Pandas Exercises and Programs

Pandas is an open-source Python Library that is made mainly for working with relational or labelled data both easily and intuitively. This Python library is built on top of the NumPy library, providing various operations and data structures for manipulating numerical data and time series. Pandas is fast and it has high performance & productivity for users.

This Pandas Exercise is designed for beginners and experienced professionals. In this article, you will learn about all the important Pandas concepts, such as Pandas DataFrames, Pandas DataFrame Rows, Pandas DataFrame Columns and more. These concepts are essential for any budding Data Analyst or Data Scientist.

List of Pandas Exercises
Pandas DataFrame – Practice Exercise with Solutions
Pandas DataFrame Rows –  Practice Exercise with Solution
Pandas Dataframe Columns – Practice Exercise with Solution
Pandas Series – Practice Exercise with Solution
Pandas Date and Time – Practice Exercise with Solution

Pandas DataFrame – Practice Exercises

• Make a Pandas DataFrame with a two-dimensional list | Python
• Python | Creating DataFrame from dict of narray/lists
• Python | Creating DataFrame from dict of narray/lists
• Creating Pandas dataframe using list of lists
• Creating a Pandas dataframe using list of tuples
• Create a Pandas DataFrame from List of Dicts
• Python | Convert list of nested dictionary into Pandas dataframe
• Replace values in Pandas dataframe using regex
• Creating a dataframe from Pandas series
• Construct a DataFrame in Pandas using string data
• Clean the string data in the given Pandas Dataframe
• Reindexing in Pandas DataFrame
• Mapping external values to dataframe values in Pandas
• Reshape a pandas DataFrame using stack, unstack and melt method
• Reset Index in Pandas Dataframe
• Python | Change column names and row indexes in Pandas DataFrame

Pandas Dataframe Rows – Practice Exercise 

• How to iterate over rows in Pandas Dataframe
• Different ways to iterate over rows in Pandas Dataframe
• Selecting rows in pandas DataFrame based on conditions
• Select any row from a Dataframe using iloc[] and iat[] in Pandas
• Limited rows selection with given column in Pandas | Python
• Drop rows from the dataframe based on certain condition applied on a column
• Insert row at given position in Pandas Dataframe
• Create a list from rows in Pandas dataframe
• Create a list from rows in Pandas DataFrame | Set 2
• Ranking Rows of Pandas DataFrame
• Sorting rows in pandas DataFrame
• Select row with maximum and minimum value in Pandas dataframe
• Get all rows in a Pandas DataFrame containing given substring
• Convert a column to row name/index in Pandas
• How to randomly select rows from Pandas DataFrame

Pandas Dataframe Columns – Practice Exercise

• Create a pandas column using for loop
• How to get column names in Pandas dataframe
• How to rename columns in Pandas DataFrame
• Collapse multiple Columns in Pandas
• Get unique values from a column in Pandas DataFrame
• Conditional operation on Pandas DataFrame columns
• Return the Index label if some condition is satisfied over a column in Pandas Dataframe
• Using dictionary to remap values in Pandas DataFrame columns
• Formatting integer column of Dataframe in Pandas
• Create a new column in Pandas DataFrame based on the existing columns
• Python | Creating a Pandas dataframe column based on a given condition
• Split a column in Pandas dataframe and get part of it
• Getting Unique values from a column in Pandas dataframe
• Split a String into columns using regex in pandas DataFrame
• Getting frequency counts of a columns in Pandas DataFrame
• Change Data Type for one or more columns in Pandas Dataframe
• Split a text column into two columns in Pandas DataFrame
• Difference of two columns in Pandas dataframe
• Get the index of maximum value in DataFrame column
• Get the index of minimum value in DataFrame column
• Get n-largest values from a particular column in Pandas DataFrame
• Get n-smallest values from a particular column in Pandas DataFrame
• How to drop one or multiple columns in Pandas Dataframe
• How to lowercase column names in Pandas dataframe
• Capitalize first letter of a column in Pandas dataframe
• Apply uppercase to a column in Pandas dataframe

Pandas Series – Practice Exercise

• Create a Pandas Series from array
• Creating a Pandas Series from Dictionary
• Creating a Pandas Series from Lists
• Create Pandas Series using NumPy functions
• Access the elements of a Series in Pandas

Pandas Date and Time – Practice Exercise

• Pandas | Basic of Time Series Manipulation
• Using Timedelta and Period to create DateTime based indexes in Pandas
• Convert the column type from string to datetime format in Pandas dataframe

FAQs on Pandas Exercise

Q1: Why do people use Pandas in Python?

Pandas are basically used in Python for data manipulation, analysis, and cleaning tasks. It provides Tabular data structures and In-built functions that simplify data handling and allow for efficient data processing.

Q2: Where can I practice Python Pandas?

There are several online platforms where you can practice Python Pandas, such as DataCamp, Kaggle, and HackerRank. Additionally, you can find exercises and tutorials on websites like GeeksforGeeks and Real Python.

Q3: What are the benefits of Pandas?

Here are some  Benefits that Pandas Offer :-

1. Data manipulation: Pandas provides efficient data structures like DataFrames, which allow for easy manipulation, filtering, and transformation of data.
2. Data analysis: It offers a wide range of In-built functions for exploratory data analysis and other statistical analysis.
3. Integration: Pandas integrates well with other libraries in the Python ecosystem, such as NumPy, Matplotlib, and Scikit-learn, enabling seamless data analysis and visualization workflows.
4. Flexibility: Pandas supports various file formats, including CSV, Excel, SQL databases, and more, making it versatile for data ingestion and export.

Q4: What are the features of Pandas?

Here are some key features of Pandas are:

• DataFrame: A two-dimensional labelled Tabular(table-like) data structure that provides columnar data manipulation capabilities.
• Data cleaning and preparation: Pandas offers functions for handling missing data, data transformation, and data normalization.
• Data aggregation and summarization: It provides methods for grouping data, computing summary statistics, and applying custom functions.
• Time series analysis: Pandas has built-in support for handling time series data, including date/time indexing, resampling, and window functions.
• Input/output tools: It supports reading and writing data in various formats, such as CSV, Excel, SQL databases, and more.
• Visualization: Pandas integrates well with Matplotlib for data visualization and provides convenient plotting functions.