In this article, we will discuss different ways to how to add a new column to dataframe in pandas i.e. using operator [] or assign() function or insert() function or using a dictionary. We will also discuss adding a new column by populating values from a list, using the same value in all indices, or calculating value on a new column based on another column.
Table of Contents
- Add column to Pandas Dataframe using [] operator
- Append column to Dataframe using assign() function
- Add multiple columns in DataFrame
- Add columns to DataFrame using Lambda Function
- Insert column to Dataframe using insert()
- Add column to Dataframe using dictionary
Let’s create a Dataframe object i.e.
import pandas as pd
# List of Tuples
students = [('jack', 34, 'Sydeny', 'Australia'),
('Riti', 30, 'Delhi', 'India'),
('Vikas', 31, 'Mumbai', 'India'),
('Neelu', 32, 'Bangalore', 'India'),
('John', 16, 'New York', 'US'),
('Mike', 17, 'las vegas', 'US')]
# Create a DataFrame object
df_obj = pd.DataFrame(students,
columns=['Name', 'Age', 'City', 'Country'],
index=['a', 'b', 'c', 'd', 'e', 'f'])
print(df_obj)
Contents of the dataframe dfobj are,
Name Age City Country a jack 34 Sydeny Australia b Riti 30 Delhi India c Vikas 31 Mumbai India d Neelu 32 Bangalore India e John 16 New York US f Mike 17 las vegas US
Now lets discuss different ways to add new columns to this data frame in pandas.
Add column to Pandas Dataframe using [] operator
Pandas: Add Column from List
Suppose we want to add a new column ‘Marks’ with default values from a list. Let’s see how to do this,
# Add column with Name Marks df_obj['Marks'] = [10, 20, 45, 33, 22, 11] print(df_obj)
Output:
Frequently Asked:
- Python Pandas : How to get column and row names in DataFrame
- Add Row to Dataframe in Pandas
- Python Pandas : Replace or change Column & Row index names in DataFrame
- Convert List to DataFrame in Python
Name Age City Country Marks a jack 34 Sydeny Australia 10 b Riti 30 Delhi India 20 c Vikas 31 Mumbai India 45 d Neelu 32 Bangalore India 33 e John 16 New York US 22 f Mike 17 las vegas US 11
As dataframe df_obj didn’t had any column with name ‘Marks’ , so it added a new column in this dataframe.
But we need to keep these things in mind i.e.
- If values provided in list are less than number of indexes then it will give ValueError.
- If Column already exists then it will replace all its values.
Pandas: Add column to DataFrame with same value
Now add a new column ‘Total’ with same value 50 in each index i.e each item in this column will have same default value 50,
# Add column with same default value df_obj['Total'] = 50 print(df_obj)
Output
Name Age City Country Marks Total a jack 34 Sydeny Australia 10 50 b Riti 30 Delhi India 20 50 c Vikas 31 Mumbai India 45 50 d Neelu 32 Bangalore India 33 50 e John 16 New York US 22 50 f Mike 17 las vegas US 11 50
It added a new column ‘Total‘ and set value 50 at each items in that column.
Pandas: Add column based on another column
Let’s add a new column ‘Percentage‘ where entry at each index will be calculated by the values in other columns at that index i.e.
# Add column to Dataframe based on another column df_obj['Percentage'] = (df_obj['Marks'] / df_obj['Total']) * 100 print(df_obj)
Output:
Name Age City Country Marks Total Percentage a jack 34 Sydeny Australia 10 50 20.0 b Riti 30 Delhi India 20 50 40.0 c Vikas 31 Mumbai India 45 50 90.0 d Neelu 32 Bangalore India 33 50 66.0 e John 16 New York US 22 50 44.0 f Mike 17 las vegas US 11 50 22.0
It added a new column ‘Percentage‘ , where each entry contains the percentage of that student, which was calculated based on Marks & Total column values for that index.
Append column to dataFrame using assign() function
In Python, Pandas Library provides a function to add columns i.e.
DataFrame.assign(**kwargs)
It accepts a keyword & value pairs, where a keyword is column name and value is either list / series or a callable entry. It returns a new dataframe and doesn’t modify the current dataframe.
Let’s add columns in DataFrame using assign().
First of all reset dataframe i.e.
import pandas as pd
# List of Tuples
students = [('jack', 34, 'Sydeny', 'Australia'),
('Riti', 30, 'Delhi', 'India'),
('Vikas', 31, 'Mumbai', 'India'),
('Neelu', 32, 'Bangalore', 'India'),
('John', 16, 'New York', 'US'),
('Mike', 17, 'las vegas', 'US')]
# Create a DataFrame object
df_obj = pd.DataFrame( students,
columns=['Name', 'Age', 'City', 'Country'],
index=['a', 'b', 'c', 'd', 'e', 'f'])
print(df_obj)
Contents dataframe df_obj are,
Name Age City Country a jack 34 Sydeny Australia b Riti 30 Delhi India c Vikas 31 Mumbai India d Neelu 32 Bangalore India e John 16 New York US f Mike 17 las vegas US
Add column to DataFrame in Pandas using assign()
Let’s add a column ‘Marks’ i.e.
