Data Visualisation with Matplotlib - 56869

Matplotlib-1 - My First Plot

Task1

Create a function named test_the_plot.
Create a figure of size 12 inches in width, and 6 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Create a list t with values [7, 14, 21, 28, 35]
Create a list d with values [i*6 for i in t]
Draw a line, by plotting t values on X-Axis and d values on Y-Axis. Use the plot function. Label the line as d = 6t.
Label X-Axis as time (seconds).
Label Y-Axis as distance (meters).
Set Title as Time vs Distance Covered.
Limit data points on X-Axis from 0 to 40.
Limit data points on Y-Axis from 0 to 220.
Add a legend.
return the figure object

Note: The results of preliminary validation doesn't impact final scoring. In-depth scorings are done at a later stage.

Once you are in the Web IDE:

Open prog.py in the jupyter lab the file and start your coding by following the instructions given above.
Once you are done with the solution , then run the following commands in the terminal to check your solutions. Click File -> New -> Terminal, run the following commands

python3 prog.py

After running the test cases, click the submit button and click on Submit Test to end the assessment.

Note - Here Rough_Work.ipynb can be used for rough work to get the solutions

Solution

import numpy as np
import matplotlib.pyplot as plt


def test_the_plot():
    fig = plt.figure(figsize=(12, 6))
    ax = fig.add_subplot(111)
    t = [7, 14, 21, 28, 35]
    d = [i * 6 for i in t]
    ax.plot(t, d, label='d = 6t')
    ax.set_xlabel('time (seconds)')
    ax.set_ylabel('distance (meters)')
    ax.set_title('Time vs Distance Covered')
    ax.set_xlim(0, 40)
    ax.set_ylim(0, 220)
    ax.legend("My First Plot")
    return fig

# Note: Do Not modify the below code
if __name__ == '__main__':
    import sys
    import subprocess
    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", 'test_plot1-0.2-py3-none-any.whl'], stdout=subprocess.DEVNULL)
    from test_plot1 import matplotlib1
    usr_fig = test_the_plot()
    matplotlib1.save_answer(usr_fig)

Matplotlib-2 - Scatter Plots

Task1

Create a function named sine_wave_plot.
Create a figure of size 13 inches in width, and 4 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Create a numpy array arr_t with 250 values between 0.0 and 3.0. Use the 'linspace' method to generate 250 values.
Create a numpy array arr_v, such that arr_v = np.sin(2.5*np.pi*arr_t).
Pass arr_t and arr_v as variables to plot function and draw a red line passing through the selected 250 points. Label the line as sin(arr_t).
Label X-Axis as Time (seconds).
Label Y-Axis as Voltage (mv).
Set Title as Sine Wave.
Limit data on X-Axis from 0 to 2.
Limit data on Y-Axis from -1 to 1.
Mark major ticks on X-Axis at 0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0.
Mark major ticks on Y-Axis at -1, 0, and 1.
Add a grid, whose linestyle is dashdot.
Add a legend.
return the figure object

Task2

Create a function named multi_curve_plot.
Create a figure of size 13 inches in width, and 4 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Create a numpy array arr_x with 25 values between 0.0 and 7.0. Use the 'linspace' method to generate 25 values.
Create three numpy arrays arr_y1, arr_y2 and arr_y3, using the expressions arr_y1 = arr_x, arr_y2 = arr_x**2 and arr_y3 = arr_x**3 respectively.
Draw a green colored line passing through arr_x and arr_y1, using the plot function. Mark the 25 data points on the line as upward pointed triangles. Label the line as y = arr_x.
Draw a blue colored line passing through arr_x and arr_y2, using the plot function. Mark the 25 data points on the line as squares. Label the line as y = arr_x**2.
Draw a red colored line passing through arr_x and arr_y3, using the plot function. Mark the 25 data points on the line as circles.
Label the line as y = arr_x**3.
Label X-Axis as arr_x.
Label Y-Axis as f(arr_x).
Set Title as Linear, Quadratic, & Cubic Equations.
Add a legend.
return the figure object

Task3

Create a function named scatter_plot.
Create a figure of size 13 inches in width, and 4 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Consider the list car_sales = [40, 65, 70, 40, 55, 60, 75, 60, 80, 95, 96, 105]. It represents the number of cars sold by a Company 'X' in each month of 2021, starting from January, 2021.
Create a list months containing numbers from 1 to 12.
Draw a scatter plot with variables months and car_sales as arguments. Mark the data points in green color. Use the scatter function for plotting.Label the points as car sales.
Limit data on X-Axis from 0 to 13.
Limit data on Y-Axis from 10 to 110.
Mark ticks on X-Axis at 1, 3, 5, 7, 9, and 11.
Label the X-Axis ticks as January, March, May, July, September, and November respectively.
Label X-Axis as Months
Label Y-Axis as No. of Cars Sold
Set Title as "Cars Sold by Company 'Z' in 2021".
Add a legend.
return the figure object

Note: The results of preliminary validation doesn't impact final scoring. In-depth scorings are done at a later stage.

