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How to take count of non-NA values in a column or row
Onyejiaku Theophilus ChidaluOverview
The count() function is used to take count of non-NA (None, NaN, NaT values) cells present in a column or row or a DataFrame object.
Syntax
DataFrame.count(axis=0, level=None, numeric_only=False)
Syntax for the count() function in Pandas
Parameters
axis: This takes either0(orindex) or1(orcolumns) for the counts to be generated for either thecolumnsor therows, respectively.level: This takes a string level name to take count along with the given level.numeric_only: This takes a boolean value indicating if only float, integer, or boolean values should be included.
Return value
This function returns a DataFrame showing the number of non-null entries in it.
Example
# A code to illustrate the count() function in Pandas
# importing the pandas and numpy libraries
import pandas as pd
import numpy as np
# creating a dataframe
df = pd.DataFrame({"Person":
["John", "Myla", "Lewis", "John", "Myla"],
"Age": [24., np.nan, 21., 33, 26],
"Single": [False, True, True, True, False]})
# printing the dataframe object
print(df)
# taking count of the non-NA values in present in the columns
print(" This is a dataframe of each columns and their Number of non-NA values: ", df.count())
# taking the count of the non-NA values in each of the rows
print("These are the number of non-NA values in each of the rows: ", df.count(axis=1))Implementing the count() function
Explanation
- Lines 4–5: We import the necessary libraries.
- Lines 8–11: We create a DataFrame object,
df. - Line 15: We print
df. - Line 18: We take count of the number of non-NA values in the columns of the DataFrame using the
count()function. - Line 21: We take count of the number of non-NA values in the rows of the DataFrame using the
count()function.
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