Learn how to use code review to improve your code and your experiments. You can learn some best practices for code review and work with the github’s code review system using pull requests.
- [Instructor] There is a law attributed to Linus…who started the Linux project.…It says given enough eyeballs, all bugs are shallow.…This means that the more people who look at the code,…the more bugs are likely to be found.…I teach a lot of classes, and my students catch my mistakes…every time before I have a chance to find them myself.…The process of letting other people look at your code…and comment on it is called code review,…and I highly recommend it as a central part…of your development process.…You'd be amazed how many bugs are caught…and how many improvements people come up with.…
There are many ways of doing code review,…from sitting together in a room and going over the code…to online tools where people comment on…the changes you make to the code.…No matter how you are doing it,…it's important to remember that…people are commenting on the code and not on you.…Some people get very defensive…when people comment on their code,…and it makes the code review process very painful.…Getting everyone to understand we're just trying…
Author
Released
7/18/2017- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Pandas
- Conda
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
Duration
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Introduction
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Welcome46s
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Mac setup1m 45s
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Windows setup59s
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Linux setup55s
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1. Scientific Python Overview
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2. The Jupyter Notebook
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Use code cells3m 4s
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Understand markdown cells3m 23s
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Edit notebooks4m 10s
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3. NumPy Basics
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Overview: NumPy2m 1s
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NumPy arrays4m 51s
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Slicing2m 24s
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Learn Boolean indexing4m 8s
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Understand broadcasting2m 32s
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Understand array operations5m 27s
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Understand ufuncs5m 7s
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4. Pandas
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Pandas overview1m 58s
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Load CSV files5m 19s
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Parse time1m 46s
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Access rows and columns6m 2s
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Use pure Python packages2m 19s
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Calculate speed6m 26s
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Display a speed box plot2m 41s
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5. Conda
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Manage environments5m 11s
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6. Folium and Geo
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Draw a track on the map4m 51s
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Use geo data with Shapely6m 10s
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Generate a report3m 41s
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7. NY Taxi Data
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Examine data2m 7s
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Load data from CSV files2m 44s
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Work with categorical data2m 50s
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Work with data: Weather data5m 30s
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8. scikit-learn
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Introduction: scikit-learn1m 15s
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Understand train/test splits2m 30s
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Preprocess data4m 32s
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Compose pipelines2m 40s
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Save and load models1m 27s
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9. Plotting
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Overview: matplotlib1m 5s
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Use styles3m 1s
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Customize Pandas output5m 38s
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Use matplotlib3m 13s
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Tips and tricks6m 1s
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Understand bokeh4m 36s
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10. Other Packages
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Other packages overview1m 19s
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Understand deep learning7m 52s
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Understand NLP: NLTK6m 43s
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Understand NLP: SpaCy2m 51s
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11. Development Process
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Overview55s
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Understand source control3m 43s
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Learn code review4m 55s
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Testing overview2m 19s
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Testing example3m 48s
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Conclusion
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Next steps1m 33s
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Video: Learn code review