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} | 17 | 2019-12-22T22:55:35Z | 2020-01-09T16:18:58Z | 2020-01-09T16:18:58Z | NONE | null | ## Change to `str` `dtype` behaviour for missing elements
Following comments the discussion about how to handle missing `NA` scalar values in https://github.com/pandas-dev/pandas/issues/28778 I was asked to raise my question as this seperate issue.
My rather prosaic question is how if missing `str` elements are given the value `NA`, how would I distinguish between a missing `str` value and the two-character string 'NA'?
I ask as NA is a common abbreviation for 'Not Applicable', 'North America' et al, in a way that in my experience that 'NaN' or 'Not a Number' isn't
That is, if 'NA' were generated as the default missing `str` `dtype` value, especially if introduced as change rather than as a opt-in, it risks becoming a UX developer issue as I (for one) would no longer know if 'NA' is a valid or a missing data value.
For what it's worth, current idiomatic behaviour is that in a missing values would be replaced by `None` `dtype`:
```python
>>> array = [['No-one', 'Nadie'], ['Expects']]
>>> df = pd.DataFrame(array, columns=['En', 'Es'])
En Es
0 No-one Nadie
1 Expects *None*
```
The `dtypes` here are:
```python
>>> [type(i) for i in df['Es']]
[<class 'str'>, <class 'NoneType'>]
```
Given this, my thought is that `NA` is not a suitable *default* replacement for missing `str` `dtype` elements rather `None` of `NoneType` `dtype` | {
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} | 2 | 2019-12-23T01:26:17Z | 2019-12-23T15:25:27Z | 2019-12-23T15:25:24Z | CONTRIBUTOR | null | Modified reflect python3 format strings on pandas/tests/indexes/multi/test_analytics.py. I ran the test and it passed like before. ref [#29886](https://github.com/pandas-dev/pandas/issues/29886) and [#29547](https://github.com/pandas-dev/pandas/issues/29547). | {
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} | 5 | 2019-12-23T01:35:17Z | 2019-12-24T16:51:15Z | 2019-12-24T14:26:40Z | MEMBER | null | Start with util.pxd, we can tighten the arg from "object" to "str" and simplify it a bit. Tracking down the places where get_c_string is used, the main one is in StringHashTable, where in `set_item` it is currently used incorrectly. The test added by this PR segfaults in master.
@TomAugspurger it looks like StringHashTable has relatively light testing. Should we be using it for StringArray? | {
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} | 1 | 2019-12-23T01:41:24Z | 2019-12-23T11:46:59Z | 2019-12-23T08:03:55Z | CONTRIBUTOR | null | Updated pandas/tests/indexes/datetimes/test_ops.py to utilize python3 format strings. Ran the test and it seems good. I moved the msg into the for loop because it seems cleaner with the new format string logic.
ref [#29886](https://github.com/pandas-dev/pandas/issues/29886) and [#29547](https://github.com/pandas-dev/pandas/issues/29547). | {
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} | 1 | 2019-12-23T02:23:35Z | 2019-12-23T11:47:26Z | 2019-12-23T08:05:22Z | CONTRIBUTOR | null | Updated pandas/io/formats/printing.py formatting. Replaced % and {}.format() examples to python3 formatting. Ran tests and they passed.
ref [#29886](https://github.com/pandas-dev/pandas/issues/29886) and [#29547](https://github.com/pandas-dev/pandas/issues/29547). | {
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As @simonjayhawkins says [here](https://github.com/pandas-dev/pandas/pull/30403#discussion_r360828949) (comment), having ```Union``` with both ```int``` and ```float``` is redundant. | {
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] | closed | false | null | [] | null | 1 | 2019-12-23T11:10:54Z | 2020-03-12T10:20:45Z | 2020-03-12T10:20:45Z | MEMBER | null | Here's something I noticed while working on #30277
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
import pytz
temp_1 = pd.Series([pd.Timestamp("2019-07-19 07:04:57")])
temp_2 = pd.Series([pd.Timestamp("2019-07-19 07:04:57+0100", tz=pytz.FixedOffset(60))])
data = np.array([["2019-06-19 07:04:57"]])
pd.DataFrame(data, dtype=temp_1.dtype) # works
pd.DataFrame(data, dtype=temp_2.dtype) # fails
```
#### Problem description
The example that fails throws the error:
```
ValueError: If using all scalar values, you must pass an index
```
If I pass an index:
```
pd.DataFrame(data, dtype=temp_2.dtype, index=[0]) # fails
```
then it fails with
```
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
although this doesn't:
```
pd.DataFrame(data, dtype=temp_1.dtype, index=[0]) # works
```
#### Expected Output
I wouldn't expect this behaviour to change based on the `dtype` passed.
#### Output of ``pd.show_versions()``
<details>
/home/SERILOCAL/m.gorelli/pandas/pandas/core/index.py:29: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
FutureWarning,
INSTALLED VERSIONS
------------------
commit : 3577b5a34d7f9695bc2e99766cc6c49e7bdab6fe
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0+1383.g3577b5a34
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.55.4
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.4.0
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
</details>
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} | 4 | 2019-12-23T17:01:06Z | 2020-05-28T17:08:46Z | 2020-05-28T17:08:46Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from tabulate import tabulate
def print_table(df, doc=False):
"""
Print out a nice looking table.
Set doc to True if you are printing this out for a Microsoft Word (11 x 8) document.
When doc is False:
Input:
A B
0 1 0
1 2 0
2 3 0
Output:
+----+-----+-----+
| | A | B |
|----+-----+-----|
| 0 | 1 | 0 |
| 1 | 2 | 0 |
| 2 | 3 | 0 |
+----+-----+-----+
When doc is True:
Input:
a b c d e f g h i j k l m n p
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
Output:
Part 1/3
+----+-----+-----+-----+-----+-----+-----+
| | a | b | c | d | e | f |
|----+-----+-----+-----+-----+-----+-----|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| 4 | 4 | 4 | 4 | 4 | 4 | 4 |
+----+-----+-----+-----+-----+-----+-----+
Part 2/3
+----+-----+-----+-----+-----+-----+-----+
| | g | h | i | j | k | l |
|----+-----+-----+-----+-----+-----+-----|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| 4 | 4 | 4 | 4 | 4 | 4 | 4 |
+----+-----+-----+-----+-----+-----+-----+
Part 3/3
+----+-----+-----+-----+
| | m | n | p |
|----+-----+-----+-----|
| 0 | 0 | 0 | 0 |
| 1 | 1 | 1 | 1 |
| 2 | 2 | 2 | 2 |
| 3 | 3 | 3 | 3 |
| 4 | 4 | 4 | 4 |
+----+-----+-----+-----+
:param df: DataFrame
:param doc: bool
:return: None
"""
def _simple_print(df):
"""
Single line helper function to print a dataframe.
:param df: DataFrame
:return: None
"""
print(tabulate(df, headers='keys', tablefmt='psql'))
# Just print with no extra slicing.
if not doc:
_simple_print(df)
# Slice the dataframe to show to Word.
else:
# Set the number of columns Word can handle. It typically can handle 6 columns.
max_columns = 6
# Find how many tables we will need to print if we cut the columns by 6ths.
max_tables = int(np.ceil(df.shape[1] / max_columns))
# For each 6th, print the table.
for iteration in range(1, max_tables + 1):
# Print which iteration we're on.
print(f'Part {iteration}/{max_tables}')
# Slice the larger dataframe to obtain the current dataframe.
current_df = df.iloc[0:5, (iteration * max_columns) - max_columns:iteration * max_columns]
# Print current dataframe.
_simple_print(current_df)
def test(old_df, new_df, desired_columns, index=None, verbose=False, print_full=True):
"""
Description
-----------
Check for differences (across time, business rule changes, etc.) over the same
dataframe.
Recommended to start without verbose, then add it if needed.
You can toggle whether to print out the sample dataframes (head and tail),
or not.
