Do you know that 60% to 80% of the work in a Data Science project goes into Data Acquisition, Cleaning, Transformation, Analysis and Feature engineering? Pandas is the foremost and de facto library for Data Analysis in Python. By mastering Pandas, you complete half the journey in Data Science. The basics of all the features provided by Pandas in a beginner-friendly way represented in this file.

Pandas is a Python library for doing data analysis. It's really fast and lets you do exploratory work incredibly quickly.

The goal of this cookbook is to give you some concrete examples for getting started with pandas. The docs are really comprehensive. However, I've often had people tell me that they have some trouble getting started, so these are examples with real-world data, and all the bugs and weirdness that entails.

If you have both conda and git on your system (otherwise, read the next section for more detailed instructions):

• conda install --yes jupyter matplotlib pandas
• git clone https://github.com/machine-learnig-ai/Pro_Pandas.git
• cd pycon-pandas-tutorial
• build/BUILD.sh
• ipython notebook

A quick tour of the IPython Notebook Shows off IPython's awesome tab completion and magic functions.

Chapter 1: Reading from a CSV

Reading your data into pandas is pretty much the easiest thing. Even when the encoding is wrong!

Chapter 2: Filtering & Sorting Your Data

It's not totally obvious how to select data from a pandas dataframe. Here I explain the basics (how to take Filtering and get columns)

Chapter 3: Grouping

The groupby/aggregate is seriously my favorite thing about pandas and I use it all the time. You should probably read this.

Chapter 4: Merging and Binding

In real life project we havee to mearge lots of table data or csv files to gather, so this is the very important part while learning pandas.

Chapter 5: Dealing with Null values

There are always null values in dataset you must have knowledge how to deal with Null values, here you will learn it with easy way with fun.

Chapter 6: Data Visualization

With out visulazation you unable to explain things to anyone, if you want to describd data with visulization anyone will understand it easily.

🐼 PANDAS 🐼.pdf