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Introducing some errors, so we can see how it looks. | {
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} | 16 | 2019-11-28T14:09:53Z | 2020-06-14T22:20:19Z | 2020-06-14T22:20:19Z | NONE | null | Hi,
With some of the hdf5 files I have, `pandas.HDFStore.groups()` returns an empty list. (as does `.keys()` which iterates over the groups). However, the data are accessible via `.get()` or `.get_node()`.
This is related to #21543 and #21372 where the `.groups()` logic was changed, in particular using `self._handle.walk_groups()` instead of `self._handle.walk_nodes()`, now to be found here:
https://github.com/pandas-dev/pandas/blob/ea2e26ae7d700d7fd363ea5bfc05d2fe3fb8a5ee/pandas/io/pytables.py#L1212
#### Current Output
```python
>>> hdf.groups()
[]
```
```python
>>> hdf.keys()
[]
```
#### Expected Ouptut
List of groups and keys as visible with e.g. `h5dump`.
**Note:** Changing the aforementioned line back to use `.walk_nodes()` fixes the issue and lists the groups and keys properly:
```python
>>> hdf.groups()
[/Data/Table Layout (Table(69462,), zlib(4)) ''
description := {
...
/Data/Array Layout/2D Parameters/Data Parameters (Table(15,)) ''
description := {
"mnemonic": StringCol(itemsize=8, shape=(), dflt=b'', pos=0),
"description": StringCol(itemsize=48, shape=(), dflt=b'', pos=1),
"isError": Int64Col(shape=(), dflt=0, pos=2),
"units": StringCol(itemsize=7, shape=(), dflt=b'', pos=3),
"category": StringCol(itemsize=31, shape=(), dflt=b'', pos=4)}
byteorder := 'little'
chunkshape := (642,)]]
```
```python
>>> hdf.keys()
['/Data/Table Layout',
'/Metadata/Data Parameters',
'/Metadata/Experiment Notes',
'/Metadata/Experiment Parameters',
'/Metadata/Independent Spatial Parameters',
'/Metadata/_record_layout',
'/Data/Array Layout/Layout Description',
'/Data/Array Layout/1D Parameters/Data Parameters',
'/Data/Array Layout/2D Parameters/Data Parameters']
```
#### Fix
One solution would be (I guess) to revert #21543, another to fix at least `.keys()` to use `._handle.walk_nodes()` instead of `.groups()` in
https://github.com/pandas-dev/pandas/blob/ea2e26ae7d700d7fd363ea5bfc05d2fe3fb8a5ee/pandas/io/pytables.py#L562
Could also be that it is a bug in `pytables`.
#### Problem background
I was trying to figure out why some hdf5 files open fine with `pandas` but fail with `dask`.
The reason is that `dask` allows wildcards and iterates over the keys to find valid ones. If `.keys()` is empty, reading the files with `dask` fails.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.27.2.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.1.post20191125
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : 3.6.1
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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```python
# Reproducing faulty behavior:
dfs = pd.DataFrame([[1,'a1',31],[2,'a2',32],[3,'a3',33]], index=['a','b','c'], columns=['c1','c2','c3'])
dfs.iloc[0] = {'c1':101, 'c2':'A1', 'c3':111}
assert dfs.iloc[0][0] == 101 #FAILED
assert dfs.iloc[0][1] == 'A1' #FAILED
assert dfs.iloc[0][2] == 111 #FAILED
```
#### Problem description
Updating first row duplicates column labels.
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} | 8 | 2019-11-28T17:21:52Z | 2019-11-30T17:53:19Z | 2019-11-30T17:47:09Z | MEMBER | null | and un-deprecated `item`, though im not sure where that goes in the whatsnew | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29919 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29919/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29919/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29919/events | https://github.com/pandas-dev/pandas/issues/29919 | 530,050,472 | MDU6SXNzdWU1MzAwNTA0NzI= | 29,919 | Dataframe.aggregate does not work with MultiIndex columns | {
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"id": 71268330,... | open | false | null | [] | null | 3 | 2019-11-28T19:08:22Z | 2021-07-23T05:06:12Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(np.random.random((4,4)))
# Working case
print("Aggregate on single indexed columns:")
print(df.aggregate(tuple, axis=0))
print("-"*80)
# Non working case
df.columns = pd.MultiIndex.from_product([[1,2],['A','B']])
print("Aggregate on multi-index indexed columns:")
print(df.aggregate(tuple, axis=0))
```
The output of the above code chunk:
```
Aggregate on single indexed columns:
0 (0.04336865512041532, 0.7908081117394874, 0.07...
1 (0.06609384319425105, 0.5889960548911211, 0.59...
2 (0.20014907395448422, 0.7652341446870041, 0.19...
3 (0.31734610713381406, 0.4574832810531657, 0.51...
dtype: object
--------------------------------------------------------------------------------
Aggregate on multi-index indexed columns:
1 2
A B A B
0 0.043369 0.066094 0.200149 0.317346
1 0.790808 0.588996 0.765234 0.457483
2 0.074382 0.598405 0.198847 0.515608
3 0.905309 0.694021 0.017308 0.552385
```
#### Problem description
When doing `aggregate` on `axis=0` I expect to perform aggregation per column and this is true if simple indexing is used. In case of multi index columns though it **returns the same dataframe unchanged** which is unexpected.
For `aggregate()` action as a NOOP and returning the same Dataframe is completely unexpected and it sounds like a bug.
#### Expected Output
If this is an invalid operation by principal I would except an exception to be raised. However with my limited experience in pandas I think the best result would be expect to return a MultiIndex series and the results of the aggregation.
A `workaround` for me atm is to convert to single index, do the aggregation and fix the index:
```python
def aggregate_multi_index_axis_0(df, func):
df_flat = df.copy()
df_flat.columns = df_flat.columns.to_flat_index()
out_series = df_flat.aggregate(func, axis=0)
# Change the index before returning
out_series.index = df.columns
return out_series
multi_index_df = pd.DataFrame(np.random.random((4,4)))
multi_index_df.columns = pd.MultiIndex.from_product([[1,2],['A','B']])
print(aggregate_multi_index_axis_0(multi_index_df, tuple))
```
The output would be:
```
1 A (0.04099305616793136, 0.8605895766376076, 0.60...
B (0.04895957253659544, 0.6242386917244872, 0.23...
2 A (0.5066341629476985, 0.92361577845554, 0.11297...
B (0.16165792280960123, 0.3757694407348352, 0.06...
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.candidate.1
python-bits : 64
OS : Linux
OS-release : 5.4.0-050400rc8-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 : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.10
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29920 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29920/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29920/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29920/events | https://github.com/pandas-dev/pandas/pull/29920 | 530,063,623 | MDExOlB1bGxSZXF1ZXN0MzQ2ODIzODIx | 29,920 | Document S3 and GCS path functionality of DataFrame.to_csv() | {
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} | 3 | 2019-11-28T20:08:28Z | 2019-12-17T18:14:19Z | 2019-12-17T18:14:09Z | CONTRIBUTOR | null | - [x] closes #8508
I made more changes than absolutely necessary for documenting this. It's a bit more complete and it reads a more similar to `pd.read_csv()` | {
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} | 1 | 2019-11-28T22:11:44Z | 2020-02-11T23:01:01Z | 2020-02-11T23:01:01Z | NONE | null | #### Code Sample
```python
import logging, sqlite3
import pandas as pd
from datetime import date, timedelta
from random import normalvariate, randint
#%% Storing function.
def store_df_data():
"""
Create a new SQLite database, generate random data (as DataFrame) and
try to save it in the database.
"""
# Defining logger.
logging.basicConfig(format="%(levelname)-5s - %(message)s", level=logging.INFO)
logger = logging.getLogger("sqlite_test")
# Create SQLite database.
conn = sqlite3.connect("financial_dataframe.db")
cursor = conn.cursor()
# Create table (only if it doesn't exist).
sql = "SELECT name FROM sqlite_master WHERE type='table' AND name='financial_data'"
cursor.execute(sql)
if cursor.fetchone() is None:
sql = "CREATE TABLE financial_data (id INTEGER PRIMARY KEY, "
sql += "date TEXT NOT NULL, cp REAL NOT NULL, volume REAL NOT NULL)"
cursor.execute(sql)
# Generate data and adjust dataframe.
df = simulate_goog_data(30, date(2019, 10, 1), 1205.1)
# Insert data one row per time.
logger.info("====================================="
+ "=====================================")
logger.info("Inserting data one per row.")
try:
df.to_sql("financial_data", con=conn, if_exists="append", index=False)
logger.info("Data inserted successfully!")
except:
logger.exception("Error while inserting data one per row:")
# Generate data again.
df = simulate_goog_data(30, date(2019, 11, 1), df.iloc[-1]["cp"])
# Insert data with multirow method.
logger.info("====================================="
+ "=====================================")
logger.info("Inserting data with multirow method.")
try:
df.to_sql("financial_data", con=conn, if_exists="append", index=False, \
method="multi")
logger.info("Data inserted successfully!")
except:
logger.exception("Error while inserting data with multirow method:")
# Generate data again.
df = simulate_goog_data(30, date(2019, 12, 1), df.iloc[-1]["cp"])
# Insert data with multirow method and chunksize.
logger.info("====================================="
+ "=====================================")
logger.info("Inserting data with multirow method and chunksize.")
try:
df.to_sql("financial_data", con=conn, if_exists="append", index=False, \
method="multi", chunksize=10)
logger.info("Data inserted successfully!")
except:
logger.exception("Error while inserting data with multirow and chunksize:")
# Close pointers.
cursor.close()
conn.close()
#%% Simulating function.
def simulate_goog_data(n_data, initial_date, initial_close):
"""
Simulate data from GOOG.
:Parameters:
n_data : Max. range of days to generate data.
initial_date : First date for the simulation.
initial_close : First close for the simulation.
