How to Concatenate Column Values in Pandas DataFrame?

Last Updated : 10 Jul, 2020

Many times we need to combine values in different columns into a single column. There can be many use cases of this, like combining first and last names of people in a list, combining day, month, and year into a single column of Date, etc. Now we'll see how we can achieve this with the help of some examples. Example 1: In this example, we'll combine two columns of first name last name to a column name. To achieve this we'll use the map function.

Python3 1==

import pandas as pd
from pandas import DataFrame 
 
# creating a dictionary of names
Names = {'FirstName':['Suzie','Emily','Mike','Robert'],
         'LastName':['Bates','Edwards','Curry','Frost']}
 
# creating a dataframe from dictionary
df = DataFrame(Names, columns=['FirstName','LastName'])
print(df)
 
print('\n')
 
# concatenating the columns
df['Name'] = df['FirstName'].map(str) + ' ' + df['LastName'].map(str)
print(df)

Output: pandas-concatenate-column-2

Example 2: Similarly, we can concatenate any number of columns in a dataframe. Let's see through another example to concatenate three different columns of the day, month, and year in a single column Date.

Python3 1==

import pandas as pd
from pandas import DataFrame 

# creating a dictionary of Dates
Dates = {'Day': [1, 29, 23, 4, 15], 
        'Month': ['Aug', 'Feb', 'Aug', 'Apr', 'Mar'], 
        'Year': [1947, 1983, 2007, 2011, 2020]}

# creating a dataframe from dictionary
df = DataFrame(Dates, columns = ['Day', 'Month', 'Year'])
print (df)

print('\n')

# concatenating the columns
df['Date'] = df['Day'].map(str) + '-' + df['Month'].map(str) + '-' + df['Year'].map(str)
print (df)

Output: pandas-concatenate-column-3

Example 3: We can take this process further and concatenate multiple columns from multiple different dataframes. In this example, we combine columns of dataframe df1 and df2 into a single dataframe.

Python3 1==

import pandas as pd
from pandas import DataFrame 

# creating a dictionary of Dates
Dates = {'Day': [1, 1, 1, 1], 
        'Month': ['Jan', 'Jan', 'Jan', 'Jan'], 
        'Year': [2017, 2018, 2019, 2020]} 

# creating a dataframe from dictionary
df1 = DataFrame(Dates, columns = ['Day', 'Month', 'Year']) 

# creating a dictionary of Rates
Rates = {'GDP': [5.8, 7.6, 5.6, 4.1], 
         'Inflation Rate': [2.49, 4.85, 7.66, 6.08]} 

# creating a dataframe from dictionary
df2 = DataFrame(Rates, columns = ['GDP', 'Inflation Rate'])

# combining columns of df1 and df2
df_combined = df1['Day'].map(str) + '-' + df1['Month'].map(str) + '-' + df1['Year'].map(str) + ': ' + 'GDP: ' + df2['GDP'].map(str) + '; ' + 'Inflation: ' + df2['Inflation Rate'].map(str)
print (df_combined)