Exercises

Exercises#

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Histogram of mountain heights#

Your task is to reproduce the plot shown below using the dataset with all mountains above meters, mountains_above_7000m.csv file. The characteristics of the plot are the following:

  • 15 bins histogram of mountain heights in metres

  • minimum and maximum values placed in text box

  • vertical red line representing the median value

  • title font size = 16

  • x and y labels font size = 14

  • legend font size = 10

  • dashed grid placed behind the histogram

Toolbox

Here are your tools to solve this exercise:

  • Define the input variable.

  • Get the maxim, min and median values of the input variable.

  • The vertical line can be plotted using matplotlib.pyplot.vlines

  • The min an max values can be plotted using matplotlib.pyplot.text

You can use the following matplotlib.pyplot functions (but not limited to): plt.figure(), plt.ylim(), plt.title(),plt.legend(),plt.rc()

More information:

  • https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html

  • https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.vlines.html

  • https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html

::: {Note}

On Jupyter lite use the following line to load the data file:

mountains_7000 = pd.read_csv('/drive/data/mountains_above_7000m.csv') :::

Bar plot of number of mountains per country#

Your task is to reproduce the plot shown below using the dataset with all mountains above 7000 meters, mountains_above_7000m.csv file. The characteristics of the plot are the following:

  • Bar plot representing the number of mountains above 7000 meters per country

  • The countries to be included are: Bhutan, China, India and Pakistan

  • The color hex color codes that represent each country are : #f1eef6, #bdc9e1, #74a9cf and #2b8cbe

  • title font size = 14

  • x and y labels font size = 12

Toolbox

Here are your tools to solve this exercise:

  • Define a list of the countries of interest.

  • Compute the number of mountains per country (see Exercise 6.5.3 for example).

  • Define a list of the given hex color codes.

  • The bar plot can be plotted using matplotlib.pyplot.bar

  • Use a for loop to place text representing the number of mountains above each bar

You can use the following matplotlib.pyplot functions (but not limited to): plt.figure(), plt.ylim(), plt.xlabel(),plt.ylabel()

More information:

  • https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html

  • https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html

::: {Note}

On Jupyter lite use the following line to load the data file:

mountains_7000 = pd.read_csv('/drive/data/mountains_above_7000m.csv') :::

Plot of two variables#

Your task is to reproduce the scatterplot with marginal histograms shown below. The dataset to be use is the sample of 100 vehicles corresponding to the 3-axle vehicle type 3C located in the file 02_01.csv. The characteristics of the plot are the following:

  • For this task you will use different Python data visualization library called seaborn

  • Scatter plot with marginal histograms representing the Axle 1 and Axle 2 measurements

  • x and y labels font size = 14

Toolbox

Here are your tools to solve this exercise:

  • Get the variables of interest (Axle 1 and Axle 2).

  • The scatter plot with marginal histograms can be plotted using seaborn.jointplot

More information:

  • https://seaborn.pydata.org/generated/seaborn.jointplot.html

::: {Note}

On Jupyter lite use the following line to load the data file:

dataset = pd.read_csv('/drive/data/02_01.csv') :::