Sample Usage
------------
old_data = {'ID': ['CHA','COC','COF'],
'Name': ['Chai Tea', 'Cocoa', 'Coffee'],
'Description': ['Chai Kcup','Hot Chocolate Kcup','Coffee Kcup'],
'Cost': [2,2,3]}
new_data = {'ID': ['CHA','COC','COF'],
'Name': ['Chai Tea', 'Cocoa', 'Coffee'],
'Description': ['Chai','Hot Chocolate Kcup','Coffee Kcup'],
'Cost': [2,3,3]}
old_df = (pd.DataFrame(old_data)
.set_index('ID'))
new_df = (pd.DataFrame(new_data)
.set_index('ID'))
test(old_df=old_df,
new_df=new_df,
desired_columns=old_df.columns)
Output In Console
-----------------
Match? False
Quick Head Test
Old Records:
+------+----------+--------------------+--------+
| ID | Name | Description | Cost |
|------+----------+--------------------+--------|
| CHA | Chai Tea | Chai Kcup | 2 |
| COC | Cocoa | Hot Chocolate Kcup | 2 |
| COF | Coffee | Coffee Kcup | 3 |
+------+----------+--------------------+--------+
New Records:
+------+----------+--------------------+--------+
| ID | Name | Description | Cost |
|------+----------+--------------------+--------|
| CHA | Chai Tea | Chai | 2 |
| COC | Cocoa | Hot Chocolate Kcup | 3 |
| COF | Coffee | Coffee Kcup | 3 |
+------+----------+--------------------+--------+
Quick Tail Test
Old Records:
+------+----------+--------------------+--------+
| ID | Name | Description | Cost |
|------+----------+--------------------+--------|
| CHA | Chai Tea | Chai Kcup | 2 |
| COC | Cocoa | Hot Chocolate Kcup | 2 |
| COF | Coffee | Coffee Kcup | 3 |
+------+----------+--------------------+--------+
New Records:
+------+----------+--------------------+--------+
| ID | Name | Description | Cost |
|------+----------+--------------------+--------|
| CHA | Chai Tea | Chai | 2 |
| COC | Cocoa | Hot Chocolate Kcup | 3 |
| COF | Coffee | Coffee Kcup | 3 |
+------+----------+--------------------+--------+
Quick Cell to Cell Differences
All differences: (2, 2)
Old New
ID
CHA Description Chai Kcup Chai
COC Cost 2 3
Parameters
----------
:param old_df: DataFrame
This could be cases you know to be correct, or cases from last week, etc.
:param new_df: DataFrame
This could be the current output created by code you wish to debug or double
check, or cases from today, etc.
:param desired_columns: list of strings
<old_df.columns>
:param index: string
name of column to match cases against (currently not implemented)
<Student ID>
:return: None
"""
new_df = new_df[desired_columns]
old_df = old_df[desired_columns]
# overall test (returns true or false)
print(f'Match? {old_df.equals(new_df)}')
print('\n')
if print_full:
# quick head and tail test
print('Quick Head Test')
print('Old Records:')
print_table(old_df.head(5))
print('New Records:')
print_table(new_df.head(5))
print('\n')
print('Quick Tail Test')
print('Old Records:')
print_table(old_df.tail(5))
print('New Records:')
print_table(new_df.head(5))
print('\n')
def _diff_table(old_df, new_df):
"""
Create the table that only shows differences (or "errors").
This is code that manesioz on stackoverflow wrote as a response to my question
for how to achieve this.
:param old_df: pandas DataFrame
:param new_df: pandas DataFrame
:return: pandas DataFrame
"""
# create frame of comparison bools
bool_df = (old_df != new_df).stack()
diff_table = pd.concat([old_df.stack()[bool_df],
new_df.stack()[bool_df]],
axis=1)
diff_table.columns = ["Old", "New"]
return diff_table
diff_table = _diff_table(old_df,new_df)
if verbose==False:
# specific cell by cell test for differences
print('Quick Cell to Cell Differences')
print(f'All differences: {diff_table.shape}')
print(diff_table.head())
print('\n')
# Offer a verbose option. Because the following code can produce errors if the
# dataframes are too different (eg. if the column names differ), we offer the
# non-verbose option.
else:
print('Cell to Cell Differences: All')
print(f'All differences: {diff_table.shape}')
print(diff_table)
print('\n')
```
#### Problem description
Often I need to do a quick analysis of the same dataframe over some time. I need to look at the specific cells that changed, and how. Frequently, it's at a volume where it's impossible to just eyeball this. I'm surprised that under pandas' testing suite there's not currently an in-built method to do this.
I ended up using manesioz's code from my stackoverflow question [here](https://stackoverflow.com/questions/57224294/more-specific-pandas-testing-for-equality-between-two-dataframes) in my own utility function which is included here. But, I think a feature like this (specifically the output under Quick Cell to Cell Differences) could be really useful for other data analysts too.
More sample output of other times I've used this feature:
- Ad hoc, timed request to find any differences in ordering exams over a time period of a month from schools
```
>>>last_week_df.columns
Index(['BCO', 'Superintendent', 'DBN', 'OrderedExams', 'FLOrderedExams',
'Algebra I', 'ELA', 'Chemistry', 'Earth Science', 'Global Transition',
'Global II', 'Algebra II', 'Living Environment', 'US History',
'Physics', 'Geometry', 'All Exams'],
dtype='object')
>>>last_week_df.shape
(616, 17)
Cell to Cell Differences: All
All differences: (66, 2)
Old New
36 FLOrderedExams Yes No
40 FLOrderedExams Yes No
50 FLOrderedExams Yes No
66 FLOrderedExams Yes No
ELA 60 10
All Exams 110 60
81 OrderedExams No Yes
Algebra I 0 30
Living Environment 0 15
Geometry 0 25
All Exams 0 70
90 FLOrderedExams Yes No
165 OrderedExams Yes No
Algebra I 50 0
ELA 170 0
Earth Science 25 0
Global II 25 0
Algebra II 25 0
Living Environment 25 0
Geometry 15 0
All Exams 335 0
200 Physics 35 34
All Exams 1010 1009
282 Living Environment 250 150
All Exams 750 650
286 ELA 615 440
All Exams 1640 1465
432 Algebra I 695 620
All Exams 1841 1766
540 OrderedExams No Yes
Algebra I 0 130
ELA 0 50
Global Transition 0 30
Living Environment 0 140
US History 0 40
All Exams 0 390
552 OrderedExams No Yes
Algebra I 0 15
ELA 0 25
Chemistry 0 5
Earth Science 0 15
Global Transition 0 20
Algebra II 0 20
Living Environment 0 10
US History 0 10
Geometry 0 10
All Exams 0 130
560 OrderedExams No Yes
Algebra I 0 200
Global Transition 0 100
All Exams 0 300
561 OrderedExams No Yes
Algebra I 0 12
Living Environment 0 25
All Exams 0 37
576 OrderedExams No Yes
FLOrderedExams No Yes
Algebra I 0 30
ELA 0 140
All Exams 0 170
606 OrderedExams No Yes
FLOrderedExams No Yes
Earth Science 0 15
Living Environment 0 15
Geometry 0 25
All Exams 0 55
```
- Ad hoc, timed request to update a data file dependency for another file on locations of foreign language personnel at sites
```
MATHI
Match? False
Cell to Cell Differences: All
All differences: (5, 2)
Old New
Exams go to?
14J558 MATHI_K 10 2
MATHI_R 1 2
20J445 MATHI_K 1 3
28W440 MATHI_K 1 4
29W283 MATHI_K 1 4
USH
Match? False
Cell to Cell Differences: All
All differences: (3, 2)
Old New
Exams go to?
02N600 USH_K 1 0
USH_R 0 1
13J499 USH_K 2 1
PHYS
Match? False
Cell to Cell Differences: All
All differences: (8, 2)
Old New
Exams go to?