"""
# Mean and standard deviation from GOOG return (october 2019 daily data).
mean_goog = 0.002240703
std_dev_goog = 0.011957324
# Minimum and maximum value for GOOG's volume.
min_vol_goog = 840000
max_vol_goog = 2615000
# Initializing lists.
date_list = list()
close_list = list()
volume_list = list()
# First close and date.
current_date = initial_date
current_close = initial_close
# Generate data (only for working days).
for _ in range(n_data):
if current_date.isoweekday() < 6:
date_list.append(current_date)
close_list.append(current_close)
volume_list.append(randint(min_vol_goog, max_vol_goog))
# Update goog's close.
current_close *= (1 + normalvariate(mean_goog, std_dev_goog))
# Move forward one day.
current_date += timedelta(days=1)
# Return dataframe.
return pd.DataFrame({"date": date_list, "cp": close_list, "volume": volume_list})
#%% Executor.
if __name__ == "__main__":
store_df_data()
```
#### Problem description
The piece of code described above tries to insert simulated financial data in a SQLite database, using method `to_sql` from pandas.DataFrame. It tries to make these insertions in three different ways:
1. Inserting all data one row at a time. The insertion performs successfully.
2. Inserting all data using attribute `method="multi"` to performs a multi-row insert. It crushes.
3. Inserting all data using attribute `method="multi"` and setting a `chunksize`. It also crushes.
It seems that it's impossible insert data in a SQLite database using multi-row option, since it throws the error:
```
TypeError: insert() takes exactly 2 arguments (1 given)
```
Could you please check this?
Thank you very much in advance!
#### Output
```
INFO - ==========================================================================
INFO - Inserting data one per row.
INFO - Data inserted successfully!
INFO - ==========================================================================
INFO - Inserting data with multirow method.
ERROR - Error while inserting data with multirow method:
Traceback (most recent call last):
File "bug_multirow_sqlite.py", line 52, in store_df_data
df.to_sql("financial_data", con=conn, if_exists="append", index=False, method="multi")
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py", line 2531, in to_sql
dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 460, in to_sql
chunksize=chunksize, dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 1547, in to_sql
table.insert(chunksize, method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 686, in insert
exec_insert(conn, keys, chunk_iter)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 609, in _execute_insert_multi
conn.execute(self.table.insert(data))
TypeError: insert() takes exactly 2 arguments (1 given)
INFO - ==========================================================================
INFO - Inserting data with multirow method and chunksize.
ERROR - Error while inserting data with multirow method and chunksize:
Traceback (most recent call last):
File "bug_multirow_sqlite.py", line 64, in store_df_data
df.to_sql("financial_data", con=conn, if_exists="append", index=False, method="multi", chunksize=10)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py", line 2531, in to_sql
dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 460, in to_sql
chunksize=chunksize, dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 1547, in to_sql
table.insert(chunksize, method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 686, in insert
exec_insert(conn, keys, chunk_iter)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 609, in _execute_insert_multi
conn.execute(self.table.insert(data))
TypeError: insert() takes exactly 2 arguments (1 given)
```
<details>
Code was run in:<br />
<ul>
<li>Windows 10 - Python 3.7.3 - pandas 0.24.2</li>
<li>Red Hat 7.6 - Python 3.6.8 - pandas 0.25.0</li>
</ul>
</details>
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} | 5 | 2019-11-28T22:29:17Z | 2019-12-11T13:57:19Z | 2019-12-11T13:18:32Z | CONTRIBUTOR | null | - [x] closes #17569
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Bug when indexing with ``.loc`` and the index is a CategoricalIndex with integer or float categories. | {
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} | 4 | 2019-11-29T00:28:51Z | 2019-12-27T00:58:07Z | 2019-12-01T17:50:19Z | CONTRIBUTOR | null | - xref https://github.com/pandas-dev/pandas/issues/30469
- [x] follow up from https://github.com/pandas-dev/pandas/pull/29212
- [x] passes `black pandas`
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.
There is more to do here. | {
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} | 10 | 2019-11-29T09:02:05Z | 2019-12-27T19:56:59Z | 2019-12-27T19:56:59Z | MEMBER | null | I know this is an optional dependency, but IMO it's still nice that running a different benchmark still works when you don't have odf installed (noticed this yesterday when running the isnull benchmarks). It's similar to pytest skipping if an optional dependency is not installed. | {
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} | 1 | 2019-11-29T09:20:58Z | 2019-12-01T18:53:07Z | 2019-11-29T17:31:42Z | MEMBER | null | DataFrame.get_value was removed.
(now we should maybe also consider deprecating `lookup`, but that is another issue) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29926/events | https://github.com/pandas-dev/pandas/pull/29926 | 530,275,876 | MDExOlB1bGxSZXF1ZXN0MzQ2OTkwMDc1 | 29,926 | BUG: Index.get_value implementation for ExtensionArray | {
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} | 3 | 2019-11-29T10:39:06Z | 2019-12-04T14:21:49Z | 2019-12-04T14:15:14Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/29708
cc @jreback @jbrockmendel | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29927/events | https://github.com/pandas-dev/pandas/pull/29927 | 530,424,534 | MDExOlB1bGxSZXF1ZXN0MzQ3MTA5NDYz | 29,927 | DEPR: passing str alias to in DatetimeTZDtype | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29928/events | https://github.com/pandas-dev/pandas/issues/29928 | 530,444,853 | MDU6SXNzdWU1MzA0NDQ4NTM= | 29,928 | Using to_json/read_json with orient='table' on a DataFrame with a single level MultiIndex does not work | {
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"description": "read_json, to_json, json... | open | false | null | [] | null | 2 | 2019-11-29T17:36:52Z | 2020-05-26T15:10:33Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas
#Scenario1
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,),(2,),(3,),(4,)]))
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
new_df=pandas.read_json(js, orient='table') # this runs but creates NaN indexes
#Scenario2
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,),(2,),(3,),(4,)], names=['ind1']))
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
try:
new_df==pandas.read_json(js, orient='table') # this fails with KeyError: 'None of [None] are in the columns'
except KeyError as e:
pass
#Scenario3
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,1),(1,2),(2,1),(2,2)], names=['ind1', 'ind2']))
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
new_df=pandas.read_json(js, orient='table') # this works as is is multi level multiindex
#Scenario4
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,),(2,),(3,),(4,)]))
df.index=df.index.to_flat_index() # a workaround?
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
new_df=pandas.read_json(js, orient='table') # This is a workaround that does produce sensible results
```
#### Problem description
I have a DataFrame that has a MultiIndex, even though that index only has one level and could easily be a normal index, I am receiving this table from ipyaggrid, so am not in control of that.
Saving this table to json with df.to_json(orient='table) and then loading from pandas.read_json(js, orient='table) leads to either an exception or the index being changed to NaN values.
Neither of these outcomes seems desirable or correct.
I have a workaround to intercept DataFrames with this feature and convert their index so a flat Index, but it would be good if the table serialisation just worked.
I was drawn to orient='Table' by the comments on this similar issue, my main requirement here is to save/load the DataFrame preserving its row and column order. You don't get this with the standard to_json/from_json modes
https://github.com/pandas-dev/pandas/issues/4889
#### Expected Output
new_df for scenario1 and scenario2 to should have a MultiIndex, in the first scenario with a level that has no name and in the second scenario with level called 'ind1'
#### Output of ``pd.show_versions()``
<details>
pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 10.0.1
setuptools : 39.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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 : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29929/events | https://github.com/pandas-dev/pandas/pull/29929 | 530,480,016 | MDExOlB1bGxSZXF1ZXN0MzQ3MTUzMjg4 | 29,929 | DEPR: tz_convert in the Timestamp constructor raises | {
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} | 4 | 2019-11-29T20:24:07Z | 2019-12-01T21:41:12Z | 2019-12-01T21:38:47Z | MEMBER | null | - [x] xref #23621
- [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 | 2019-11-29T22:02:49Z | 2019-11-29T23:26:11Z | 2019-11-29T23:07:40Z | MEMBER | null | This one was deprecated back in 0.17.0, so long time coming.
There's some more cleanup that can be done around this method in a follow-up. | {
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} | 1 | 2019-11-30T00:14:05Z | 2019-12-01T23:22:21Z | 2019-12-01T23:22:15Z | CONTRIBUTOR | null | Follow-up work on #29761 - parametrize over different operations and frequencies
- [x] xref #21096
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"gists... | null | 5 | 2019-11-30T01:31:04Z | 2019-12-01T14:36:01Z | 2019-12-01T13:26:52Z | NONE | null | Sorry to bother with a mere typo, but the sentence has no closing parentheses.
> Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the dtype keyword, a passed ndarray, or a passed Series, then it will be preserved in DataFrame operations.
> https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html
Thank you in advance. | {
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- [x] tests added / passed
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} | 3 | 2019-12-01T15:18:48Z | 2019-12-03T22:36:38Z | 2019-12-03T22:36:37Z | CONTRIBUTOR | null | `Series.interpolate(method='index')` use [numpy.interp](https://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html) under the hood, which cannot work with unsorted X values. Here we sort the known data for `numpy.interp`.
- [x] closes #21037
- [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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... | open | false | null | [] | null | 31 | 2019-12-01T20:04:47Z | 2021-10-04T07:42:42Z | null | NONE | null | - [X] closes #29688
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is an attempt at #29688 i.e. group some columns on the same ax when calling `df.plot`
See [this gist](https://gist.github.com/NicolasHug/4d1f60212ba69e6000982951ae47f203) for illustration of different supported plots
Thanks! | {
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} | 12 | 2019-12-01T21:35:10Z | 2019-12-22T23:45:27Z | 2019-12-22T23:45:25Z | NONE | null | **df_manager.py**
```python
from multiprocessing.managers import BaseManager
import pandas as pd
def init_dataframe():
return pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
def get_df():
return df
df = init_dataframe()
manager = BaseManager(('', 37844), b'password')
manager.register('get_df', get_df)
server = manager.get_server()
server.serve_forever()
```
**data_handler.py**
```python
from multiprocessing.managers import BaseManager
import pandas as pd
def get_df():
manager = BaseManager(('', 37844), b'password')
manager.register('get_df')
manager.connect()
return manager.get_df()
def data():
df = get_df()
return df.to_dict()
if __name__ == '__main__':
data()
```
I'm trying to use multiprocessing to create a service that handles my pandas dataframe to give to Flask requests. Unfortunately, this throws an exception when attempting to call `manager.get_df()` in `data_handler.py`. This seems to be caused by DataFrames specifically, as returning `df.to_json()` instead of just `df` in `df_manager.py` seems to work fine.
