Pandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.
According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world. That is quite an achievement!
Pandas is the first library we import when we fire up our Jupyter notebooks (‘import pandas as pd’ is indelibly etched in our minds!). It is a super flexible tool that enables us to perform data analysis and data manipulation on Pandas dataframes in double-quick time.
- What is Pandas?
- How can I install Pandas in Python?
- Where is Pandas used in data science?
- How difficult is it to learn Pandas?
- What is a Pandas dataframe?
- Do I need to know Python to learn Pandas?
- What kind of data analysis can I perform using Pandas?
- Will Pandas help me become a better data scientist?
- What kind of data formats can I import using Pandas?
If you’ve asked any of these questions before or are looking to learn Pandas from scratch, you’ve come to the right place.
The great thing about Pandas is the sheer number of tasks you can perform in Python. It is often called the Swiss Army Knife of data analysis! That should give you a good idea of what you can expect from this powerful library.
Here’s a taste of what we’ll cover in this course:
- Basics of the Pandas library
- How to import data using Pandas: Read data into Python
- How to write data using Pandas
- Perform data analysis and manipulation using Pandas:
- Select columns and rows in Pandas
- Manipulate columns in Pandas - Rename columns, sort data in Pandas dataframe, binning data using Pandas, etc.
- How to deal with missing values using Pandas
- What is the Apply function in Pandas and how you can use it
- Aggregate data in Pandas - a very handy tool for quick data analysis
- How to merge and join multiple Pandas dataframes
- Pivot tables in Pandas (yes, you can draw up pivot tables in Python using Pandas!)
And we have even included an illustrated Pandas cheatsheet just for you!
Beginners in Python who are curious about Pandas and how to use it for data analysis and data manipulation
Anyone who wants to start their data science career (Pandas is the first library you’ll import!)
Anyone looking to get into a data analyst role using Python programming
Anyone who wants to jump from Excel into Python
- Introduction to the Course
- Pandas Installation
- Loan Prediction
- Big Mart Sales
- Understanding File System & shell commands
- Reading Excel & CSV files
- Writing Data using Pandas
- Quiz: Reading a csv file using Pandas
- What are Pandas Dataframes & its operations?
- Selecting Columns & Rows in Pandas (Indexing)
- Quiz: DataFrames and basic operations
- Basic Descriptive Statistics using Pandas
- Plotting using Pandas
- Quiz: Data Exploration using Pandas
- Renaming Column using Pandas
- Sorting Data in Pandas DataFrame
- Binning using Pandas
- Handling Missing Values
- Apply Function in Pandas for Element wise Operations
- Quiz: Pandas Apply Function
- Types of Aggregations in Pandas
- Aggregations using Pandas in action
- Quiz: Aggregations in Pandas
- Merging Data in Pandas Dataframes
- Quiz: Merging Data using Pandas
- Pandas Cheatsheet
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What is Pandas?
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation library built in Python.
Pandas is THE most popular Python library in data science and the 4th most popular library in the world (according to StackOverflow’s global survey). The open source nature of Pandas isone of the primary reasons for its popularity and adoption rate in the community.
Here’s a golden nugget about Pandas from Wikipedia: The name ‘Pandas’ is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals.
How can I install Pandas in Python?
The easiest way to install Pandas is to install it as part of the Anaconda distribution. Anaconda is free and easy to install (it’s the installer we use for setting up Python as well).
We highly recommend doing this instead of trying to install Pandas from scratch (it will be a slightly difficult process if you’re not familiar with Python or programming in general).
Where is Pandas used in data science?
Pandas is primarily used in data science and machine learning in the form of dataframes. As we’ve mentioned above, Pandas enables us to perform all sorts of data analysis and manipulation tasks in Python, including importing different data files like CSV, Excel, JSON, etc.
Most data science projects use Pandas to perform aggregating functions like GroupBy, merge and join dataframes, impute missing values in Python, among other things.
