How to Insert a Row to Pandas DataFrame

As a data scientist, one of the most common tasks you will encounter is working with pandas DataFrames. Pandas is a powerful library for data manipulation and analysis in Python that provides comprehensive data structures for working with tabular data. In this article, we will discuss how to insert a row to a pandas DataFrame.

What Is a Pandas DataFrame?

Before we dive into inserting a row to a pandas DataFrame, let’s first understand what a DataFrame is. A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It is similar to a spreadsheet or a SQL table, but with optimized performance and functionality for data analysis.

How to Insert a Row to a Pandas DataFrame

To insert a row to a Pandas DataFrame, the append or loc method can be employed. Below are the typical steps for accomplishing this task:

1. Create a dictionary with the data for the new row
2. Append the dictionary to the DataFrame using the append or loc method
3. Reset the index if needed

Using append method

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Create a dictionary with the data for the new row
new_row = {'Name': 'David', 'Age': 40}

# Append the dictionary to the DataFrame
df = df.append(new_row, ignore_index=True)

# Reset the index
df = df.reset_index(drop=True)

print(df)

Output:

       Name  Age
0     Alice   25
1       Bob   30
2   Charlie   35
3     David   40

As we can see in the example above, we first create a DataFrame with three rows and two columns. Then, we create a dictionary with the data for the new row, which includes the name and age of the person we want to add. We then append the dictionary to the DataFrame using the append method, and set the ignore_index parameter to True to generate a new index for the DataFrame. Finally, we reset the index using the reset_index method to ensure that the index is sequential and starts from 0.

Using loc method

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Create a dictionary with the data for the new row
new_row = {'Name': 'David', 'Age': 40}

# Inserting the new row
df.loc[len(df)] = new_row

# Reset the index
df = df.reset_index(drop=True)

print(df)

Output:

       Name  Age
0     Alice   25
1       Bob   30
2   Charlie   35
3     David   40

In the code above, the new_row dictionary contains the information for the person we want to add. Using df.loc[len(df)], we insert the new row at the end of the DataFrame. The length of the DataFrame (len(df)) serves as the index for the new row.

Conclusion

In this article, we have learned how to insert a row to a pandas DataFrame. Pandas is a powerful library for data manipulation and analysis in Python, and being able to insert rows to a DataFrame is a fundamental skill for any data scientist or software engineer working with tabular data. By following the simple steps outlined in this article, you can easily add new rows to your DataFrame and continue your data analysis with confidence.

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