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Introduction to Data Visualization with Matplotlib

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

# Importing the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Importing the course datasets 
climate_change = pd.read_csv('datasets/climate_change.csv', parse_dates=["date"], index_col="date")
medals = pd.read_csv('datasets/medals_by_country_2016.csv', index_col=0)
summer_2016 = pd.read_csv('datasets/summer2016.csv')
austin_weather = pd.read_csv("datasets/austin_weather.csv", index_col="DATE")
weather = pd.read_csv("datasets/seattle_weather.csv", index_col="DATE")

# 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 notes here

# 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.
  • https://desktop.arcgis.com/es/arcmap/latest/manage-data/raster-and-images/what-is-raster-data.htm

Plot time-series data To plot time-series data, we use the Axes object plot command. The first argument to this method are the values for the x-axis and the second argument are the values for the y-axis.

This exercise provides data stored in a DataFrame called climate_change. This variable has a time-index with the dates of measurements and two data columns: "co2" and "relative_temp".

In this case, the index of the DataFrame would be used as the x-axis values and we will plot the values stored in the "relative_temp" column as the y-axis values. We will also properly label the x-axis and y-axis.

Instructions Add the data from climate_change to the plot: use the DataFrame index for the x value and the "relative_temp" column for the y values. Set the x-axis label to 'Time'. Set the y-axis label to 'Relative temperature (Celsius)'. Show the figure.

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()

Using a time index to zoom in When a time-series is represented with a time index, we can use this index for the x-axis when plotting. We can also select a range of dates to zoom in on a particular period within the time-series using pandas' indexing facilities. In this exercise, you will select a portion of a time-series dataset and you will plot that period.

The data to use is stored in a DataFrame called climate_change, which has a time-index with dates of measurements and two data columns: "co2" and "relative_temp".

Instructions

Use plt.subplots to create a Figure with one Axes called fig and ax, respectively. Create a variable called seventies that includes all the data between "1970-01-01" and "1979-12-31". Add the data from seventies to the plot: use the DataFrame index for the x value and the "co2" column for the y values.

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()

Plotting two variables If you want to plot two time-series variables that were recorded at the same times, you can add both of them to the same subplot.

If the variables have very different scales, you'll want to make sure that you plot them in different twin Axes objects. These objects can share one axis (for example, the time, or x-axis) while not sharing the other (the y-axis).

To create a twin Axes object that shares the x-axis, we use the twinx method.

In this exercise, you'll have access to a DataFrame that has the climate_change data loaded into it. This DataFrame was loaded with the "date" column set as a DateTimeIndex, and it has a column called "co2" with carbon dioxide measurements and a column called "relative_temp" with temperature measurements. Plotting two variables If you want to plot two time-series variables that were recorded at the same times, you can add both of them to the same subplot.

If the variables have very different scales, you'll want to make sure that you plot them in different twin Axes objects. These objects can share one axis (for example, the time, or x-axis) while not sharing the other (the y-axis).

To create a twin Axes object that shares the x-axis, we use the twinx method.

In this exercise, you'll have access to a DataFrame that has the climate_change data loaded into it. This DataFrame was loaded with the "date" column set as a DateTimeIndex, and it has a column called "co2" with carbon dioxide measurements and a column called "relative_temp" with temperature measurements.

Instructions Use plt.subplots to create a Figure and Axes objects called fig and ax, respectively. Plot the carbon dioxide variable in blue using the Axes plot method. Use the Axes twinx method to create a twin Axes that shares the x-axis. Plot the relative temperature variable in red on the twin Axes using its plot method.

# 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()

Defining a function that plots time-series data Once you realize that a particular section of code that you have written is useful, it is a good idea to define a function that saves that section of code for you, rather than copying it to other parts of your program where you would like to use this code.

Here, we will define a function that takes inputs such as a time variable and some other variable and plots them as x and y inputs. Then, it sets the labels on the x- and y-axis and sets the colors of the y-axis label, the y-axis ticks and the tick labels. Instructions Define a function called plot_timeseries that takes as input an Axes object (axes), data (x,y), a string with the name of a color and strings for x- and y-axis labels. Plot y as a function of in the color provided as the input color. Set the x- and y-axis labels using the provided input xlabel and ylabel, setting the y-axis label color using color. Set the y-axis tick parameters using the tick_params method of the Axes object, setting the colors key-word to color.

# 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)

Using a plotting function Defining functions allows us to reuse the same code without having to repeat all of it. Programmers sometimes say "Don't repeat yourself".

In the previous exercise, you defined a function called plot_timeseries:

plot_timeseries(axes, x, y, color, xlabel, ylabel) that takes an Axes object (as the argument axes), time-series data (as x and y arguments) the name of a color (as a string, provided as the color argument) and x-axis and y-axis labels (as xlabel and ylabel arguments). In this exercise, the function plot_timeseries is already defined and provided to you.

Use this function to plot the climate_change time-series data, provided as a pandas DataFrame object that has a DateTimeIndex with the dates of the measurements and co2 and relative_temp columns. Instructions In the provided ax object, use the function plot_timeseries to plot the "co2" column in blue, with the x-axis label "Time (years)" and y-axis label "CO2 levels". Use the ax.twinx method to add an Axes object to the figure that shares the x-axis with ax. Use the function plot_timeseries to add the data in the "relative_temp" column in red to the twin Axes object, with the x-axis label "Time (years)" and y-axis label "Relative temperature (Celsius)".

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