Learn Data Analysis With Pandas In 2023

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Federico Azzurro

7:49:26

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01 - Getting Started

001 Introduction.mp4

01:19

002 Installing Anaconda.mp4

05:25

003 Jupyter Notebook.mp4

09:17

004 Resources.mp4

00:58

external-links.txt

02 - Series

001 Introduction.mp4

05:05

002 Attributes.mp4

05:15

003 Methods.mp4

04:43

004 Handling CSV files.mp4

05:21

004 TopRichestInWorld.csv

005 head() & tail().mp4

02:15

006 Sorting values in a Series.mp4

05:39

007 Counting values in a Series.mp4

05:38

008 Accessing elements via position.mp4

04:22

009 Accessing elements via index.mp4

03:20

010 Homework.mp4

01:39

010 series-homework.zip

011 Homework (Solutions).mp4

03:31

external-links.txt

03 - Excel

001 Handling excel files.mp4

04:16

04 - DataFrames (Part 1)

001 Introduction.mp4

06:15

002 Methods.mp4

03:17

003 describe() & info().mp4

08:43

003 imdb-top-1000.csv

004 nlargest() & nsmallest().mp4

05:34

005 set index() & reset index().mp4

03:15

006 Removing columnsrows in a DataFrame with drop().mp4

03:30

007 Adding columns to a DataFrame.mp4

01:59

008 dropna().mp4

05:04

009 fillna().mp4

04:16

010 Homework.mp4

01:04

010 homework-dataframes-001.zip

011 Homework (Solutions).mp4

03:37

external-links.txt

05 - DataFrames (Part 2)

001 Titanic.xls.mp4

02:19

001 titanic3.xls

002 .loc[].mp4

03:30

003 .loc[] (Continued).mp4

02:37

004 .iloc[].mp4

03:53

005 .iloc[] (Continued).mp4

03:42

006 Broadcasting.mp4

04:32

007 Conditions.mp4

02:18

008 Multiple conditions.mp4

05:15

009 between().mp4

02:06

010 isin(), isnull(), & notnull().mp4

02:50

011 Renaming.mp4

06:04

012 Homework.mp4

01:36

012 homework3.zip

013 Homework (Solutions).mp4

05:05

external-links.txt

06 - DataFrames (Part 3)

