Analyzing Data Using Python: Cleaning & Analyzing Data in Pandas

Python 3.8
  • 13 Videos | 1h 58m 47s
  • Includes Assessment
  • Earns a Badge
For data analysis to be useful and accurate, the analyzed data needs to be cleaned and curated. There are copious methods to achieve this in pandas. In this course, you'll learn how to identify and eliminate duplicates in pandas. You'll start by using the pandas cut method to discretize data into bins, using bins to plot histograms and identify outliers using box-and-whisker plots. You'll parse and work with datetime objects read in from strings and convert string columns to datetime using the dateutils python library. Moving on, you'll master different pandas methods for aggregating data - including the groupby, pivot, and pivot_table methods. Lastly, you'll perform various joins - inner, left outer, right outer, and full outer - using both the merge and join methods.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    identify and deal with duplicate records
    summarize records into bins or categories
    compute aggregations on data
    perform common grouping and aggregation operations
    use pivot tables to explore data
    use pivot tables to summarize data
  • combine and merge records
    perform inner join operations using the merge() method
    perform left and right join operations using the merge() method
    implement joins using the join() method
    manipulate and analyze time series data
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    3m 17s
    UP NEXT
  • Playable
    2. 
    Identifying Duplicates in Data
    6m 29s
  • Locked
    3. 
    Categorizing and Binning Data
    6m 48s
  • Locked
    4. 
    Computing Aggregations on Data
    10m 42s
  • Locked
    5. 
    Grouping and Aggregating Data
    12m 54s
  • Locked
    6. 
    Viewing Data Using Pivot Tables
    9m 50s
  • Locked
    7. 
    Summarizing Data Using Pivot Tables
    13m 10s
  • Locked
    8. 
    Combining Data in DataFrames
    10m 19s
  • Locked
    9. 
    Implementing Inner Joins
    8m 32s
  • Locked
    10. 
    Implementing Left and Right Joins
    7m 50s
  • Locked
    11. 
    Performing Joins Using DataFrame Indexes
    7m 20s
  • Locked
    12. 
    Analyzing Time Series Data
    13m 25s
  • Locked
    13. 
    Course Summary
    2m 41s

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