Add Rows to Pandas DataFrame

Adding rows to a Pandas DataFrame is a common operation in data manipulation and analysis. Pandas DataFrames are powerful data structures that allow you to store and manipulate tabular data, including adding new rows. In this article, we will explore the different methods to add rows to a Pandas DataFrame, including using the `loc` attribute, the `concat` function, and the `append` method.

Introduction to Pandas DataFrames

Pandas is a popular Python library used for data manipulation and analysis. It provides data structures and functions to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables. A Pandas DataFrame is a two-dimensional table of data with columns of potentially different types. It is similar to an Excel spreadsheet or a table in a relational database.

Creating a Pandas DataFrame

To create a Pandas DataFrame, you can use the pd.DataFrame() constructor and pass in a dictionary or a list of lists. Here is an example:

import pandas as pd

# Create a dictionary
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
        'Age': [28, 24, 35, 32],
        'Country': ['USA', 'UK', 'Australia', 'Germany']}

# Create a DataFrame
df = pd.DataFrame(data)

print(df)

Adding Rows to a Pandas DataFrame

There are several ways to add rows to a Pandas DataFrame. Here are a few methods:

Method 1: Using the loc Attribute

The loc attribute is a label-based data selection method that allows you to access a group of rows and columns by label(s). You can use it to add a new row to a DataFrame by assigning a new row to a specific index.

# Add a new row to the DataFrame
df.loc[len(df)] = ['Phil', 36, 'Canada']

print(df)

Method 2: Using the concat Function

The concat function is used to concatenate two or more DataFrames. You can use it to add a new row to a DataFrame by creating a new DataFrame with the new row and then concatenating it with the original DataFrame.

# Create a new DataFrame with the new row
new_row = pd.DataFrame({'Name': ['Lucy'], 'Age': [29], 'Country': ['France']})

# Concatenate the new row with the original DataFrame
df = pd.concat([df, new_row], ignore_index=True)

print(df)

Method 3: Using the append Method

The append method is used to add a new row to a DataFrame. However, it is not as efficient as the other methods and is generally not recommended.

# Add a new row to the DataFrame
df = df._append({'Name': 'Mike', 'Age': 31, 'Country': 'Italy'}, ignore_index=True)

print(df)