# Add new column to DataFrame in Pandas using assign() mod_fd = df_obj.assign( Marks=[10, 20, 45, 33, 22, 11]) print(mod_fd)
It will return a new dataframe with a new column ‘Marks’ in that Dataframe. Values provided in list will used as column values.
Contents of new dataframe mod_fd are,
Name Age City Country Marks a jack 34 Sydeny Australia 10 b Riti 30 Delhi India 20 c Vikas 31 Mumbai India 45 d Neelu 32 Bangalore India 33 e John 16 New York US 22 f Mike 17 las vegas US 11
Add multiple columns in DataFrame using assign()
We can also add multiple columns using assign() i.e.
# Add two columns in the Dataframe
df_obj = df_obj.assign( Marks=[10, 20, 45, 33, 22, 11],
Total=[50] * 6)
print(df_obj)
It added both column Marks & Total. Contents of the returned dataframe is,
Name Age City Country Marks Total a jack 34 Sydeny Australia 10 50 b Riti 30 Delhi India 20 50 c Vikas 31 Mumbai India 45 50 d Neelu 32 Bangalore India 33 50 e John 16 New York US 22 50 f Mike 17 las vegas US 11 50
Add a columns in DataFrame based on other column using lambda function
Add column ‘Percentage’ in dataframe, it’s each value will be calculated based on other columns in each row i.e.
# Add a column Percentage based on columns Marks & Total df_obj = df_obj.assign(Percentage = lambda x: (x['Marks'] / x['Total']) * 100) print(df_obj)
Contents of the returned dataframe are,
Name Age City Country Marks Total Percentage a jack 34 Sydeny Australia 10 50 20.0 b Riti 30 Delhi India 20 50 40.0 c Vikas 31 Mumbai India 45 50 90.0 d Neelu 32 Bangalore India 33 50 66.0 e John 16 New York US 22 50 44.0 f Mike 17 las vegas US 11 50 22.0
Pandas: Insert column to Dataframe using insert()
First of all reset dataframe i.e.
import pandas as pd
# List of Tuples
students = [('jack', 34, 'Sydeny', 'Australia'),
('Riti', 30, 'Delhi', 'India'),
('Vikas', 31, 'Mumbai', 'India'),
('Neelu', 32, 'Bangalore', 'India'),
('John', 16, 'New York', 'US'),
('Mike', 17, 'las vegas', 'US')]
# Create a DataFrame object
df_obj = pd.DataFrame( students,
columns=['Name', 'Age', 'City', 'Country'],
index=['a', 'b', 'c', 'd', 'e', 'f'])
print(df_obj)
Contents dataframe df_obj are,
Name Age City Country a jack 34 Sydeny Australia b Riti 30 Delhi India c Vikas 31 Mumbai India d Neelu 32 Bangalore India e John 16 New York US f Mike 17 las vegas US
In all the previous solution, we added new column at the end of the dataframe, but suppose we want to add or insert a new column in between the other columns of the dataframe, then we can use the insert() function i.e.
# Insert column at the 2nd position of Dataframe
df_obj.insert(2, # column position
"Marks", # column name
[10, 20, 45, 33, 22, 11], # column values
True) # Allow duplicates
print(df_obj)
Output:
Name Age Marks City Country a jack 34 10 Sydeny Australia b Riti 30 20 Delhi India c Vikas 31 45 Mumbai India d Neelu 32 33 Bangalore India e John 16 22 New York US f Mike 17 11 las vegas US
It inserted the column ‘Marks’ in between other columns.
Pandas: Add a column to Dataframe using dictionary
Create a dictionary with keys as the values of new columns and values in dictionary will be the values of any existing column i.e.
ids = [11, 12, 13, 14, 15, 16] # Provide 'ID' as the column name and for values provide dictionary df_obj['ID'] = dict(zip(ids, df_obj['Name'])) print(df_obj)
Output:
Name Age Marks City Country ID a jack 34 10 Sydeny Australia 11 b Riti 30 20 Delhi India 12 c Vikas 31 45 Mumbai India 13 d Neelu 32 33 Bangalore India 14 e John 16 22 New York US 15 f Mike 17 11 las vegas US 16
Here we created a dictionary by zipping the a list of values and existing column ‘Name’. Then set this dictionary as the new column ‘ID’ in the dataframe.
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