In Python, “DataFrames” are an essential component of data analysis. The “pandas” library offers several methods to work with DataFrames. Inserting or adding rows to a DataFrame is a fundamental approach when analyzing or manipulating data. Python provides various methods to insert rows in Pandas DataFrame.

This Python post presents a detailed tutorial on how to add/insert a row to Pandas DataFrame utilizing several examples.

How to Add/Insert a Row to Pandas DataFrame in Python?

To add/insert a row to Python Pandas DataFrame, the below methods are utilized in Python:

• “Dataframe.loc[ ]” Method.
• “pandas.concat()” Function.
• “dataframe.append()” Function.

Method 1: Add/Insert a Row to Pandas DataFrame Using the “dataframe.loc[ ]” Method

The “df.loc[ ]” method adds a row to a Pandas DataFrame. This method allows the user to select a specific location within the DataFrame and assign values.

Example

Here is an example code:

In the above code:

• The “pandas” library is imported and “DataFrame” with three columns is created.
• The dictionary “new_row” is initialized in the program. This dictionary represents the values of the new row we want to add.
• Finally, use the “df.loc[len(df)] = new_row” method to add a new row to the DataFrame. The “len(df)” ensures that the new row is included at the end/last of the DataFrame.

Output

The particular DataFrame has been updated with the new row.

Method 2: Add/Insert a Row to Pandas DataFrame Using the “pandas.concat()” Function

Another method to add a row to a Pandas DataFrame is via the “pd.concat()” function. This function concatenates two DataFrames along a particular axis.

Example

Here is an example code:

In the above code lines:

• The “pandas” library is imported, and “DataFrame” with three columns is created.
• Now, likewise, define a new DataFrame named “new_row” that represents the values of the new row we want to add.
• Finally, use the “pd.concat()” function to concatenate the “data1” DataFrame and “new_row” DataFrame along the rows axis (axis=0).
• The “ignore_index=True” parameter ensures that the index of the resulting DataFrame is reset.

Output

The particular DataFrame has been updated with the new row.

Method 3: Add/Insert a Row to Pandas DataFrame Utilizing the “dataframe.append()” Function

The “dataframe.append()” function can also be used to add a row to a Pandas DataFrame. This function appends a row to a DataFrame and retrieves a new DataFrame object.

Example

Let’s overview the below example code:

In this code:

• The “pandas” library is imported, and “DataFrame” with three columns is created.
• The dictionary “new_row” is initialized in the program. This dictionary represents the values of the new row that needs to be added.
• The “append()” function is used to append the dictionary “new_row” to the target DataFrame.
• The “ignore_index=True” parameter ensures that the resulting DataFrame has a new index.

Output

Conclusion

To add/insert a row to Pandas DataFrame, the “dataframe.loc[ ]” method, “pandas.concat()” function or the “dataframe.append()” function is used in Python. The “df.loc()” method is used along with the “len()” function to add a complete row to the specified DataFrame. Similarly, the “pandas.concat()” and “append()” functions can also be used to add the new row to the specified DataFrame. This Python post presented a detailed guide on how to add/insert a row to Pandas DataFrame using numerous examples.