Once you are in the Web IDE:

Open prog.py in the jupyter lab the file and start your coding by following the instructions given above.
Once you are done with the solution , then run the following commands in the terminal to check your solutions. Click File -> New -> Terminal, run the following commands
```
python3 prog.py
```
After running the test cases, click the submit button and click on Submit Test to end the assessment.

Note - Here Rough_Work.ipynb can be used for rough work to get the solutions

Solution

import numpy as np
import matplotlib.pyplot as plt


def sine_wave_plot():

    # Write your functionality below
    fig = plt.figure(figsize=(13, 4))
    ax = fig.add_subplot(111)
    arr_t = np.linspace(0.0, 3.0, num = 250)
    arr_v = np.sin(2.5 * np.pi * arr_t)
    ax.plot(arr_t, arr_v, label="sin(arr_t)")
    ax.set_xlabel("Time (seconds)")
    ax.set_ylabel("Voltage (mv)")
    ax.set_title("Sine Wave")
    ax.set_xlim(0, 2)
    ax.set_ylim(-1, 1)
    ax.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0])
    ax.set_yticks([-1, 0, 1])
    ax.grid(linestyle="dashdot")
    ax.legend(["Voltage Speed"])
    return fig


def multi_curve_plot():

    # Write your functionality below
    fig = plt.figure(figsize=(13, 4))
    ax = fig.add_subplot(111)
    arr_x = np.linspace(0.0, 7.0, num = 25)
    arr_y1 = arr_x
    arr_y2 = arr_x ** 2
    arr_y3 = arr_x ** 3

    ax.plot(arr_x, arr_y1, color="green", marker="^", label ="y = arr_x")
    ax.plot(arr_x, arr_y2, color="blue", marker="s", label ="y = arr_x**2")
    ax.plot(arr_x, arr_y3, color="red", marker="o", label ="y = arr_x**3")
    ax.set_xlabel("arr_x")
    ax.set_ylabel("f(arr_x)")
    ax.set_title("Linear, Quadratic, & Cubic Equations")
    ax.legend(["Linear", "Quadratic", "Cubic"])
    return fig



def scatter_plot():

    # Write your functionality below
    fig = plt.figure(figsize=(13, 4))
    ax = fig.add_subplot(111)
    car_sales = [40, 65, 70, 40, 55, 60, 75, 60, 80, 95, 96, 105]
    months = np.array(range(1, 13))
    ax.scatter(months, car_sales, color="green", label="car sales")
    ax.set_xlim(0, 13)
    ax.set_ylim(10, 110)
    ax.set_xticks(np.array(range(1, 13, 2)), ["January", "March", "May", "July", "September", "November"])
    ax.set_xlabel("Months")
    ax.set_ylabel("No. of Cars Sold")
    ax.set_title("Cars Sold by Company 'Z' in 2021")
    ax.legend(["Count of Cars Sold"])
    return fig


# Note: Do Not modify the below code
if __name__ == '__main__':
    import sys
    import subprocess
    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", 'test_plot2-0.1-py3-none-any.whl'], stdout=subprocess.DEVNULL)
    from test_plot2 import matplotlib2
    usr_fig1 = sine_wave_plot()
    usr_fig2 = multi_curve_plot()
    usr_fig3 = scatter_plot()
    matplotlib2.save_answer(usr_fig1, usr_fig2, usr_fig3)

Matplotlib-3 - Bar Plots

Task1

Create a function named barplot_of_iris_sepal_length.
Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Define a list species, with elements ['setosa', 'versicolor', 'virginica'].
Define a list index, with values [0.4, 1.4, 2.4].
Define another list sepal_len with values [6.01, 6.94, 7.69]. These values represent the mean sepal length of iris flowers belonging to three species.
Draw a bar plot using the bar function, such that the height of each vertical bar displays the sepal length of a species label it as Sepal Length.
Use index and sepal_len as variables. Set bar width as 0.4, color as blue, and border color of the bar as red.
Label X-Axis as Species.
Label Y-Axis as Sepal Length (cm).
Set Title as Mean Sepal Length of Iris Species.
Limit X-Axis from 0 to 3.
Limit Y-Axis from 0 to 9.
Set ticks on X-Axis at 0.4, 1.4, and 2.4.
Set X-Axis tick labels to ['setosa', 'versicolor', 'virginica'].
Add a legend.
return the figure object.