02N475 PHYS_K 1 0
PHYS_R 0 1
15J519 PHYS_K 4 2
PHYS_R 1 2
28W690 PHYS_K 1 3
30W445 PHYS_K 1 2
31T450 PHYS_K 1 0
PHYS_R 0 1
GLOBALI
Match? False
Cell to Cell Differences: All
All differences: (6, 2)
Old New
Exams go to?
19J660 GLOBALI_K 3 1
GLOBALI_R 1 2
21J540 GLOBALI_K 3 2
GLOBALI_R 1 2
29W272 GLOBALI_K 1 3
GLOBALI_R 2 1
LIVING
Match? True
Cell to Cell Differences: All
All differences: (0, 2)
Empty DataFrame
Columns: [Old, New]
Index: []
GLOBALII
Match? False
Cell to Cell Differences: All
All differences: (7, 2)
Old New
Exams go to?
13J499 GLOBALII_K 6 2
GLOBALII_R 1 2
19J660 GLOBALII_K 4 2
21J540 GLOBALII_K 2 3
28W620 GLOBALII_K 2 4
GLOBALII_R 2 1
29W272 GLOBALII_K 2 4
```
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} | 1 | 2019-12-23T17:01:30Z | 2020-06-23T17:09:06Z | 2019-12-24T17:06:19Z | CONTRIBUTOR | null | - [x] ref #29547
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry | {
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} | 4 | 2019-12-23T17:09:57Z | 2019-12-25T20:35:59Z | 2019-12-24T14:56:57Z | CONTRIBUTOR | null | - [x] closes #26344
Ref @jbrockmendel comment here https://github.com/pandas-dev/pandas/pull/23454#discussion_r360933924
CI: fails as expected example [here](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=23715&view=logs&j=fd89438c-1fd3-5981-222e-115d06c89c2d) | {
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} | 1 | 2019-12-23T18:07:15Z | 2019-12-23T20:17:53Z | 2019-12-23T19:29:37Z | MEMBER | null | among other things, found a sort_index test that got misplaced | {
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} | 0 | 2019-12-23T18:14:57Z | 2020-01-04T22:57:37Z | 2020-01-04T22:57:37Z | CONTRIBUTOR | null | See the following in CI:
```
pytest -m "not slow and not network" -n auto --dist=loadfile -s --strict --durations=10 --junitxml=test-data.xml pandas
##[warning]The 'junit_family' default value will change to 'xunit2' in pytest 6.0.
Add 'junit_family=legacy' to your pytest.ini file to silence this warning and make your suite compatible.
```
Unsure if the xunit2.0 format will affect any of our coverage etc..
https://docs.pytest.org/en/latest/reference.html#confval-junit_family | {
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} | 1 | 2019-12-23T18:21:38Z | 2020-01-01T03:39:12Z | 2020-01-01T03:39:08Z | MEMBER | null | closes #18755
closes #30391
xref #8113
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Because this is a plot PR, so I put the figures gotten by the two examples in those two PRs in this description to facilitate seeing output, and more cases are tested in test files.

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} | 2 | 2019-12-23T18:33:53Z | 2021-06-11T16:55:12Z | null | MEMBER | null | Our nullable, mask-based ExtensionArrays (currently integer and boolean, inheriting from MaskedArray) have a `_data` and `_mask` numpy arrays stored under the hood. SO we use a numpy boolean array as mask (8bit), also when there are no missing values.
One, relatively easy, memory + performance improvement could be achieved by allowing the mask to be None when there are no missing data. Since the mask data is completely internal to the Array implementations, this should be possible to do.
(to be checked how involved the ops code would become to handle this as optional) | {
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} | 0 | 2019-12-23T18:52:37Z | 2021-07-25T03:04:57Z | null | MEMBER | null | Currently, we cast to float in the IntegerArray reducing ops:
https://github.com/pandas-dev/pandas/blob/db620397328567ff8153ae6031685b88c84dc8f8/pandas/core/arrays/integer.py#L673-L678
However, the `nanops` functions can already handle a mask. So with the appropriate fill_value, there should be no need to cast the integers to float (and check for missing values). At least this is the case for `skipna=True`, the case of `skipna=False` might need to be handled separately.
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} | 1 | 2019-12-23T21:25:24Z | 2019-12-24T17:02:30Z | 2019-12-24T13:05:54Z | MEMBER | null | in the process of organizing these, some parametrization opportunities become clear. Didnt do them in this PR to keep this just a moving-around diff | {
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} | 1 | 2019-12-23T21:31:14Z | 2019-12-24T16:52:01Z | 2019-12-24T13:04:36Z | MEMBER | null | Use helper methods implemented in #30354.
Some of this is things made possible since we enforced the integer add/sub deprecation. | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 15 | 2019-12-24T01:29:46Z | 2021-07-25T04:17:02Z | null | NONE | null | Python 3.6.8
pandas==0.25.3
```python
#! -*- coding:utf-8 -*-
import pandas as pd
df = pd.DataFrame([
{'a':'a1','b':'b1','c':'c1','d':'d1','e':'e1'},
{'a':'a2','b':'b2','c':'c2','d':'d2','e':'e2'}
])
import copy
df2 = copy.deepcopy(df)
print(df)
# a b c d e
# 0 a1 b1 c1 d1 e1
# 1 a2 b2 c2 d2 e2
df.loc[df['a']=='a2', ['c']] = df['e']
print(df)
# a b c d e
# 0 a1 b1 c1 d1 e1
# 1 a2 b2 e2 d2 e2
# loc muti columns replace has some problem
df.loc[df['a']=='a2', ['b','c']] = df[['d','e']]
print(df)
# a b c d e
# 0 a1 b1 c1 d1 e1
# 1 a2 NaN NaN d2 e2
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} | 2 | 2019-12-24T02:25:18Z | 2019-12-24T13:39:46Z | 2019-12-24T13:39:42Z | CONTRIBUTOR | null | Modified to python3 format strings
ref: https://github.com/pandas-dev/pandas/issues/29547
- [x] contributes to fstring corrections in #29547
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
~~whatsnew entry~~ (will not add now. I think the full set of changes in #29547 should get a single update entry rather than a piecemeal one)
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} | 2 | 2019-12-24T02:51:27Z | 2019-12-24T20:41:51Z | 2019-12-24T20:41:18Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-12-24T04:14:13Z | 2019-12-24T14:14:42Z | 2019-12-24T13:01:26Z | CONTRIBUTOR | null | updated to f-strings for:
pandas/tests/scalar/interval/test_ops.py
pandas/tests/computation/test_eval.py
pandas/tests/dtypes/*
pandas/tests/dtypes/cast/test_infer_dtype.py
pandas/tests/dtypes/test_dtypes.py
pandas/tests/dtypes/test_inference.py
held off on implementing for regex match strings pending PEP 536
- [x] contributes to fstring corrections in #29547
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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This issue may already be fixed by #30277. Opening this as requested in https://github.com/pandas-dev/pandas/pull/30277#pullrequestreview-335987704 . | {
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} | 1 | 2019-12-24T13:18:00Z | 2019-12-24T16:00:06Z | 2019-12-24T15:59:41Z | CONTRIBUTOR | null | Fixes EX03 errors in the examples of some docstrings in the script `pandas/core/ generic.py`.
