Pandas Add Column to DataFrame

In Pandas, you can add a new column to an existing DataFrame using the DataFrame.insert() function, which updates the DataFrame in place. Alternatively, you can use DataFrame.assign() to insert a new column, but this method returns a new DataFrame with the added column.

In this article, I will cover examples of adding multiple columns, adding a constant value, deriving new columns from an existing column to the Pandas DataFrame.

Key Points -

A new column can be created by assigning values directly to a new column name with df['new_column'] = values.
The assign() method adds columns and returns a modified copy of the DataFrame, leaving the original DataFrame unchanged unless reassigned.
Adding a column directly modifies the DataFrame in place, while using assign() creates a new DataFrame.
Lambda functions within assign() enable complex calculations or conditional logic to define values for the new column.
The insert() method allows adding a new column at a specific position within the DataFrame, providing flexibility for organizing columns.
Using functions like np.where() or apply(), you can populate a new column based on conditional values.

Quick Examples of Adding Column

If you are in a hurry, below are some quick examples of adding column to pandas DataFrame.


# Quick examples of add column to dataframe

# Add new column to the dataframe
tutors = ['William', 'Henry', 'Michael', 'John', 'Messi']
df2 = df.assign(TutorsAssigned=tutors)

# Add a multiple columns to the dataframe
MNCCompanies = ['TATA','HCL','Infosys','Google','Amazon']
df2 =df.assign(MNCComp = MNCCompanies,TutorsAssigned=tutors )

# Derive new Column from existing column
df = pd.DataFrame(technologies)
df2=df.assign(Discount_Percent=lambda x: x.Fee * x.Discount / 100)

# Add a constant or empty value to the DataFrame
df = pd.DataFrame(technologies)
df2=df.assign(A=None,B=0,C="")

# Add new column to the existing DataFrame
df = pd.DataFrame(technologies)
df["MNCCompanies"] = MNCCompanies

# Add new column at the specific position
df = pd.DataFrame(technologies)
df.insert(0,'Tutors', tutors )

# Add new column by mapping to the existing column
df = pd.DataFrame(technologies)
tutors = {"Spark":"William", "PySpark":"Henry", "Hadoop":"Michael","Python":"John", "pandas":"Messi"}
df['Tutors'] = df['Courses'].map(tutors)
print(df)

To run some examples of adding column to DataFrame, let’s create DataFrame using data from a dictionary.


# Create DataFrame
import pandas as pd
import numpy as np

technologies= {
    'Courses':["Spark","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000,25000,23000,24000,26000],
    'Discount':[1000,2300,1000,1200,2500]
          }

df = pd.DataFrame(technologies)
print("Create a DataFrame:\n", df)

Yields below output.

Add Column to DataFrame

DataFrame.assign() is used to add/append a column to the DataFrame, this method generates a new DataFrame incorporating the added column, while the original DataFrame remains unchanged.

Below is the syntax of the assign() method.


# Syntax of DataFrame.assign()
DataFrame.assign(**kwargs)

Now let's add a column 'TutorsAssigned" to the DataFrame. Using assign() we cannot modify the existing DataFrame inplace instead it returns a new DataFrame after adding a column. The below example adds a list of values as a new column to the DataFrame.


# Add new column to the DataFrame
tutors = ['William', 'Henry', 'Michael', 'John', 'Messi']
df2 = df.assign(TutorsAssigned=tutors)
print("Add column to DataFrame:\n", df2)

Yields below output.

Add Multiple Columns to the DataFrame

You can add multiple columns to a Pandas DataFrame by using the assign() function.


# Add multiple columns to the DataFrame
MNCCompanies = ['TATA','HCL','Infosys','Google','Amazon']
df2 = df.assign(MNCComp = MNCCompanies,TutorsAssigned=tutors )
print("Add multiple columns to DataFrame:\n", df2)

Yields below output.


# Output:
# Add multiple columns to DataFrame:
    Courses    Fee  Discount  MNCComp TutorsAssigned
0    Spark  22000      1000     TATA        William
1  PySpark  25000      2300      HCL          Henry
2   Hadoop  23000      1000  Infosys        Michael
3   Python  24000      1200   Google           John
4   Pandas  26000      2500   Amazon          Messi

Adding a Column From Existing

In real-time scenarios, there's often a need to compute and add new columns to a dataset based on existing ones. The following demonstration calculates the Discount_Percent column based on Fee and Discount. In this instance, I'll utilize a lambda function to generate a new column from the existing data.


# Derive New Column from Existing Column
df = pd.DataFrame(technologies)
df2 = df.assign(Discount_Percent=lambda x: x.Fee * x.Discount / 100)
print("Add column to DataFrame:\n", df2)

You can explore deriving multiple columns and appending them to a DataFrame within a single statement. This example yields the below output.


# Output:
# Add column to DataFrame:
   Courses    Fee  Discount  Discount_Percent
0    Spark  22000      1000          220000.0
1  PySpark  25000      2300          575000.0
2   Hadoop  23000      1000          230000.0
3   Python  24000      1200          288000.0
4   Pandas  26000      2500          650000.0

Add a Constant or Empty Column

The below example adds 3 new columns to the DataFrame, one column with all None values, a second column with 0 value, and the third column with an empty string value.