001 Pokemon.csv

001 Pokemon.csv.mp4

03:31

002 apply().mp4

06:53

003 map() & applymap().mp4

04:57

004 astype().mp4

05:07

005 replace().mp4

02:53

006 where().mp4

02:37

007 agg().mp4

06:13

008 copy().mp4

03:27

009 Multi-indexing.mp4

03:40

010 Multi-indexing (Continued).mp4

06:26

011 Homework.mp4

01:51

011 homework-df3.zip

012 Homework (Solutions).mp4

05:28

external-links.txt

07 - String Operations

001 .str.mp4

04:25

002 startswith() & endswith().mp4

03:57

003 Index & columns.mp4

02:36

08 - Reshaping Datasets

001 Introduction.mp4

01:51

001 exams.csv

002 transpose().mp4

04:27

003 stack() & unstack().mp4

04:41

004 melt().mp4

03:16

004 olympics.csv

005 people.csv

005 pivot().mp4

04:10

006 people2.csv

006 pivot table().mp4

06:30

007 groupby() - Part 1.mp4

05:21

008 groupby() - Part 2.mp4

04:01

009 groupby() - Part 3.mp4

02:15

010 Homework.mp4

01:02

010 reshape-homework.zip

011 Homework (Solutions).mp4

03:09

external-links.txt

09 - Combining Datasets

001 Introduction.mp4

00:27

002 concat().mp4

09:11

003 company21.csv

003 company22.csv

003 merge().mp4

02:01

004 Outer join.mp4

05:03

005 Inner join.mp4

03:53

006 Left & right join.mp4

01:43

007 Left & right join (Minus).mp4

02:29

008 Outer join (Minus).mp4

01:00

009 Merging with different column names.mp4

02:53

10 - Time Series

001 Introduction.mp4

00:43

002 Timestamp & DatetimeIndex.mp4

06:48

003 date range().mp4

04:47

004 Period & PeriodIndex.mp4

04:06

005 Timedelta & TimedeltaIndex.mp4

06:39

006 Accessing time attributes through .dt.mp4

03:17

007 Timestamp methods & attributes.mp4

05:31

008 AAPL.csv

008 Time Series in files (Part 1).mp4

04:03

009 Time Series in files (Part 2).mp4

06:37

010 loc[] & iloc[] with DatetimeIndex.mp4

03:55

011 reindex().mp4

06:26

012 resample().mp4

05:53

013 Homework.mp4

01:49

013 time-series-homework.zip

014 Homework (Solutions).mp4

06:12

external-links.txt

11 - Matplotlib

001 Introduction.mp4

01:24

002 Line plots.mp4

06:49

003 Bar plots.mp4

03:40

004 Histograms.mp4

02:52

005 Pie charts.mp4

05:43

006 Styles.mp4

04:35

007 Interactive plots.mp4

02:19

008 Scatter.mp4

02:08

009 Candlestick charts (Bonus).mp4

06:10

010 Color by value (Bonus).mp4

07:31

010 gap.csv

external-links.txt

12 - Bar Chart Race

001 Preview.mp4

00:42

002 Installation.mp4

01:50

002 languages.csv

003 Implementation.mp4

08:49

external-links.txt

13 - NumPy

001 Introduction.mp4

02:27

002 Lists vs. NumPy arrays.mp4

02:41

003 What are arrays.mp4

02:14

004 More information.mp4

01:59

005 Array basics.mp4

03:46

006 Sorting and concatenating arrays.mp4

01:55

007 Getting the shape and size of an array.mp4

01:41

008 Reshaping an array.mp4

02:09

009 Adding a new axis to an array.mp4

02:31

010 Indexing and slicing.mp4

04:31

011 Creating arrays from existing data.mp4

03:32

012 Basic operations.mp4

03:00

013 Broadcasting.mp4

01:59

014 Matrices.mp4

04:05

015 Generating random numbers.mp4

01:51

016 Finding unique elements.mp4

02:33

017 Transposing and reshaping arrays.mp4

01:51

018 Reversing arrays.mp4

02:46

019 Reshaping & flattening multidimensional arrays.mp4

01:43

020 Saving & loading arrays.mp4

03:36

external-links.txt

14 - Final

001 Whats next.mp4

01:30

external-links.txt

Description

Learn Data Analysis with Pandas, Matplotlib, & Python in 2023

What You'll Learn?

How to install Anaconda
How to use Pandas
How to use Matplotlib
How to use Jupyter Notebook
How to create plots for significant data
The basics of using NumPy

Who is this for?

Python developers who are interested in learning Data Science

What You Need to Know?

You should be familiar with the basics of Python

You will need a computer and access to internet

More details

Description
Are you ready to embark on your journey as a professional Data Analyst, and learn some of the most demanded skills on the market in programming for 2023?

Who is this course for?
This course is for anyone who wants to build a strong foundation for Data Science with Python. It will cover everything you need to know about using Pandas for Data Analysis, and it will also cover how you can use Matplotlib to create some very insightful charts to display your data in a visually attractive way! The only requirement is that you have some experience with Python, for that's what we will be using in this course.

Why should you pick this course and not the others?
There are thousands of Python courses on the internet, so why should you pick this one? Well, to put it simply, I believe that I teach programming concepts in a far more effective way than a majority of the courses on the Internet. I make sure to only teach what's essential and needed, so that you don't waste time with code that you will never see or use in your entire career. I'm a self-taught professional and will teach you how you can be the same!

30 Day Money-Back Guarantee
At any point of this course you can opt in to get your money back. Whether you feel that this course is not right for you, or changed your mind about learning Data Analysis with Pandas, you can easily request a refund which will be immediately refunded to your account with no questions asked through Udemy!
Who this course is for:
Python developers who are interested in learning Data Science

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Ciao! My name is Federico and I'm a programming instructor.I started teaching programming back in 2019. During this time I’ve taught I wide variety of topics: Android development, iOS development, Web development, JavaScript, Cross-Platform Development with React Native, Machine Learning with Python, Python Core-Concepts, C++, Kotlin, & much, much more!

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