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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

Plotting time-series

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Import pandas as pd

import pandas as pd

``````import matplotlib.pyplot as plt
fig, ax = plt.subplots()

# Add the time-series for "relative_temp" to the plot
ax.plot(climate_change.index,climate_change["relative_temp"])

# Set the x-axis label
ax.set_xlabel('Time')

# Set the y-axis label
ax.set_ylabel('Relative temperature (Celsius)')

# Show the figure
plt.show()``````
``````import matplotlib.pyplot as plt

# Use plt.subplots to create fig and ax
fig, ax = plt.subplots()

# Create variable seventies with data from "1970-01-01" to "1979-12-31"
seventies = climate_change["1970-01-01":"1979-12-31"]

# Add the time-series for "co2" data from seventies to the plot
ax.plot(seventies.index, seventies["co2"])

# Show the figure
plt.show()``````
``````import matplotlib.pyplot as plt

# Initalize a Figure and Axes
fig, ax = plt.subplots()

# Plot the CO2 variable in blue
ax.plot(climate_change.index, climate_change["co2"], color='blue')

# Create a twin Axes that shares the x-axis
ax2 = ax.twinx()

# Plot the relative temperature in red
ax2.plot(climate_change.index, climate_change["relative_temp"], color="red")

plt.show()``````
``````# Define a function called plot_timeseries
def plot_timeseries(axes, x, y, color, xlabel, ylabel):

# Plot the inputs x,y in the provided color
axes.plot(x, y, color=color)

# Set the x-axis label
axes.set_xlabel(xlabel)

# Set the y-axis label
axes.set_ylabel(ylabel, color=color)

# Set the colors tick params for y-axis
axes.tick_params('y', colors=color)
fig, ax = plt.subplots()

# Plot the CO2 levels time-series in blue
plot_timeseries(ax, climate_change.index, climate_change["co2"], "blue", "Time (years)", "CO2 levels")

# Create a twin Axes object that shares the x-axis
ax2 = ax.twinx()

# Plot the relative temperature data in red
plot_timeseries(ax2, climate_change.index, climate_change["relative_temp"], "red", "Time (years)", "Relative temperature (Celsius)")

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

# Plot the relative temperature data
ax.plot(climate_change.index,  climate_change["relative_temp"])

# Annotate the date at which temperatures exceeded 1 degree
ax.annotate('>1 degree', xy=(pd.Timestamp('2015-10-06'), 1))

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

# Plot the CO2 levels time-series in blue
plot_timeseries(ax, climate_change.index,climate_change["co2"], 'blue', "Time (years)", "CO2 levels")

# Create an Axes object that shares the x-axis
ax2 = ax.twinx()

# Plot the relative temperature data in red
plot_timeseries(ax2, climate_change.index , climate_change["relative_temp"], 'red', "Time (years)", "Relative temp (Celsius)")

# Annotate point with relative temperature >1 degree
ax2.annotate(">1 degree", xy=(pd.Timestamp('2015-10-06 00:00:00'), 1),  xytext=(pd.Timestamp('2008-10-06'), -0.2),arrowprops={"arrowstyle":"->", "color":"gray"})

plt.show()``````

Quantitative comparisons and statistical visualizations

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

# Plot a bar-chart of gold medals as a function of country
ax.bar(medals.index,medals["Gold"])

# Set the x-axis tick labels to the country names
ax.set_xticklabels(medals.index, rotation=90)

# Set the y-axis label
ax.set_ylabel("Number of medals")

plt.show()``````
``````# Add bars for "Gold" with the label "Gold"
ax.bar(medals.index, medals["Gold"], label="Gold")

# Stack bars for "Silver" on top with label "Silver"
ax.bar(medals.index, medals["Silver"], bottom=medals["Gold"], label="Silver")

# Stack bars for "Bronze" on top of that with label "Bronze"
ax.bar(medals.index, medals["Bronze"], bottom=medals["Gold"] + medals["Silver"], label="Bronze")

# Display the legend
ax.legend()

plt.show()``````

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.