Learn Data Analysis With Pandas In 2023

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Federico Azzurro

5:57:52

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1. Getting Started

1. Introduction.mp4

01:19

2.1 Anaconda.html

2.2 JupyterThemes.html

2. Installing Anaconda.mp4

05:25

3. Jupyter Notebook.mp4

09:17

4. Resources.mp4

00:58

2. Series

1.1 Pandas Documentation.html

1. Introduction.mp4

05:05

2.1 Documentation.html

2. Attributes.mp4

05:15

3.1 Documentation.html

3. Methods.mp4

04:43

4.1 Richest People CSV (Source).html

4.2 TopRichestInWorld.csv

4. Handling CSV files.mp4

05:20

5. head() And tail().mp4

02:15

6. Sorting values in a Series.mp4

05:38

7. Counting values in a Series.mp4

05:38

8. Accessing elements via position.mp4

04:22

9. Accessing elements via index.mp4

03:20

10.1 series homework.zip

10. Homework.mp4

01:39

11. Homework (Solution).mp4

03:31

3. Excel

1. Handling excel files.mp4

04:16

4. DataFrames (Part 1)

1. Introduction.mp4

06:15

2. Methods.mp4

03:17

3.1 IMDB Movies CSV (Source).html

3.2 imdb top 1000.csv

3. describe() And info().mp4

08:43

4. nlargest() And nsmallest().mp4

05:34

5. set index() And reset index().mp4

03:15

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

03:30

7. Adding columns to a DataFrame.mp4

01:59

8. dropna().mp4

05:04

9. fillna().mp4

04:15

10.1 homework dataframes 001.zip

10. Homework.mp4

01:04

11. Homework (Solution).mp4

03:37

5. DataFrames (Part 2)

1.1 Titanic Dataset (Source).html

1.2 titanic3.xls

1. Titanic.xls.mp4

02:19

2. .loc[].mp4

03:30

3. .loc[] (Continued).mp4

02:37

4. .iloc[].mp4

03:53

5. .iloc[] (Continued).mp4

03:41

6. Broadcasting.mp4

04:32

7. Conditions.mp4

02:18

8. Multiple conditions.mp4

05:15

9. between().mp4

02:06

10. isin(), isnull(), And notnull().mp4

02:50

11. Renaming.mp4

06:04

12.1 homework3.zip

12. Homework.mp4

01:36

13. Homework (Solution).mp4

05:05

6. DataFrames (Part 3)

1.1 Pokemon Dataset (Source).html

1.2 Pokemon.csv

1. Pokemon.csv.mp4

03:31

2. apply().mp4

06:53

3. map() And applymap().mp4

04:57

4. astype().mp4

05:07

5. replace().mp4

02:52

6. where().mp4

02:37

7. agg().mp4

06:13

8. copy().mp4

03:27

9. Multi-indexing.mp4

03:39

10. Multi-indexing (Continued).mp4

06:25

11.1 homework df3.zip

11. Homework.mp4

01:51

12. Homework (Solution).mp4

05:28

7. String Operations

1. .str.mp4

04:24

2. startswith() And endswith().mp4

03:57

3. Index And columns.mp4

02:36

8. Reshaping Datasets

1.1 Exams Dataset (Source).html

1.2 exams.csv

1. Introduction.mp4

01:50

2. transpose().mp4

04:27

3. stack() And unstack().mp4

04:41

4.1 olympics.csv

4. melt().mp4

03:16

5.1 people.csv

5. pivot().mp4

04:10

6.1 people2.csv

6. pivot table().mp4

06:30

7. groupby() - Part 1.mp4

05:21

8. groupby() - Part 2.mp4

04:01

9. groupby() - Part 3.mp4

02:15

9. Combining Datasets

1. Introduction.mp4

00:27

2. concat().mp4

09:10

3.1 company21.csv

3.2 company22.csv

3. merge().mp4

02:01

4. Outer join.mp4

05:03

5. Inner join.mp4

03:53

6. Left And right join.mp4

01:43

7. Left And right join (Minus).mp4

02:28

8. Outer join (Minus).mp4

01:00

9. Merging with different column names.mp4

02:53

10. Time Series

1. Introduction.mp4

00:43

2. Timestamp And DatetimeIndex.mp4

06:48

3.1 Frequency aliases.html

3. date range().mp4

04:47

4. Period And PeriodIndex.mp4

04:05

5. Timedelta And TimedeltaIndex.mp4

06:39

6. Accessing time attributes through .dt.mp4

03:17

7.1 Timestamp (Documentation).html

7. Timestamp methods And attributes.mp4

05:31

8.1 AAPL.csv

8.2 Apple Dataset (Source).html

8. Time Series in files (Part 1).mp4

04:03

9.1 Format Codes.html

9. Time Series in files (Part 2).mp4

06:37

10. loc[] And iloc[] with DatetimeIndex.mp4

03:55

11. reindex().mp4

06:26

12. resample().mp4

05:53

11. Matplotlib

1. Introduction.mp4

01:24

2. Line plots.mp4

06:48

12. Conclusion

1.1 Kaggle.com.html

1.2 Pandas (Documentation).html

1. Whats next.mp4

01:30

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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