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Datacamp introduction to Data Visualization with Matplotlib

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Datacamp introduction to Data Visualization with Matplotlib: - Customizing your plots - Small multiples - Plotting time-series data - Plotting time-series with different variables - Annotating time-series data - Quantitative comparisons: bar-charts - Quantitative comparisons: histograms - Statistical plotting - Quantitative comparisons: scatter plots - Preparing your figures to share with others - Sharing your visualizations with others - Automating figures from data - Where to go next - Matplotlib cheat sheet

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Introduction to Da
Visualization with
Matplotlib
I N T R O D U C T I O N T O D ATA V I S U A L I Z AT I O N W I T H




Ariel Rokem
Data Scientist

,Data visualization




Image credit: Gytis Dudas and Andrew Rambaut

,Introducing the pyplot interface
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plt.show()

, Adding data to axes
seattle_weather["MONTH"] seattle_weather["MLY-TAVG-NORMAL"]


DATE 1 42.1
1 Jan 2 43.4
2 Feb 3 46.6
3 Mar 4 50.5
4 Apr 5 56.0
5 May 6 61.0
6 Jun 7 65.9
7 Jul 8 66.5
8 Aug 9 61.6
9 Sep 10 53.3
10 Oct 11 46.2
11 Nov 12 41.1
12 Dec Name: MLY-TAVG-NORMAL, dtype: float64
Name: MONTH, dtype: object
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