Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A multi-index dataframe has multi-level, or hierarchical indexing. We can easily convert the multi-level index into the column by the reset_index() method.
DataFrame.reset_index() is used to reset the index to default and make the index a column of the dataframe.
Step 1: Creating a multi-index dataframe.
Let’s see an example by making a multi-index dataframe first.
Code:
Python3
import pandas as pd # Creating index for muti-index dataframe tuples = [('A', 'a'), ('A', 'b'), ('B', 'a'), ('B', 'b')] index = pd.MultiIndex.from_tuples(tuples) # Value corresponding to the index data = [2, 4, 6, 8] # Creating dataframe using 'data' and 'index' df = pd.DataFrame(data = data, index = index, columns = ['value']) print(df) |
Output:
Step 2: Converting index into the column.
Here we can see the hierarchical indexing, we are going to convert it into a column using the reset_index() method.
Python3
reset_df = df.reset_index() print(reset_df) |
Output:
Here we can see that the index is default now and our multi-index now becomes columns with the default. We can also select which level of multi-index to reset using parameter level.
Code:
Python3
# rest only index 'indx1' reset_indx1 = df.reset_index(level='indx1') print(reset_indx1) |
Output:
Here, we can see that only indx1 is reset becomes a column, not both the indices. Also, notice that there is no default index in this case because there is still an index (indx2) left.
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