```python-traceback
Traceback (most recent call last):
File "src/data_handler.py", line 15, in <module>
data()
File "src/data_handler.py", line 11, in data
df = get_df()
File "src/data_handler.py", line 8, in get_df
return manager.get_df()
File "/usr/lib/python3.7/multiprocessing/managers.py", line 724, in temp
token, exp = self._create(typeid, *args, **kwds)
File "/usr/lib/python3.7/multiprocessing/managers.py", line 609, in _create
id, exposed = dispatch(conn, None, 'create', (typeid,)+args, kwds)
File "/usr/lib/python3.7/multiprocessing/managers.py", line 82, in dispatch
raise convert_to_error(kind, result)
multiprocessing.managers.RemoteError:
---------------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python3.7/multiprocessing/managers.py", line 201, in handle_request
result = func(c, *args, **kwds)
File "/usr/lib/python3.7/multiprocessing/managers.py", line 391, in create
exposed = public_methods(obj)
File "/usr/lib/python3.7/multiprocessing/managers.py", line 122, in public_methods
return [name for name in all_methods(obj) if name[0] != '_']
File "/usr/lib/python3.7/multiprocessing/managers.py", line 113, in all_methods
func = getattr(obj, name)
File "/home/admin/dev/pandas-multiprocessing/venv/lib/python3.7/site-packages/pandas/core/frame.py", line 392, in _constructor_expanddim
raise NotImplementedError("Not supported for DataFrames!")
NotImplementedError: Not supported for DataFrames!
---------------------------------------------------------------------------
```
If I'm missing something obvious, please let me know. I think what I'm trying to do should be possible, though.
I've posted this on [StackOverflow](https://stackoverflow.com/questions/59097332/flask-multiprocessing-remoteerror) as well if you'd like to get some rep.
**EDIT** Removed Flask dependency in example code. | {
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} | 2 | 2019-12-01T22:28:46Z | 2019-12-02T01:45:40Z | 2019-12-02T01:44:56Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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} | 1 | 2019-12-01T23:56:52Z | 2019-12-02T00:58:23Z | 2019-12-02T00:55:05Z | MEMBER | null | Feeling strange about opening a PR just for this, but my OCD is killing me. | {
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} | 1 | 2019-12-02T00:17:58Z | 2019-12-02T11:04:09Z | 2019-12-02T01:24:13Z | MEMBER | null | - [x] ref #29886
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} | 1 | 2019-12-02T00:41:44Z | 2019-12-02T01:53:30Z | 2019-12-02T01:45:36Z | MEMBER | null | We have a few methods that take `**kwargs` and ignore them. It turns out the reason for this is in `_write_to_group`, where we pass `**kwargs` to two separate functions (`_create_storer` and `write`), each of which actually needs a specific subset. By making kwargs explicit in _write_to_group, we can pass the correct ones to create_storer and write, and remove some unused kwargs from other signatures. | {
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} | 2 | 2019-12-02T01:26:19Z | 2019-12-02T23:29:29Z | 2019-12-02T23:29:21Z | CONTRIBUTOR | null | 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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} | 5 | 2019-12-02T01:31:19Z | 2019-12-10T12:08:07Z | 2019-12-10T12:04:50Z | MEMBER | null | - [x] ref #29886
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-12-02T01:44:02Z | 2019-12-07T22:28:57Z | 2019-12-07T22:27:25Z | MEMBER | null | Part of my perpetual goal to simplify this. Right now we override the built-in capabilities of the JSON encoder and use multiple layers of loosely typed functions. I think this is pretty confusing, so I am trying to reduce the callbacks and set native values directly during iteration
This isn't a full solve - rather just numeric values. I plan on tackling string representations in a follow up; just stopping here because the code movement gets large fast.
In addition to less code, note that this also reduces the amount of compiler warnings from 68 down to 60 when compiling with `-Wextra`
master:
<details>
```sh
pandas/_libs/src/ujson/python/objToJSON.c:388:28: warning: unused parameter 'obj' [-Wunused-parameter]
static void *CDouble(JSOBJ obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:389:30: warning: unused parameter '_outLen' [-Wunused-parameter]
size_t *_outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:395:26: warning: unused parameter 'obj' [-Wunused-parameter]
static void *CLong(JSOBJ obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:396:28: warning: unused parameter '_outLen' [-Wunused-parameter]
size_t *_outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:402:34: warning: unused parameter '_obj' [-Wunused-parameter]
static void *PyLongToINT64(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:403:36: warning: unused parameter '_outLen' [-Wunused-parameter]
size_t *_outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:408:60: warning: unused parameter 'tc' [-Wunused-parameter]
static void *NpyFloatToDOUBLE(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:409:39: warning: unused parameter '_outLen' [-Wunused-parameter]
size_t *_outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:415:59: warning: unused parameter 'tc' [-Wunused-parameter]
static void *PyFloatToDOUBLE(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:416:38: warning: unused parameter '_outLen' [-Wunused-parameter]
size_t *_outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:422:57: warning: unused parameter 'tc' [-Wunused-parameter]
static void *PyBytesToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:422:67: warning: unused parameter 'outValue' [-Wunused-parameter]
static void *PyBytesToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:429:69: warning: unused parameter 'outValue' [-Wunused-parameter]
static void *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:515:40: warning: unused parameter '_obj' [-Wunused-parameter]
static void *NpyDatetime64ToJSON(JSOBJ _obj, JSONTypeContext *tc,
^
pandas/_libs/src/ujson/python/objToJSON.c:554:40: warning: unused parameter 'obj' [-Wunused-parameter]
static int NpyTypeToJSONType(PyObject *obj, JSONTypeContext *tc, int npyType,
^
pandas/_libs/src/ujson/python/objToJSON.c:624:40: warning: unused parameter '_obj' [-Wunused-parameter]
static void NpyArr_freeItemValue(JSOBJ _obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:633:31: warning: unused parameter '_obj' [-Wunused-parameter]
int NpyArr_iterNextNone(JSOBJ _obj, JSONTypeContext *tc) { return 0; }
^
pandas/_libs/src/ujson/python/objToJSON.c:633:54: warning: unused parameter 'tc' [-Wunused-parameter]
int NpyArr_iterNextNone(JSOBJ _obj, JSONTypeContext *tc) { return 0; }
^
pandas/_libs/src/ujson/python/objToJSON.c:690:37: warning: unused parameter 'obj' [-Wunused-parameter]
void NpyArrPassThru_iterBegin(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:690:59: warning: unused parameter 'tc' [-Wunused-parameter]
void NpyArrPassThru_iterBegin(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:769:33: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ NpyArr_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:774:32: warning: unused parameter 'obj' [-Wunused-parameter]
char *NpyArr_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:828:33: warning: unused parameter 'obj' [-Wunused-parameter]
char *PdBlock_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:850:43: warning: unused parameter 'obj' [-Wunused-parameter]
char *PdBlock_iterGetName_Transpose(JSOBJ obj, JSONTypeContext *tc,
^
pandas/_libs/src/ujson/python/objToJSON.c:896:38: warning: unused parameter 'obj' [-Wunused-parameter]
void PdBlockPassThru_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1128:26: warning: unused parameter 'obj' [-Wunused-parameter]
void Tuple_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1128:48: warning: unused parameter 'tc' [-Wunused-parameter]
void Tuple_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1130:32: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Tuple_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1134:31: warning: unused parameter 'obj' [-Wunused-parameter]
char *Tuple_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1134:53: warning: unused parameter 'tc' [-Wunused-parameter]
char *Tuple_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1134:65: warning: unused parameter 'outLen' [-Wunused-parameter]
char *Tuple_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1147:25: warning: unused parameter 'obj' [-Wunused-parameter]
int Iter_iterNext(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1165:25: warning: unused parameter 'obj' [-Wunused-parameter]
void Iter_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1177:31: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Iter_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1181:30: warning: unused parameter 'obj' [-Wunused-parameter]
char *Iter_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1181:52: warning: unused parameter 'tc' [-Wunused-parameter]
char *Iter_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1181:64: warning: unused parameter 'outLen' [-Wunused-parameter]
char *Iter_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1197:24: warning: unused parameter 'obj' [-Wunused-parameter]
void Dir_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1283:30: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Dir_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1288:29: warning: unused parameter 'obj' [-Wunused-parameter]
char *Dir_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1314:25: warning: unused parameter 'obj' [-Wunused-parameter]
void List_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1314:47: warning: unused parameter 'tc' [-Wunused-parameter]
void List_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1316:31: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ List_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1320:30: warning: unused parameter 'obj' [-Wunused-parameter]
char *List_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1320:52: warning: unused parameter 'tc' [-Wunused-parameter]
char *List_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1320:64: warning: unused parameter 'outLen' [-Wunused-parameter]
char *List_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1327:28: warning: unused parameter 'obj' [-Wunused-parameter]
void Index_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1363:26: warning: unused parameter 'obj' [-Wunused-parameter]
void Index_iterEnd(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:1363:48: warning: unused parameter 'tc' [-Wunused-parameter]
void Index_iterEnd(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:1365:32: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Index_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1369:31: warning: unused parameter 'obj' [-Wunused-parameter]
char *Index_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1377:29: warning: unused parameter 'obj' [-Wunused-parameter]
void Series_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1418:27: warning: unused parameter 'obj' [-Wunused-parameter]
void Series_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1424:33: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Series_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1428:32: warning: unused parameter 'obj' [-Wunused-parameter]
char *Series_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1436:32: warning: unused parameter 'obj' [-Wunused-parameter]
void DataFrame_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1482:30: warning: unused parameter 'obj' [-Wunused-parameter]
void DataFrame_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1488:36: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ DataFrame_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1492:35: warning: unused parameter 'obj' [-Wunused-parameter]
char *DataFrame_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1502:27: warning: unused parameter 'obj' [-Wunused-parameter]
void Dict_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1507:25: warning: unused parameter 'obj' [-Wunused-parameter]
int Dict_iterNext(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1535:25: warning: unused parameter 'obj' [-Wunused-parameter]
void Dict_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1544:31: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Dict_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1548:30: warning: unused parameter 'obj' [-Wunused-parameter]
char *Dict_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:2291:34: warning: unused parameter 'obj' [-Wunused-parameter]
void Object_endTypeContext(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:2403:5: warning: missing field 'errorMsg' initializer [-Wmissing-field-initializers]
}};
^
pandas/_libs/src/ujson/python/objToJSON.c:2403:6: warning: missing field 'npyCtxtPassthru' initializer [-Wmissing-field-initializers]
}};
^
pandas/_libs/src/ujson/python/objToJSON.c:2357:31: warning: unused parameter 'self' [-Wunused-parameter]
PyObject *objToJSON(PyObject *self, PyObject *args, PyObject *kwargs) {
^
68 warnings generated.
