Pandas Rows: The ELI5 Guide

Pandas Rows: The ELI5 Guide

If you’re a data scientist or analyst, you’ve probably heard of Pandas. Pandas is a powerful Python library for data manipulation and analysis. It’s widely used in the industry and academia, and it’s a must-have tool in your arsenal.

One of the fundamental concepts in Pandas is the row. A row is a horizontal slice of data in a Pandas DataFrame. Understanding how rows work is essential for working with data in Pandas.

In this ELI5 (Explain Like I’m 5) guide, we’ll cover everything you need to know about Pandas rows. We’ll start with the basics and gradually move to more advanced topics.

What is a Pandas row?

A Pandas row is a horizontal slice of data in a DataFrame. Think of a DataFrame as a table, where each row represents a record, and each column represents a variable or feature.

For example, let’s say we have a DataFrame that contains information about customers:

Each row in this DataFrame represents a customer, and each column represents a variable (customer ID, name, age, and gender).

How to access rows in a Pandas DataFrame?

There are several ways to access rows in a Pandas DataFrame. The most common way is to use the loc method, which allows you to select rows by label.

For example, to select the row with label 2 (which represents Bob), you can use the following code:

df.loc[2]

This will return a Pandas Series object that contains the data for Bob:

Customer ID      2
Name           Bob
Age             30
Gender           M
Name: 2, dtype: object

You can also use the iloc method to select rows by integer position. For example, to select the second row (which represents Bob), you can use the following code:

df.iloc[1]

This will return the same result as before:

Customer ID      2
Name           Bob
Age             30
Gender           M
Name: 2, dtype: object

How to select multiple rows in a Pandas DataFrame?

You can select multiple rows in a Pandas DataFrame by using the loc or iloc method with a list of labels or integer positions.

For example, to select the rows with labels 2 and 3 (which represent Bob and Carol), you can use the following code:

df.loc[[2, 3]]

This will return a new DataFrame that contains only the selected rows:

You can also use slicing to select a range of rows. For example, to select the first two rows, you can use the following code:

df.iloc[:2]

This will return a new DataFrame that contains only the first two rows:

How to add a new row to a Pandas DataFrame?

To add a new row to a Pandas DataFrame, you can use the loc method with a new label and a dictionary of values.

For example, let’s say we want to add a new customer to our DataFrame:

new_customer = {'Customer ID': 5, 'Name': 'Eve', 'Age': 45, 'Gender': 'F'}
df.loc[5] = new_customer

This will add a new row to our DataFrame:

How to delete a row from a Pandas DataFrame?

To delete a row from a Pandas DataFrame, you can use the drop method with the label or integer position of the row.

For example, let’s say we want to delete the row with label 5 (which represents Eve):

df = df.drop(5)

This will remove the row from our DataFrame:

Conclusion

In this ELI5 guide, we’ve covered everything you need to know about Pandas rows. We’ve learned what a row is, how to access and select rows, how to add and delete rows, and more.

Understanding rows is essential for working with data in Pandas, and we hope this guide has been helpful. If you have any questions or feedback, please let us know in the comments below.