# Add a constant or empty value to the DataFrame.
df = pd.DataFrame(technologies)
df2=df.assign(A=None,B=0,C="")
print("Add column to DataFrame:\n", df2)

Yields below output.


# Output:
# Add column to DataFrame:
    Courses    Fee  Discount     A  B C
0    Spark  22000      1000  None  0  
1  PySpark  25000      2300  None  0  
2   Hadoop  23000      1000  None  0  
3   Python  24000      1200  None  0  
4   Pandas  26000      2500  None  0

Append Column to Existing Pandas DataFrame

The above examples create a new DataFrame after adding new columns instead of appending a column to an existing DataFrame. The example explained in this section is used to append a new column to the existing DataFrame.


# Add New column to the existing DataFrame
df = pd.DataFrame(technologies)
df["MNCCompanies"] = MNCCompanies
print("Add column to DataFrame:\n", df2)

Yields below output.


# Output:
# Add column to DataFrame:
   Courses    Fee  Discount MNCCompanies
0    Spark  22000      1000         TATA
1  PySpark  25000      2300          HCL
2   Hadoop  23000      1000      Infosys
3   Python  24000      1200       Google
4   Pandas  26000      2500       Amazon

You can also use this approach to add a new column by deriving from an existing column,


# Derive a new column from existing column
df2 = df['Discount_Percent'] = df['Fee'] * df['Discount'] / 100
print("Add column to DataFrame:\n", df2)

# Output:
# Add column to DataFrame:
#  0    220000.0
# 1    575000.0
# 2    230000.0
# 3    288000.0
# 4    650000.0
dtype: float64

Add Column to Specific Position of DataFrame

The DataFrame.insert() method offers the flexibility to add columns at any position within an existing DataFrame. While many examples often showcase appending columns at the end of the DataFrame, this method allows for insertion at the beginning, in the middle, or at any specific column index of the DataFrame.


# Add new column at the specific position
# Add new column to the DataFrame
tutors = ['William', 'Henry', 'Michael', 'John', 'Messi']
df.insert(0,'Tutors', tutors )
print("Add column to DataFrame:\n", df)

# Insert 'Tutors' column at the specified position
# Add new column to the DataFrame
position = 0
df.insert(position, 'Tutors', tutors)
print("Add column to DataFrame:\n", df)

Yields below output.


# Output:
# Add column to DataFrame:
    Tutors  Courses    Fee  Discount
0  William    Spark  22000      1000
1    Henry  PySpark  25000      2300
2  Michael   Hadoop  23000      1000
3     John   Python  24000      1200
4    Messi   Pandas  26000      2500

Add a Column From Dictionary Mapping

If you want to add a column with specific values for each row based on an existing value, you can do this using a Dictionary. Here, The values from the dictionary will be added as Tutors column in df, by matching the key value with the column 'Courses'.


# Add new column by mapping to the existing column
df = pd.DataFrame(technologies)
tutors = {"Spark":"William", "PySpark":"Henry", "Hadoop":"Michael","Python":"John", "pandas":"Messi"}
df['Tutors'] = df['Courses'].map(tutors)
print("Add column to DataFrame:\n", df)

Note that it is unable to map pandas as the key in the dictionary is not exactly matched with the value in the Courses column (case sensitive). This example yields the below output.


# Output:
# Add column to DataFrame:
   Courses    Fee  Discount   Tutors
0    Spark  22000      1000  William
1  PySpark  25000      2300    Henry
2   Hadoop  23000      1000  Michael
3   Python  24000      1200     John
4   Pandas  26000      2500      NaN

Using loc[] Add Column

Using pandas loc[] you can access rows and columns by labels or names however, you can also use this for adding a new column to pandas DataFrame. This loc[] property uses the first argument as rows and the second argument for columns hence, I will use the second argument to add a new column.


# Assign the column to the DataFrame
df = pd.DataFrame(technologies)
tutors = ['William', 'Henry', 'Michael', 'John', 'Messi']
df.loc[:, 'Tutors'] = tutors
print("Add column to DataFrame:\n", df)

Yields the same output as above.

FAQ on Add Column to Pandas DataFrame

You can add a column to an existing DataFrame in Pandas using direct assignment or the insert method.

You can use the insert method to add a column at a specific position in the DataFrame. Specify the column index where you want to insert the new column.

you can add a column using data from another DataFrame if the indices match. For example, df2 = pd.DataFrame({'existing_column': [value1, value2, value3]}) df['existing_column'] = df2['existing_column']

You can add a column based on calculations from existing columns. You can use functions, lambda expressions, or any other method to compute values for the new column.

Conclusion

In conclusion, adding columns to a pandas DataFrame is a fundamental operation. This can be accomplished using methods like DataFrame.assign() and DataFrame.insert(). While DataFrame.assign() adds new columns and returns a new DataFrame, leaving the original unchanged, DataFrame.insert() allows for inserting columns at specific positions within the DataFrame, directly modifying it.

Pandas Add Column to DataFrame

Quick Examples of Adding Column

Add Column to DataFrame

Add Multiple Columns to the DataFrame

Adding a Column From Existing

Add a Constant or Empty Column

Append Column to Existing Pandas DataFrame

Add Column to Specific Position of DataFrame

Add a Column From Dictionary Mapping

Using loc[] Add Column

FAQ on Add Column to Pandas DataFrame

Conclusion

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References