```
</details>
This PR
<details>
```sh
pandas/_libs/src/ujson/python/objToJSON.c:388:57: warning: unused parameter 'tc' [-Wunused-parameter]
static void *PyBytesToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:388:67: warning: unused parameter 'outValue' [-Wunused-parameter]
static void *PyBytesToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:395:69: warning: unused parameter 'outValue' [-Wunused-parameter]
static void *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue,
^
pandas/_libs/src/ujson/python/objToJSON.c:481:40: warning: unused parameter '_obj' [-Wunused-parameter]
static void *NpyDatetime64ToJSON(JSOBJ _obj, JSONTypeContext *tc,
^
pandas/_libs/src/ujson/python/objToJSON.c:520:40: warning: unused parameter 'obj' [-Wunused-parameter]
static int NpyTypeToJSONType(PyObject *obj, JSONTypeContext *tc, int npyType,
^
pandas/_libs/src/ujson/python/objToJSON.c:610:40: warning: unused parameter '_obj' [-Wunused-parameter]
static void NpyArr_freeItemValue(JSOBJ _obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:619:31: warning: unused parameter '_obj' [-Wunused-parameter]
int NpyArr_iterNextNone(JSOBJ _obj, JSONTypeContext *tc) { return 0; }
^
pandas/_libs/src/ujson/python/objToJSON.c:619:54: warning: unused parameter 'tc' [-Wunused-parameter]
int NpyArr_iterNextNone(JSOBJ _obj, JSONTypeContext *tc) { return 0; }
^
pandas/_libs/src/ujson/python/objToJSON.c:676:37: warning: unused parameter 'obj' [-Wunused-parameter]
void NpyArrPassThru_iterBegin(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:676:59: warning: unused parameter 'tc' [-Wunused-parameter]
void NpyArrPassThru_iterBegin(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:755:33: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ NpyArr_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:760:32: warning: unused parameter 'obj' [-Wunused-parameter]
char *NpyArr_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:814:33: warning: unused parameter 'obj' [-Wunused-parameter]
char *PdBlock_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:836:43: warning: unused parameter 'obj' [-Wunused-parameter]
char *PdBlock_iterGetName_Transpose(JSOBJ obj, JSONTypeContext *tc,
^
pandas/_libs/src/ujson/python/objToJSON.c:882:38: warning: unused parameter 'obj' [-Wunused-parameter]
void PdBlockPassThru_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1114:26: warning: unused parameter 'obj' [-Wunused-parameter]
void Tuple_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1114:48: warning: unused parameter 'tc' [-Wunused-parameter]
void Tuple_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1116:32: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Tuple_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1120:31: warning: unused parameter 'obj' [-Wunused-parameter]
char *Tuple_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1120:53: warning: unused parameter 'tc' [-Wunused-parameter]
char *Tuple_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1120:65: warning: unused parameter 'outLen' [-Wunused-parameter]
char *Tuple_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1133:25: warning: unused parameter 'obj' [-Wunused-parameter]
int Iter_iterNext(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1151:25: warning: unused parameter 'obj' [-Wunused-parameter]
void Iter_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1163:31: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Iter_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1167:30: warning: unused parameter 'obj' [-Wunused-parameter]
char *Iter_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1167:52: warning: unused parameter 'tc' [-Wunused-parameter]
char *Iter_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1167:64: warning: unused parameter 'outLen' [-Wunused-parameter]
char *Iter_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1183:24: warning: unused parameter 'obj' [-Wunused-parameter]
void Dir_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1269:30: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Dir_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1274:29: warning: unused parameter 'obj' [-Wunused-parameter]
char *Dir_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1300:25: warning: unused parameter 'obj' [-Wunused-parameter]
void List_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1300:47: warning: unused parameter 'tc' [-Wunused-parameter]
void List_iterEnd(JSOBJ obj, JSONTypeContext *tc) {}
^
pandas/_libs/src/ujson/python/objToJSON.c:1302:31: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ List_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1306:30: warning: unused parameter 'obj' [-Wunused-parameter]
char *List_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1306:52: warning: unused parameter 'tc' [-Wunused-parameter]
char *List_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1306:64: warning: unused parameter 'outLen' [-Wunused-parameter]
char *List_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1313:28: warning: unused parameter 'obj' [-Wunused-parameter]
void Index_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1349:26: warning: unused parameter 'obj' [-Wunused-parameter]
void Index_iterEnd(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:1349:48: warning: unused parameter 'tc' [-Wunused-parameter]
void Index_iterEnd(JSOBJ obj, JSONTypeContext *tc) { PRINTMARK(); }
^
pandas/_libs/src/ujson/python/objToJSON.c:1351:32: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Index_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1355:31: warning: unused parameter 'obj' [-Wunused-parameter]
char *Index_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1363:29: warning: unused parameter 'obj' [-Wunused-parameter]
void Series_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1404:27: warning: unused parameter 'obj' [-Wunused-parameter]
void Series_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1410:33: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Series_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1414:32: warning: unused parameter 'obj' [-Wunused-parameter]
char *Series_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1422:32: warning: unused parameter 'obj' [-Wunused-parameter]
void DataFrame_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1468:30: warning: unused parameter 'obj' [-Wunused-parameter]
void DataFrame_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1474:36: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ DataFrame_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1478:35: warning: unused parameter 'obj' [-Wunused-parameter]
char *DataFrame_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1488:27: warning: unused parameter 'obj' [-Wunused-parameter]
void Dict_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1493:25: warning: unused parameter 'obj' [-Wunused-parameter]
int Dict_iterNext(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1521:25: warning: unused parameter 'obj' [-Wunused-parameter]
void Dict_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1530:31: warning: unused parameter 'obj' [-Wunused-parameter]
JSOBJ Dict_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:1534:30: warning: unused parameter 'obj' [-Wunused-parameter]
char *Dict_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
^
pandas/_libs/src/ujson/python/objToJSON.c:2278:34: warning: unused parameter 'obj' [-Wunused-parameter]
void Object_endTypeContext(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:2304:35: warning: unused parameter 'obj' [-Wunused-parameter]
JSINT64 Object_getLongValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:2308:36: warning: unused parameter 'obj' [-Wunused-parameter]
double Object_getDoubleValue(JSOBJ obj, JSONTypeContext *tc) {
^
pandas/_libs/src/ujson/python/objToJSON.c:2380:5: warning: missing field 'errorMsg' initializer [-Wmissing-field-initializers]
}};
^
pandas/_libs/src/ujson/python/objToJSON.c:2380:6: warning: missing field 'npyCtxtPassthru' initializer [-Wmissing-field-initializers]
}};
^
pandas/_libs/src/ujson/python/objToJSON.c:2334:31: warning: unused parameter 'self' [-Wunused-parameter]
PyObject *objToJSON(PyObject *self, PyObject *args, PyObject *kwargs) {
^
60 warnings generated.
```
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} | 4 | 2019-12-02T02:10:23Z | 2019-12-09T16:02:44Z | 2019-12-09T09:10:56Z | MEMBER | null | - [X] closes #27203
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
A quick summary of the deprecation details can be found here: https://github.com/pandas-dev/pandas/pull/26403#issuecomment-507351745 | {
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} | 1 | 2019-12-02T02:37:39Z | 2019-12-02T12:35:21Z | 2019-12-02T12:35:15Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fixed a typo in the user-guide/Styling: https://pandas-docs.github.io/pandas-docs-travis/user_guide/style.html#Sharing-Styles
In the line:
`Notice that you’re able share the styles even though they’re data aware. The styles are re-evaluated on the new DataFrame they’ve been used upon.` | {
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} | 6 | 2019-12-02T02:42:54Z | 2019-12-04T16:40:14Z | 2019-12-04T14:06:40Z | MEMBER | null | Made possible by #29951, progress towards #20903
@jreback input needed on whether certain not-covered-in-tests cases should be considered possible. See that both `put` and `append` still contain `**kwargs`. In all extant tests, kwargs is always empty. But there are remaining keywords that write_to_group (which both put and append call) accepts that are not in the put/append signatures.
So the question is: is there a reason why put/append would accept different subsets of the writing keywords? If not, they should both have the same explicit kwargs as write_to_group.
Once this is pinned down, we can get explicit all the way up the stack to NDFrame.to_hdf | {
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} | 0 | 2019-12-02T03:37:07Z | 2019-12-02T16:12:00Z | 2019-12-02T12:34:19Z | MEMBER | null | Things I find myself doing in a bunch of local branches.
cc @WillAyd. | {
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} | 1 | 2019-12-02T08:41:53Z | 2019-12-02T14:06:49Z | 2019-12-02T12:45:03Z | MEMBER | null | - [x] ref #29886
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"id": 7171206... | open | false | null | [] | null | 0 | 2019-12-02T09:07:02Z | 2021-04-10T16:08:02Z | null | CONTRIBUTOR | null | can you also create an issue on the pandas side so we know what we need to update once dask is at a certain version (you can just point to this line here)
_Originally posted by @jreback in https://github.com/_render_node/MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDM1MjM4MzI0NA==/comments/review_comment_
**Note:** This will be on hold for quite a while (first merging my PR, then waiting until dask has silenced the warning on their side, then bumping our dask dependency) | {
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} | 12 | 2019-12-02T09:14:44Z | 2019-12-04T20:05:24Z | 2019-12-04T19:27:34Z | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/29597 and https://github.com/pandas-dev/pandas/pull/29555, now actually using the `pd.NA` scalar in `BooleanArray`. | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 3 | 2019-12-02T10:46:52Z | 2021-07-23T05:08:26Z | null | MEMBER | null | From https://github.com/pandas-dev/pandas/pull/27929#discussion_r350469472. In `__getitem__` or `Categorical.min(..)`, we always return `np.nan` as scalar missing value, regardless of the dtype:
```
In [7]: cat = pd.Categorical([pd.Timestamp("2012"), None], ordered=True)
In [8]: cat
Out[8]:
[2012-01-01, NaT]
Categories (1, datetime64[ns]): [2012-01-01]
In [9]: cat[1]
Out[9]: nan
In [10]: cat.min(skipna=False)
Out[10]: nan
```
In the above, this could also be `pd.NaT` instead?
