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.idxmax() function returns index of first occurrence of maximum over requested axis. While finding the index of the maximum value across any index, all NA/null values are excluded.
Syntax: DataFrame.idxmax(axis=0, skipna=True)
Parameters :
axis : 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise
skipna : Exclude NA/null values. If an entire row/column is NA, the result will be NAReturns : idxmax : Series
Example #1: Use idxmax() function to function to find the index of the maximum value along the index axis.
# importing pandas as pdimport pandas as pd # Creating the dataframe df = pd.DataFrame({"A":[4, 5, 2, 6], "B":[11, 2, 5, 8], "C":[1, 8, 66, 4]}) # Print the dataframedf |
Now apply the idxmax() function along the index axis.
# applying idxmax() function.df.idxmax(axis = 0) |
Output :
If we look at the values in the dataframe, we can verify the result returned by the function. The function returned a pandas series object containing the index of maximum value in each column.
Example #2: Use idxmax() function to find the index of the maximum value along the column axis. The dataframe contains NA values.
# importing pandas as pdimport pandas as pd # Creating the dataframe df = pd.DataFrame({"A":[4, 5, 2, None], "B":[11, 2, None, 8], "C":[1, 8, 66, 4]}) # Skipna = True will skip all the Na values# find maximum along column axisdf.idxmax(axis = 1, skipna = True) |
Output :
The output is a pandas series containing the column label for each row which has the maximum value.
