An introduction to data visualization with Matplotlib
This blog post is about my learning journey for data science with python through Datacamp. Datasets, code excerpts & illustration belong to Datacamp and used here for future references and learning purpose only.
You can see my detailed notebook for this particular journey at Kaggle, Github
# Import the matplotlib.pyplot submodule and name it plt
import matplotlib.pyplot as plt
# Create a Figure and an Axes with plt.subplots
fig, ax = plt.subplots()
# Plot MLY-PRCP-NORMAL from seattle_weather against the MONTH
ax.plot(seattle_weather["MONTH"], seattle_weather["MLY-PRCP-NORMAL"])
# Plot MLY-PRCP-NORMAL from austin_weather against MONTH
ax.plot(austin_weather["MONTH"], austin_weather["MLY-PRCP-NORMAL"])
# Call the show function
plt.show()
Customizing Data Appearance
# Plot Seattle data, setting data appearance
ax.plot(seattle_weather["MONTH"], seattle_weather["MLY-PRCP-NORMAL"], color="b",marker="o",linestyle="--")
# Plot Austin data, setting data appearance
ax.plot(austin_weather["MONTH"], austin_weather["MLY-PRCP-NORMAL"], color="r",marker="v")
# Call show to display the resulting plot
plt.show()
ax.plot(seattle_weather["MONTH"], seattle_weather["MLY-PRCP-NORMAL"])
ax.plot(austin_weather["MONTH"], austin_weather["MLY-PRCP-NORMAL"])
# Customize the x-axis label
ax.set_xlabel("Time (months)")
# Customize the y-axis label
ax.set_ylabel("Precipitation (inches)")
# Add the title
ax.set_title("Weather patterns in Austin and Seattle")
# Display the figure
plt.show()
Small multiples
# Create a Figure and an array of subplots with 2 rows and 2 columns
fig, ax = plt.subplots(2, 2)
# Addressing the top left Axes as index 0, 0, plot month and Seattle precipitation
ax[0, 0].plot(seattle_weather["MONTH"], seattle_weather["MLY-PRCP-NORMAL"])
# In the top right (index 0,1), plot month and Seattle temperatures
ax[0, 1].plot(seattle_weather["MONTH"], seattle_weather["MLY-TAVG-NORMAL"])
# In the bottom left (1, 0) plot month and Austin precipitations
ax[1,0].plot(austin_weather["MONTH"], austin_weather["MLY-PRCP-NORMAL"])
# In the bottom right (1, 1) plot month and Austin temperatures
ax[1,1].plot(austin_weather["MONTH"], austin_weather["MLY-TAVG-NORMAL"])
plt.show()
Small multiples with shared y axis
# Create a figure and an array of axes: 2 rows, 1 column with shared y axis
fig, ax = plt.subplots(2, 1, sharey=True)
# Plot Seattle precipitation in the top axes
ax[0].plot(seattle_weather["MONTH"], seattle_weather["MLY-PRCP-NORMAL"], color='b')
ax[0].plot(seattle_weather["MONTH"], seattle_weather["MLY-PRCP-25PCTL"], color='b', linestyle='--')
ax[0].plot(seattle_weather["MONTH"], seattle_weather["MLY-PRCP-75PCTL"], color='b', linestyle='--')
# Plot Austin precipitation in the bottom axes
ax[1].plot(austin_weather["MONTH"], austin_weather["MLY-PRCP-NORMAL"], color='r')
ax[1].plot(austin_weather["MONTH"], austin_weather["MLY-PRCP-25PCTL"], color='r', linestyle='--')
ax[1].plot(austin_weather["MONTH"], austin_weather["MLY-PRCP-75PCTL"], color='r', linestyle='--')
plt.show()
Plotting time series data
# Read the data from file using read_csv
climate_change = pd.read_csv("climate_change.csv", parse_dates=["date"], index_col="date")
print(climate_change.head())
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()
Time index to zoom in
# Use plt.subplots to create fig and ax
fix,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
# 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()
By using plotting function
# 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()
Annotating a plot of time series data
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 00:00:00'),-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()
Stacked bar chart
# 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"])
# Display the legend
ax.legend()
plt.show()
Quantitative comparisons using histogram