(similar issue will come up once we can use the EAs that use the new NA scalar in categoricals)
However, `CategoricalDtype.na_value` now also returns `np.nan` (which should be consistent with what we return in the cases above):
```
In [13]: cat.dtype.na_value
Out[13]: nan
```
We can of course let the `CategoricalDtype.na_value` be dependent on the `na_value` of the dtype of the categories. But I am not fully sure we want such values-dependent behaviour? | {
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} | 4 | 2019-12-02T11:03:13Z | 2019-12-20T14:41:40Z | 2019-12-10T14:49:36Z | MEMBER | null | - [x] ref #29886
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 12 | 2019-12-02T12:45:04Z | 2019-12-30T17:55:21Z | 2019-12-30T17:55:18Z | CONTRIBUTOR | null | Just a WIP for now. I haven't closely reviewed this since pd.NA was merged into master.
cc @jorisvandenbossche
Closes https://github.com/pandas-dev/pandas/issues/15375 | {
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] | closed | false | null | [] | null | 5 | 2019-12-02T13:11:28Z | 2021-07-23T05:11:39Z | 2021-07-23T05:11:39Z | NONE | null | Using pandas version of newest anaconda windows setup-installer:
pd.__version__
Out[11]: '0.25.1'
While importing larger csv-files there occures a shift in column names.
Simple version works:
```
df = pd.read_csv(relpath + "\data.csv", sep=';', header=0,
skiprows=[1], parse_dates=False, keep_date_col=True)
df.head()
Out[4]:
Timestamp PMaxEVU U_L1_L2 Frequency
0 01.12.2019 00:01 100.0 20.688 49.965
1 01.12.2019 00:02 100.0 20.704 49.947
2 01.12.2019 00:03 100.0 20.700 49.971
3 01.12.2019 00:04 100.0 20.701 49.988
4 01.12.2019 00:05 100.0 20.701 49.986
```
while normal mode leads to shift (see Timestamp has PMaxEVU-values)
```
df1 = pd.read_csv(relpath + "\ALL_DATA_01.csv", sep=';', header=0,
skiprows=[1], parse_dates=False, keep_date_col=True)
df1 = df1.set_index('Timestamp')
df1.head()
Out[9]:
PMaxEVU PMaxEVU_Active CosPhi_OP ... ER ER_Cap Unnamed: 59
Timestamp ...
100.0 0.0 -0.01 -100.0 ... 0.0 0.0 NaN
100.0 0.0 -0.01 -100.0 ... 0.0 0.0 NaN
100.0 0.0 -0.01 -100.0 ... 0.0 0.0 NaN
100.0 0.0 -0.01 -100.0 ... 0.0 0.0 NaN
100.0 0.0 -0.01 -100.0 ... 0.0 0.0 NaN
[5 rows x 59 columns]
```
this occures even with direct importing with parse_dates=True,
```
df1 = pd.read_csv( relpath + "\ALL_DATA_01.csv", sep=';', header=0,
index_col=0, skiprows=[1], parse_dates=True,
keep_date_col=False)
```
Useing string name leads to KeyError - this may be an error on my side
```
df1 = pd.read_csv( relpath + "\ALL_DATA_01.csv", sep=';', header=0,
index_col='Timestamp', skiprows=[1], parse_dates=True,
keep_date_col=False)
```
```
File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 2100, in read
values = data.pop(self.index_col[i])
KeyError: 'Timestamp'
``` | {
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] | closed | false | null | [] | null | 1 | 2019-12-02T15:08:42Z | 2020-05-22T16:58:24Z | 2020-05-22T16:58:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# when concatenating on Multi-index, 1 and 0 are automatically converted to booleans if there are other booleans
s1 = pd.Series([10,20],index=pd.MultiIndex.from_tuples( [('is true', True), ('is true', False)],names=['Question', 'Answer']))
s2 = pd.Series([51,12,123],index=pd.MultiIndex.from_tuples( [('how many', 0), ('how many', 1), ('how many', 2),],names=['Question', 'Answer']))
to_concat =[s1,s2]
print('with issue:')
print(pd.concat(to_concat))
#no issue
s1 = pd.Series([10,20],index=pd.MultiIndex.from_tuples( [('is true', 1), ('is true', 0)],names=['Question', 'Answer']))
to_concat =[s1,s2]
print('\nwithout issue:')
print(pd.concat(to_concat))
```
#### Problem description
When concatenating on Multi-index, 1 and 0 are automatically converted to Booleans if there are other Booleans. In my view the type should remain the same after concatenating.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
#### Expected Output
```with issue:
Question Answer
is true True 10
False 20
how many False 51
True 12
2 123
dtype: int64
without issue:
Question Answer
is true 1 10
0 20
how many 0 51
1 12
2 123
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
'0.25.3'
</details>
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} | 1 | 2019-12-02T15:15:27Z | 2020-01-01T17:27:21Z | 2020-01-01T17:27:21Z | CONTRIBUTOR | null | ```
In [10]: i = Index(['c','a','d','b'])
In [12]: i.get_slice_bound('a',side="left",kind="ix")
Out[12]: 1
In [13]: mi = MultiIndex.from_arrays([['c','a','d','b']])
In [15]: mi.get_slice_bound('a',side="left",kind="ix")
---------------------------------------------------------------------------
UnsortedIndexError Traceback (most recent call last)
<ipython-input-15-fbc28595630f> in <module>
----> 1 mi.get_slice_bound('a',side="left",kind="ix")
~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py in get_slice_bound(self, label, side, kind)
2523 if not isinstance(label, tuple):
2524 label = (label,)
-> 2525 return self._partial_tup_index(label, side=side)
2526
2527 def slice_locs(self, start=None, end=None, step=None, kind=None):
~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _partial_tup_index(self, tup, side)
2585 raise UnsortedIndexError(
2586 "Key length (%d) was greater than MultiIndex"
-> 2587 " lexsort depth (%d)" % (len(tup), self.lexsort_depth)
2588 )
2589
UnsortedIndexError: 'Key length (1) was greater than MultiIndex lexsort depth (0)'
```
But, If `MultiIndex` is sorted,
```
In [17]: mi = MultiIndex.from_arrays([['a','b','c','d']])
In [18]: mi.get_slice_bound('a',side="left",kind="ix")
Out[18]: 0
```
If only sorted 'MultiIndex' can work is not bug but intended it. then I think need documentation for 'MultiIndex'. Because just documentation on 'Index.get_slice_bound' is not enough to recognize '.get_slice_bound' only works with sorted 'MultiIndex'
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-36-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ko_KR.UTF-8
LOCALE : ko_KR.UTF-8
pandas : 0.25.1
numpy : 1.16.1
pytz : 2018.7
dateutil : 2.7.5
pip : 19.3.1
setuptools : 40.6.3
Cython : 0.29.2
pytest : 5.1.0
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.15
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
</details>
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} | 1 | 2019-12-02T16:34:11Z | 2019-12-02T23:19:09Z | 2019-12-02T23:16:38Z | MEMBER | null | Side-note, in `create_index`, none of our tests hit the `v.remove_index()` calls. If we could rule those out, this function could be simplified quite a bit. | {
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"de... | open | false | null | [] | null | 1 | 2019-12-02T16:41:56Z | 2021-01-16T23:08:04Z | null | CONTRIBUTOR | null | There's some duplicate / slightly diverging functionality between `pd.array`, `infer_dtype` and `convert_objects`. Both `pd.array` and `convert_objects` are roughly "convert this list-like thing to this better array". See the conversation in https://github.com/pandas-dev/pandas/pull/29799#discussion_r352255141 for more.
Hopefully this can be deduplicated.
cc @jreback.
Followup to https://github.com/pandas-dev/pandas/pull/29799 | {
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"... | null | 3 | 2019-12-02T16:57:03Z | 2020-05-16T04:06:53Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pyodbc
import sqlalchemy as sa
import urllib
query = """
drop table if exists #MyTempTable;
select
1 ID
, 'A' Letter
into #MyTempTable
union
select 2, 'B' union
select 3, 'C' union
select 4, 'D' union
select 5, 'E'
;
select
*
from #MyTempTable
"""
connection_string = urllib.parse.quote_plus(
'DRIVER={SQL Server Native Client 11.0};\
SERVER={0},{1};\
trusted_Connection=yes\
;encrypt=yes;\
trustServerCertificate=yes;'.format(server, port)
)
eng = sa.create_engine('mssql+pyodbc:///?odbc_connect={0}'.format(connection_string))
df = pd.read_sql(query, con=eng)
```
#### Problem description
The problem is that SQLAlchemy throws an exception when running the snippet of code above:
```Python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\sqlalchemy\engine\result.py in _fetchall_impl(self)
1162 try:
-> 1163 return self.cursor.fetchall()
1164 except AttributeError:
AttributeError: 'NoneType' object has no attribute 'fetchall'
During handling of the above exception, another exception occurred:
ResourceClosedError Traceback (most recent call last)
<ipython-input-2-018f2f8f6b89> in <module>
----> 1 df = pd.read_sql(query, con=eng)
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\pandas\io\sql.py in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)
395 sql, index_col=index_col, params=params,
396 coerce_float=coerce_float, parse_dates=parse_dates,
--> 397 chunksize=chunksize)
398
399
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\pandas\io\sql.py in read_query(self, sql, index_col, coerce_float, parse_dates, params, chunksize)
1106 parse_dates=parse_dates)
1107 else:
-> 1108 data = result.fetchall()
1109 frame = _wrap_result(data, columns, index_col=index_col,
1110 coerce_float=coerce_float,
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\sqlalchemy\engine\result.py in fetchall(self)
1216 except BaseException as e:
1217 self.connection._handle_dbapi_exception(
-> 1218 e, None, None, self.cursor, self.context
1219 )
1220
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\sqlalchemy\engine\base.py in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1458 util.raise_from_cause(sqlalchemy_exception, exc_info)
1459 else:
-> 1460 util.reraise(*exc_info)
1461
1462 finally:
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\sqlalchemy\util\compat.py in reraise(tp, value, tb, cause)
275 if value.__traceback__ is not tb:
276 raise value.with_traceback(tb)
--> 277 raise value
278
279
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\sqlalchemy\engine\result.py in fetchall(self)
1211
1212 try:
-> 1213 l = self.process_rows(self._fetchall_impl())
1214 self._soft_close()
1215 return l
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\sqlalchemy\engine\result.py in _fetchall_impl(self)
1163 return self.cursor.fetchall()
1164 except AttributeError:
-> 1165 return self._non_result([])
1166
1167 def _non_result(self, default):
c:\users\dscally\appdata\local\programs\python\python37\lib\site-packages\sqlalchemy\engine\result.py in _non_result(self, default)
1168 if self._metadata is None:
1169 raise exc.ResourceClosedError(
-> 1170 "This result object does not return rows. "
1171 "It has been closed automatically."
