Pandas use various methods to reshape the dataframe and series. Let’s see about the some of that reshaping method.
Let’s import a dataframe first.
# import pandas module import pandas as pd # making dataframe # it was print the first 5-rows print(df.head()) |
Output:
Using stack() method:
Stack method works with the MultiIndex objects in DataFrame, it returning a DataFrame with an index with a new inner-most level of row labels. It changes the wide table to a long table.
# import pandas module import pandas as pd # making dataframe df = pd.read_csv("nba.csv") # reshape the dataframe using stack() method df_stacked = df.stack() print(df_stacked.head(26)) |
Output:
Using unstack() method:
unstack is similar to stack method, It also works with multi-index objects in dataframe, producing a reshaped DataFrame with a new inner-most level of column labels.
# import pandas module import pandas as pd # making dataframe df = pd.read_csv("nba.csv") # unstack() method df_unstacked = df_stacked.unstack() print(df_unstacked.head(10)) |
Using melt() method:
Melt in pandas reshape dataframe from wide format to long format. It uses the “id_vars[‘col_names’]” for melt the dataframe by column names.
# import pandas module import pandas as pd # making dataframe df = pd.read_csv("nba.csv") # it takes two columns "Name" and "Team" df_melt = df.melt(id_vars =['Name', 'Team']) print(df_melt.head(10)) |
Output:
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