Task2

Create a function named barplot_of_iris_measurements.
Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Define the following lists:
sepal_len = [6.01, 6.94, 7.59]
sepal_wd = [4.42, 3.77, 3.97]
petal_len = [2.46, 5.26, 6.55]
petal_wd = [1.24, 2.33, 3.03]
species = ['setosa', 'versicolor', 'virginica']
species_index1 = [0.7, 1.7, 2.7]
species_index2 = [0.9, 1.9, 2.9]
species_index3 = [1.1, 2.1, 3.1]
species_index4 = [1.3, 2.3, 3.3]
Draw vertical bars showing the mean sepal length of a species. Set the color of the bars to m, boundary line color to grey, width of bars as 0.2, and label it as Sepal Length. Use bar with species_index1 and sepal_len.
Draw vertical bars showing mean sepal length of a species. Set the color of the bars to y, boundary line color to grey, width of bars as 0.2, and label it as Sepal Width. Use bar with species_index2 and sepal_wd.
Draw vertical bars showing mean sepal length of a species. Set the color of the bars to c, boundary line color to grey, width of bars as 0.2, and label it as Petal Length. Use bar with species_index3 and petal_len.
Draw vertical bars showing mean sepal length of a species. Set the color of the bars to orange, boundary line color to grey, width of bars as 0.2, and label it as Petal Width. Use bar with species_index4 and petal_wd.
Label X-Axis as Species.
Label Y-Axis as Iris Measurements (cm).
Set Title as Mean Measurements of Iris Species.
Limit X-Axis from 0.5 to 3.5 .
Limit Y-Axis from 0 to 10.
Mark major ticks on X-Axis at 1.0, 2.0, and 3.0.
Label the major ticks on X-Axis as setosa, versicolor and virginica respectively.
Add a legend.
return the figure object.

Task3

Create a function named hbarplot_of_iris_petal_length.
Create a figure of size 15 inches in width, and 5 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Define a list species, with elements ['setosa', 'versicolor', 'virginica'].
Define a list index, with values [0.1, 1.1, 2.1].
Define another list petal_len with values [2.67, 5.49, 6.37]. These values represent the mean petal length of iris flowers belonging to three species.
Draw a horizontal bar plot using barh function, such that the width of each bar display the petal length of a species and label it as Petal Length.
Use index and petal_len as variables. Set bar height as 0.4, color as m, and border color of the bar as c.
Label Y-Axis as Species
Label X-Axis as `Petal Length (cm).
Set Title as Mean Petal Length of Iris Species.
Mark major ticks on Y-Axis at 0.10, 1.10 and 2.10.
Label the major ticks on Y-Axis as setosa, versicolor and virginica respectively.
Add a legend.
return the figure object.

Note: The results of preliminary validation doesn't impact final scoring. In-depth scorings are done at a later stage.

Once you are in the Web IDE:

Open prog.py in the jupyter lab the file and start your coding by following the instructions given above.
Once you are done with the solution , then run the following commands in the terminal to check your solutions. Click File -> New -> Terminal, run the following commands
```
python3 prog.py
```
After running the test cases, click the submit button and click on Submit Test to end the assessment.

Note - Here Rough_Work.ipynb can be used for rough work to get the solutions

Solution

import numpy as np
import matplotlib.pyplot as plt


def barplot_of_iris_sepal_length():

    # Write your functionality below
    fig = plt.figure(figsize=(9, 7))
    ax = fig.add_subplot(111)
    species = ['setosa', 'versicolor', 'virginica']
    index = [0.4, 1.4, 2.4]
    sepal_len = [6.01, 6.94, 7.69]
    ax.bar(index, sepal_len, label="Sepal Length", width=0.4, color="blue", edgecolor="red")
    ax.set_xlabel("Species")
    ax.set_ylabel("Sepal Length (cm)")
    ax.set_title("Mean Sepal Length of Iris Species")
    ax.set_xlim(0, 3)
    ax.set_ylim(0, 9)
    ax.set_xticks(index)
    ax.set_xticklabels(species)
    ax.legend(["Sepal Length"])
    return fig


def barplot_of_iris_measurements():

