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 MultiIndex.set_labels() function set new labels on MultiIndex. Defaults to returning new index.
Syntax: MultiIndex.set_labels(labels, level=None, inplace=False, verify_integrity=True)
Parameters :
labels : new labels to apply
level : level(s) to set (None for all levels)
inplace : if True, mutates in place
verify_integrity : if True, checks that levels and labels are compatible
Returns: new index (of same type and class…etc)
Example #1: Use MultiIndex.set_labels() function to reset the labels of the MultiIndex.
Python3
# importing pandas as pdimport pandas as pd# Create the MultiIndexmidx = pd.MultiIndex.from_tuples([(10, 'Ten'), (10, 'Twenty'), (20, 'Ten'), (20, 'Twenty')], names =['Num', 'Char'])# Print the MultiIndexprint(midx) |
Output :
Now let’s reset the labels of the MultiIndex.
Python3
# resetting the labels the MultiIndexmidx.set_labels([[1, 1, 0, 0], [0, 1, 1, 0]]) |
Output :
As we can see in the output, the labels of the MultiIndex has been reset.
Example #2: Use MultiIndex.set_labels() function to reset any specific label only in the MultiIndex.
Python3
# importing pandas as pdimport pandas as pd# Create the MultiIndexmidx = pd.MultiIndex.from_tuples([(10, 'Ten'), (10, 'Twenty'), (20, 'Ten'), (20, 'Twenty')], names =['Num', 'Char'])# Print the MultiIndexprint(midx) |
Output :
Now let’s reset the ‘Char’ label of the MultiIndex.
Python3
# resetting the labels the MultiIndexmidx.set_labels([0, 1, 1, 0], level ='Char') |
Output :
As we can see in the output, the ‘Char’ label of the MultiIndex has been reset to the desired value.
