Python | Pandas dataframe.all ()

DataFrame.all() checks if all elements are true, possibly along the axis. It returns True if all elements are in row or along the Dataframe axis is nonzero, not empty, or false.

Syntax: DataFrame.all (axis = 0, bool_only = None, skipna = True, level = None, ** kwargs)

Parameters:
axis: {0 or `index`, 1 or `columns`, None} , default 0
Indicate which axis or axes should be reduced.
0 / `index`: reduce the index, return a Series whose index is the original column labels.
1 / `columns` : reduce the columns, return a Series whose index is the original index.
None: reduce all axes, return a scalar.

skipna: Exclude NA / null values … If an entire row / column is NA, the result will be NA.
level: If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
bool_only: Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.
** kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns: all: Series or DataFrame (if level specified)

Note. The value Nan will be treated as a non-empty value and therefore will be evaluated as True.

To link to the CSV file used in the code, click here

Example # 1: Suffix _col in each _col in _col .

# import pandas as pd

import pandas as pd

 
# Create frame data from the CSV file

df = pd .read_csv ( "nba.csv" )

 
# Print first 10 lines
# data frame for rendering

df [: 10 ]

# check the "Name" column

df.Name. all ()

Output:

Example # 2. Behavior evaluation by columns

dataframe.all () The default behavior checks if all column values ​​return True.

# Check all columns in the data frame

df. all ( )

Output:

Example # 3: checking for line-by-line elements

Specify axis = & # 39; columns & # 39; to check if all string values ​​return True. if all values ​​in any particular row evaluate to true, then the total row evaluates to true.

# import pandas as pd

import pandas as pd

 
# Create data frame from CSV file

df = pd.read_csv ( "nba.csv" )

 
# Check through a row

df. all (axis = `columns` )

Output:

all ( ) evaluates all values ​​across all rows in a data frame and outputs a boolean value for each row.

Example # 4: validating all values ​​in a data frame

Specify axis = None to make each value true in the data frame.

# import pandas as pd

import pandas as pd

 
# Create data frame from CSV file

df = pd.read_csv ( "nba.csv" )

 
# Check through a row

df. all (axis = None )

Output: