on all of the timelike indices (DTI, TDI, PI)
we should remove this and prefer the 3 rounding methods | {
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} | 1 | 2019-06-28T16:46:38Z | 2019-07-23T22:04:24Z | 2019-07-23T22:04:24Z | NONE | null | #### My Code Sample
When I tried to launch a join operation between multiIndexed dataframes as the following code describes
Fully code is posted on:
https://github.com/ericksc/pandas_studies
```python
# My code here
dates = pd.date_range(start='1980-1-1', end='2050-1-1')
strings = np.array(
[
'apple', 'grape', 'orange' ,'pear', 'melon', 'banana',
'watermelon', 'lemon', 'strawberry', 'berry', 'blackberry',
'cherry','pineapple' , 'mango', 'papaya', 'peach', 'coffee',
'planes', 'cars', 'houses', 'dogs', 'cats', 'computers',
'servers', 'sun', 'moon', 'chairs', 'tables', 'screens',
'keyboards', 'shoes', 't-shirts', 'tv', 'radio', 'door', 'windows',
'bed', 'spoon', 'key', 'paper', 'foot', 'bee', 'ants' ,'worm',
'pack', 'phone', 'boat', 'hair', 'yellow', 'disc'
])
start = time.time()
idx = pd.MultiIndex.from_product([dates,strings], names=['date', 'words'])
end = time.time()
print("MultiIndex execution time:", end-start)
lenght = idx.shape[0]
start = time.time()
df_1 = pd.DataFrame({'col_A':np.random.randn(lenght),
'col_2':np.arange(lenght),
'col_b' : np.random.choice(a=[False, True], size=(lenght)),
'col_i' : np.random.random_integers(low=0,high=100, size=lenght),
}, index=idx)
end = time.time()
print("data frame creation execution time:", end-start)
df_2 = pd.DataFrame({'col_x':np.arange(lenght)},
index=idx)
assert df_1.shape == (1278450, 4)
start = time.time()
df_1 = df_1.join(df_2, how='left')
end = time.time()
print("Join execution time:", end - start)
assert df_1.shape == (1278450, 5)
```
#### Problem description
The join operation between multiIndexed dataframes (at least 2 levels) with a large combination of labels shows a change of time execution performance to worst from pandas 0.19.1 to 0.23.4, including 0.24.2.
- pandas 0.19.1-> join execution time -> 0.1129 seconds
- pandas 0.23.4-> join execution time -> 1.2409 seconds
- pandas 0.24.2-> join execution time -> 1.2309 seconds
This is around 10.9x times.
#### Expected Output
The expected output is a similar performance.
#### Output of ``pd.show_versions()``
<details>
### p36-pandas 0.19.1
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
### p36-pandas 0.23.4
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 4.6.3
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
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xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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} | 13 | 2019-06-28T17:26:18Z | 2020-01-24T19:47:30Z | 2019-07-02T15:41:45Z | MEMBER | null | - [X] closes #16316, closes #23705, closes #25087, closes #25860, closes #24931
- [X] tests added / passed
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I left the `test_interval.py` and `test_interval_new.py` files separate for now, in order to make it more obvious what is being changed. Will condense these files in a follow-up. | {
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} | 29 | 2019-06-28T18:03:18Z | 2020-01-16T00:35:00Z | 2019-07-03T17:02:36Z | MEMBER | null | - [ ] closes #25632
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} | 0 | 2019-06-28T18:39:24Z | 2019-06-28T20:04:18Z | 2019-06-28T20:04:13Z | MEMBER | null | - [x] closes #25220
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Let me know if the whatsnew note should be a special section in the `Deprecation` section. | {
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} | 2 | 2019-06-28T19:12:43Z | 2019-06-29T02:37:45Z | 2019-06-29T02:37:45Z | CONTRIBUTOR | null | closes #27084
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} | 0 | 2019-06-28T19:15:56Z | 2020-11-09T20:27:35Z | 2020-11-09T20:27:35Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
In [2]: df = pd.DataFrame({'col':[1, 2, 3, 4, 5], 'ind1':['a','b','c','d',np.nan], 'ind2':[1,2,3,4,5] }).set_index(['ind1', 'ind2'])
In [3]: df.loc['a']
Out[3]:
col
ind2
1 1
In [4]: df.loc[['a']]
Out[4]:
col
ind1 ind2
a 1 1
NaN 5 5
```
#### Problem description
``Out[3]`` is correct, ``Out[4]`` is wrong - the ``NaN`` line should not be there.
From #15107.
