Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/pandas-dev/pandas/issues/46653 below:

add validation checks on levels keyword from pd.concat · Issue #46653 · pandas-dev/pandas · GitHub

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
     A
x x  1
y y  1


```python
>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], levels=[["x", "y"]])
   A
x  1
y  1

First, we should raise when levels duplicated. Second, when levels specified and keys not given, then we should also raise since this is unreasonable. (DOC only mentions infering levels from keys but not infering keys from levels)

>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
ValueError: Level values not unique: ['x', 'y', 'y']

>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], levels=[["x", "y"]])
ValueError: levels supported only when keys not None

1.4.1

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4