Working with data often involves modifying it to suit your analysis needs. One common operation is inserting a new row into a DataFrame. In this post, we’ll explore several methods to achieve this in pandas, a powerful data manipulation library in Python.

Method 1: Using append()

The append() method is straightforward and easy to use when you want to add a new row to your DataFrame. Here’s an example:

import pandas as pd

# Create a DataFrame
data = {'Column1': [1, 2, 3],
        'Column2': ['A', 'B', 'C']}
df = pd.DataFrame(data)

# New row data
new_row = {'Column1': 4, 'Column2': 'D'}

# Append the new row
df = df.append(new_row, ignore_index=True)

print(df)

Output:

   Column1 Column2
0        1       A
1        2       B
2        3       C
3        4       D

Method 2: Using loc[] Indexer

The loc[] indexer allows you to add a new row by specifying the index for the new row. This method is efficient and avoids the need to create a new DataFrame.

import pandas as pd

# Create a DataFrame
data = {'Column1': [1, 2, 3],
        'Column2': ['A', 'B', 'C']}
df = pd.DataFrame(data)

# New row data
new_row = pd.Series({'Column1': 4, 'Column2': 'D'})

# Get the index for the new row
new_index = len(df)

# Use loc to insert the new row with the new index
df.loc[new_index] = new_row

print(df)

Output:

   Column1 Column2
0        1       A
1        2       B
2        3       C
3        4       D

Method 3: Using concat()

If you have multiple rows to add, it’s more efficient to create a new DataFrame with the new rows and concatenate it with the existing DataFrame.

import pandas as pd

# Create a DataFrame
data = {'Column1': [1, 2, 3],
        'Column2': ['A', 'B', 'C']}
df = pd.DataFrame(data)

# New rows data
new_rows = pd.DataFrame({'Column1': [4, 5], 'Column2': ['D', 'E']})

# Concatenate the DataFrames
df = pd.concat([df, new_rows], ignore_index=True)

print(df)

Output:

  Column1 Column2
0        1       A
1        2       B
2        3       C
3        4       D
4        5       E

Conclusion

Inserting a new row in a pandas DataFrame can be done in several ways, each with its advantages. The append() method is simple and straightforward, while loc[] is more efficient for single-row additions. For adding multiple rows, concat() is the most efficient approach. Choose the method that best fits your needs and data size.