Advanced Data Analysis & Wrangling with Python Pandas

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Richard Wang

15:29:43

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1. Introduction

1. Introduction.mp4

02:41

2. What makes this course different.mp4

07:03

3. What is pandas.mp4

11:17

4. Course content and structure.mp4

07:01

2. Installation of Python, Pandas and Jupyter Notebook

1. Install Python and Pandas.mp4

05:05

2. Install Jupyter NotebookLab.mp4

04:30

3. Series in Pandas

1. Pandas vs NumPy.mp4

03:11

2. Basics of series.mp4

11:50

3. Advanced series operations.mp4

06:30

4. DataFrame An Introduction

1. Data Frames Basics.mp4

09:23

2. Data Frame basics operations and Gotchas!.mp4

04:53

3. Data Frame computations and new columns.mp4

04:49

4. Useful data frame methods.mp4

07:56

5. Add and drop columns.mp4

11:15

5. Read and Write Data Files

1.1 data files.zip

1. Overview of Data File Formats.mp4

04:34

2.1 data files.zip

2. How to Read CSV files.mp4

07:31

3. Read CSV Files with DateTime Columns.mp4

09:01

4. Dataset with headers and footers (Fama-French).mp4

02:50

5. How to write to CSV files.mp4

02:50

6. How to read and write Parquet files.mp4

06:16

7. How to read and write tab-deliminated and other formats.mp4

03:12

8. How to read and write JSON from the web.mp4

21:51

6. Data Selection and Filtering

1.1 california restaurants.zip

1. Basic data selection in data frames.mp4

13:01

2. Gotchas!.mp4

06:24

3. The .loc selector.mp4

13:03

4. How to conditionally modify rows using .loc selector.mp4

08:17

5. The .iloc selector.mp4

05:45

6. Reset the index.mp4

01:30

7. Filter rows with logical conditions.mp4

13:53

8. Chaining complex operations in pandas.mp4

07:32

7. Sorting, Counting, Uniquing and Dealing with Duplicated Values

1.1 california restaurants.zip

1. Sort by a single column.mp4

04:26

2. Sort by multiple columns.mp4

11:33

3. Counting rows & values.mp4

03:43

4. Finding unique values.mp4

06:41

5. Duplicated values part 1.mp4

08:42

6. Duplicated values part 2.mp4

08:11

8. Missing Values Handling

1.1 data.zip

1. How to find missing values.mp4

09:44

2. Missing value propogation.mp4

16:48

3. How to fill missing values basics.mp4

00:56

4. How to forward and backward fill missing values in a time-series.mp4

05:46

5. How to fill missing values with averages.mp4

10:11

6. How to use the replace method to good effect.mp4

03:02

7. How to interpolate missing values in a time-series.mp4

03:21

9. Aggregation

1.1 aggregation data.zip

1. Aggregation vs. transformation.mp4

10:19

2.1 aggregation data.zip

2. Aggregation basics.mp4

14:03

3. Multiple statistics for multiple columns at once.mp4

02:20

4. Specific statistics for specific columns at once.mp4

07:47

5. idxmax and idxmin.mp4

04:14

6. Pandas build-in aggregation functions.mp4

05:11

7. Pandas statistic functions.mp4

03:48

8. User Defined Functions (UDF) for aggregation.mp4

07:12

10. Transformation

1.1 transformation.zip

1. Basics of transformation.mp4

18:17

2. Time series transform lag, shift, diff and pct change.mp4

08:44

3. The transform( ) function itself.mp4

10:40

4. User Defined Functions (UDF) for transformation.mp4

14:01

11. Apply, Map and Lambda Functions

1.1 realtor-data.zip

1. Apply.mp4

08:51

2. Map.mp4

04:02

3. Lambda Functions.mp4

05:06

12. Mid-course talk

1. Study tips.mp4

05:03

13. Groupby Operations

1.1 data.zip

1. Introduction to the Split-Apply-Combine Strategy in data analytics.mp4

05:57

2.1 data.zip

2. Groupby basics.mp4

08:03

3. Aggregationstatistics by group.mp4

15:46

4. The agg function and California restaurants.mp4

06:59

5. Transformation by group & stock prices.mp4

13:35

6. Caveat on transformation by group.mp4

07:42

14. Vectorized String Manipulations

1.1 templates.zip

1. String data types in pandas, concatenate & change cases.mp4

14:33

2. Split strings.mp4

03:26

3. Replace, strip, pad, zerofill strings.mp4

09:02

4. Removing prefixsuffix, string slicing, length & count.mp4

06:36

15. Vectorized Data & Time Manipulations

1.1 data.zip

1. How pandas store date and time.mp4

01:19

2. The time stamp.mp4

08:47

3. Frequencies Part 1.mp4

09:19

4. Frequences Part 2.mp4

09:49

5. The .dt accessor magic.mp4

07:06

6. Date & time calculations Absolute Time Delta.mp4

04:14

7. More sensible date & time calculations Offsets.mp4

10:15

8. DateTime resampling the basics.mp4

10:41

9. DateTime resampling by group.mp4

09:07

16. Reshaping Data and Pivot Tables

1.1 data.zip

1. Reshape from long to wide formats pivot( ).mp4

13:28

2. Reshapepivot from long to wide with multiple columns.mp4

03:02

3. Excel-style pivot tables.mp4

11:23

4. Differences between pivot( ) and pivot table( ).mp4

01:14

5. Reshape from wide to long format melt( ).mp4

08:40

6. Financial ratios case study.mp4

14:28

17. MergeJoin Data Frames

1.1 data.zip

1. Introduction to joining data frames.mp4

04:06

2.1 data.zip

2. Vertical merge (concat).mp4

07:09

3. Horizontal merge inner join.mp4

07:57

4. Horizontal merge outerleftright joins.mp4

09:42

5. Financial ratios case setup.mp4

05:20