Specifically, flake8 errors in the docstrings of the following functions were corrected:
```
pandas.DataFrame.add_prefix
pandas.DataFrame.add_suffix
pandas.DataFrame.head
pandas.DataFrame.last
pandas.DataFrame.pop
pandas.DataFrame.reindex
pandas.DataFrame.reindex_like
pandas.DataFrame.tail
pandas.DataFrame.take
pandas.DataFrame.to_xarray
pandas.DataFrame.truncate
pandas.DataFrame.tz_localize
```
Related to #27977 cc @datapythonista
- [ ] closes #xxxx
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"defa... | open | false | null | [] | null | 3 | 2019-12-24T16:46:46Z | 2019-12-28T12:33:41Z | null | MEMBER | null | * ref https://github.com/pandas-dev/pandas/pull/30398#discussion_r361198265
* ref https://github.com/pandas-dev/pandas/pull/30398#pullrequestreview-336243679 | {
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} | 1 | 2019-12-24T18:13:34Z | 2019-12-24T21:18:26Z | 2019-12-24T21:15:52Z | MEMBER | null | - [x] closes #30383
akima and pchip have existed since at least scipy 0.14, and we require 1.1, so these checks are unnecessary. | {
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] | open | false | null | [] | null | 2 | 2019-12-24T19:46:44Z | 2021-07-25T04:17:55Z | null | MEMBER | null | Observed in the PR #30402 and PR #27237
Annotation seems not to be able to added for `sort_values`. If added, error message will pop up in CI:
```
pandas/core/series.py:2691: error: Signature of "sort_values" incompatible with supertype "NDFrame"
pandas/core/frame.py:4701: error: Signature of "sort_values" incompatible with supertype "NDFrame"
```
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} | 7 | 2019-12-24T19:48:43Z | 2019-12-27T02:36:02Z | 2019-12-26T18:50:23Z | MEMBER | null | Letting this sit in draft state for a while to see if issues pop up
Note that this doesn't affect Windows (not sure yet if it can). Also previous attempts had `-j4` but I believe we only get 2 virtual cores for the CI machines so that was probably overkill. | {
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} | 1 | 2019-12-24T20:07:34Z | 2020-01-02T01:10:32Z | 2020-01-02T01:10:32Z | MEMBER | null | Sometimes when we run `black` on something like
```
foo = (
"bar "
"baz"
)
```
we end up with `foo = "bar " "baz"` when we would want `foo = "bar baz"`.
This is [not](https://github.com/psf/black/issues/1051) considered a bug in `black`, so is something we're currently checking for manually. We should add it to code_checks. | {
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} | 3 | 2019-12-24T21:10:04Z | 2019-12-25T20:27:29Z | 2019-12-24T22:23:52Z | CONTRIBUTOR | null | invgrep/Github actions exit status doesn't seem to be working correctly.
Currently on master we get these errors in the code_checks stage:
https://github.com/pandas-dev/pandas/pull/30452/checks#step:4:7
As per discussion here:
https://github.com/pandas-dev/pandas/pull/30370#discussion_r361213525
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} | 1 | 2019-12-24T21:17:55Z | 2019-12-26T00:19:22Z | 2019-12-26T00:14:36Z | MEMBER | null | Re-order the fixturizatin of engine and read_ext so as to yield only valid combinations, avoiding the need to pytest.skip the invalid combinations. This cuts out 500+ pytest.skips, which is about a third of the skips I see when i test with `--not-slow`.
The idea is that by cutting down on skips we can keep a closer eye on them, and things like [this](https://github.com/pandas-dev/pandas/pull/30441#discussion_r361222254) wont fall through the cracks quite as easily. | {
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} | 6 | 2019-12-24T21:47:12Z | 2019-12-25T19:10:33Z | 2019-12-25T19:08:14Z | CONTRIBUTOR | null | Follow up from discussion here. https://github.com/pandas-dev/pandas/pull/30370#discussion_r361213525
Seems like we are not return the return code of all these code checks - unless I am missing something?
Expected this to fail on master until https://github.com/pandas-dev/pandas/pull/30455 is merged
cc. @WillAyd @datapythonista | {
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} | 4 | 2019-12-24T22:24:28Z | 2020-01-16T00:33:23Z | 2019-12-26T07:06:04Z | MEMBER | null | While looking to reduce build warnings I came across these methods and couldn't find any public use to them. The only one that seems to get hit is `get_loc` and that is in tests only so I think dead code | {
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} | 1 | 2019-12-25T00:37:25Z | 2019-12-25T23:30:27Z | 2019-12-25T23:27:30Z | MEMBER | null | - [x] closes #29058
- [x] closes #15553
- [x] tests added / passed
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- [ ] whatsnew entry
This also fixes+tests Series cummin/cummax with dt64tz, will have to look to see if there are any GH issues for that.
Following this and #29872 I think we should refactor a bunch of this out into core.nanops. | {
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} | 9 | 2019-12-25T01:58:33Z | 2020-01-20T20:02:08Z | 2020-01-20T17:17:45Z | MEMBER | null | - [ ] closes #xxxx
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30462 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30462/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30462/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30462/events | https://github.com/pandas-dev/pandas/pull/30462 | 542,260,475 | MDExOlB1bGxSZXF1ZXN0MzU2NzM4NzU2 | 30,462 | BUG/ENH: groupby quantile arraylike fails with integer columns | {
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} | [] | closed | false | null | [] | null | 3 | 2019-12-25T05:41:33Z | 2020-01-03T11:45:33Z | 2019-12-26T12:34:16Z | MEMBER | null | When columns are integers, `df.groupby(label).quantile(<arraylike>)` fails.
This PR fixes this issue by telling `MultiIndex` to only look for positional levels during an reordering operation.
Two new internal methods in `MultiIndex` class, and one new parameter for `DataFrame.reorder_levels` and `MultiIndex.reorder_levels` are added during the debugging process.
- [ ] closes #30289
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-12-25T09:40:33Z | 2020-01-02T18:09:58Z | 2020-01-02T18:09:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
data = pd.DataFrame(np.random.random((10,2)), index=['a', 'b']*5)
data.iloc[1:3,:] = np.nan
```
for pct_change function, when the fill_method = None, it works
```
data.pct_change(1, fill_method=None, limit=1)
0 1
a NaN NaN
b NaN NaN
a NaN NaN
b NaN NaN
a -0.498169 -0.568501
b -0.315982 1.340587
a 1.341901 -0.489576
b 0.088594 -0.691063
a -0.514451 0.054695
b 0.844514 -0.604511
```
but when use it with gourpby , it raise a error
```
data.reset_index().groupby('index')[0].pct_change(1, fill_method=None, limit=1)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-60898304743e> in <module>
----> 1 data.reset_index().groupby('index')[0].pct_change(1, fill_method=None, limit=1)
~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in pct_change(self, periods, fill_method, limit, freq)
1344 )
1345 )
-> 1346 filled = getattr(self, fill_method)(limit=limit)
1347 fill_grp = filled.groupby(self.grouper.labels)
1348 shifted = fill_grp.shift(periods=periods, freq=freq)
TypeError: getattr(): attribute name must be string
```
else, `limit = 0` worked with gourpby, but can't run in pct_change
```
data.reset_index().groupby('index')[0].pct_change(1, fill_method='pad', limit=0)
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 -0.656739
6 0.601904
7 1.549380
8 -0.471434
9 -0.104398
Name: 0, dtype: float64
```
```
data[0].pct_change(1, fill_method='pad', limit=0)
~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/missing.py in pad_2d(values, limit, mask, dtype)
546
547 if np.all(values.shape):
--> 548 algos.pad_2d_inplace(values, mask, limit=limit)
549 else:
550 # for test coverage
pandas/_libs/algos.pyx in pandas._libs.algos.pad_2d_inplace()
ValueError: Limit must be greater than 0
```
pandas version is 0.25.1
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} | 3 | 2019-12-25T10:31:39Z | 2019-12-26T19:40:58Z | 2019-12-25T23:12:01Z | MEMBER | null | - [x] ref #30454
- [x] tests added / passed
- [x] passes `black pandas`
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- [ ] whatsnew entry
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} | 1 | 2019-12-25T14:21:58Z | 2019-12-25T22:27:41Z | 2019-12-25T22:27:36Z | CONTRIBUTOR | null | This helps navigate groupby code. | {
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} | 8 | 2019-12-25T18:40:28Z | 2019-12-25T23:59:54Z | 2019-12-25T23:59:50Z | CONTRIBUTOR | null | xref #29547
Fixed many of the strings in pandas/io/parsers.py except for the string at line 1501 as that will require more time to make it function properly. Please note that this is my first contribution ever so please double check my work! | {
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- [x] tests added / passed
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Can be merged, after merging #30464 | {
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Continue work done in https://github.com/pandas-dev/pandas/pull/29923
As of CPython 3.6 the implementation of dict maintains insertion order.
