Pandas for Data Analysis in Python
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31 Lectures
4h 45m 55s
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Curriculum For This Course
- 1. Installation Instructions 49s
- 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
- 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
- 1. General pandas Concepts 10m 40s
- 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
- 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
- 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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