    # Write your functionality below
    fig = plt.figure(figsize=(9, 7))
    ax = fig.add_subplot(111)
    sepal_len = [6.01, 6.94, 7.59]
    sepal_wd = [4.42, 3.77, 3.97]
    petal_len = [2.46, 5.26, 6.55]
    petal_wd = [1.24, 2.33, 3.03]
    species = ['setosa', 'versicolor', 'virginica']
    species_index1 = [0.7, 1.7, 2.7]
    species_index2 = [0.9, 1.9, 2.9]
    species_index3 = [1.1, 2.1, 3.1]
    species_index4 = [1.3, 2.3, 3.3]

    ax.bar(species_index1, sepal_len, color="m", edgecolor="grey", width=0.2, label="Sepal Length")
    ax.bar(species_index2, sepal_wd, color="y", edgecolor="grey", width=0.2, label="Sepal Width")
    ax.bar(species_index3, petal_len, color="c", edgecolor="grey", width=0.2, label="Petal Length")
    ax.bar(species_index4, petal_wd, color="orange", edgecolor="grey", width=0.2, label="Petal Width")
    ax.set_xlabel("Species")
    ax.set_ylabel("Iris Measurements (cm)")
    ax.set_title("Mean Measurements of Iris Species")
    ax.set_xlim(0.5, 3.5)
    ax.set_ylim(0, 10)
    ax.set_xticks([1.0, 2.0, 3.0])
    ax.set_xticklabels(species)
    ax.legend(["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"])

    return fig


def hbarplot_of_iris_petal_length():

    # Write your functionality below
    fig = plt.figure(figsize=(15, 5))
    ax = fig.add_subplot(111)
    species = ['setosa', 'versicolor', 'virginica']
    index = [0.1, 1.1, 2.1]
    petal_len = [2.67, 5.49, 6.37]

    ax.barh(index, petal_len, height=0.4, label="Petal Length", color="m", edgecolor="c")
    ax.set_xlabel("Species")
    ax.set_ylabel("Petal Length (cm)")
    ax.set_title("Mean Petal Length of Iris Species")
    ax.set_yticks(index)
    ax.set_yticklabels(species)
    ax.legend(["Petal Length"])

    return fig


# Note: Do Not modify the below code
if __name__ == '__main__':
    import sys
    import subprocess
    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", 'test_plot3-0.1-py3-none-any.whl'], stdout=subprocess.DEVNULL)
    from test_plot3 import matplotlib3
    usr_fig1 = barplot_of_iris_sepal_length()
    usr_fig2 = barplot_of_iris_measurements()
    usr_fig3 = hbarplot_of_iris_petal_length()
    matplotlib3.save_answer(usr_fig1, usr_fig2, usr_fig3)

Matplotlib-4 - Histograms and Box Plots

Task1

Create a function named hist_of_a_sample_normal_distribution.
Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Set random seed to 100 using the expression np.random.seed(100).
Create a normal distribution dist_arr of 1000 values, with mean 35 and standard deviation 3.0. Use np.random.randn.
Draw a histogram of dist_arr with 35 bins. Use the hist function.
Label X-Axis as dist_arr
Label Y-Axis as Bin Count
Set Title as Histogram of a Single Dataset
return the figure object.

Task2

Create a function named boxplot_of_four_normal_distribution.
Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Set random seed to 100 using the expression np.random.seed(100).
Create a normal distribution arr_1 of 2000 values, with mean 35 and standard deviation 6.0. Use np.random.randn.
Create a normal distribution arr_2 of 2000 values, with mean 25 and standard deviation 4.0. Use np.random.randn.
Create a normal distribution arr_3 of 2000 values, with mean 45 and standard deviation 8.0. Use np.random.randn.
Create a normal distribution arr_4 of 2000 values, with mean 55 and standard deviation 10.0. Use np.random.randn.
Create a list labels with elements ['arr_1', 'arr_2', 'arr_3', 'arr_4].
Draw a Boxplot arr_1, arr_2, arr_3, arr_4 with notches and label it using the labels list. Use the boxplot function.
Choose o symbol for outlier, and fill color inside boxes by setting patch_artist argument to True.
Label X-Axis as Dataset
Label Y-Axis as Value
Set Title as Box plot of Multiple Datasets
return the figure object.

Note: The results of preliminary validation doesn't impact final scoring. In-depth scorings are done at a later stage.

Once you are in the Web IDE:

Open prog.py in the jupyter lab the file and start your coding by following the instructions given above.
Once you are done with the solution , then run the following commands in the terminal to check your solutions. Click File -> New -> Terminal, run the following commands
```
python3 prog.py
```
After running the test cases, click the submit button and click on Submit Test to end the assessment.