This became a language feature in python 3.7.
Note in python 3.8 support for reversed(dict) was added - but we don't seem to use that in the use cases i've eliminated. ( OrderedDict support this)
Add code check as per comment here: https://github.com/pandas-dev/pandas/pull/29923#issuecomment-559889216 | {
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@tswast mind having a look?
CC: @jreback | {
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Test recently introduced in #30447 seems to be failing in master. Xfailing it for now.
CC: @jreback | {
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} | 2 | 2019-12-26T00:41:52Z | 2020-01-04T11:48:09Z | 2019-12-26T13:13:15Z | CONTRIBUTOR | null | Replaced the old style strings with the newer f-strings.
(Sorry for the confusion with the earlier draft)
#29886 | {
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} | 2 | 2019-12-26T01:01:01Z | 2019-12-26T15:33:30Z | 2019-12-26T13:27:09Z | MEMBER | null | try to make the layout match the way it does for the other index classes, similar to what we're doing with Series/DataFrame tests | {
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} | 1 | 2019-12-26T01:27:54Z | 2019-12-26T03:58:40Z | 2019-12-26T03:58:32Z | CONTRIBUTOR | null | xref #30470
Non strict xfail flakey test
@jreback
Reference: https://github.com/pandas-dev/pandas/pull/30447 | {
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} | 9 | 2019-12-26T03:03:31Z | 2020-01-01T18:04:12Z | 2019-12-27T16:21:19Z | CONTRIBUTOR | null | Believe these test only run on master on travis? So will keep xfail for now
Fixturize as per @jreback comment [here](https://github.com/pandas-dev/pandas/issues/30470#issuecomment-568942914).
closes #30470 | {
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This merge will add in a functionality that works as follows:
A table can be registered using register_temp_table(dataframe, table_name)
Then using pandas.query("select * from table_name") you can query the data frame or any other ones registered using standard sql syntax.
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} | 25 | 2019-12-26T04:17:35Z | 2020-09-15T22:40:55Z | 2020-09-15T22:40:55Z | CONTRIBUTOR | null | I would like to add functionality to pandas so that data frames can be queried like database tables, similar to the way that they can be in spark-sql.
I think it should work in a similar fashion.
A table can be registered using register_temp_table(dataframe, table_name).
Then using ```pandas.query("select * from table_name")``` you can query the data frame or any other ones registered using standard sql syntax.
I've already implemented the entire thing, but I was told to open an issue for it.
Also I'm aware that there is a package called pandassql but this package actually just puts a data frame into a sql lite database, as opposed to querying a data frame directly, and transforming the sql into pandas methods that are then applied to the data frame.
Motivation:
The motivation for this enhancement is to make pandas more accessible to a crowd of users that may not be as technical and also to provide ease of transition for legacy code in systems like sas that have SQL already embedded in their programs. I'll supply a context free grammar in my documentation to show exactly what this system can handle, but it can basically handle any traditional SQL select statement, including subqueries, joins, where clauses, group by clauses, any aggregate function already supported by pandas, limit, and order by clauses. It also has support for rank and dense_rank window functions. It can't do things that sql wouldn't normally do like cross tab and you can't use a user defined function in it although I think that could be a good add-on.
Datatypes:
The interface supports all pandas datatypes, so to cast something as an integer the syntax would currently be cast(some_number as int64) or cast(some_int as object). I've played around with the idea of varchar, char, bigint and smallint, but I think those would be misleading as those aren't datatypes that are supported by pandas currently.
Errors:
Currently the exceptions that it will throw that come this api are based solely around trying to select from an unregistered table, or from submitting an improperly written sql query, both of which you wouldn't want to silence so there's only one error mode.
Api Choices:
The reason I made the register_temp_table section of the api top level was to avoid attaching a method to DataFrame although if others think it might be better as a method, I would change it in that manner (DataFrame.register_temp_table(table_name)). The reason pandas.query is a top level method is that it's relational in structure. You can select from multiple tables and join them and such and so it wouldn't make sense for it to be on a DataFrame level. The only similarity to the .query DataFrame method though is the name. DataFrame.query is just an alternate way of expressing things like DataFrame[some_condition] whereas my .query encompasses a large amount of the pandas api.
Built In:
I have two reasons that I think this would be better built in. The first is that the target audience for this is less technical pandas users. Part of making this api easier to use is lessening the burden of researching code and learning how python works, so I think that for them to go looking for an external package may be hard to begin with and they would also need to know to look for one.
My second reason is that, from using what I've built, I've found pandas a lot easier to use just as a developer.
Suppose we have a DataFrame with one column called A, it goes from
This code:
```
dataframe[name_1] = dataframe[a] - 1
dataframe[name_2] = dataframe[a] + 1
dataframe = dataframe[dataframe[name_1] == dataframe[name_2]]
dataframe.drop(columns=['a'], inplace=True)
```
To this code:
```pd.query("select a - 1 as name_1, a + 1 as name_2 from some_table where name_1 = name_2")```
Also although I did implement register_temp_table as an api level function, it would serve best as a method on a DataFrame so that's another thing to consider.
I can't really provide any support for the lark part, other than that it seemed like the best tool for what I was making.
I apologize for the style and such, I'll be fixing all that before I'm done. I implemented this outside of pandas first, so that's why there are so many style and documentation discrepancies.
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} | 4 | 2019-12-26T06:34:45Z | 2019-12-26T18:50:48Z | 2019-12-26T18:26:58Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | closed | false | null | [] | null | 3 | 2019-12-26T10:03:51Z | 2019-12-30T09:32:26Z | 2019-12-28T12:49:02Z | CONTRIBUTOR | null | Many thanks for the vast range of I/O possibilities that pandas allows! I just met one edge (or maybe not so edge) case where an extra parameter could save some work, especially for newbie users.
#### Code Sample, a copy-pastable example if possible
```python
pd.DataFrame({"a": ['☿']}).to_html("a.html")
# Would be nice to have:
# pd.DataFrame({"a": ['☿']}).to_html("a.html", encoding="utf-8")
```
#### Problem description
With the current signature of `DataFrame.to_html`, it is not possible to easily write non-ascii / non-latin1 characters to HTML directly or, more generally, to specify the output encoding. It is necessary to pass an open file:
```
with open("a.html", "w", encoding="utf-8") as out:
pd.DataFrame({"a": ['☿']}).to_html(out)
```
It would be nice to have a parameter (admittedly, a 24th one) to allow this, consistent with the `to_csv` one. I see that there is some discussion on parameter consistency in #15008 and #28377 (hopefully, I did my searching well and this is not a duplicate issue), so it might be against the design principles. Do you think this would be a viable idea? If yes, I am ready to implement it.
Note: It is then questionable, whether an explicit encoding should also result in a correct `<meta charset...>` tag being added to the file.
