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Pandas is the most popular library for Data Science in Python. In this course we'll take it to the next level, explaining all the fundamental data structures, including Data Frames, Series, Indices, Plotting and more.
Duration
14 hours
Difficulty
professional
INE's world-class IT trainingInstructor for this course
Santiago Basulto
This course is composed by the following modules
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Module 1
Intro to Pandas2 videos
What is Pandas?
Pandas Playground
Module 2
Pandas Series Essentials16 videos
Intro to Pandas Series
Data Types Playground
Add index to Series
Get Series shape and size
Get Series elements type
Your custom reversed Pandas Series
Pandas Series - Selection and Indexing
Custom price selector
Show price difference
Adding new elements to a Series
Removing elements from the Series
Increasing Series element value
Using .loc method to select a Series element
Using .loc method to select multiple Series elements
Using .iloc to get Series elements by index
Using .iloc to select Series range of elements
Module 3
Conditional Selection and Operations on Pandas Series11 videos
Vectorized Operations and Methods on Pandas Series
Salaries datataset overview
Create a new Series using another Series
Getting best paid players
Conditional Selection and Filtering on Pandas Series
Show richer players
Lowest salary players
Using operators while selecting data on Series
Modifying Series using Conditional Selection
Pandas Series - Sorting
Sorting salaries
Module 4
Pandas DataFrames Essentials18 videos
Intro to Pandas DataFrames
Create your first Pandas DataFrame
Get DataFrame information
Column Types on Pandas DataFrames
Fixing DataFrame column
NBA Position Codes
Removing elements from Pandas DataFrames
Remove following count
Dropping last players
Pandas DataFrames - Selection and Indexing
Using .loc method to select DataFrame elements
Using .loc method to select multiple DataFrame elements
Using .iloc to get DataFrame elements by index
Using .iloc to select DataFrame range of elements
Add and Modify elements on Pandas DataFrames
Adding new Twitter data
Creating Twitter activity data
NBA additional info
Module 5
Conditional Selection and Operations on Pandas DataFrames16 videos
Vectorized Operations on Pandas DataFrames
Counting NBA players per position
Team roster size
Calculating player efficiency
Conditional Selection and Filtering on Pandas DataFrames
Pragmatic filter with DataFrame.query
Conditional Selection on NBA players data
Using operators while selecting data on DataFrames
High scorers
Best young talents
Modifying a DataFrame using Conditional Selection
Pandas DataFrames - Sorting
NBA ranking
Useful statistical methods for DataFrames
Most minutes played
Top 3-Point Field Goal Scorers
Module 6
Index Object, Immutability and Plotting5 videos
Pandas Indexes
Add index to DataFrame
Indexing NBA players
Pandas Immutability and the inplace parameter
Intro to Pandas Plotting
Module 7
Final Project1 video
Bike Store Sales Analysis using Pandas
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