The last_valid_index
method is used to return the index for the last non-NA or None
value. The method returns None
for the following conditions:
None
.Note: Click here to learn more about the pandas library.
DataFrame.last_valid_index()
The method has no parameters.
import pandas as pdimport numpy as npdef index(dataframe):print("DataFrame is:")print(dataframe)print("Index of the last non-NA/null value is - ", dataframe.last_valid_index())df = pd.DataFrame([[np.nan, np.nan, np.nan],[np.nan, 100,3],[1, 2, 3],[np.nan,-7,np.nan],[-9, -8, -7],[10, 8, 12]],columns=list('XYZ'))df1 = pd.DataFrame([[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan],[np.nan,-3,np.nan],[10, 8, 12],[np.nan, np.nan, np.nan]],columns=list('XYZ'))df2 = pd.DataFrame([[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan]],columns=list('XYZ'))index(df)index(df1)index(df2)
pandas
and numpy
modules.index
that accepts a data frame, prints it and finds the last non-NA/null value index using the last_valid_index
method.index
method with different DataFrames.df
indicates that the 5th row has all values as non-NA/non-null from the end.df1
indicates that the 4th row has all values as non-NA/non-null from the end.df2
indicates that none of the rows have a non-null/non-NA value.