1172 )
ResourceClosedError: This result object does not return rows. It has been closed automatically.
```
The exception implies that the problem is caused by the fact that the `DROP` and `SELECT...INTO` statements are both causing the server to return messages along the lines of `(5 rows affected)`, and Pandas is picking up the first of these and trying to treat it as the results of the query. This is backed up by the fact that you can split out the `SELECT` statement, and then things work nicely:
```Python
prelim = """
drop table if exists #MyTempTable;
select
1 ID
, 'A' Letter
into #MyTempTable
union
select 2, 'B' union
select 3, 'C' union
select 4, 'D' union
select 5, 'E'
;
"""
query = """
select
*
from #MyTempTable
"""
connection_string = urllib.parse.quote_plus(
'DRIVER={SQL Server Native Client 11.0};\
SERVER={0},{1};\
trusted_Connection=yes\
;encrypt=yes;\
trustServerCertificate=yes;'.format(server, port)
)
eng = sa.create_engine('mssql+pyodbc:///?odbc_connect={0}'.format(connection_string))
eng.execute(prelim)
df = pd.read_sql(query, con=eng)
```
And that then returns everything all hunky-dory. I think this is a little user unfriendly, and could be improved.
#### Expected Output
More like "desired" than "expected", but the simplest method that occurs to me would be to iterate over the returned resultsets using SQLAlchemy's `cursor.nextset()`, and inspection of the results using `cursor.returns_rows` to see if the use of `cursor.fetchall()` would be legal, and returning the first available set where that condition is met.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4.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: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 41.0.1
Cython: 0.29.6
numpy: 1.16.1
scipy: 1.2.1
pyarrow: 0.14.0
xarray: 0.14.0
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
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: 1.2.1
lxml.etree: 4.3.4
bs4: None
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
Not included in `pd.show_versions()` but relevant here:
```Python
>>> print(pyodbc.version)
'4.0.26'
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29975 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29975/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29975/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29975/events | https://github.com/pandas-dev/pandas/issues/29975 | 531,296,168 | MDU6SXNzdWU1MzEyOTYxNjg= | 29,975 | pd.NA `na_rep` truncated in to_csv | {
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} | 1 | 2019-12-02T16:59:14Z | 2020-01-01T18:18:10Z | 2020-01-01T18:18:10Z | MEMBER | null | Just found this while messing around with how the new pd.NA sentinel would work with extension modules
```python
>>> pd.Series(["a", pd.NA, "c"]).to_csv(na_rep="ZZZZZ")
',0\n0,a\n1,ZZZZZ\n2,c\n'
>>> pd.Series(["a", pd.NA, "c"], dtype="string").to_csv(na_rep="ZZZZZ")
',0\n0,a\n1,ZZ\n2,c\n'
```
Note the latter is truncated to two letters
@jorisvandenbossche | {
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} | 2 | 2019-12-02T17:09:16Z | 2019-12-18T17:27:03Z | 2019-12-18T16:56:42Z | MEMBER | null | cc @jorisvandenbossche
im using pyarrow 0.15.1 locally
```
_____________________________________________ TestParquetPyArrow.test_additional_extension_arrays _____________________________________________
self = <pandas.tests.io.test_parquet.TestParquetPyArrow object at 0x129bc4050>, pa = 'pyarrow'
@td.skip_if_no("pyarrow", min_version="0.15.0")
def test_additional_extension_arrays(self, pa):
# test additional ExtensionArrays that are supported through the
# __arrow_array__ protocol
df = pd.DataFrame(
{
"a": pd.Series([1, 2, 3], dtype="Int64"),
"b": pd.Series(["a", None, "c"], dtype="string"),
}
)
if LooseVersion(pyarrow.__version__) >= LooseVersion("0.15.1.dev"):
expected = df
else:
# de-serialized as plain int / object
expected = df.assign(a=df.a.astype("int64"), b=df.b.astype("object"))
> check_round_trip(df, pa, expected=expected)
pandas/tests/io/test_parquet.py:522:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/io/test_parquet.py:175: in check_round_trip
compare(repeat)
pandas/tests/io/test_parquet.py:167: in compare
df.to_parquet(path, **write_kwargs)
pandas/core/frame.py:2061: in to_parquet
**kwargs,
pandas/io/parquet.py:255: in to_parquet
**kwargs,
pandas/io/parquet.py:101: in write
table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
pyarrow/table.pxi:1057: in pyarrow.lib.Table.from_pandas
???
/usr/local/lib/python3.7/site-packages/pyarrow/pandas_compat.py:555: in dataframe_to_arrays
for c, f in zip(columns_to_convert, convert_fields)]
/usr/local/lib/python3.7/site-packages/pyarrow/pandas_compat.py:555: in <listcomp>
for c, f in zip(columns_to_convert, convert_fields)]
/usr/local/lib/python3.7/site-packages/pyarrow/pandas_compat.py:546: in convert_column
raise e
/usr/local/lib/python3.7/site-packages/pyarrow/pandas_compat.py:540: in convert_column
result = pa.array(col, type=type_, from_pandas=True, safe=safe)
pyarrow/array.pxi:196: in pyarrow.lib.array
???
pyarrow/array.pxi:92: in pyarrow.lib._handle_arrow_array_protocol
???
pandas/core/arrays/string_.py:200: in __arrow_array__
return pa.array(self._ndarray, type=type, from_pandas=True)
pyarrow/array.pxi:207: in pyarrow.lib.array
???
pyarrow/array.pxi:78: in pyarrow.lib._ndarray_to_array
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E pyarrow.lib.ArrowTypeError: ("Expected a string or bytes object, got a 'NAType' object", 'Conversion failed for column b with type string')
``` | {
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} | 2 | 2019-12-02T17:17:57Z | 2019-12-02T23:44:14Z | 2019-12-02T23:23:30Z | MEMBER | null | Questions for @jreback on follow-ups:
1) Note in `read_hdf` kwargs is still present in the signature, but i put an assertion that they are empty. In principle we could pass more junk to HDFStore, but we never do in tests. Should we allow it, or can those kwargs be deleted altogether?
2) the current signature for `select_column` is `(self, key: str, column: str, **kwargs)`. These kwargs are passed to `read_column`, which would accept "where", "start", "stop". In tests we pass start and stop, but never where. Can we rule out "where"?
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"default": f... | open | false | null | [] | null | 5 | 2019-12-02T18:03:43Z | 2021-07-23T05:21:00Z | null | CONTRIBUTOR | null | In https://github.com/numpy/numpy/pull/14995 I have tried to make numpy consistent with respect to coercing dataframes (and other array-likes which also implement the sequence protocol) to numpy arrays.
With the new PR/behaviour, the `__array__` interface would be fully preferred, and no mixed/inconsistent behaviour with respect to also being a sequence-like (with different behaviour) would occur.
Unfortunately, pandas DataFrames have this behaviour, since they are squence-like. This behaviour kicks in during DataFrame coercion, in the following case:
```
df1 = pd.DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]})
df2 = pd.DataFrame([df1, df1])
```
Where `df2` is currently coerced as a dataframe with dataframes inside. Currently this happens due to the following logic:
```python
try:
if is_list_like(values[0]) or hasattr(values[0], 'len'): # <-- is hit
# following convert does nothing; `np.array()` than raises Error...
values = np.array([convert(v) for v in values])
elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
# GH#21861
values = np.array([convert(v) for v in values])
else:
values = convert(values)
except (ValueError, TypeError):
values = convert(values) # <-- Ends up getting called and forces object array.
```
EDIT: addtional code details: `convert` is a thin wrapper around:
```python
def maybe_convert_platform(values):
""" try to do platform conversion, allow ndarray or list here """
if isinstance(values, (list, tuple, range)):
values = construct_1d_object_array_from_listlike(values)
# more logic
```
This takes the first branch (`values` is a list), which in turn forces a 1-D object array:
```python
def construct_1d_object_array_from_listlike(values):
# numpy will try to interpret nested lists as further dimensions, hence
# making a 1D array that contains list-likes is a bit tricky:
result = np.empty(len(values), dtype='object')
result[:] = values
return result
```
because `np.array([df1, df1])` will raise an error due to the inconsistencies within NumPy, it ends up calling `convert([df1, df1])` which in turn creates a NumPy ``dtype=object`` array with two dataframes inside.
However, the new/correct behaviour for NumPy would be to that `np.array([df1, df1])` will return a 3 dimensional array. This ends up raising an error because pandas refuses to coerce a 3D array to a DataFrame.
It seems safest to not try to squeeze this into the upcoming NumPy release (it is planned in a few days). However, I would like to change it in master soon after branching. I am not sure if you see the current behaviour as important or not, but it would be nice if you can look into what the final intend will be here. If we (can) change this in NumPy I am not sure there is a way for pandas to retain the old behaviour. | {
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} | 1 | 2019-12-02T21:04:51Z | 2019-12-04T16:57:56Z | 2019-12-04T14:01:50Z | MEMBER | null | The end goal is to minimize the amount of attribute setting that is done outside of the constructor (or create_for_block which is a classmethod and ill give it a pass).
create_for_block is called in one place, and immediately after that set_atom is called on the result. set_atom does a ton of attribute setting, so the idea here is to gradually move that attribute setting up into create_for_block.
