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
# Creating index for multi-index dataframetuples = [('A', 'a'), ('A', 'b'), ('B', 'a'), ('B', 'b')]index = pd.MultiIndex.from_tuples(tuples, names=['index_1', 'index_2'])# Value corresponding to the indexdata = [2, 4, 6, 8]# Creating dataframe using 'data' and 'index'df = pd.DataFrame(data = data, index = index, columns = ['value'])print(df) |
Output:
DataFrame created with multi-index
Step 2: Converting index into the column.
Here we can see the hierarchical indexing, we are converting each index into a separate column using the reset_index() method.
Python3
new_df = df.reset_index()print(new_df) |
Output:
DataFrame after using reset_index() method
Here we can see that the all the levels of index are converted to columns.
We can also select which level of multi-index to reset using parameter level.
level parameter can be an index name or can be a tuple or list of indices to remove from the dataframe and make them as dataframe columns.
Code:
Python3
new_df_2 = df.reset_index(level=("index_2",))new_df_2.head() |
Output:
DataFrame after using reset_index with level parameter set.
Note: Here, we can see that only “index_2” is reset from index and has become a column, but not “index_1“.
