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Markdown Rules
Windows OS
Modules vs Packages vs Libraries
Data Types
String

Python Misc Concepts

Working with Files

Exception Handling

Pandas Introduction

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
Data Types
String

Python Misc Concepts

Working with Files

Exception Handling

Pandas Introduction

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. Data Types
4. String
5. Boolean
- 5.1. Checking if true or false
6. List
7. Dictionary
8. Date and Time
9. Looping
- 9.1. Break and Continue
10. Python Misc Concepts
11. Regex
12. Working with Files
13. Exception Handling
- 13.1. Exceptional Handling

Pandas 101

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

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

Dataframe

3. Dataframe#

This chapter covers some useful Dataframe concepts

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2.2. Manipulating a Series

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3.1. Creating a Dataframe

By Sahil Choudhary

Last updated on None.