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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

Run the hidden code cell below to import the data used in this course.

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

# Importing the course datasets

# 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``````

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

``# Add your code snippets here``

Explore Datasets

Use the DataFrames imported in the first cell to explore the data and practice your skills!

• 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`.
• Try out the different Matplotlib styles available and save your visualizations as a PNG file.
``climate_change``
``````fig, ax = plt.subplots()

plt.title = 'Climate change'
ax.plot(climate_change.index, climate_change.co2)
ax.set_xlabel('Time (years)')
ax.set_ylabel('CO2 emissions (ppm)')
plt.show()``````
``````fig, ax = plt.subplots(2,1)

ax[0].plot(climate_change.index, climate_change.co2, linewidth = '0.5')
ax[0].set_title('Evolution of carbon dioxide emissions over time')
ax[0].set_xlabel('Time (years)')
ax[0].set_ylabel('CO2 emissions (ppm)')

ax[1].plot(climate_change.index, climate_change.relative_temp, linewidth = 0.5, color = 'red')
ax[1].set_title('Evolution of the relative temperature over time')
ax[1].set_xlabel('Time (years)')
ax[1].set_ylabel('Relative temperature (Celsius)')

plt.show()``````
``````plt.style.use('fivethirtyeight')

fig = plt.figure(figsize=(40, 40))
ax = plt.subplot(2, 1, 1, frameon=True)
ax.plot(climate_change.index, climate_change.co2, linewidth = 1.5, color = 'blue')
ax.set_title('Evolution over time', fontweight ="bold",  fontsize = 60.0)
ax.set_xlabel('Time (years)', fontsize = 40.0 )
ax.set_ylabel('CO2 emissions (ppm)',  fontsize = 40.0)
ax.tick_params(axis='x', labelsize=30)
ax.tick_params(axis='y', labelsize=30)
ax.autoscale(enable=True, axis='both', tight=True)

ax2 = plt.subplot(2, 1, 2, frameon = True)
ax2.plot(climate_change.index, climate_change.relative_temp, linewidth = 0.8, color = 'red')
ax2.set_xlabel('Time (years)', fontsize = 40.0 )
ax2.set_ylabel('Relative temperature (Celsius)', fontsize = 40.0 )
ax2.tick_params(axis='x', labelsize=30)
ax2.tick_params(axis='y', labelsize=30)
ax2.autoscale(enable=True, axis='both', tight=True)``````
``````fig = plt.figure(figsize=(40, 40))
ax = plt.subplot(2, 1, 1, frameon=True)
ax.plot(climate_change.index, climate_change.co2, linewidth = 1.5, color = 'blue')
ax.set_title('Evolution over time', fontweight ="bold",  fontsize = 60.0)
ax.set_xlabel('Time (years)', fontsize = 40.0 )
ax.set_ylabel('CO2 emissions (ppm)',  fontsize = 40.0)
ax.tick_params(axis='x', labelsize=30)
ax.tick_params(axis='y', labelsize=30)
ax.autoscale(enable=True, axis='both', tight=True)

ax2 = plt.subplot(2, 1, 2, frameon = True)
ax2.plot(climate_change.index, climate_change.relative_temp, linewidth = 0.8, color = 'red')
ax2.set_xlabel('Time (years)', fontsize = 40.0 )
ax2.set_ylabel('Relative temperature (Celsius)', fontsize = 40.0 )
ax2.tick_params(axis='x', labelsize=30)
ax2.tick_params(axis='y', labelsize=30)
ax2.autoscale(enable=True, axis='both', tight=True)``````
``````plt.style.use('default')
fig, ax = plt.subplots()

ax.plot(climate_change.index, climate_change.co2, linewidth = 0.8, color = 'blue')
ax.set_title('Evolution over time')
ax.set_xlabel('Time (years)')
ax.set_ylabel('CO2 emissions (ppm)')

ax2 = ax.twinx()
ax2.plot(climate_change.index, climate_change.relative_temp, linewidth = 0.8, color = 'red')
ax2.set_xlabel('Time (years)')
ax2.set_ylabel('Relative temperature (Celsius)')

plt.show()``````
``````eighties = climate_change['1980':'1989']
nineties = climate_change['1990':'1999']``````
``````fig, ax = plt.subplots()

ax.set_title('Evolution during the 80s')
ax.plot(eighties.index, eighties.co2, linewidth = 1.5, color = 'blue')
ax.set_xlabel('Time (years)')
ax.set_ylabel('CO2 emissions (ppm)', color = 'blue')
ax.tick_params('y', colors = 'blue')

ax2 = ax.twinx()
ax2.plot(eighties.index, eighties.relative_temp, linewidth = 1.5, color = 'red')
ax2.set_xlabel('Time (years)')
ax2.set_ylabel('Relative temperature (Celsius)', color = 'red')
ax2.tick_params('y', colors = 'red')

plt.show()``````
``````fig, ax = plt.subplots()

ax.set_title('Evolution during the 90s')
ax.plot(nineties.index, nineties.co2, linewidth = 1.5, color = 'blue')
ax.set_xlabel('Time (years)')
ax.set_ylabel('CO2 emissions (ppm)', color = 'blue')
ax.tick_params('y', colors = 'blue')

ax2 = ax.twinx()
ax2.plot(nineties.index, nineties.relative_temp, linewidth = 1.5, color = 'red')
ax2.set_xlabel('Time (years)')
ax2.set_ylabel('Relative temperature (Celsius)', color = 'r')
ax2.tick_params('y', colors = 'red')

plt.show()``````