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.max() function returns the maximum of the values in the given object. If the input is a series, the method will return a scalar which will be the maximum of the values in the series. If the input is a dataframe, then the method will return a series with maximum of values over the specified axis in the dataframe. By default the axis is the index axis.
Syntax: DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.Returns : max : Series or DataFrame (if level specified)
Example #1: Use max() function to find the maximum value over the index axis.
# importing pandas as pdimport pandas as pd # Creating the dataframe df = pd.DataFrame({"A":[12, 4, 5, 44, 1], "B":[5, 2, 54, 3, 2], "C":[20, 16, 7, 3, 8], "D":[14, 3, 17, 2, 6]}) # Print the dataframedf |
Let’s use the dataframe.max() function to find the maximum value over the index axis
# Even if we do not specify axis = 0, # the method will return the max over# the index axis by defaultdf.max(axis = 0) |
Output :
Example #2: Use max() function on a dataframe which has Na values. Also find the maximum over the column axis.
# importing pandas as pdimport pandas as pd # Creating the dataframe df = pd.DataFrame({"A":[12, 4, 5, None, 1], "B":[7, 2, 54, 3, None], "C":[20, 16, 11, 3, 8], "D":[14, 3, None, 2, 6]}) # skip the Na values while finding the maximumdf.max(axis = 1, skipna = True) |
Output :
