Pandas for Data Analysis in Python

Pandas for Data Analysis in Python

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Introduction to the Course

  • 1. Installation Instructions
    49s

IPython Notebooks and Raw Python Data Analysis

  • 1. Tools of the Trade, The IPython Notebook
    10m 6s
  • 2. Maps
    7m 7s
  • 3. Filters
    5m 45s
  • 4. List Comprehensions
    6m 40s
  • 5. Lambda Functions
    9m 44s

The Basics of NumPy

  • 1. NumPy Array Basics
    6m 9s
  • 2. Boolean Selection
    10m 20s
  • 3. Helpful Methods and Shortcuts
    12m 59s
  • 4. Vectorization
    7m 8s
  • 5. Multi-Dimensional Arrays
    14m 12s
  • 6. Querying Slicing, Combining, and Splitting Arrays
    12m 42s

Pandas Basics

  • 1. General pandas Concepts
    10m 40s

pandas Series

  • 1. Overview of the pandas Series
    7m 25s
  • 2. Look Ups, Selections, and Indexing
    11m 1s
  • 3. Advanced Indexing Options
    6m 56s
  • 4. Handling NaN Values, Reindexing, Filling Methods and Series Addition
    12m 6s
  • 5. Series Multiplication, More Reindexing, and Mapping
    6m 5s

pandas DataFrame

  • 1. DataFrame Basics
    10m 4s
  • 2. Reading Files, Plotting, and Basic Methods
    10m 45s
  • 3. More Plotting, Joins, Basic DateTime Indexing, and Writing to Files
    11m 53s
  • 4. Adding & Reseting Columns, Mapping with Functions
    7m 14s
  • 5. More Mapping, Filling NaN values, Plotting, Correlations, and Histograms
    10m 17s
  • 6. More Plotting, Rolling Calculations, Basic DateTime Indexing
    5m 52s
  • 7. Analysis Concepts, Filling NaN Values, Cumulative Sums and Value Counts
    9m 20s
  • 8. Data Maintenance, Adding/ Removing Columns and Rows
    8m 18s
  • 9. Basic Grouping, Concepts of Aggregate Functions
    9m 8s

BONUS: Advanced pandas Topics

  • 1. pandas.IO.Data, Panels and Hierarchical Indexing
    11m 42s
  • 2. Advanced Reading Csvs/HTML, Binning, Categorical Data
    12m 43s
  • 3. Advanced Groupings and Aggregate Functions
    7m 56s
  • 4. More Grouping Functions including Apply and Transform
    12m 49s
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