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.to_hierarchical() function return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle. It is useful to replicate and rearrange a MultiIndex for combination with another Index with n_repeat items.
Syntax: MultiIndex.to_hierarchical(n_repeat, n_shuffle=1)
Parameters :
n_repeat : Number of times to repeat the labels on self
n_shuffle : Controls the reordering of the labels. If the result is going to be an inner level in a MultiIndex, n_shuffle will need to be greater than one. The size of each label must divisible by n_shuffleReturns : MultiIndex
Example #1: Use MultiIndex.to_hierarchical() function to repeat the labels in the MultiIndex.
# importing pandas as pd import pandas as pd # Create the MultiIndex midx = pd.MultiIndex.from_tuples([(10, 'Ten'), (10, 'Twenty'), (20, 'Ten'), (20, 'Twenty')], names =['Num', 'Char']) # Print the MultiIndex print(midx) |
Output :
Now let’s repeat the labels of the MultiIndex 2 times.
# repeat the labels in the MultiIndex 2 times. midx.to_hierarchical(n_repeat = 2) |
Output :
As we can see in the output, the labels in the returned MultiIndex is repeated 2 times.
Example #2: Use MultiIndex.to_hierarchical() function to repeat as well as reshuffle the labels in the MultiIndex.
# importing pandas as pd import pandas as pd # Create the MultiIndex midx = pd.MultiIndex.from_tuples([(10, 'Ten'), (10, 'Twenty'), (20, 'Ten'), (20, 'Twenty')], names =['Num', 'Char']) # Print the MultiIndex print(midx) |
Output :
Now let’s repeat and reshuffle the labels of the MultiIndex 2 times.
# resetting the labels the MultiIndex midx.to_hierarchical(n_repeat = 2, n_shuffle = 2) |
Output :
As we can see in the output, the labels are repeated as well as reshuffled twice in the returned MultiIndex.