Note - Here Rough_Work.ipynb can be used for rough work to get the solutions

Solution

import numpy as np
import matplotlib.pyplot as plt


def hist_of_a_sample_normal_distribution():

    # Write your functionality below
    fig = plt.figure(figsize=(9,7))
    ax = fig.add_subplot(111)
    np.random.seed(100)
    dist_arr = np.random.randn(1000) * 3.0 + 35
    ax.hist(dist_arr, bins=35)
    ax.set_xlabel("dist_arr")
    ax.set_ylabel("Bin Count")
    ax.set_title("Histogram of a Single Dataset")
    return fig


def boxplot_of_four_normal_distribution():

    # Write your functionality below
    fig = plt.figure(figsize=(9,7))
    ax = fig.add_subplot(111)
    np.random.seed(100)
    arr_1 = np.random.randn(2000) * 6.0 + 35
    arr_2 = np.random.randn(2000) * 4.0 + 25
    arr_3 = np.random.randn(2000) * 8.0 + 45
    arr_4 = np.random.randn(2000) * 10.0 + 55
    elements = ['arr_1', 'arr_2', 'arr_3', 'arr_4']
    ax.boxplot([arr_1, arr_2, arr_3, arr_4], labels=elements, patch_artist=True, sym='o')
    ax.set_xlabel("Dataset")
    ax.set_ylabel("Value")
    ax.set_title("Box plot of Multiple Datasets")
    return fig


# Note: Do Not modify the below code
if __name__ == '__main__':
    import sys
    import subprocess
    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", 'test_plot4-0.1-py3-none-any.whl'], stdout=subprocess.DEVNULL)
    from test_plot4 import matplotlib4
    usr_fig1 = hist_of_a_sample_normal_distribution()
    usr_fig2 = boxplot_of_four_normal_distribution()
    matplotlib4.save_answer(usr_fig1, usr_fig2)

Matplotlib-5 - Applying Styles

Task1

Create a function named generate_plot_with_style1.
Generate the following barplot with 'ggplot' style. Use 'with' to apply the style to the code generating the barplot.
Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
Create an axis, associated with figure fig, using add_subplot. Name it as ax.
Define the following lists
sepal_len = [6.01, 6.94, 7.59]
sepal_wd = [4.42, 3.77, 3.97]
petal_len = [2.46, 5.26, 6.55]
petal_wd = [1.24, 2.33, 3.03]
species = ['setosa', 'versicolor', 'virginica']
species_index1 = [0.8, 1.8, 2.8]
species_index2 = [1.0, 2.0, 3.0]
species_index3 = [1.2, 2.2, 3.2]
species_index4 = [1.4, 2.4, 3.4]
Draw vertical bars showing mean sepal length of a species. Set width of the bars as 0.2, and label it as Sepal Length. Use bar with species_index1 and sepal_len.
Draw vertical bars showing mean sepal length of a species. Set width of bars as 0.2 and label it as Sepal Width. Use bar with species_index2 and sepal_wd.
Draw vertical bars showing mean sepal length of a species. Set width of bars as 0.2 and label it as Petal Length. Use bar with species_index3 and petal_len.
Draw vertical bars showing mean sepal length of a species. Set width of bars as 0.2 and label it as Petal Width. Use bar with species_index4 and petal_wd.
Label X-Axis as Species.
Label Y-Axis as Iris Measurements (cm).
Set Title as Mean Measurements of Iris Species.
Limit X-Axis from 0.5 to 3.7.
Limit Y-Axis from 0 to 10.
Mark major ticks on X-Axis at 1.0, 2.0, and 3.0.
Label the major ticks on X-Axis as setosa, versicolor, and virginica respectively.
Add a legend.
return the figure object.

Task2

Create a function named generate_plot_with_style2.
Regenerate the barplot defined in 'generate_plot_with_style1' using 'seaborn-colorblind' style. Use 'with' for applying the style.
Limit X-Axis from 0.5 to 3.5.
return the figure object.

Task3

Create a function named generate_plot_with_style3.
Regenerate the barplot defined in 'generate_plot_with_style1' using 'grayscale' style. Use 'with' for applying the style.
Limit X-Axis from 0.5 to 3.5.
return the figure object.

Note: The results of preliminary validation doesn't impact final scoring. In-depth scorings are done at a later stage.

Once you are in the Web IDE:

Open prog.py in the jupyter lab the file and start your coding by following the instructions given above.
Once you are done with the solution , then run the following commands in the terminal to check your solutions. Click File -> New -> Terminal, run the following commands
```
python3 prog.py
```
After running the test cases, click the submit button and click on Submit Test to end the assessment.