Related but kept for a separate branch, most of the affected methods have `block` arguments, these can all be changed to operate on block.values, which will narrow the exposure to internals. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29980 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29980/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29980/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29980/events | https://github.com/pandas-dev/pandas/issues/29980 | 531,555,402 | MDU6SXNzdWU1MzE1NTU0MDI= | 29,980 | pandas.core.dtypes.cast.find_common_type upcasts CategoricalDtype list to object | {
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} | [] | closed | false | null | [] | null | 6 | 2019-12-02T21:38:15Z | 2019-12-10T23:25:18Z | 2019-12-10T23:25:17Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
from pandas.core.dtypes.cast import find_common_type
types = [
pd.CategoricalDtype([1,2,3], ordered=False),
pd.CategoricalDtype([1,4,5], , ordered=False),
]
find_common_types(types)
```
#### Problem description
The `find_common_types` is upcasting a list of `CategoricalDtype` to `object`. Ideally, it would create a `CategoricalDtype` that included the union of all of the values in the `categories` field of the `types` parameter.
The fix for this is relatively easy, I will propose a fix in a comment reply to this issue.
#### Expected Output
```python
CategoricalDtype(categories=[1, 2, 3, 4, 5], ordered=False)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.15.0
pytz : 2018.5
dateutil : 2.7.3
pip : 19.2.3
setuptools : 41.0.1
Cython : 0.29.10
pytest : 3.9.3
hypothesis : None
sphinx : 1.8.1
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.5
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.0.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.5.8
pandas_gbq : 0.6.1
pyarrow : 0.14.0.RAY
pytables : None
s3fs : 0.2.0
scipy : 1.2.0
sqlalchemy : 1.2.17
tables : 3.4.4
xarray : 0.11.3
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 1 | 2019-12-02T22:41:16Z | 2019-12-03T04:38:49Z | 2019-12-03T04:38:27Z | MEMBER | null | - [ ] ref ####
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-12-02T23:55:35Z | 2020-04-05T17:35:01Z | 2019-12-03T18:53:03Z | MEMBER | null | #29977 included an assertion that I meant to remove before merging. This removes that, followed by some misc cleanup: annotations, docstrings, remove is_shape_reversed (which was for Panel compat) | {
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} | 1 | 2019-12-03T00:14:25Z | 2019-12-04T13:59:42Z | 2019-12-04T13:59:39Z | MEMBER | null | Looks like GitHub Actions provides a specific command to add to the PATH, which is simpler (and also works on Windows, which is not required in this case, but will be when we add the test jobs). | {
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} | 2 | 2019-12-03T02:08:53Z | 2019-12-03T16:09:16Z | 2019-12-03T10:20:59Z | MEMBER | null | i think this should fix the failing windows-py36 azure build | {
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} | 5 | 2019-12-03T02:28:23Z | 2019-12-12T15:51:24Z | 2019-12-12T13:48:13Z | MEMBER | null | includes a couple of whatsnew notes that fell through the cracks | {
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] | closed | false | null | [] | null | 9 | 2019-12-03T03:09:13Z | 2020-05-04T18:31:00Z | 2019-12-04T09:18:51Z | NONE | null | Hi! Please help me with this problem. Thank you in advance for your help.
Source code and data are attached.
Executing this statement:
**labeled_features = labeled_features.fillna(method='bfill', limit=7) ## where "labeled_features" is a dataframe**
it hangs with this error:
**"NotImplementedError: specifying a limit for fillna has not been implemented yet"**
The code belongs to "Modeling Guide for Predictive Maintenance Python 3 Notebook", downloaded from
"https://notebooks.azure.com/Microsoft/projects/PredictiveMaintenance".
Operating System: Windows 10
Google Chrome
I tried with Anaconda3-5.2.0-Windows-x86_64 (Python 3.6), and afterwards I tried with Python 3.8, Pandas 0.25.3, and iPython.
Actually with Anaconda3-2019.10-Windows-x86_64, Jupyter 6.0.1, Pandas 0.25.1, Python 3.7.4, Numpy 1.16.5
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-27-259154593492> in <module>
[PredictiveMaintenance_SourceAndData.zip](https://github.com/pandas-dev/pandas/files/3914325/PredictiveMaintenance_SourceAndData.zip)
1 labeled_features = final_feat.merge(failures, on=['datetime', 'machineID'], how='left')
----> 2 labeled_features = labeled_features.fillna(method='bfill', limit=7) # fill backward up to 24h
3 labeled_features = labeled_features.fillna('none')
4 labeled_features.head()
~\Anaconda3\lib\site-packages\pandas\core\frame.py in fillna(self, value, method, axis, inplace, limit, downcast, **kwargs)
4242 limit=limit,
4243 downcast=downcast,
-> 4244 **kwargs
4245 )
4246
~\Anaconda3\lib\site-packages\pandas\core\generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
6235 inplace=inplace,
6236 coerce=True,
-> 6237 downcast=downcast,
6238 )
6239 else:
~\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in interpolate(self, **kwargs)
567
568 def interpolate(self, **kwargs):
--> 569 return self.apply("interpolate", **kwargs)
570
571 def shift(self, **kwargs):
~\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
436 kwargs[k] = obj.reindex(b_items, axis=axis, copy=align_copy)
437
--> 438 applied = getattr(b, f)(**kwargs)
439 result_blocks = _extend_blocks(applied, result_blocks)
440
~\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in interpolate(self, method, axis, inplace, limit, fill_value, **kwargs)
1947 values = self.values if inplace else self.values.copy()
1948 return self.make_block_same_class(
-> 1949 values=values.fillna(value=fill_value, method=method, limit=limit),
1950 placement=self.mgr_locs,
1951 )
~\Anaconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
206 else:
207 kwargs[new_arg_name] = new_arg_value
--> 208 return func(*args, **kwargs)
209
210 return wrapper
~\Anaconda3\lib\site-packages\pandas\core\arrays\categorical.py in fillna(self, value, method, limit)
1842 if limit is not None:
1843 raise NotImplementedError(
-> 1844 "specifying a limit for fillna has not " "been implemented yet"
1845 )
1846
NotImplementedError: specifying a limit for fillna has not been implemented yet
[PredictiveMaintenance_SourceAndData.zip](https://github.com/pandas-dev/pandas/files/3914330/PredictiveMaintenance_SourceAndData.zip)
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} | 2 | 2019-12-03T04:46:21Z | 2019-12-04T15:34:34Z | 2019-12-04T13:57:06Z | MEMBER | null | - [x] ref https://github.com/pandas-dev/pandas/issues/29547
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 9 | 2019-12-03T05:22:01Z | 2019-12-04T22:59:22Z | 2019-12-04T22:51:48Z | MEMBER | null | The cleanups in libreduction are made possible by SparseArray.values going away. | {
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"descript... | closed | false | null | [] | null | 13 | 2019-12-03T06:08:06Z | 2020-05-08T00:43:27Z | 2020-05-08T00:43:27Z | 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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} | 2 | 2019-12-03T11:55:37Z | 2019-12-04T19:51:43Z | 2019-12-04T13:53:35Z | MEMBER | null | mypy 0.750 has an experimental feature for static inference of annotations see https://mypy.readthedocs.io/en/latest/mypy_daemon.html#static-inference-of-annotations
have tried it out on functions in core.generic. The annotations add here are mainly the ones that matched the docstrings.
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... | closed | false | null | [] | null | 2 | 2019-12-03T12:01:08Z | 2021-07-23T05:22:05Z | 2021-07-23T05:22:05Z | NONE | null | Copy pasting clipboard values to excel arbitrarily (?) fills nans with 0s.
### Steps
```python
from pandas import Timestamp
from numpy import nan
data = {'asset': {Timestamp('2010-06-29 00:00:00'): nan,
Timestamp('2010-06-30 00:00:00'): -0.002511469339115502,
Timestamp('2010-07-01 00:00:00'): -0.07847255560218203,
Timestamp('2010-07-02 00:00:00'): -0.12568297475787682,
Timestamp('2010-07-06 00:00:00'): -0.160937491617839,
Timestamp('2010-07-07 00:00:00'): -0.019242767272329764,
Timestamp('2010-07-08 00:00:00'): 0.10506322784810118,
Timestamp('2010-07-09 00:00:00'): -0.0034363690398837576,
Timestamp('2010-07-12 00:00:00'): -0.020114999999999883,
Timestamp('2010-07-13 00:00:00'): 0.06392962251786649},
'strat_long': {Timestamp('2010-06-29 00:00:00'): nan,
Timestamp('2010-06-30 00:00:00'): nan,
Timestamp('2010-07-01 00:00:00'): nan,
Timestamp('2010-07-02 00:00:00'): nan,
Timestamp('2010-07-06 00:00:00'): nan,
Timestamp('2010-07-07 00:00:00'): nan,
Timestamp('2010-07-08 00:00:00'): nan,
Timestamp('2010-07-09 00:00:00'): nan,
Timestamp('2010-07-12 00:00:00'): nan,
Timestamp('2010-07-13 00:00:00'): nan},
'strat_short': {Timestamp('2010-06-29 00:00:00'): nan,
Timestamp('2010-06-30 00:00:00'): nan,
Timestamp('2010-07-01 00:00:00'): nan,
Timestamp('2010-07-02 00:00:00'): nan,
Timestamp('2010-07-06 00:00:00'): nan,
Timestamp('2010-07-07 00:00:00'): nan,
Timestamp('2010-07-08 00:00:00'): nan,
Timestamp('2010-07-09 00:00:00'): nan,
Timestamp('2010-07-12 00:00:00'): nan,
Timestamp('2010-07-13 00:00:00'): nan},
'strat': {Timestamp('2010-06-29 00:00:00'): 0.0,
Timestamp('2010-06-30 00:00:00'): 0.0,
Timestamp('2010-07-01 00:00:00'): 0.0,
Timestamp('2010-07-02 00:00:00'): 0.0,
Timestamp('2010-07-06 00:00:00'): 0.0,
Timestamp('2010-07-07 00:00:00'): 0.0,
Timestamp('2010-07-08 00:00:00'): 0.0,
Timestamp('2010-07-09 00:00:00'): 0.0,
Timestamp('2010-07-12 00:00:00'): 0.0,
Timestamp('2010-07-13 00:00:00'): 0.0}}
df = pd.DataFrame.from_dict(data)
```
which creates the following dataset:
<img width="345" alt="Screenshot 2019-12-03 at 11 57 09" src="https://user-images.githubusercontent.com/3731173/70049203-0f23d000-15c4-11ea-9156-7474784a2f1d.png">
Copy pasting clipboard values to excel:
```python
df.to_clipboard()
```
arbitrarily (?) fills nans with 0s:
<img width="415" alt="Screenshot 2019-12-03 at 11 54 32" src="https://user-images.githubusercontent.com/3731173/70049306-51e5a800-15c4-11ea-85a5-e563d66c3687.png">
Passing the `excel=False` flag works (counter-intuitively) as expected:
```
df.to_clipboard(excel=False)
```
<img width="407" alt="Screenshot 2019-12-03 at 11 55 04" src="https://user-images.githubusercontent.com/3731173/70049367-73469400-15c4-11ea-85d2-43c7dc0b3625.png">
### pd.show_versions()
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
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
```
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} | 13 | 2019-12-03T13:52:20Z | 2021-03-08T14:33:57Z | 2019-12-08T17:35:44Z | CONTRIBUTOR | null | Should `pow(1, pd.NA)` be `1` or `NA`?
