How to Add One Row in an Existing Pandas DataFrame?

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Adding a row to an existing Pandas DataFrame is a common task when working with data in Python. This guide will walk you through different methods to achieve this, ensuring you understand each step with clear explanations and examples.

Method 1: Using loc Method

The loc method allows you to access a group of rows and columns by labels or a boolean array. You can use it to add a new row by specifying the index and the values.

Example:

Python
import pandas as pd

# Creating a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

# Displaying the original DataFrame
print("Original DataFrame:")
print(df)

# Adding a new row using loc
df.loc[3] = ['David', 40, 'Houston']

# Displaying the updated DataFrame
print("\nDataFrame after adding a new row using loc:")
print(df)

Output:

Markdown
Original DataFrame:
      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

DataFrame after adding a new row using loc:
     Name  Age         City
0   Alice   25     New York
1     Bob   30  Los Angeles
2 Charlie   35      Chicago
3   David   40      Houston

This example shows how to add a new row to the DataFrame using the loc method by specifying the index and the corresponding values for each column.

Method 2: Using _append Method

The _append method is a convenient way to add rows to a DataFrame. It creates a new DataFrame with the additional row(s).

Example:

Python
# Creating a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

# Displaying the original DataFrame
print("Original DataFrame:")
print(df)

# Creating a new row as a DataFrame
new_row = pd.DataFrame({'Name': ['David'], 'Age': [40], 'City': ['Houston']})

# Adding the new row using append
df = df._append(new_row, ignore_index=True)

# Displaying the updated DataFrame
print("\nDataFrame after adding a new row using append:")
print(df)

Output:

Markdown
Original DataFrame:
      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

DataFrame after adding a new row using append:
      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago
3    David   40      Houston

This example demonstrates how to use the _append method to add a new row to the DataFrame, creating a new DataFrame with the additional row and maintaining the original index.

Note: The append method has been replaced by _append in recent versions of pandas, but it might be removed in future releases. It’s advisable to use it with caution. It’s recommended to use the concat function for adding rows to a DataFrame.

Method 3: Using concat Function

The concat function is another way to add rows to a DataFrame. It is particularly useful when you need to add multiple rows at once.

Example:

Python
# Creating a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

# Displaying the original DataFrame
print("Original DataFrame:")
print(df)

# Creating new rows as a DataFrame
new_rows = pd.DataFrame({
    'Name': ['David', 'Eve'],
    'Age': [40, 45],
    'City': ['Houston', 'Phoenix']
})

# Adding the new rows using concat
df = pd.concat([df, new_rows], ignore_index=True)

# Displaying the updated DataFrame
print("\nDataFrame after adding new rows using concat:")
print(df)

Output:

Markdown
Original DataFrame:
      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

DataFrame after adding new rows using concat:
      Name  Age      City
0    Alice   25   New York
1      Bob   30 Los Angeles
2  Charlie   35    Chicago
3    David   40    Houston
4      Eve   45    Phoenix

In this example, the concat function is used to add multiple rows to the DataFrame. This method is efficient for appending multiple rows at once and combines the original and new DataFrames into a single one.

Conclusion

Adding a row to a Pandas DataFrame can be done using several methods, each with its advantages. The loc method is direct and straightforward, _append is convenient for adding single rows, and concat is powerful for adding multiple rows at once. Choose the method that best fits your needs and workflow.

If you have any questions or need further assistance, leave a comment below. Don’t forget to check out our other tutorials for more Pandas tips and tricks!

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