Note - Here Rough_Work.ipynb can be used for rough work to get the solutions

Solution

import numpy as np
import matplotlib.pyplot as plt


def generate_plot_with_style1():

    # Write your functionality below
    fig = None
    sepal_len = [6.01, 6.94, 7.59]
    sepal_wd = [4.42, 3.77, 3.97]
    petal_len = [2.46, 5.26, 6.55]
    petal_wd = [1.24, 2.33, 3.03]
    species = ['setosa', 'versicolor', 'virginica']
    species_index1 = [0.8, 1.8, 2.8]
    species_index2 = [1.0, 2.0, 3.0]
    species_index3 = [1.2, 2.2, 3.2]
    species_index4 = [1.4, 2.4, 3.4]
    with plt.style.context('ggplot'):
        fig = plt.figure(figsize=(9, 7))
        ax = fig.add_subplot(111)
        ax.set(title="Mean Measurements of Iris Species", xlabel="Species", ylabel="Iris Measurements (cm)")
        ax.bar(species_index1, sepal_len, width=0.2, label="Sepal Length")
        ax.bar(species_index2, sepal_wd, width=0.2, label="Sepal Width")
        ax.bar(species_index3, petal_len, width=0.2, label="Petal Length")
        ax.bar(species_index4, petal_wd, width=0.2, label="Petal Width")
        ax.set_xlim(0.5, 3.7)
        ax.set_ylim(0, 10)
        ax.set_xticks([1.0, 2.0, 3.0])
        ax.set_xticklabels(species)
        ax.legend(["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"])
    return fig


def generate_plot_with_style2():

    # Write your functionality below
    fig = None
    sepal_len = [6.01, 6.94, 7.59]
    sepal_wd = [4.42, 3.77, 3.97]
    petal_len = [2.46, 5.26, 6.55]
    petal_wd = [1.24, 2.33, 3.03]
    species = ['setosa', 'versicolor', 'virginica']
    species_index1 = [0.8, 1.8, 2.8]
    species_index2 = [1.0, 2.0, 3.0]
    species_index3 = [1.2, 2.2, 3.2]
    species_index4 = [1.4, 2.4, 3.4]
    with plt.style.context('seaborn-colorblind'):
        fig = plt.figure(figsize=(9, 7))
        ax = fig.add_subplot(111)
        ax.set(title="Mean Measurements of Iris Species", xlabel="Species", ylabel="Iris Measurements (cm)")
        ax.bar(species_index1, sepal_len, width=0.2, label="Sepal Length")
        ax.bar(species_index2, sepal_wd, width=0.2, label="Sepal Width")
        ax.bar(species_index3, petal_len, width=0.2, label="Petal Length")
        ax.bar(species_index4, petal_wd, width=0.2, label="Petal Width")
        ax.set_xlim(0.5, 3.5)
        ax.set_ylim(0, 10)
        ax.set_xticks([1.0, 2.0, 3.0])
        ax.set_xticklabels(species)
        ax.legend(["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"])

    return fig


def generate_plot_with_style3():

    # Write your functionality below
    fig = None
    sepal_len = [6.01, 6.94, 7.59]
    sepal_wd = [4.42, 3.77, 3.97]
    petal_len = [2.46, 5.26, 6.55]
    petal_wd = [1.24, 2.33, 3.03]
    species = ['setosa', 'versicolor', 'virginica']
    species_index1 = [0.8, 1.8, 2.8]
    species_index2 = [1.0, 2.0, 3.0]
    species_index3 = [1.2, 2.2, 3.2]
    species_index4 = [1.4, 2.4, 3.4]
    with plt.style.context('grayscale'):
        fig = plt.figure(figsize=(9, 7))
        ax = fig.add_subplot(111)
        ax.set(title="Mean Measurements of Iris Species", xlabel="Species", ylabel="Iris Measurements (cm)")
        ax.bar(species_index1, sepal_len, width=0.2, label="Sepal Length")
        ax.bar(species_index2, sepal_wd, width=0.2, label="Sepal Width")
        ax.bar(species_index3, petal_len, width=0.2, label="Petal Length")
        ax.bar(species_index4, petal_wd, width=0.2, label="Petal Width")
        ax.set_xlim(0.5, 3.5)
        ax.set_ylim(0, 10)
        ax.set_xticks([1.0, 2.0, 3.0])
        ax.set_xticklabels(species)
        ax.legend(["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"])

    return fig


# Note: Do Not modify the below code
if __name__ == '__main__':
    import sys
    import subprocess
    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", 'test_plot5-0.1-py3-none-any.whl'], stdout=subprocess.DEVNULL)
    from test_plot5 import matplotlib5
    usr_fig1 = generate_plot_with_style1()
    usr_fig2 = generate_plot_with_style2()
    usr_fig3 = generate_plot_with_style3()
    matplotlib5.save_answer(usr_fig1, usr_fig2, usr_fig3)

