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Pandas DataFrame | all method

Pandas
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DataFrame
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Data Indexing and Masks
schedule Jul 1, 2022
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Pandas DataFrame.all(~) method checks each row or column, and returns True for that row/column if all its values evaluate to True.

Parameters

1. axislink | int or string | optional

Whether to check each row, column or the entire DataFrame:

Axis

Description

Checks each column.

0 or "index"

Checks each row.

1 or "columns"

None

Returns True if all values in the source DataFrame evaluates to True.

By default, axis=0.

2. bool_only | None or boolean | optional

Whether or not to only consider rows or columns that just have boolean entries in them. By default, bool_only=None.

3. skipna | boolean | optional

  • If True, then NaNs are ignored. If all values in the row/column are NaN, then the method returns a True.

  • If False, then NaNs are treated as True.

By default, skipna=True.

4. level | int or string | optional

The level to target. This is only relevant if the source DataFrame is a multi-index. By default, level=None.

Return value

If axis=None, then a single boolean is returned. Otherwise, a DataFrame of booleans is returned.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,0],"B":[True,1]})
df
   A  B
0  3  True
1  0  1

Checking each column

For each column, to check whether or not all its values evaluate to True:

df.all()   # axis=0
A False
B True
dtype: bool

Here, note the following:

  • we get False for column A because the value 0 is internally equivalent to False.

  • similarly, True is returned for column B because the value 1 represents a True boolean.

Checking each row

For each row, to check whether or not all its values evaluate to True:

df.all(axis=1)
0 True
1 False
dtype: bool

Checking entire DataFrame

To check whether or not all the values in the DataFrame evaluates to True:

df.all(axis=None)
False
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Published by Isshin Inada
Edited by 0 others
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