Learn how to combine Pandas plotting with matplotlib. You can also see how to generate interactive charts on the notebook and use IPyWidegts to add controls to Jupyter notebook.
- [Instructor] We saw how to plot attendance,…and we saw how to plot with mat properly.…Now let's see how we can combine them together.…Say we'd like to have pandas plot…on the matplotlib access we created.…We can do this easily by supplying the ax key…with argument to panda's plotting functions.…We'll open a new notebook, and this one we'll call tips.…We're going to use some stock information here,…so the usual matplotlib inline…and import matplotlib.pyplot as plt,…and import pandas as pd.…
And now let's read the data.…df = pd.read_csv( 'lnkd.csv', and, parse_dates=['Date'],…and we'll also set the index column to be the date.…And let's take a look at what we have.…The usual stock information, date, open price,…high price, low price, close and volume.…Now let's create some access.…We're going to use subplots command.…So fig, and (ax1, ax2) = plt.subplots…and this time subplots in plural.…
One row, two column, and the figsize will be 15 and six.…And now we tell the data frame to plot only the April data,…2016-04, and the close column, and plot it…
Author
Miki Tebeka
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
Views
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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: Tips and tricks