Python Pandas - Modifying DataFrame

---

---

Pandas DataFrame is two-dimensional data structure that can be used for storing and manipulating tabular data. It consists of rows and columns making it similar to a spreadsheet or SQL table. Modifying a Pandas DataFrame is a crucial step in data preprocessing, data analysis, and data cleaning.

Some of the most common DataFrame modifications include −

• Renaming column or row labels.

• Adding or inserting new columns.

• Updating or replacing existing column values.

• Removing unnecessary columns.

In this tutorial, we will learn about how to modify Pandas DataFrames in different ways.

Renaming Column Labels in a DataFrame

Renaming column or row labels improves data readability and helps standardize column names. The rename() method in Pandas allows renaming one or more columns or row labels.

Example

The following example uses the DataFrame.rename() method to rename a columns name of a DataFrame.

 import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [1, 2, 3],'B': [4, 5, 6]}) # Display original DataFrame print("Original DataFrame:") print(df) # Rename column 'A' to 'aa' df = df.rename(columns={'A': 'aa'}) # Display modified DataFrame print("Modified DataFrame:") print(df) 

Following is the output of the above code −

 Original DataFrame: 

	A	B
0	1	4
1	2	5
2	3	6

Modified DataFrame:

	aa	B
0	1	4
1	2	5
2	3	6

Renaming Row Labels in a DataFrame

Similarly, you can rename row labels using of a Pandas DataFrame using the index parameter of the rename() method.

Example

This example demonstrates how to rename the row labels of a Pandas DataFrame using the rename(index={}) method.

 import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [1, 2, 3],'B': [4, 5, 6]}, index=['x', 'y', 'z']) # Display original DataFrame print("Original DataFrame:") print(df) # Rename the multiple row labels df = df.rename(index={'x': 'r1', 'y':'r2', 'z':'r3'}) # Display modified DataFrame print("Modified DataFrame:") print(df) 

Following is the output of the above code −

 Original DataFrame: 

	A	B
x	1	4
y	2	5
z	3	6

Modified DataFrame:

	A	B
r1	1	4
r2	2	5
r3	3	6

Adding or Inserting Columns in a DataFrame

Adding a new column to an existing DataFrame is straightforward. The simplest way is to directly assign values to the DataFrame using a new column name. Additionally, you can use the DataFrame.insert() method to insert a new column at a specified location.

Example: Adding a New Column Directly

The following example demonstrates how to add a new column directly to a DataFrame.

 import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [1, 2, 3],'B': [4, 5, 6]}) # Add a new column 'C' with values df['C'] = [7, 8, 9] # Display updated DataFrame print("DataFrame after adding a new column 'C':") print(df) 

Following is the output of the above code −

 DataFrame after adding a new column 'C': 

	A	B	C
0	1	4	7
1	2	5	8
2	3	6	9

Example: Inserting a Column at a Specific Position

This example demonstrates how to insert a column at a specific index of a DataFrame using the DataFrame.insert() method. In this example we will insert the columns D at index position 1.

 import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [1, 2, 3],'B': [4, 5, 6]}) # Insert a new column 'D' at position 1 df.insert(1, 'D', [10, 11, 12]) # Display updated DataFrame print("DataFrame after inserting column 'D' at position 1:") print(df) 

Following is the output of the above code −

 DataFrame after inserting column 'D' at position 1: 

	A	D	B
0	1	10	4
1	2	11	5
2	3	12	6

Replacing the Contents of a DataFrame

Replacing the contents of the DataFrame can be done by multiple ways, one of the easiest way is assigning new values directly to the particular part of the DataFrame.

Example: Replacing a Columns Values

The following example demonstrates how to replace/update particular column values of a DataFrame using the direct assignment way.

 import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [1, 2, 3],'B': [4, 5, 6]}) # Replace the contents of column 'A' with new values df['A'] = [10, 20, 30] # Display updated DataFrame print("DataFrame after replacing column 'A':") print(df) 

Following is the output of the above code −

 DataFrame after replacing column 'A': 

	A	B
0	10	4
1	20	5
2	30	6

Replacing Specific Values using the replace() method

You can also use the DataFrame.replace() method to replace specific values within a column of a DataFrame.

Example

This example demonstrates how to replace specific values in a DataFrame using the DataFrame.replace() method.

 import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [1, 2, 3],'B': [4, 5, 6]}) # Display the Input DataFrame print("Original DataFrame:", df, sep='\n') # Replace the contents df.replace({'A': 1, 'B': 6}, 100, inplace=True) # Display updated DataFrame print("DataFrame after replacing column 'A':") print(df) 

Following is the output of the above code −

 Original DataFrame: 

	A	B
0	1	4
1	2	5
2	3	6

DataFrame after replacing column 'A':

	A	B
0	100	4
1	2	5
2	3	100

Deleting Columns

Removing unnecessary columns is essential for data cleaning. You can delete single or multiple columns of a DataFrame using the DataFrame.drop() method.

Example

Here is an example that demonstrates how to delete multiple columns from a Pandas DataFrame using the DataFrame.drop() method.

 import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [1, 2, 3],'B': [4, 5, 6],'C': [7, 8, 9]}) # Display the original DataFrame print("Original DataFrame:", df, sep='\n') # Delete columns 'A' and 'B' df = df.drop(columns=['A', 'B']) # Display updated DataFrame print("DataFrame after deleting columns 'A' and 'B':") print(df) 

Following is the output of the above code −

 Original DataFrame: 

	A	B	C
0	1	4	7
1	2	5	8
2	3	6	9

DataFrame after deleting columns 'A' and 'B':

	C
0	7
1	8
2	9