Handling JSON in Python

In this post we would look at scenarios where we have to work with Java script object notation(JSON). The nature of the task as well as resources have been summarized in the following sections:

Problem Statement: Many a times we are given a situation where we need to extract information from JSON object before it can be used further.This is a typical case when dealing with creation of tools to process information or while extracting information from some other source or while exchanging information. The aim is to understand JSON and how it can be converted into a structured data using python.

Data Download Link: 

Download example1 and example2 from this link

https://drive.google.com/drive/folders/1xjw6a17zCaa8sYj8ZerXv7SmGeuuEeVq?usp=sharing

Learning Objectives:

• Handling JSON

• Understanding JSON object 
• Using json library
• Converting it into a table (data frame) 

Please download the jupyter file from the following link

Jupyter File link: https://drive.google.com/drive/folders/1xjw6a17zCaa8sYj8ZerXv7SmGeuuEeVq?usp=sharing
Download Handling Json.ipynb file from this link

Link to Html version:
https://drive.google.com/drive/folders/1xjw6a17zCaa8sYj8ZerXv7SmGeuuEeVq?usp=sharing

Pandas Complete Guide

This blog contains collection of all the codes snippets related to pandas. It covers the following things:

1. Introduction to Series: https://mlmadeeasy.blogspot.com/2019/02/introduction-to-series.html
2. Hierarchical Indexing: https://mlmadeeasy.blogspot.com/2019/02/hierarchical-indexing.html
3. Combining and Merging Data sets: https://mlmadeeasy.blogspot.com/2019/02/combining-and-merging-datasets.html
4. Pivoting using Pandas: https://mlmadeeasy.blogspot.com/2019/02/pivoting-using-pandas.html
5. Frequency Profiling using Pandas: https://mlmadeeasy.blogspot.com/2019/02/frequency-profiling-using-pandas.html
6. Data Manipulation in Python Part 1: https://mlmadeeasy.blogspot.com/2019/01/data-manipulation-with-python.html
7. Data Manipulation in Python Part 2 (Retail Case Study):https://mlmadeeasy.blogspot.com/2019/01/data-manipulation-with-python-part-2.html

Frequency Profiling using Pandas

This blog contains introduction to frequency profiling. This is common place with data engineers who do ETL. It is also used at the start of every exploratory data analysis.

The blog consists of the following attachments:

1. Jupyter Notebook https://drive.google.com/file/d/1mL3GoVQjGTTUeWMmwDKtytGNRF0evE6D/view?usp=sharing
2. Html doc:   https://drive.google.com/file/d/1ExuLhp1CgCjE0DCTR_G09LnD63I_Txp4/view?usp=sharing

Pivoting using Pandas

This blog contains introduction to pivoting using pandas. It gives different scenarios of creating summaries using pivot table functions

The blog consists of the following attachments:

1. Jupyter Notebook https://drive.google.com/file/d/1Vsui7dQH9pJN1unw6oz47hZH1WZifObQ/view?usp=sharingW7mTz2k8hnx8XjcW4JL5JeoPw/view?usp=sharing
2. Html doc: https://drive.google.com/file/d/1DFcwajEK2qqYXcKXjd_srQsjkYvuBGON/view?usp=sharing

Combining and Merging Datasets

This blog contains introduction to merging data frames. It gives different scenarios of using keys from different data frames.

The blog consists of the following attachments:

1. Jupyter Notebook https://drive.google.com/file/d/100RXan-W7mTz2k8hnx8XjcW4JL5JeoPw/view?usp=sharing
2. Html doc: https://drive.google.com/file/d/1t0J8QBD7kNb-DZDevPs-y36imb0G4I6g/view?usp=sharing

Hierarchical Indexing

This blog contains introduction to Hierarchical Indexing. It starts with challenges associated with multi level group by and how to tackle it using hierarchical indexing. 

The blog consists of the following attachments:

1. Jupyter Notebook https://drive.google.com/file/d/1ruXC1cGMbx_hDQisz8oe3drdAzYFrpo9/view?usp=sharing
2. Html doc: https://drive.google.com/file/d/1Kf8qHLYY_ihos_z5WS8fpyTUwacXldnD/view?usp=sharing

Introduction to Series

This blog contains introduction to series and all the functions associated with it. It starts with how to store data, indexing, data manipulation, handling NaN, etc. It is very important to understand series as it forms the basis to understand pandas data frames.

The blog consists of the following attachments:

1. Jupyter Notebook https://drive.google.com/file/d/1lV07jr9RHCzd9e_d1ukj3hcficx2M2R8/view?usp=sharing
2. Html doc: https://drive.google.com/file/d/16bPtQUuOJoXqoi8P5tJsB--GDnygintV/view?usp=sharing