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6

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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    مدت زمان: 930 دقیقه
    تاریخ انتشار: ۲۱ آذر ۱۴۰۲
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