Comprehensive Guide to Pandas DataFrame Row Operations

Pandas is a powerful library in Python that provides easy-to-use data structures and data analysis tools. One of the most common data structures used in Pandas is the DataFrame. It is a two-dimensional labeled data structure with columns of potentially different types. In this article, we will explore all possible row operations that can be performed on a Pandas DataFrame.

Note 1: This article is an extension to the main Pandas DataFrame article below:

Note 2: We will be using Google Colaboratory Python notebooks to avoid setup and environment delays. The focus of this article is to get you up and running in Machine Learning with Python, and we can do all that we need there.

We will be using the following DataFrame for our examples:

import pandas as pd

data = {'Name': ['John', 'Emma', 'Sarah', 'Michael'],
       'Age': [25, 28, 30, 35],
       'Country': ['USA', 'Canada', 'Australia', 'UK']}

df = pd.DataFrame(data)        

Rows Info: df.index

df.index        

df.index is an attribute that represents the row index labels of a DataFrame. The row index labels provide a unique identifier for each row in the DataFrame.

When you access df.index, it returns the current index of the DataFrame, which can be either a numeric index (default range index) or a custom index specified during the DataFrame creation.

Here's an example to illustrate this:

import pandas as pd

data = {'Name': ['John', 'Emma', 'Sarah', 'Michael'],
       'Age': [25, 28, 30, 35],
       'Country': ['USA', 'Canada', 'Australia', 'UK']}

df = pd.DataFrame(data)

print(df.index)
        

Output:

RangeIndex(start=0, stop=4, step=1)
        

In the above code, the DataFrame df is created from a dictionary data. Since we didn't explicitly specify an index, a default range index is assigned to the DataFrame. The output shows a RangeIndex with a start value of 0, stop value of 4, and a step of 1. This indicates that the DataFrame has four rows with index labels ranging from 0 to 3.

The df.index attribute can be useful to access and manipulate the row index labels of a DataFrame. You can assign new values to df.index to change the index labels or use various index-related methods to perform operations like reindexing, resetting the index, etc.

Changing Index: set_index()

You might want to change the index from a range of numbers to some other column. However, you need to make sure it is unique per row. In this DataFrame, the 'Name' column does not have duplicate. Let's demonstrate how to change the Index to this Column:

df.set_index('Name')        

Note that Name is now the label of the index instead of a regular column

set_index by default generates a new DataFrame. You can modify the original df by adding inplace=True

to return back to the numeric index you can run

df.reset_index()        

Accessing Rows:

Accessing One Row: df.iloc[row_number]:

• This method allows accessing a specific row by its integer position. It returns a Series object containing the row.

Accessing Multiple Rows: df.iloc[start:stop]

You can access multiple rows using the slice:

df.iloc[start:end] # end is exclusive        

Accessing a row with df.loc[label]:

• This method allows accessing a row by its label. It returns a Series object containing the row.

Note that you have to have labels for the index as we demonstrated in the previous example and setting the index to 'Name'.

Accessing Multiple Rows: df.loc[[label1, label2, ....]]

Adding Rows:

df.append(row, ignore_index=True)

This method appends a row to the DataFrame. The row parameter is a dictionary or Series object containing the values for each column. The ignore_index parameter is optional and when set to True, it resets the index after appending the row.

Note: You will need to set it to True if you are adding a dictionary as in the example below

Deleting Rows:

df.drop(index):

This method deletes a row by its index. It returns a new DataFrame without the deleted row. The index parameter accepts either a single index value or a list of index values.

Updating Rows:

• df.at[index, column] = new_value: This method allows updating a specific value in a row based on its index and column name. It directly modifies the DataFrame.
• df.iat[row_number, column_number] = new_value: This method allows updating a specific value in a row based on its integer position. It directly modifies the DataFrame.

Filtering Rows:

• df[df['column_name'] > value]: This method filters the DataFrame based on a specific condition. It returns a new DataFrame containing only the rows that satisfy the condition.

Sorting Rows:

• df.sort_values(by='column_name'): This method sorts the DataFrame based on a specific column. It returns a new DataFrame with the rows sorted in ascending order based on the values in the specified column.

Grouping Rows:

• df.groupby('column_name'): This method groups the rows based on a specific column. It returns a GroupBy object that allows performing aggregate functions on the groups.

Iterating through Rows:

• for index, row in df.iterrows(): This method allows iterating through each row in the DataFrame. The index variable contains the index of the row, and the row variable contains a Series object representing the row data.

These are some of the most commonly used row operations in Pandas DataFrame. They provide a wide range of functionalities to manipulate and analyze data efficiently. By utilizing these operations, one can perform various data transformations and calculations on large datasets with ease.