Author
Released
11/3/2017- DataFrames
- Working with plots
- Boolean indexing
- String handling
- Indexing
- Grouping data
- Reshaping
- Creating your own colormaps
Skill Level Intermediate
Duration
Views
- [Jonathan] Welcome. I'm Jonathan Fernandes and I'm really excited about this course as it includes two things I enjoy, Python's Pandas and the Olympics. So Pandas is an open source library that provides easy to use data analysis tools for the Python programming language. So the purpose of this course is to be hands on and very practical. So each section builds the knowledge that you would have picked up in earlier parts of the course. So you learn how to manipulate and query data more effectively and the tools that you learn in this course will help you become a better data scientist.
So we'll cover topics such as Series and DataFrames, plotting, indexing, Groupby, stack and unstack, and some data visualizations. Remember, if you want to get the most out of this course, work through the challenge exercises. The data set we'll be using are the medal winners for the Summer Olympics from 1896 to 2008, and I can't wait to get started. See you at the Olympics.
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pandas for Data Science
with Charles Kelly2h 3m Intermediate
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Introduction
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Welcome1m
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Exercise files32s
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1. Technical Setup
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Installing Anaconda2m 59s
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Downloading the data set1m 37s
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Using the Jupyter notebook3m 59s
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Using Pandas2m 51s
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2. Series and DataFrames
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DataFrames1m 19s
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Series4m 32s
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Challenge41s
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Solution2m 41s
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3. Data Input and Validation
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Using read_csv()3m 24s
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Using shape1m 25s
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Using head() and tail()1m 48s
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Using info()1m 2s
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4. Basic Analysis
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Using value_counts()3m 13s
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Using sort_values()3m 33s
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Boolean indexing2m 30s
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String handling1m 33s
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Challenge1m 15s
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Solution7m 40s
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5. Basic Plotting
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Basic plotting1m 39s
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Plot types4m 10s
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Colors1m 1s
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Figsize40s
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Colormaps1m 29s
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Seaborn basic plotting3m 29s
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Challenge52s
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Solution5m 15s
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6. Indexing
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Index1m 30s
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Using set_index()2m 51s
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Using reset_index()1m 23s
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Using sort_index()2m 25s
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Using loc[]2m 7s
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Using iloc[]2m 3s
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Challenge33s
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Solution7m 39s
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7. Groupby
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Groupby2m 9s
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Iterate through a group1m 56s
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Groupby computations5m 55s
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Challenge38s
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Solution3m 43s
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8. Reshaping
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Reshaping3m 34s
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Using stack()1m 41s
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Using unstack()1m 27s
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Challenge43s
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Solution6m 7s
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9. Data Visualizations
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Learning heatmaps5m 38s
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Creating your own colormaps4m 31s
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10. Challenge
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Final challenge34s
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Conclusion
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Next steps1m 1s
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Video: Welcome