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Introduction to Data Visualization with Matplotlib
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• ## .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Introduction to Data Visualization with Matplotlib

ðŸ‘‹ Welcome to your workspace! Here, you can write and run Python code and add text in Markdown. Below, we've imported the datasets from the course Introduction to Data Visualization with Matplotlib as DataFrames as well as the packages used in the course. This is your sandbox environment: analyze the course datasets further, take notes, or experiment with code!

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Importing course packages; you can add more too!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Importing course datasets as DataFrames
# Some pre-processing on the weather datasets, including adding a month column
seattle_weather = weather[weather["STATION"] == "USW00094290"]
month = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
seattle_weather["MONTH"] = month
austin_weather["MONTH"] = month

austin_weather.head() # Display the first five rows of this DataFrame``````
``# Begin writing your own code here!``

#### Don't know where to start?

• Using `austin_weather` and `seattle_weather`, create a Figure with an array of two Axes objects that share a y-axis range (`MONTHS` in this case). Plot Seattle's and Austin's `MLY-TAVG-NORMAL` (for average temperature) in the top Axes and plot their `MLY-PRCP-NORMAL` (for average precipitation) in the bottom axes. The cities should have different colors and the line style should be different between precipitation and temperature. Make sure to label your viz!
• Using `climate_change`, create a twin Axes object with the shared x-axis as time. There should be two lines of different colors not sharing a y-axis: `co2` and `relative_temp`. Only include dates from the 2000s and annotate the first date at which `co2` exceeded 400.
• Create a scatter plot from `medals` comparing the number of Gold medals vs the number of Silver medals with each point labeled with the country name.
• Explore if the distribution of `Age` varies in different sports by creating histograms from `summer_2016`.