Pandas DataFrame | all method
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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. |
| Checks each row. |
| Returns |
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, thenNaNs are ignored. If all values in the row/column areNaN, then the method returns aTrue.If
False, thenNaNs are treated asTrue.
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
A B0 3 True1 0 1
Checking each column
For each column, to check whether or not all its values evaluate to True:
df.all() # axis=0
A FalseB Truedtype: bool
Here, note the following:
we get
Falsefor columnAbecause the value0is internally equivalent toFalse.similarly,
Trueis returned for columnBbecause the value1represents aTrueboolean.
Checking each row
For each row, to check whether or not all its values evaluate to True:
df.all(axis=1)
0 True1 Falsedtype: bool
Checking entire DataFrame
To check whether or not all the values in the DataFrame evaluates to True:
df.all(axis=None)
False