Matplotlib-6 - Multiple Plots

Task1

Create a function named generate_figure1.
Define a numpy array 'x' with expression 'np.arange(0.0, 10.0, 0.01)'.
Define another numpy array 'arr_s1' with expression 'np.sin(3*np.pi*x)'
Define one more numpy array 'arr_s2' with expression 'np.sin(6*np.pi*x)'.
Create a figure of size 12 inches in width, and 7 inches in height. Name it as fig.
Create an axes, using plt.subplot function. Name it as axes1. The subplot must point to the first virtual grid created by 2 rows and 1 column. Set 'title' argument to 'Sin(3*pi*x)'.
Draw a line plot of 'x' and 'arr_s1' using the 'plot' function on 'axes1`.
Create another axes, using the plt.subplot function. Name it as axes2. The subplot must point to the second virtual grid created by 2 rows and 1 column. Set 'title' argument to 'Sin(6*pi*x)'. Set 'sharex' argument to 'axes1' and 'sharey' argument to 'axes1'.
Draw a line plot of 'x' and 'arr_s2' using the 'plot' function on 'axes2`.
return the figure object

Task2

Create a function named generate_figure2.
Set random seed to 1500 using the expression 'np.random.seed(1500)'.
Define a numpy array 'x' with expression 'np.random.rand(10)'.
Define another numpy array 'y' with expression 'np.random.rand(10)'.
Define one more numpy array 'z' with expression 'np.sqrt(x**2 + y**2)'.
Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
Create an axes, using plt.subplot function. Name it as axes1. The subplot must point to the first virtual grid created by 2 rows and 2 columns. Set 'title' argument to 'Scatter plot with Diamond Markers'.
Draw a scatter plot of 'x' and 'y' using 'scatter' function on 'axes1`. Set argument 's' to 80, 'c' to z and 'marker' to 'd'.
Add ticks on X-Axis at 0.0, 0.5, 1.0, 1.5 and ticks on Y-Axis at -0.2, 0.2, 0.6, 1.0 respectively
Create an axes, using plt.subplot function. Name it as axes2. The subplot must point to the Second virtual grid created by 2 rows and 2 columns. Set 'title' argument to 'Scatter plot with Circle Markers'.
Draw a scatter plot of 'x' and 'y' using 'scatter' function on 'axes2`. Set argument 's' to 80, 'c' to 'z' and 'marker' to 'o'.
Add ticks on X-Axis at 0.0, 0.5, 1.0, 1.5 and ticks on Y-Axis at -0.2, 0.2, 0.6, 1.0 respectively.
Create an axes, using plt.subplot function. Name it as axes3. The subplot must point to the Third virtual grid created by 2 rows and 2 columns. Set 'title' argument to 'Scatter plot with Plus Markers'.
Draw a scatter plot of 'x' and 'y' using 'scatter' function on 'axes3`. Set argument 's' to 80, 'c' to 'z' and 'marker' to '+'.
Add ticks on X-Axis at 0.0, 0.5, 1.0, 1.5 and ticks on Y-Axis at -0.2, 0.2, 0.6, 1.0 respectively.
Create an axes, using plt.subplot function. Name it as axes4. The subplot must point to the Fourth virtual grid created by 2 rows and 2 columns. Set 'title' argument to 'Scatter plot with Upper Triangle Markers'.
Draw a scatter plot of 'x' and 'y' using 'scatter' function on 'axes4`. Set argument 's' to 80, 'c' to 'z' and 'marker' to '^'.
Add ticks on X-Axis at 0.0, 0.5, 1.0, 1.5 and ticks on Y-Axis at -0.2, 0.2, 0.6, 1.0 respectively
Adjust the entire layout with expression 'plt.tight_layout()'.
return the figure object

Task3

Create a function named generate_figure3.
Define a numpy array 'x' with expression 'np.arange(1, 301, 3)'.
Define another numpy array 'y1' with expression 'y1 = x'.
Define another numpy array 'y2' with expression 'y2 = x**2'.
Define another numpy array 'y3' with expression 'y3 = x**3'.
Create a figure of size 9 inches in width, and 7 inches in height. Name it as fig.
Define a grid 'gr' of 2 rows and 2 columns, using 'GridSpec' function. Ensure that 'matplotlib.gridspec' is imported, before defining the grid.
Create an axes, using plt.subplot function. Name it as axes1. The subplot must span the 1st row and 1st column of the defined grid 'gr'. Set 'title' argument to 'y = x'.
Draw a line plot of 'x' and 'y1' using 'plot' function on 'axes1`.
Create an axes, using plt.subplot function. Name it as axes2. The subplot must span 2nd row and 1st column of defined grid 'gr'. Set 'title' argument to 'y = x**2'.
Draw a line plot of 'x' and 'y2' using 'plot' function on 'axes2`.
Create an axes, using plt.subplot function. Name it as axes3. The subplot must span all rows of 2nd column of defined grid 'gr'. Set 'title' argument to 'y = x**3'.
Draw a line plot of 'x' and 'y3' using 'plot' function on 'axes3`.
Adjust the entire layout with the expression 'plt.tight_layout()'.
return the figure object

Note: The results of preliminary validation doesn't impact final scoring. In-depth scorings are done at a later stage.