#### Expected Output
Same as ``Out[3]``.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 45ea26763da832189747ac9f86630fece84b4f18
python : 3.7.3.candidate.1
python-bits : 64
OS : Linux
OS-release : 4.9.0-9-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 0.25.0.dev0+824.g45ea26763
numpy : 1.16.4
pytz : 2016.7
dateutil : 2.8.0
pip : 9.0.1
setuptools : 41.0.1
Cython : 0.29.2
pytest : 3.0.6
hypothesis : 3.6.1
sphinx : 1.4.9
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 4.3.2
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: 0.2.1
bs4 : 4.5.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.2
numexpr : 2.6.9
openpyxl : 2.3.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.0.15
tables : 3.4.4
xarray : None
xlrd : 1.0.0
xlwt : 1.3.0
xlsxwriter : 0.9.6
</details>
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} | 0 | 2019-06-28T19:31:01Z | 2019-06-28T20:00:21Z | 2019-06-28T19:54:51Z | MEMBER | null | - [x] closes #14411
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 4 | 2019-06-28T19:50:26Z | 2019-06-28T22:57:10Z | 2019-06-28T22:53:54Z | MEMBER | null | - [x] xref #https://github.com/pandas-dev/pandas/issues/18262
- [x] tests added / passed
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- [x] whatsnew entry
Removes:
`Series.put`
`Index.dtype_str`
`Series.real`
`Series.imag` | {
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... | closed | false | null | [] | null | 4 | 2019-06-28T21:31:45Z | 2020-09-21T11:34:08Z | 2020-09-21T11:34:08Z | CONTRIBUTOR | null | edit: see https://github.com/pandas-dev/pandas/issues/27108#issuecomment-527633246
---
I'd like to be able to have an index, and ensure that no operation introduces duplicates.
```python
idx = pd.Index(..., allow_duplicates=False)
s = pd.Series(..., index=idx)
```
From here, any pandas operation that introduces duplicates (e.g. `s.loc[['a', 'a']]`) would raise, rather than return an Index with two values. | {
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} | 5 | 2019-06-28T21:44:44Z | 2019-07-31T20:38:29Z | 2019-07-31T19:50:29Z | MEMBER | null | - [x] closes #21359
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 5 | 2019-06-28T22:00:32Z | 2019-07-01T21:44:53Z | 2019-07-01T21:40:58Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Addresses one of the problems behind #26864, but probably doesn't address the statefullness problem there.
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} | 0 | 2019-06-28T22:10:48Z | 2019-06-30T03:46:34Z | 2019-06-30T03:46:34Z | MEMBER | null | If you run the `tests/io/excel/test_readers.py` with only openpyxl installed, the xlrd tests are skipped, but some of the openpyxl tests still fail on needing xlrd.
eg:
```
____________________________________________________________ TestExcelFileRead.test_read_excel_engine_value[openpyxl-.xlsm-None] _____________________________________________________________
self = <pandas.tests.io.excel.test_readers.TestExcelFileRead object at 0x7fca3bb6d198>, read_ext = '.xlsm', excel_engine = None
@pytest.mark.parametrize('excel_engine', [
'xlrd',
None
])
def test_read_excel_engine_value(self, read_ext, excel_engine):
# GH 26566
> xl = ExcelFile("test1" + read_ext, engine=excel_engine)
../excel/test_readers.py:853:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../../io/excel/_base.py:791: in __init__
self._reader = self._engines[engine](self._io)
../../../io/excel/_xlrd.py:21: in __init__
import_optional_dependency("xlrd", extra=err_msg)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
name = 'xlrd', extra = 'Install xlrd >= 1.0.0 for Excel support', raise_on_missing = True, on_version = 'raise'
def import_optional_dependency(
name: str,
extra: str = "",
raise_on_missing: bool = True,
on_version: str = "raise",
):
"""
Import an optional dependency.
By default, if a dependency is missing an ImportError with a nice
message will be raised. If a dependency is present, but too old,
we raise.
Parameters
----------
name : str
The module name. This should be top-level only, so that the
version may be checked.
extra : str
Additional text to include in the ImportError message.
raise_on_missing : bool, default True
Whether to raise if the optional dependency is not found.
When False and the module is not present, None is returned.
on_version : str {'raise', 'warn'}
What to do when a dependency's version is too old.
* raise : Raise an ImportError
* warn : Warn that the version is too old. Returns None
* ignore: Return the module, even if the version is too old.
It's expected that users validate the version locally when
using ``on_version="ignore"`` (see. ``io/html.py``)
Returns
-------
maybe_module : Optional[ModuleType]
The imported module, when found and the version is correct.
None is returned when the package is not found and `raise_on_missing`
is False, or when the package's version is too old and `on_version`
is ``'warn'``.
"""
try:
module = importlib.import_module(name)
except ImportError:
if raise_on_missing:
> raise ImportError(message.format(name=name, extra=extra)) from None
E ImportError: Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd.
```
The actual excel reading (interactively) is not affected, so certainly not a blocking issue. | {
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} | 1 | 2019-06-28T22:14:59Z | 2019-06-29T01:56:07Z | 2019-06-29T01:56:04Z | MEMBER | null | - [x] xerf #18262
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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