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Markdown Rules
Windows OS
Modules vs Packages vs Libraries
Datastructures 101
Data Types

Python Misc Concepts

Working with Files

Exception Handling

Pandas Introduction

Creating Arrays

Dimensions in Array

Indexing and Slicing Arrays

Arrays Operations

Compatible Data Types

Object Oriented Method

Chart Formatting in Seaborn

Grouped Bar chart

Random Variables

Conditional Probability

Probability Distribution

Central Limit Theorem

Analysis of Variance(ANOVA)

Database Internals

Data Warehousing 101

Scheduling in SnowFlake

Site Navigation

Markdown Rules
Windows OS
Modules vs Packages vs Libraries
Datastructures 101
Data Types

Python Misc Concepts

Working with Files

Exception Handling

Pandas Introduction

Creating Arrays

Dimensions in Array

Indexing and Slicing Arrays

Arrays Operations

Compatible Data Types

Object Oriented Method

Chart Formatting in Seaborn

Grouped Bar chart

Random Variables

Conditional Probability

Probability Distribution

Central Limit Theorem

Analysis of Variance(ANOVA)

Database Internals

Data Warehousing 101

Scheduling in SnowFlake

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Introduction to the Book

Markdown 101

1. Markdown Rules

Python 101

1. Windows OS
2. Modules vs Packages vs Libraries
3. Datastructures 101
4. Data Types
5. String
6. Boolean
- 6.1. Checking if true or false
7. List
8. Dictionary
9. Date and Time
10. Looping
- 10.1. Break and Continue
11. Python Misc Concepts
12. Regex
13. Working with Files
14. Exception Handling
- 14.1. Exceptional Handling

Pandas 101

1. Pandas Introduction
2. Series
- 2.1. Creating a Series
- 2.2. Manipulating a Series
3. Dataframe

Numpy 101

1. Numpy
2. Arrays
3. Creating Arrays
4. Dimensions in Array
5. Indexing and Slicing Arrays
6. Arrays Operations
7. Random Numbers

Matplotlib 101

1. Matplotlib
2. Compatible Data Types
3. Plotly API
4. Object Oriented Method

Seaborn 101

1. Seaborn
3. Chart Formatting in Seaborn
4. Bar chart
5. Line chart
6. Grouped Bar chart
7. Histogram
8. Boxplot
9. Pairplot
10. Heatmap

Stats and Probability 101

1. Random Variables
3. Expectation
4. Variance
5. Set Theory
6. Conditional Probability
7. Probability Distribution
9. Central Limit Theorem
10. Analysis of Variance(ANOVA)

Tableau 101

1. Tableau Server

DATA 101

1. Database Internals
2. ETL Processes
3. Data Warehousing 101
4. Snowflake
11. Scheduling in SnowFlake

Domain Knowledge 101

1. Inventory 101

.ipynb

Date and Time

9. Date and Time#

This chapter covers some useful Date/Time/Datetime concepts

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8.5. Dictionary Advance concepts

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9.1. Playing with Date and Time

By Sahil Choudhary

Last updated on None.