PANDAS EXCEL
Pandas is probably the best tool to do real-world data analysis in Python. It allows us to clean data, wrangle data, make visualizations, and more.
You can think of Pandas as a supercharged Microsoft Excel. Most of the tasks you can do in Excel can be done in Pandas too and vice versa. That said, there are many areas where Pandas outperforms Excel.
In this introduction to Pandas, we will compare Pandas dataframes and Excel Spreadsheet, learn different ways to create a dataframe, and how to make pivot tables. dataframe has rows and columns (also known as series). On top of a dataframe, you will see the name of the columns and on the left side, there’s the index. By default index in Pandas start with 0.
The intersection of a row and a column is called a data value or simply data. We can store different types of data such as integers, strings, boolean, and so on.
Here’s a picture of a dataframe that shows US states ranked by population. I’m going to show you the code to create a dataframe like this later, but now let’s analyze this dataframe.
Creating a dataframe by reading an Excel/CSV file
This is without a doubt the easiest way to create a dataframe in Pandas. We only need to import pandas, use the read_csv() method and write the name of the Excel/CSV file within parentheses.
👇CLICK BELOW SEE EXAMPLE PROGRAMS👇
👉EXAMPLE PROGRAMS ON PANDAS EXCEL👈