6. Financial ratios case merge.mp4

08:21

7. Financial ratios case ratios calculation.mp4

09:06

8. Financial ratios case solutions.mp4

13:13

18. Rolling Windows Operations

1.1 data.zip

1. The basic idea of rolling windows.mp4

03:34

2.1 data.zip

2. Moving windows basics.mp4

19:31

3. Moving windows by group.mp4

08:40

4. Exponential moving averages.mp4

03:50

19. Data Visualization with Pandas

1.1 data.zip

1. Introduction to visualization.mp4

01:45

2. Preparation and setup.mp4

05:58

3. Line plots.mp4

13:29

4. Subplots.mp4

05:18

5. Bar plots.mp4

05:18

6. Scatter plots.mp4

08:54

7. Histograms.mp4

03:11

8. Area plots.mp4

02:14

9. Pie charts.mp4

01:37

20. Capstone Case Study Does the Stock Momentum Strategy Work

1.1 data.zip

1. Introduction to the case.mp4

08:59

2.1 data.zip

2. Data cleaning and preparation.mp4

11:43

3. Calculating stock momentum.mp4

12:01

4. Calculating forward returns.mp4

06:00

5. Forming decile porfolios.mp4

05:17

6. Calculating the results.mp4

04:43

7. Visualizing the results.mp4

07:31

21. Appendix A NumPy Basics

1. Introduction to NumPy.mp4

07:38

2. How to create numpy arrays.mp4

04:40

3. How to create special arrays.mp4

05:53

4. How to reshape numpy arrays.mp4

03:21

5. How to generate random numbers in NumPy.mp4

11:00

6. How to do random shuffling and selections in NumPy.mp4

03:50

7. Element-wise computations & broadcasting.mp4

05:57

8. Matrix math in NumPy.mp4

09:01

9. NumPys indexing approach.mp4

11:49

Description

Learn Advanced Data Wrangling, Analytics & Manipulation with pandas

What You'll Learn?

Learn Python pandas package for advanced data analysis and wrangling
Data Frames & Series
Input and Output into Pandas
Data selection and filtering
Sort, count, unique, duplicated values
Handling missing values
Data Aggregation
Data Transformation
apply, map
Complex Groupby (Split-Apply-Combine)
Vectorized string manipulation
Vectorized date/time manipulation
reshape and pivot
Joins/Merge
Rolling Windows Operations
Data Visualization
Stock Market Case Study

Who is this for?

Data Analysts & Data Scientists

Anyone who is interested in series data manipulation and wrangling in Python

Researchers in all fields

Business analysts and marketing researchers

What You Need to Know?

Some basic Python coding skill required.

I will teach you everything about pandas.

More details

Description
This course of the Fantastic Python Series is an advanced course on data manipulation and wrangling with the pandas package in Python. Pandas is one of the most important packages in the Python eco-system and it is where most data scientists spend 80% of their time on. It is essential to have a deep and complete understanding of how pandas work to conduct analysis more effectively and efficiently.

This course offers a complete guide on all areas of Pandas functionalities, from the foundamentals, all the way to highly advanced and complex skills such as rolling windows and time series resampling. It will teach data scientists from all fields, including IT, business, finance, etc, how data manipulation and wrangling is done effectively in pandas and how to avoid potential pitfalls ("Gotchas").

The advanced parts of this course is particularly helpful for those analysts/scientists who work with time series data (and panel data) as the pandas offers an extensive array of features for time series calculations. So finance professionals and physists will find it especially relevant to their field of work.

This course is proceeds from the foundations of data series and data frame, and then proceeds to intermediate level data manipulations, and eventually dive deep into advanced data wrangling topics such as complex groupby operations, sophisticated joins/merges and reshaping from wide format to long and vice versa.

Finally, a stock market case study is offered as a capstone for this entire course. This case study will draw together most, if not all, areas of knowledge of pandas and analyze real-world financial data.

Who this course is for:
Data Analysts & Data Scientists
Anyone who is interested in series data manipulation and wrangling in Python
Researchers in all fields
Business analysts and marketing researchers

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Dr Richard Wang, PhD, CFA, CMA, is a former college business professor who turned into a Fintech entrepreneur. He is the CEO of MyCEO AI Corp, a financial portal website. He has 10+ years of university teaching experience at (among other places) Texas A&M University-Commerce and Eastern Illinois University, and is highly ranked by his students (both undergrad and graduate). Dr. Wang's expertise spans both software development and Accounting/Finance. On the finance side, he is a Chartered Financial Analyst (CFA) Charterholder and a Certified Management Accountant (CMA). On the IT side, he has extensive coding experience in both Python and R for data science and Javascript for Web Development. Dr Wang's passion lies in combining all aspects of data science and machine learning in business decision-making and investments.

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