```python
In [1]: import pandas as pd
In [2]: 1 ** pd.NA
Out[2]: NA
In [3]: import numpy as np
In [5]: 1 ** np.nan
Out[5]: 1.0
```
cc @jorisvandenbossche | {
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} | 3 | 2019-12-03T15:37:08Z | 2019-12-05T15:54:48Z | 2019-12-05T15:47:31Z | MEMBER | null | broken off #28339
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} | 2 | 2019-12-03T15:43:12Z | 2019-12-03T16:23:43Z | 2019-12-03T16:22:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'a': [1, 2, None], 'b':[1, 2, 1]}, dtype=pd.Int64Dtype())
df.set_index('a').reset_index().dtypes
```
a float64
b Int64
dtype: object
#### Problem description
Nullable Int64 datatype is lost if the column is used as index.
This means that it is lost whenever we do a groupby followed by a reset_index and if we try to merge it with the original dataframe we get errors (at least if we try to merge int64 with Int64).
If the column contained NaNs it will be converted to float, if it did not contain NaNs it will be converted to int64 (not nullable).
#### Expected Output
a Int64
b Int64
dtype: object
#### Output of ``pd.show_versions()``
[paste the output of ``pd.show_versions()`` here below this line]
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 158 Stepping 10, 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: 1.2.1
pyarrow: None
xarray: None
IPython: 7.9.0
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: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"default... | closed | false | null | [] | null | 2 | 2019-12-03T16:23:58Z | 2019-12-04T03:04:10Z | 2019-12-04T02:58:38Z | CONTRIBUTOR | null | As described in the following issue
, usage of !r is currently redundant and so changing to f strings in place of it.
- [x] ref https://github.com/pandas-dev/pandas/issues/29886
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-12-03T16:27:10Z | 2019-12-05T18:06:24Z | 2019-12-05T15:41:29Z | MEMBER | null | Input/help needed on two outstanding questions:
1) Do we just remove `pandas.tseries.converter` after this?
2) Some of these deprecations are old enough that I couldn't track them down via the blame, so they currently have "(:issue:`????`)" in the whatsnew. | {
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} | 1 | 2019-12-03T16:30:23Z | 2019-12-03T18:53:53Z | 2019-12-03T18:35:57Z | MEMBER | null | the need for this looks like the result of a typo on my part | {
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"description": "pd.eval,... | open | false | null | [] | null | 2 | 2019-12-03T17:12:57Z | 2020-04-26T21:11:32Z | null | CONTRIBUTOR | null | `query` provides an interesting interface to query data from a pandas dataframe. It also opens up possibilities to have these queries be processed by different kind of engines. Today these options are 'python' and 'numexpr', where 'numexpr' is the default here.
The problematic part here is, is that numexpr is often not installed. As a fallback pandas will use the 'python' engine. This wouldn't be a problem if the difference is only speed, but the 'python' engine provides more features than the 'numexpr' which can cause somebody to write code that works perfectly on his/her machine, but can unexpectedly break on another machine where numexpr happens to be installed. This for example was the case with issue #29027.
I think such a hidden pitfall should not exist, and there should be someway to prevent this from happening or warn the user if it can happen.
Some possibilities I thought about:
* Set engine='python' the default. Simple and probably the best solution.
* Set engine='numexpr' the default and require an explicit engine='python' whenever numexpr is not installed. (So do not silenty fallback.)
* Alternative, throw an warning whenever numexpr is not installed, which can be suppressed by setting engine='python' explicitly. (So fallback with sound.)
* Instead throw an error whenever numexpr is not installed.
* Have numexpr as a dependency.
* Figure out if `.query` is called with syntax not allowed for numexpr and throw a warning whenever engine='python' is not explicitly set. I don't know if this is possible. This might be also more work in the future whenever something changes to the parser, but is probably the most ideal solution. | {
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... | closed | false | null | [] | null | 2 | 2019-12-03T19:00:40Z | 2021-06-28T07:52:01Z | 2021-06-28T07:52:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
df = pd.DataFrame((np.random.rand(5, 4)*100), columns=list('ABCD'))
df_style = df.style.set_properties(**{'background-color': 'black',
'color': 'lawngreen',
'border-color': 'white'})
display(df_style)
df_style.to_excel('test.xlsx')
#### Problem description
I was using the Styler.set_properties() function to format the Pandas Dataframe, while it (df_style) shows up nicely in Jupyter Notebook, it caused an error in the Styler.to_excel() function. Could you please advise how I can fix it?
[Issue also posted in stackoverflow.com: https://stackoverflow.com/questions/59161827/error-in-pandas-styler-set-properties-with-styler-to-excel]
#### Expected Output
Error in Styler.to_excel():
TypeError: tuple indices must be integers or slices, not dict
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1054-aws
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.1.post20191125
Cython : 0.29.14
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
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 : None
pytables : None
s3fs : None
scipy : 1.3.1
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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} | 6 | 2019-12-03T19:53:29Z | 2020-04-05T17:35:09Z | 2019-12-04T01:46:58Z | MEMBER | null | tested on OSX 36 and 37, we'll see if that generalizes | {
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} | 9 | 2019-12-03T20:32:45Z | 2019-12-05T10:22:04Z | 2019-12-04T20:16:44Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-12-03T20:42:24Z | 2019-12-12T14:53:49Z | 2019-12-12T14:53:49Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
print(pd.to_datetime(['19850212', '19890611'], format = '%Y-%m-%d'))
# => DatetimeIndex(['1985-02-12', '1989-06-11'], dtype='datetime64[ns]', freq=None)
print(pd.to_datetime(['19850212', '19890611', None], format = '%Y-%m-%d'))
# => DatetimeIndex(['1985-02-12', '1989-06-11', 'NaT'], dtype='datetime64[ns]', freq=None)
print(pd.to_datetime(['19850212', '19890611'], format = '%Y%m%d'))
# => DatetimeIndex(['1985-02-12', '1989-06-11'], dtype='datetime64[ns]', freq=None)
print(pd.to_datetime(['19850212', '19890611', None], format = '%Y%m%d'))
# => TypeError, ValueError
```
#### Problem description
When `format` is `'%Y%m%d'`, `pd.to_datetime` chokes on the `None`, rather than passing it through as `NaT` as I would expect and as occurred in a previous version of pandas (I'm not sure which). The error is raised even if `pd.to_datetime`'s `errors` argument is set to `'ignore'` or `'raise'`. The `cache` argument doesn't make a difference, either.
Related: #23055
<details>
<summary>Tracebacks</summary>
```
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/pandas/core/tools/datetimes.py", line 448, in _convert_listlike_datetimes
values, tz = conversion.datetime_to_datetime64(arg)
File "pandas/_libs/tslibs/conversion.pyx", line 200, in pandas._libs.tslibs.conversion.datetime_to_datetime64
TypeError: Unrecognized value type: <class 'str'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/tmp/example.py", line 6, in <module>
print(pd.to_datetime(["19850212", "19890611", None], format = "%Y%m%d"))
File "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py", line 208, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pandas/core/tools/datetimes.py", line 794, in to_datetime
result = convert_listlike(arg, box, format)
File "/usr/local/lib/python3.7/dist-packages/pandas/core/tools/datetimes.py", line 451, in _convert_listlike_datetimes
raise e
File "/usr/local/lib/python3.7/dist-packages/pandas/core/tools/datetimes.py", line 409, in _convert_listlike_datetimes
"cannot convert the input to " "'%Y%m%d' date format"
ValueError: cannot convert the input to '%Y%m%d' date format
```
</details>
#### Output of ``pd.show_versions()``
<details>
```
commit : None
python : 3.7.5.candidate.1
python-bits : 64
OS : Linux
OS-release : 5.3.0-19-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 : 1.16.2
pytz : 2019.2
dateutil : 2.7.3
pip : 18.1
setuptools : 41.1.0
Cython : None
pytest : 4.5.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : 0.13.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
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} | 4 | 2019-12-03T21:24:51Z | 2019-12-24T15:04:02Z | 2019-12-24T15:04:02Z | MEMBER | null | ```python-traceback
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+1155.ged20822a5'
In [2]: {pd.NA, hash(pd.NA)}
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
In [3]: {pd.NA} | {hash(pd.NA)}
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
In [4]: {pd.NA: 'foo', hash(pd.NA): 'bar'}
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
In [5]: d = {pd.NA: 'foo'}
...: d[hash(pd.NA)]
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
```
The above behavior is due to Python using equality as a fallback when hash collisions occur and our defined behavior of `bool(pd.NA)` raising. This might be something we have to live with if we want to maintain the current behavior of `bool(pd.NA)`, or at least an immediate workaround doesn't come to mind.
The examples above are admittedly a bit contrived, so I think it'd be fairly rare to see this occur in practice. My main concern is that this could result in some confusing behavior when `pd.NA` is included in containers, e.g. `set(IntegerArray)` could fail in rare circumstances.
cc @jorisvandenbossche @TomAugspurger | {
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"url": "https://api.github.com/repos/pandas-dev/pandas/issues/30013/reactions"
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