Python | Pandas dataframe.count()
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.count()
is used to count the no. of non-NA/null observations across the given axis. It works with non-floating type data as well.
Syntax: DataFrame.count(axis=0, level=None, numeric_only=False)
Parameters:
axis : 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame
numeric_only : Include only float, int, boolean data
Returns: count : Series (or DataFrame if level specified)
Example #1: Use count()
function to find the number of non-NA/null value across the row axis.
import pandas as pd
df = pd.DataFrame({ "A" :[ - 5 , 8 , 12 , None , 5 , 3 ],
"B" :[ - 1 , None , 6 , 4 , None , 3 ],
"C:[" sam ", " haris ", " alex ", np.nan, " peter ", " nathan"]})
df
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Now find the count of non-NA value across the row axis
Output :
Example #2: Use count()
function to find the number of non-NA/null value across the column.
import pandas as pd
df = pd.DataFrame({ "A" :[ - 5 , 8 , 12 , None , 5 , 3 ],
"B" :[ - 1 , None , 6 , 4 , None , 3 ],
"C:[" sam ", " haris ", " alex ", np.nan, " peter ", " nathan"]})
df.count(axis = 1 )
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Output :
Last Updated :
20 Nov, 2018
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