How to Append Rows to a Pandas DataFrame

As a data scientist or software engineer, you’re likely familiar with Pandas, a popular data manipulation library in Python. One of the most fundamental tasks when working with data is appending rows to a DataFrame. In this article, we’ll cover how to do just that.

What is a Pandas DataFrame?

A Pandas DataFrame is a two-dimensional, size-mutable, tabular data structure with labeled axes (rows and columns). It is akin to a spreadsheet or SQL table, but with more powerful features. A DataFrame can hold data of different types, including integers, floats, strings, and more.

Why Append Rows to a Pandas DataFrame?

In data science, it’s common to receive new data over time. To keep our analyses up-to-date, we need to append new rows to our existing data. Appending rows is also useful when we want to combine multiple data sources.

How to Append Rows to a Pandas DataFrame

To append rows to a Pandas DataFrame, we can use the append() method. This method returns a new DataFrame with the appended rows.

Here’s an example:

import pandas as pd

# create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# create a new row
new_row = {'A': 7, 'B': 8}

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

print(df)

Output:

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

In this example, we first create a DataFrame with two columns A and B. We then create a new row new_row with the same column names as the DataFrame. We use the append() method to append the new row to the DataFrame and set the ignore_index parameter to True to reset the index of the resulting DataFrame.

Appending Multiple Rows to a Pandas DataFrame

We can also append multiple rows to a Pandas DataFrame using the append() method. To do this, we first create a list of dictionaries, where each dictionary represents a row to append. We then pass this list to the append() method.

Here’s an example:

import pandas as pd

# create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# create a list of new rows
new_rows = [{'A': 7, 'B': 8}, {'A': 9, 'B': 10}]

# append the new rows to the DataFrame
df = df.append(new_rows, ignore_index=True)

print(df)

Output:

   A   B
0  1   4
1  2   5
2  3   6
3  7   8
4  9  10

In this example, we create a list of two dictionaries new_rows, each representing a row to append. We pass this list to the append() method to append the new rows to the DataFrame.

Conclusion

Appending rows to a Pandas DataFrame is a common and important task in data science. In this article, we covered how to append a single row and multiple rows to a DataFrame using the append() method. Remember to set the ignore_index parameter to True to reset the index of the resulting DataFrame.

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