We generally want to concat two or more dataframes when working with some data. So, when we concat these dataframes we need to actually want to provide an identifier column in order to identify the concatenated dataframes. In this article, we’ll see with the help of examples of how we can do this.
Example 1:
To add an identifier column, we need to specify the identifiers as a list for the argument “keys” in concat() function, which creates a new multi-indexed dataframe with two dataframes concatenated. Now we’ll use reset_index to convert multi-indexed dataframe to a regular pandas dataframe.
Python3
import pandas as pdimport numpy as npdict = {'Name':['Martha', 'Tim', 'Rob', 'Georgia'], 'Maths':[87, 91, 97, 95], 'Science':[83, 99, 84, 76] }df1 = pd.DataFrame(dict)dict = {'Name':['Amy', 'Maddy'], 'Maths':[89, 90], 'Science':[93, 81] }df2 = pd.DataFrame(dict)# Concatenating two dataframesdf = pd.concat([df1,df2],keys=['t1', 't2'])display(df)df = pd.concat([df1,df2], keys=['t1', 't2']).reset_index()display(df) |
Output:
In the output, we can see a column with the identifiers of each dataframe where “t1” represents the first dataframe and “t2” represents the second dataframe.
Example 2:
We can do this similarly for any number of dataframes. In this example, we’ll combine three dataframes.
Python3
import pandas as pdimport numpy as npdict = {'Name': ['Martha', 'Tim', 'Rob', 'Georgia'], 'Maths': [87, 91, 97, 95], 'Science': [83, 99, 84, 76] }df1 = pd.DataFrame(dict)dict = {'Name': ['Amy', 'Maddy'], 'Maths': [89, 90], 'Science': [93, 81] }df2 = pd.DataFrame(dict)dict = {'Name': ['Rob', 'Rick', 'Anish'], 'Maths': [89, 90, 87], 'Science': [93, 81, 90] }df3 = pd.DataFrame(dict)# Concatenating Dataframesdf = pd.concat([df1, df2, df3], keys=['t1', 't2', 't3'])display(df)df = pd.concat([df1, df2, df3], keys=['t1', 't2', 't3']).reset_index()display(df) |
Output:
In the output, we can see a column with the identifiers of each dataframe where “t1”, “t2” and “t3” represent first, second and third dataframe respectively.