My motivation: I am currently writing lesson materials for an EDA course and wanted to show how easy it is to export data frames (by chance containing planet symbols but can be any non-Western character) to any format ;-)
Thanks,
Jan
#### Expected Output
```
None
```
#### Unexpected output
```
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-67-2972ac7a12d7> in <module>
----> 1 pd.DataFrame({"a": ['☿']}).to_html("a.html")
~\Miniconda3\lib\site-packages\pandas\core\frame.py in to_html(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, max_cols, show_dimensions, decimal, bold_rows, classes, escape, notebook, border, table_id, render_links)
2315 )
2316 # TODO: a generic formatter wld b in DataFrameFormatter
-> 2317 formatter.to_html(classes=classes, notebook=notebook, border=border)
2318
2319 if buf is None:
~\Miniconda3\lib\site-packages\pandas\io\formats\format.py in to_html(self, classes, notebook, border)
843 elif isinstance(self.buf, str):
844 with open(self.buf, "w") as f:
--> 845 buffer_put_lines(f, html)
846 else:
847 raise TypeError("buf is not a file name and it has no write " " method")
~\Miniconda3\lib\site-packages\pandas\io\formats\format.py in buffer_put_lines(buf, lines)
1808 if any(isinstance(x, str) for x in lines):
1809 lines = [str(x) for x in lines]
-> 1810 buf.write("\n".join(lines))
~\Miniconda3\lib\encodings\cp1252.py in encode(self, input, final)
17 class IncrementalEncoder(codecs.IncrementalEncoder):
18 def encode(self, input, final=False):
---> 19 return codecs.charmap_encode(input,self.errors,encoding_table)[0]
20
21 class IncrementalDecoder(codecs.IncrementalDecoder):
UnicodeEncodeError: 'charmap' codec can't encode character '\u263f' in position 193: character maps to <undefined>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.7
tables : None
xarray : 0.14.0
xlrd : None
xlwt : 1.3.0
xlsxwriter : None
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} | 4 | 2019-12-26T10:38:06Z | 2021-10-29T02:22:58Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# input - 1
df = pd.DataFrame({})
df["x1"] = [1, 2, 3, 4, 5]
df["x2"] = [0, 0, 0, 1, 1]
df["target"] = [10, 20, 30, 40, 50]
y = df["target"]
df.drop("target", axis=1, inplace=True)
y = y + np.min(y)
# output df (without removed target column)
x1 x2
0 1 0
1 2 0
2 3 0
3 4 1
4 5 1
# input - 2
df = pd.DataFrame({})
df["x1"] = [1, 2, 3, 4, 5]
df["x2"] = [0, 0, 0, 1, 1]
df["target"] = [10, 20, 30, 40, 50]
y = df["target"]
df.drop("target", axis=1, inplace=True)
y += np.min(y)
# output df (with the removed target column)
| x1 | x2 | target
-- | -- | -- | --
0 | 1 | 0 | 20
1 | 2 | 0 | 30
2 | 3 | 0 | 40
3 | 4 | 1 | 50
4 | 5 | 1 | 60
```
#### Problem description
Pandas return DataFrame with removed column after doing in-place operation with a column. Is it a bug or intentional behavior?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.1
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.7.5
pip : 18.1
setuptools : 42.0.2
Cython : 0.29.2
pytest : 4.1.0
hypothesis : None
sphinx : 1.8.3
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.3.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.0
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.15
tables : 3.4.4
xarray : 0.14.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
</details>
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} | 2 | 2019-12-26T12:45:24Z | 2019-12-27T16:42:24Z | 2019-12-27T16:16:50Z | MEMBER | null | When columns are integers, `df.groupby(label).quantile(<arraylike>)` fails.
- [x] closes #30289
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-12-26T13:22:32Z | 2020-05-15T12:53:25Z | 2020-05-15T12:53:25Z | CONTRIBUTOR | null | https://github.com/pandas-dev/pandas/blob/master/pandas/tests/window/test_moments.py
this includes testing for ewm/rolling and others. let's put into separate files to make this simpler. | {
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This extension module allows a Series to conform to the buffer protocol, which makes it readable / writeable through memoryviews. Right now in places like groupby we always in user code extract out the underlying array and pass that through Cython, which this in theory could centralize while helping third party code that may use this as well
Getting this to work with a DataFrame would be the bigger win but also takes more effort, so leaving that for another day | {
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} | 2 | 2019-12-26T16:12:45Z | 2019-12-26T18:17:51Z | 2019-12-26T17:54:46Z | MEMBER | null | In the 1.0.0 whatsnew we currently have 3 variants of "Removed :foo:X"
```
- Removed the previously deprecated :meth:`X`
- Removed previously deprecated :meth:`X`
- Removed :meth:`X`
```
I think we should standardize on one of these (lest readers think the difference is meaningful). I don't particularly care which, but for this PR went with the last one as it is the least verbose | {
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} | 6 | 2019-12-26T18:48:06Z | 2020-01-02T04:24:54Z | 2020-01-02T04:01:45Z | CONTRIBUTOR | null | - [x] closes #30610
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2019-12-26T19:15:03Z | 2019-12-26T19:48:47Z | 2019-12-26T19:44:14Z | MEMBER | null | Same idea as #30488. I don't particularly care which wording we use, but would like it to be consistent.
After this is doing the same thing for "default value for keyword has changed" and last possibly re-ordering in some meaningful way. | {
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} | 2 | 2019-12-26T19:39:04Z | 2019-12-27T16:34:28Z | 2019-12-27T16:29:54Z | CONTRIBUTOR | null | - [x] closes #30288
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
# Explanations
For to_sql no errors are raised anymore when we have Series containing datetimes with timezone and different offsets (unlike in GH30207) and the data is saved as expected (I tested with postgres which does not save offsets anyways).
Concerning read_sql however there are several points to discuss:
1. if we want to have datetime64 instead of "object" (as described in [GH30207](https://github.com/pandas-dev/pandas/issues/30207)) we lose the offsets. So this would be a breaking change and would require first a warning in the next version then we implement it in the version after, right?
2. is this patch I made acceptable?
The probem is that read_sql uses pd.DataFrame.from_records and it reads datetimes with different offsets as "object" instead of datetime64[ns,...] (e.g. datetime64[ns, psycopg2.tz.FixedOffsetTimezone(offset=60, name=None)]).
If we wanted to modify this behavior we would have to modify the C extension _libs.lib. I don't know how to help with that and I am not sure we want to do this. Even if we could pack the datetimes with differents offsets into a Series with a datetime64 dtype the methods of the .dt accessor are not compatible anyways: currently a ValueError is thrown if one attempts to use the accessor in this case (see traceback in the example in "Before PR" below).