fig, ax = plt.subplots()
# Plot a histogram of "Weight" for mens_rowing
ax.hist(mens_rowing["Weight"])
# Compare to histogram of "Weight" for mens_gymnastics
ax.hist(mens_gymnastics["Weight"])
# Set the x-axis label to "Weight (kg)"
ax.set_xlabel("Weight (kg)")
# Set the y-axis label to "# of observations"
ax.set_ylabel("# of observations")
plt.show()
Adding steps to histogram
fig, ax = plt.subplots()
# Plot a histogram of "Weight" for mens_rowing
ax.hist(mens_rowing["Weight"],label="Rowing",bins=5)
# Compare to histogram of "Weight" for mens_gymnastics
ax.hist(mens_gymnastics["Weight"],label="Gymnastics",bins=5)
ax.set_xlabel("Weight (kg)")
ax.set_ylabel("# of observations")
# Add the legend and show the Figure
ax.legend()
plt.show()
With histtype
fig, ax = plt.subplots()
# Plot a histogram of "Weight" for mens_rowing
ax.hist(mens_rowing["Weight"],label="Rowing",bins=5,histtype="step")
# Compare to histogram of "Weight" for mens_gymnastics
ax.hist(mens_gymnastics["Weight"],label="Gymnastics",bins=5,histtype="step")
ax.set_xlabel("Weight (kg)")
ax.set_ylabel("# of observations")
# Add the legend and show the Figure
ax.legend()
plt.show()
Statistical Plotting
Adding error bars
fig, ax = plt.subplots()
# Add a bar for the rowing "Height" column mean/std
ax.bar("Rowing", mens_rowing["Height"].mean(), yerr=mens_rowing["Height"].std())
# Add a bar for the gymnastics "Height" column mean/std
ax.bar("Gymnastics",mens_gymnastics["Height"].mean(),yerr=mens_gymnastics["Height"].std())
# Label the y-axis
ax.set_ylabel("Height (cm)")
plt.show()
fig, ax = plt.subplots()
# Add Seattle temperature data in each month with error bars
ax.errorbar(seattle_weather["MONTH"],seattle_weather["MLY-TAVG-NORMAL"] , seattle_weather["MLY-TAVG-STDDEV"])
# Add Austin temperature data in each month with error bars
ax.errorbar(austin_weather["MONTH"],austin_weather["MLY-TAVG-NORMAL"],austin_weather["MLY-TAVG-STDDEV"])
# Set the y-axis label
ax.set_ylabel("Temperature (Fahrenheit)")
plt.show()
Boxplots
fig, ax = plt.subplots()
# Add a boxplot for the "Height" column in the DataFrames
ax.boxplot([mens_rowing["Height"],mens_gymnastics["Height"]])
# Add x-axis tick labels:
ax.set_xticklabels(["Rowing","Gymnastics"])
# Add a y-axis label
ax.set_ylabel("Height (cm)")
plt.show()
Quantitative comparisons: scatter plots
fig, ax = plt.subplots()
# Add data: "co2" on x-axis, "relative_temp" on y-axis
ax.scatter(climate_change["co2"],climate_change["relative_temp"])
# Set the x-axis label to "CO2 (ppm)"
ax.set_xlabel("CO2 (ppm)")
# Set the y-axis label to "Relative temperature (C)"
ax.set_ylabel("Relative temperature (C)")
plt.show()
eighties = climate_change["1980-01-01":"1989-12-31"]
nineties = climate_change["1990-01-01":"1999-12-31"]
fig, ax = plt.subplots()
ax.scatter(eighties["co2"], eighties["relative_temp"],color="red", label="eighties")
ax.scatter(nineties["co2"], nineties["relative_temp"],color="blue", label="nineties")
ax.legend()
ax.set_xlabel("CO2 (ppm)")
ax.set_ylabel("Relative temperature (Celsius)")
plt.show()
fig, ax = plt.subplots()
# Add data: "co2", "relative_temp" as x-y, index as color
ax.scatter(climate_change["co2"],climate_change["relative_temp"],c=climate_change.index)
# Set the x-axis label to "CO2 (ppm)"
ax.set_xlabel("CO2 (ppm)")
# Set the y-axis label to "Relative temperature (C)"
ax.set_ylabel("Relative temperature (C)")
plt.show()
Automate you visualization
fig, ax = plt.subplots()
# Loop over the different sports branches
for sport in sports:
# Extract the rows only for this sport
sport_df = summ2016[summ2016["Sport"]==sport]
# Add a bar for the "Weight" mean with std y error bar
ax.bar(sport,sport_df["Weight"].mean(),
yerr=sport_df["Weight"].std())
ax.set_ylabel("Weight")
ax.set_xticklabels(sports, rotation=90)
# Save the figure to file
fig.savefig("sports_weights.png")
plt.show()
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