Once you are in the Web IDE:

Open prog.py in the jupyter lab the file and start your coding by following the instructions given above.
Once you are done with the solution , then run the following commands in the terminal to check your solutions. Click File -> New -> Terminal, run the following commands
```
python3 prog.py
```
After running the test cases, click the submit button and click on Submit Test to end the assessment.

Note - Here Rough_Work.ipynb can be used for rough work to get the solutions

Solution

import numpy as np
import matplotlib.pyplot as plt


def generate_figure1():

    # Write your functionality below
    fig = plt.figure(figsize=(12, 7))
    x = np.arange(0.0, 10.0, 0.01)
    arr_s1 = np.sin(3 * np.pi * x)
    arr_s2 = np.sin(6 * np.pi * x)

    axes1 = plt.subplot(2,1,1,title="Sin(3*pi*x)")
    axes1.plot(x, arr_s1)

    axes2 = plt.subplot(2,1,2,title="Sin(6*pi*x)")
    axes2.plot(x, arr_s2)
    return fig


def generate_figure2():

    # Write your functionality below
    fig = plt.figure(figsize=(9, 7))
    np.random.seed(1500)
    x = np.random.rand(10)
    y = np.random.rand(10)
    z = np.sqrt(x**2 + y**2)

    axes1 = plt.subplot(2, 2, 1, title="Scatter plot with Diamond Markers")
    axes1.scatter(x, y, s=80, c=z, marker='d')
    axes1.set_xticks([0.0, 0.5, 1.0, 1.5])
    axes1.set_yticks([-0.2, 0.2, 0.6, 1.0])

    axes2 = plt.subplot(2, 2, 2, title="Scatter plot with Circle Markers")
    axes2.scatter(x, y, s=80, c=z, marker='o')
    axes2.set_xticks([0.0, 0.5, 1.0, 1.5])
    axes2.set_yticks([-0.2, 0.2, 0.6, 1.0])

    axes3 = plt.subplot(2, 2, 3, title="Scatter plot with Plus Markers")
    axes3.scatter(x, y, s=80, c=z, marker='+')
    axes3.set_xticks([0.0, 0.5, 1.0, 1.5])
    axes3.set_yticks([-0.2, 0.2, 0.6, 1.0])

    axes4 = plt.subplot(2, 2, 4, title="Scatter plot with Upper Triangle Markers")
    axes4.scatter(x, y, s=80, c=z, marker='^')
    axes4.set_xticks([0.0, 0.5, 1.0, 1.5])
    axes4.set_yticks([-0.2, 0.2, 0.6, 1.0])

    plt.tight_layout()
    return fig


def generate_figure3():
    import matplotlib.gridspec as gridspec
    # Write your functionality below
    x = np.arange(1, 301, 3)
    y1 = x
    y2 = x**2
    y3 = x**3

    fig = plt.figure(figsize=(9, 7))
    gr = gridspec.GridSpec(2,2)

    axes1 = plt.subplot(gr[0,0], title='y = x')
    axes1.plot(x, y1)

    axes2 = plt.subplot(gr[1,0], title="y = x**2")
    axes2.plot(x, y2)

    axes3 = plt.subplot(gr[:,1], title="y = x**3")
    axes3.plot(x, y3)

    plt.tight_layout()
    return fig


# Note: Do Not modify the below code
if __name__ == '__main__':
    import sys
    import subprocess
    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", 'test_plot6-0.2-py3-none-any.whl'], stdout=subprocess.DEVNULL)
    from test_plot6 import matplotlib6
    usr_fig1 = generate_figure1()
    usr_fig2 = generate_figure2()
    usr_fig3 = generate_figure3()
    matplotlib6.save_answer(usr_fig1, usr_fig2, usr_fig3)

Anitesh'er Khata (Anitesh's Notes)

Sharing my notes and questions from my life courses

Data Visualisation with Matplotlib || Fresco Play || 56869

Data Visualisation with Matplotlib - 56869

Matplotlib-1 - My First Plot

Solution

Matplotlib-2 - Scatter Plots

Solution

Matplotlib-3 - Bar Plots

Solution

Matplotlib-4 - Histograms and Box Plots

Solution

Matplotlib-5 - Applying Styles

Solution

Matplotlib-6 - Multiple Plots

Solution