In short, Pandas is an essential part of a data science project!
What is a Pandas dataframe?
In Pandas, a dataframe is a data structure used to store and manipulate tabular data. The tabular data has it’s columns, column names and rows - we can easily perform operations on large dataframes using Pandas functions.
A Pandas dataframe is also the standard structure used to store the data from common formats of data like CSV file, Excel sheets and others.
How difficult is it to learn Pandas?
It’s actually quite straightforward! Even though Pandas has a ton of features and functions, you can easily pick those up with a bit of practice.
And that’s exactly how we’ve designed the course! You’ll learn all the different Pandas functions in Python and then work on various exercises after each lesson to solidify what you’ve learned.
Do I need to know Python to learn Pandas?
It would definitely help to have basic Python programming knowledge if you want to maximize your Pandas experience. The ability to merge or join Pandas dataframes, manipulating data and so on will require a bit of Python programming.
If you’re completely new to Python, we recommend taking our free Python for Data Science course.
What kind of data analysis can I perform using Pandas?
You can perform all kinds of data analysis and data manipulation using Pandas. We cover the key points in this course but here is a list for your reference:
● Reading and writing data from different file formats like CSV, Excel, JSON, etc.
● Data alignment
● Handling missing data
● Reshaping data and building pivot tables using Pandas
● Label-based slicing, fancy indexing, and subsetting of large datasets
● Column insertion and deletion using Pandas dataframes
● Group by allowing split-apply-combine operations on datasets
● Dataset merging and joining
● Filtering dataframes, and so on
Will Pandas help me become a better data scientist?
Short answer - yes. Which data science project won’t appreciate someone who can perform quick analysis and data manipulation? This is a key part of any data science project and mastering Pandas will go a long way towards making you a better and more efficient data scientist.
Additionally, this will also help you in your interview rounds when you’re asked to analyze certain data (Pandas is your best friend!).
What kind of data formats can I import using Pandas?
The beauty about Pandas is the remarkable number of files we can read into Python. Here’s a quick list:
1. Comma-separated values (CSV)
4. Plain Text (txt)
9. Hierarchical Data Format
- A working laptop / desktop with 4 GB RAM
- A working Internet connection
- Basic knowledge of Machine Learning
- Basic knowledge of Python - check out this Course first, if you are new to Python
This is all it takes for you to learn one of the most popular and useful library for data analysis in Python.
What are you waiting for?
Who should take Pandas for Data Analysis in Python course?
This course is for people who wants to start their journey in machine learning and data analysis. Pandas is one of the most popular data analysis libraries in Python.
I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?
The course assumes no prior background in Machine Learning. So, you can start this course.
What is the fee for this course?
This course is free of cost.
How long would I have access to "Pandas for Data Analysis in Python" course?
Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
How much effort will this course take?
You can complete Pandas for Data Analysis in a few hours. You are also expected to apply Pandas and its functions to perform data analysis.
How can I apply and test my learnings about Pandas for Data Analysis?
You can start by doing the tests at the end of various chapters. In addition, you can apply Pandas for Data Analysis to solve various Practice problems on Analytics Vidhya DataHack Platform
Can I download videos from this course?
We regularly update "Pandas for Data Analysis in Python" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.
Which programming language is used to teach Pandas in this course?
As the name suggests, this course teaches Pandas for Data Analysis in Python.
Do I get a certificate upon completion of the course?
No, there is no certificate for this course.
I just completed Pandas for Data Analysis in Python course, what should I do next?
Congratulations! We would highly recommend that you continue your machine learning journey by taking our Applied Machine Learning Course
I don't have Python Installed in my machine, what can I do?
You can go ahead and install Anaconda distribution - it will come pre-installed with everything you need including Pandas and scikit-learn libraries
How is a Free Course different from a paid course on Analytics Vidhya?
Our free courses are just the tip of the iceberg. They are good to get you started, where as paid course provide you with the depth required for industry roles.