Advanced Data Analysis & Wrangling with Python Pandas

سرفصل های دوره

Learn Advanced Data Wrangling, Analytics & Manipulation with pandas

1. Introduction

1. Introduction

2. What makes this course different

3. What is pandas

4. Course content and structure

2. Installation of Python, Pandas and Jupyter Notebook

1. Install Python and Pandas

2. Install Jupyter NotebookLab

3. Series in Pandas

1. Pandas vs NumPy

2. Basics of series

3. Advanced series operations

4. DataFrame An Introduction

1. Data Frames Basics

2. Data Frame basics operations and Gotchas!

3. Data Frame computations and new columns

4. Useful data frame methods

5. Add and drop columns

5. Read and Write Data Files

1.1 data files.zip

1. Overview of Data File Formats

2.1 data files.zip

2. How to Read CSV files

3. Read CSV Files with DateTime Columns

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

5. How to write to CSV files

6. How to read and write Parquet files

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

8. How to read and write JSON from the web

6. Data Selection and Filtering

1.1 california restaurants.zip

1. Basic data selection in data frames

2. Gotchas!

3. The .loc selector

4. How to conditionally modify rows using .loc selector

5. The .iloc selector

6. Reset the index

7. Filter rows with logical conditions

8. Chaining complex operations in pandas

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

1.1 california restaurants.zip

1. Sort by a single column

2. Sort by multiple columns

3. Counting rows & values

4. Finding unique values

5. Duplicated values part 1

6. Duplicated values part 2

8. Missing Values Handling

1.1 data.zip

1. How to find missing values

2. Missing value propogation

3. How to fill missing values basics

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

5. How to fill missing values with averages

6. How to use the replace method to good effect

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

9. Aggregation

1.1 aggregation data.zip

1. Aggregation vs. transformation

2.1 aggregation data.zip

2. Aggregation basics

3. Multiple statistics for multiple columns at once

4. Specific statistics for specific columns at once

5. idxmax and idxmin

6. Pandas build-in aggregation functions

7. Pandas statistic functions

8. User Defined Functions (UDF) for aggregation

10. Transformation

1.1 transformation.zip

1. Basics of transformation

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

3. The transform( ) function itself

4. User Defined Functions (UDF) for transformation

11. Apply, Map and Lambda Functions

1.1 realtor-data.zip

1. Apply

2. Map

3. Lambda Functions

12. Mid-course talk

1. Study tips

13. Groupby Operations

1.1 data.zip

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

2.1 data.zip

2. Groupby basics

3. Aggregationstatistics by group

4. The agg function and California restaurants

5. Transformation by group & stock prices

6. Caveat on transformation by group

14. Vectorized String Manipulations

1.1 templates.zip

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

2. Split strings

3. Replace, strip, pad, zerofill strings

4. Removing prefixsuffix, string slicing, length & count

15. Vectorized Data & Time Manipulations

1.1 data.zip

1. How pandas store date and time

2. The time stamp

3. Frequencies Part 1

4. Frequences Part 2

5. The .dt accessor magic

6. Date & time calculations Absolute Time Delta

7. More sensible date & time calculations Offsets

8. DateTime resampling the basics

9. DateTime resampling by group

16. Reshaping Data and Pivot Tables

1.1 data.zip

1. Reshape from long to wide formats pivot( )

2. Reshapepivot from long to wide with multiple columns

3. Excel-style pivot tables

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

5. Reshape from wide to long format melt( )

6. Financial ratios case study

17. MergeJoin Data Frames

1.1 data.zip

1. Introduction to joining data frames

2.1 data.zip

2. Vertical merge (concat)

3. Horizontal merge inner join

4. Horizontal merge outerleftright joins

5. Financial ratios case setup

6. Financial ratios case merge

7. Financial ratios case ratios calculation

8. Financial ratios case solutions

18. Rolling Windows Operations

1.1 data.zip

1. The basic idea of rolling windows

2.1 data.zip

2. Moving windows basics

3. Moving windows by group

4. Exponential moving averages

19. Data Visualization with Pandas

1.1 data.zip

1. Introduction to visualization

2. Preparation and setup

3. Line plots

4. Subplots

5. Bar plots

6. Scatter plots

7. Histograms

8. Area plots

9. Pie charts

20. Capstone Case Study Does the Stock Momentum Strategy Work

1.1 data.zip

1. Introduction to the case

2.1 data.zip

2. Data cleaning and preparation

3. Calculating stock momentum

4. Calculating forward returns

5. Forming decile porfolios

6. Calculating the results

7. Visualizing the results

21. Appendix A NumPy Basics

1. Introduction to NumPy

2. How to create numpy arrays

3. How to create special arrays

4. How to reshape numpy arrays

5. How to generate random numbers in NumPy

6. How to do random shuffling and selections in NumPy

7. Element-wise computations & broadcasting

8. Matrix math in NumPy

9. NumPys indexing approach

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