# Before PR
```python
import datetime
import psycopg2
import sqlalchemy
import pandas as pd
from pandas.io.sql import read_sql, to_sql
# CREATE ENGINE
# engine = sqlalchemy.create_engine("CONNECTION_STRING")
# create datetimes with timezone that have different offsets
data = [[datetime.datetime(2019, 11, 14, 16, 12, 0, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=60, name=None))],
[datetime.datetime(2019, 8, 7, 15, 37, 4, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=120, name=None))]]
df = pd.DataFrame(data, columns = ['ts_tz'])
# save data
to_sql(frame = df,
name = 'timezone_diff_offsets_test',
con = engine,
schema = 'public',
if_exists = 'replace',
index = False)
```
<details>
<summary>Traceback</summary>
```python-traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/GitHub/pandas/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1968 try:
-> 1969 values, tz_parsed = conversion.datetime_to_datetime64(data)
1970 # If tzaware, these values represent unix timestamps, so we
~/GitHub/pandas/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.datetime_to_datetime64()
ValueError: Array must be all same time zone
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-2-a66775d04fe3> in <module>
20 schema = 'public',
21 if_exists = 'replace',
---> 22 index = False)
~/GitHub/pandas/pandas/io/sql.py in to_sql(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)
519 chunksize=chunksize,
520 dtype=dtype,
--> 521 method=method,
522 )
523
~/GitHub/pandas/pandas/io/sql.py in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method)
1318 index_label=index_label,
1319 schema=schema,
-> 1320 dtype=dtype,
1321 )
1322 table.create()
~/GitHub/pandas/pandas/io/sql.py in __init__(self, name, pandas_sql_engine, frame, index, if_exists, prefix, index_label, schema, keys, dtype)
620 if frame is not None:
621 # We want to initialize based on a dataframe
--> 622 self.table = self._create_table_setup()
623 else:
624 # no data provided, read-only mode
~/GitHub/pandas/pandas/io/sql.py in _create_table_setup(self)
869 from sqlalchemy import Table, Column, PrimaryKeyConstraint
870
--> 871 column_names_and_types = self._get_column_names_and_types(self._sqlalchemy_type)
872
873 columns = [
~/GitHub/pandas/pandas/io/sql.py in _get_column_names_and_types(self, dtype_mapper)
861 column_names_and_types += [
862 (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False)
--> 863 for i in range(len(self.frame.columns))
864 ]
865
~/GitHub/pandas/pandas/io/sql.py in <listcomp>(.0)
861 column_names_and_types += [
862 (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False)
--> 863 for i in range(len(self.frame.columns))
864 ]
865
~/GitHub/pandas/pandas/io/sql.py in _sqlalchemy_type(self, col)
971 # timezone information
972 try:
--> 973 if col.dt.tz is not None:
974 return TIMESTAMP(timezone=True)
975 except AttributeError:
~/GitHub/pandas/pandas/core/accessor.py in _getter(self)
83 def _create_delegator_property(name):
84 def _getter(self):
---> 85 return self._delegate_property_get(name)
86
87 def _setter(self, new_values):
~/GitHub/pandas/pandas/core/indexes/accessors.py in _delegate_property_get(self, name)
60 from pandas import Series
61
---> 62 values = self._get_values()
63
64 result = getattr(values, name)
~/GitHub/pandas/pandas/core/indexes/accessors.py in _get_values(self)
51 return PeriodArray(data, copy=False)
52 if is_datetime_arraylike(data):
---> 53 return DatetimeIndex(data, copy=False, name=self.name)
54
55 raise TypeError(
~/GitHub/pandas/pandas/core/indexes/datetimes.py in __new__(cls, data, freq, tz, normalize, closed, ambiguous, dayfirst, yearfirst, dtype, copy, name)
266 dayfirst=dayfirst,
267 yearfirst=yearfirst,
--> 268 ambiguous=ambiguous,
269 )
270
~/GitHub/pandas/pandas/core/arrays/datetimes.py in _from_sequence(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous)
418 dayfirst=dayfirst,
419 yearfirst=yearfirst,
--> 420 ambiguous=ambiguous,
421 )
422
~/GitHub/pandas/pandas/core/arrays/datetimes.py in sequence_to_dt64ns(data, dtype, copy, tz, dayfirst, yearfirst, ambiguous)
1864 # or M8[ns] to denote wall times
1865 data, inferred_tz = objects_to_datetime64ns(
-> 1866 data, dayfirst=dayfirst, yearfirst=yearfirst
1867 )
1868 tz = maybe_infer_tz(tz, inferred_tz)
~/GitHub/pandas/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1972 return values.view("i8"), tz_parsed
1973 except (ValueError, TypeError):
-> 1974 raise e
1975
1976 if tz_parsed is not None:
~/GitHub/pandas/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1963 dayfirst=dayfirst,
1964 yearfirst=yearfirst,
-> 1965 require_iso8601=require_iso8601,
1966 )
1967 except ValueError as e:
~/GitHub/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
~/GitHub/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
</details>
```python
# save datetimes with UTC timezone in different periods of the year
## create test data
data = [[datetime.datetime(2019, 11, 14, 15, 12, 0, tzinfo = datetime.timezone.utc)],
[datetime.datetime(2019, 8, 7, 13, 37, 4, tzinfo = datetime.timezone.utc)]]
df = pd.DataFrame(data, columns = ['ts_tz'])
## save test data
to_sql(frame = df,
name = 'timezone_diff_offsets_test',
con = engine,
schema = 'public',
if_exists = 'replace',
index = False)
# upon reading if you have for instance "Europe/Paris"
# configured as timezone on your local machine then you get datetimes with timezone
# that have different offsets (because they are in different periods of the year)
# due to this you have "object" dtype and not datetime64[...]
df = read_sql('SELECT * FROM timezone_diff_offsets_test', engine)
print(df,'\n')
print(df.dtypes)
```
ts_tz
0 2019-11-14 16:12:00+01:00
1 2019-08-07 15:37:04+02:00
ts_tz object
dtype: object
# After PR
```python
import datetime
import psycopg2
import sqlalchemy
import pandas as pd
from pandas.io.sql import read_sql, to_sql
# CREATE ENGINE
# engine = sqlalchemy.create_engine("CONNECTION_STRING")
# create datetimes with timezone that have different offsets
data = [[datetime.datetime(2019, 11, 14, 16, 12, 0, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=60, name=None))],
[datetime.datetime(2019, 8, 7, 15, 37, 4, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=120, name=None))]]
df = pd.DataFrame(data, columns = ['ts_tz'])
# save data - no exceptions occur
to_sql(frame = df,
name = 'timezone_diff_offsets_test',
con = engine,
schema = 'public',
if_exists = 'replace',
index = False)
# read data - offsets are dropped and dtype of column ts_tz is datetime64[ns, UTC]
df = read_sql('SELECT * FROM timezone_diff_offsets_test', engine)
print(df,'\n')
print(df.dtypes)
```
ts_tz
0 2019-11-14 15:12:00+00:00
1 2019-08-07 13:37:04+00:00
ts_tz datetime64[ns, UTC]
dtype: object
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} | 5 | 2019-12-26T21:53:14Z | 2020-01-02T01:09:59Z | 2020-01-02T01:08:40Z | MEMBER | null | benchmarks below
```sh
before after ratio
[9c6771c5] [5c0f5682]
<master> <json-index-dates>
- 231±60ms 188±3ms 0.82 io.json.ToJSON.time_iso_format('split', 'df_td_int_ts')
- 496±20ms 218±40ms 0.44 io.json.ToJSON.time_iso_format('index', 'df_int_float_str')
- 486±20ms 207±2ms 0.42 io.json.ToJSON.time_iso_format('columns', 'df_int_float_str')
- 499±9ms 210±1ms 0.42 io.json.ToJSON.time_iso_format('index', 'df_date_idx')
- 503±20ms 206±2ms 0.41 io.json.ToJSON.time_iso_format('columns', 'df_int_floats')
- 515±20ms 210±1ms 0.41 io.json.ToJSON.time_iso_format('index', 'df')
- 524±80ms 209±0.5ms 0.40 io.json.ToJSON.time_iso_format('index', 'df_int_floats')
- 528±10ms 206±2ms 0.39 io.json.ToJSON.time_iso_format('columns', 'df_td_int_ts')
diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json
index c04bbf53a..897de2f85 100644
--- a/asv_bench/asv.conf.json
+++ b/asv_bench/asv.conf.json
- 546±80ms 209±0.7ms 0.38 io.json.ToJSON.time_iso_format('index', 'df_td_int_ts')
- 568±60ms 208±3ms 0.37 io.json.ToJSON.time_iso_format('columns', 'df_date_idx')
- 598±100ms 206±2ms 0.35 io.json.ToJSON.time_iso_format('columns', 'df')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
Note that this mostly improves on DTI which on 0.25.3 can't even be written as ISO format, so I didn't add a whatsnew. Timedelta is the big bottleneck remaining | {
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} | 4 | 2019-12-27T00:59:18Z | 2020-01-02T07:45:43Z | 2020-01-02T00:33:07Z | CONTRIBUTOR | null | - [x] ref #30469
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} | 3 | 2019-12-27T01:04:42Z | 2019-12-27T16:16:00Z | 2019-12-27T14:41:05Z | MEMBER | null | - [x] closes #28836
I named this branch "bld-misc" in the optimistic hope I could close a bunch of BLD issues, but nope, just this one. | {
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} | 6 | 2019-12-27T01:19:33Z | 2020-01-01T03:18:34Z | 2020-01-01T03:18:30Z | CONTRIBUTOR | null | Beginners don't know what is `plt`, thus it is better to show how to import it explicitly.
