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Python Pandas Tutorial for Beginners: How to Use Pandas for Data Analysis

A beginner-friendly approach on how to use Pandas to create data structures, sort, select and visualize data
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Manage data in Excel with Python
Manage other types of data with Python
Visualize data
Generate instant statistics
Various ways of using Pandas to manage data

Python is the go-to programming language when people decide to conduct data analysis. Why? Well, Python offers benefits of easier syntax conventions, a multitude of libraries for different purposes, and the opportunity to process, clean, and explore your data carefully. In this Python Pandas tutorial, I am showing the advantages of Pandas, a popular high-level library for storing and manipulating data. 

Learning how to use Pandas begins from the very basics. I will review the proper way of importing the library, defining different sequences, DataFrame basics, statistics, sorting, etc. I will also show you how to convert various data files into Pandas DataFrames automatically. This Python Pandas tutorial will end with the presentation of visualization opportunities to make your data less dull to look at for your colleagues or business partners. 

Why Python for data analysis?

Why should you choose to learn Python and Pandas? One of the greatest advantages of Python is its easy-to-follow syntax. Python is also highly scalable, making it less challenging to deal with the growing number of lines in your code. Another appreciated feature of Python is its large quantity of libraries. To take advantage of all benefits offered by Python, you should learn how to use Pandas and other libraries related to data management. 

Reasons to learn Python Pandas

For an effective data analysis with no boundaries, analysts use a combination of Python tools. The matplotlib library is used for visualizing data in graphs or other forms, and NumPy is the main library for scientific computing in Python. In fact, Pandas is based on NumPy. 

In this Python Pandas tutorial, we will be reviewing the Pandas library, which makes data manipulation more productive. Specifically, the Pandas tool is perfect for presenting data in structures that are more suited for data analysis (DataFrames, 3D tables, etc.). You can easily convert text, Excel, or CSV files into Pandas DataFrames. 

What will you find in this course? 

My Python Pandas tutorial is beginner-friendly, meaning that you can be a complete rookie when it comes to using the Pandas library. However, this course does require you to know how to use the Python programming language. 

I will start by explaining the process of Pandas data analysis. Each lecture of this Python Pandas tutorial will discuss a different step of the procedure.

  • The first lecture introduces one of the possible data structures: series. Starting from the proper way of importing the Pandas library, I will move on to the definition of one-dimensional arrays. 
  • The second lecture explains the fundamental concept of Pandas data analysis: DataFrames. For single rows, you would always use series, but for multiple ones, DataFrames is the more suitable option. 
  • Third, fourth, fifth, and sixth lessons discuss the ways of converting different files to the DataFrames of Pandas. 
  • The seventh lesson reveals some tricks to improve the way you work with DataFrames. 
  • The eighth lecture shows methods for manipulating numbers in your DataFrame and generating statistical information.
  • The ninth lesson focuses on the method used for sorting data (for instance, according to salary or age). 
  • The tenth lecture shows how you can select specific data from the DataFrame.
  • The final lesson reveals the most basic way of visualizing data by using matplotlib library. 
Python Pandas Tutorial for Beginners: How to Use Pandas for Data Analysis
$ 10
per course
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