Data Analysis and Visualization in Hindi

Overview

Pandas : Dataframe to Dictionary.Pandas : Conditional Selection and Conditional Filtering.Pandas Exercise. This course is an introduction to Data Analysis and Visualization in Hindi language, using the Anaconda programming environment. It covers topics such as Implementation of Jupyter Notebook, Numpy Overview, Creation of Numpy Arrays, Reshaping Data, Seed Function, Indexing and Slicing, View Vs Copy, Conditional Selection, Operations on Array with scaler, Operations on Two or more array, Pandas Overview, Selecting Columns and Rows, Dataframe to Dictionary, Conditional Selection and Conditional Filtering. Exercises and solutions are provided in the course to help students practice and apply the concepts learnt.

Syllabus

Why Data Analysis and Visualization(Hindi).
Download and Install Anaconda(Hindi).
Jupyter Notebook Tutorial (Hindi).
NumPy Overview (Hindi).
Create NumPy Arrays (Hindi).
Create Arrays Using Numpy Functions(Hindi).
NumPy : Reshaping data(Hindi).
NumPy: -1 in reshape(Hindi).
NumPy : Seed Function (Hindi).
NumPy : Indexing And Slicing of 1-d array(Hindi).
Numpy : 2-d Matrix - Indexing and Slicing (Hindi).
Numpy : View Vs Copy(Hindi).
NumPy : Conditional Selection(Hindi).
Numpy: Operations on array with scaler(Hindi).
NumPy : operations on two or more array(Hindi).
Some more useful numpy functions(Hindi).
NumPy Axis (Hindi).
NumPy : shuffle and unique (Hindi).
NumPy : Horizontal and Vertical Stacking(Hindi).
NumPy Exercise (Hindi).
NumPy : Exercise Solution (Hindi).
Numpy vs python lists [Hindi].
Pandas Overview(Hindi).
Read CSV Files using pandas.
Pandas : Selecting Columns(Hindi).
Pandas : Selecting rows using square brackets.
Pandas : iloc (Hindi).
Pandas : Loc.
Analyse World Happiness Report Pandas Exercise.
[Solution]Analyse World Happiness Report Solution.
Pandas Series Part 1.
Pandas Series [2].
Different Ways to Create Pandas Series.
Pandas : Sort Series.
Series to list.
Pandas Series : nsmalest nlargest idxmin idxmax.
Pandas : Index Object.
Pandas : Filter Data.
Pandas: Filter Data Part 2.
pd.DataFrame.