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 dataReturns: 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 pdimport pandas as pd # Creating a dataframe using dictionarydf = 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 dataframedf |
Now find the count of non-NA value across the row axis
# axis = 0 indicates rowdf.count(axis = 0) |
Output :
Example #2: Use count() function to find the number of non-NA/null value across the column.
# importing pandas as pdimport pandas as pd # Creating a dataframe using dictionarydf = 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 columnsdf.count(axis = 1) |
Output :
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