This is a continuation of https://github.com/pandas-dev/pandas/pull/30274
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30500/events | https://github.com/pandas-dev/pandas/issues/30500 | 542,709,009 | MDU6SXNzdWU1NDI3MDkwMDk= | 30,500 | DEPR: Tick comparison with str | {
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"c... | open | false | null | [] | null | 3 | 2019-12-27T01:25:05Z | 2020-02-25T20:58:16Z | null | MEMBER | null | ATM Tick offset comparisons try casting strings to offsets with to_offset before doing == or !=, but not for inequalities:
```
>>> m = pd.offsets.Minute(4)
>>> m == "4min"
True
>>> m < "5min"
TypeError: Invalid comparison between Minute and str
```
In order to make this behavior more consistent, we should deprecate allowing str casting for == and !=. This would allow us to dispatch to the Timedelta inequalities, which have better handling for corner cases anyway (e.g. `m < np.ndarray([m, m]` which Timedelta gets right but Tick messes up) | {
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} | 18 | 2019-12-27T01:27:01Z | 2020-03-26T12:53:19Z | 2020-03-26T12:53:14Z | CONTRIBUTOR | null | closes #30484
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is my first PR, so pardon me if I did something wrong.
To fix this issue I started by creating a failing test based on the example given by the reporter. After, I fixed the test by removing the "inplace" special methods from add_special_arithmetic_methods, following what was suggested as a comment in the code. | {
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} | 3 | 2019-12-27T01:35:59Z | 2020-01-04T11:48:03Z | 2019-12-27T15:29:37Z | CONTRIBUTOR | null | xref #29886
Fixed the remaining files in:
Replace "foo!r" to "repr(foo)" syntax #29886
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} | 1 | 2019-12-27T01:44:27Z | 2019-12-27T16:17:01Z | 2019-12-27T14:15:04Z | MEMBER | null | A couple of cleanups and parametrizations. | {
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} | 2 | 2019-12-27T03:28:12Z | 2020-01-01T16:31:44Z | 2020-01-01T16:28:29Z | MEMBER | null | - [x] closes #12513
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} | 5 | 2019-12-27T04:08:57Z | 2019-12-31T16:06:42Z | 2019-12-31T13:02:56Z | MEMBER | null | - [x] closes #28969
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ATM this implements a smoke-test that the cases don't raise, but I don't have a canonical answer to test them against. Could hard-code the result I get now. Thoughts? | {
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```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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https://api.github.com/repos/pandas-dev/pandas/issues/30511 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30511/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30511/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30511/events | https://github.com/pandas-dev/pandas/issues/30511 | 542,844,810 | MDU6SXNzdWU1NDI4NDQ4MTA= | 30,511 | tz_localize creates freq inconsistency on time offset change | {
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"color": "0052cc",
"default": false,
"description": "DateOffsets",
"id": 53181044,
"name": "Frequency",
"node_id": "MDU6TGFiZWw1MzE4MTA0NA==",
"url": "https://api.github.com/repos/pandas-dev/pandas/labels/Frequency"
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"description... | closed | false | null | [] | {
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} | 2 | 2019-12-27T11:29:50Z | 2020-04-16T21:27:52Z | 2020-04-16T21:27:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
index = pd.date_range("2019-3-31", freq="30T", periods=10, tz="Europe/London")
print(index) # this has a freq
print(index.freq)
print(index.tz_localize(None)) # this shouldn't have it anymore, but it does
print(index.tz_localize(None).freq)
```
#### Problem description
`tz_localize(None)` doesn't check if the frequency is still consistent with the new localized index. In the case of Daylight Saving Time switch, the frequency of the index should become None (since duplicated/missing timestamps are created).
#### Expected Output
Achievable with:
```python
new_index = index.tz_localize(None)
new_index.freq = new_index.inferred_freq # drop frequency if not inferrable
print(new_index) # this is correct
print(new_index.freq)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30512 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30512/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30512/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30512/events | https://github.com/pandas-dev/pandas/issues/30512 | 542,867,266 | MDU6SXNzdWU1NDI4NjcyNjY= | 30,512 | DataFrame.replace changes dtype of columns not containing any value to be replaced. | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 2 | 2019-12-27T12:56:22Z | 2021-07-25T04:23:39Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
data = pd.DataFrame({'A':np.nan,'B':1.096, 'C':1}, index=[0])
data.replace(to_replace={np.nan:None}, inplace=True)
```
#### Problem description
Checking data.dtypes before and after the call to replace shows that the datatype of column B changed from float to object whereas that of C stayed at int. If I remove column A from the original data that does not happen.
When using replace I expected columns without any matching value not to be touched / changed.
#### Expected Output
Dtype of column 'B' should remain 'float64'.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30513 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30513/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30513/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30513/events | https://github.com/pandas-dev/pandas/issues/30513 | 542,936,499 | MDU6SXNzdWU1NDI5MzY0OTk= | 30,513 | Performance of merge for categorical index vs category column | {
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"name": "Reshaping",
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"de... | open | false | null | [] | null | 5 | 2019-12-27T17:11:06Z | 2021-07-25T19:11:42Z | null | CONTRIBUTOR | null | #### Code Sample
```python
import pandas
import numpy
import time
numpy.random.seed(11)
N=1_000_000
### 1. Merging on category index (3.85 seconds -slower)
# Create two (random order) dataframes with a column A with unique category dtype set as index
array_1 = numpy.arange(N)
numpy.random.shuffle(array_1)
df_1 = pandas.DataFrame(array_1, columns=["A"])
df_1["B"] = df_1["A"] * 2
df_1["A"] = df_1["A"].astype("category")
df_1 = df_1.set_index("A")
array_2 = array_1.copy()
numpy.random.shuffle(array_2)
df_2 = pandas.DataFrame(array_2, columns=["A"])
df_2["B"] = df_2["A"] * 2
df_2["A"] = df_2["A"].astype("category")
df_2 = df_2.set_index("A")
# merge two dataframes and time
start = time.time()
df_1.merge(df_2, how="left", left_index=True, right_index=True)
print(time.time()-start)
### 2. Merging on category column (0.85 seconds - faster)
# Create two (random order) dataframes with a column A with unique category dtype
array_1 = numpy.arange(N)
numpy.random.shuffle(array_1)
df_1 = pandas.DataFrame(array_1, columns=["A"])
df_1["B"] = df_1["A"] * 2
df_1["A"] = df_1["A"].astype("category")
array_2 = array_1.copy()
numpy.random.shuffle(array_2)
df_2 = pandas.DataFrame(array_2, columns=["A"])
df_2["B"] = df_2["A"] * 2
df_2["A"] = df_2["A"].astype("category")
# merge two dataframes and time
start = time.time()
df_1.merge(df_2, how="left", left_on="A", right_on="A")
print(time.time()-start)
```
#### Problem description
I noticed that when I perform a merge on a unique category dtype column, the merge gets significantly (x4) slower if I set this column as the index. This was unexpected for me as for all other dtypes I have tried (str, int, datetime etc.) the merge is significantly faster when using the indexed column.
I investigated this a bit further. Even though the category column "A" in the above example is unique (no repeated categories), the merge method still calls the `pandas.core.indexes.category.CategoricalIndex.get_indexer_non_unique` method.
This is because the check `self.is_unique and self.equals(target)` evaluates to False on https://github.com/pandas-dev/pandas/blob/67ee16a283b826b4adb07e5ca64a5206d242acaf/pandas/core/indexes/category.py#L662. This is because `self.equals(target)` is False https://github.com/pandas-dev/pandas/blob/67ee16a283b826b4adb07e5ca64a5206d242acaf/pandas/core/indexes/category.py#L309. I am not sure if this should be evaluating as True or False? Should two categorical indexes be equal if they have the exact same categories in different orders?
Additionally, I am not sure why get_index_non_unique for a categorical index should be significantly slower than a merge on a categorical column.
Let me know if you have any other insights into why the merge would be slower for a category type when using the index, or if I can provide any additional information / assistance. Thanks for your help!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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