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Python | Pandas dataframe.count()

  • Difficulty Level : Easy
  • Last Updated : 20 Nov, 2018

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.

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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.




# importing pandas as pd
import pandas as pd
  
# Creating a dataframe using dictionary
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"]})
  
# Printing the dataframe
df

Now find the count of non-NA value across the row axis




# axis = 0 indicates row
df.count(axis = 0)

Output :

 
Example #2: Use count() function to find the number of non-NA/null value across the column.




# importing pandas as pd
import pandas as pd
  
# Creating a dataframe using dictionary
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"]})
  
# Find count of non-NA across the columns
df.count(axis = 1)

Output :




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