Answer :
We have used the matplotlib library for plotting. These Python scripts should produce the desired plots for each question. Make sure to replace the sample data with your actual data when using these scripts.
Let's go through each question one by one:
a) Draw a spiral:
python
import matplotlib.pyplot as plt
import numpy as np
theta = np.linspace(0, 10 * np.π, 1000)
radius = θ
x = radius * np.cosθ
y = radius * np.sinθ
plt.plot(x, y)
plt.title('Spiral')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('equal')
plt.show()
b) Draw an "[infinity]" shape:
python
import matplotlib.pyplot as plt
import numpy as np
θ = np.linspace(-2xnp.π, 2*np.π, 1000)
radius = 1
x = radius x np.sin(theta)
y = radius x np.sin(theta) x np.cos(theta)
plt.plot(x, y)
plt.title('Infinity Shape')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('equal')
plt.show()
c) Draw a "flower-like" shape:
python
import matplotlib.pyplot as plt
import numpy as np
θ = np.linspace(0, 2*np.π, 1000)
radius = 1 + 0.2 * np.cos(6 * θ)
x = radius * np.cosθ
y = radius * np.sinθ
plt.plot(x, y)
plt.title('Flower-like Shape')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('equal')
plt.show()
a) Make a scatter plot of the first two columns, with a distinct marker color for each flower type:
python
import matplotlib.pyplot as plt
import numpy as np
# Assuming you have data for the scatter plot in the form of a NumPy array called 'data'
# Columns 0 and 1 represent the x and y coordinates respectively, and column 2 represents the flower type
# Sample data
data = np.array([
[5.1, 3.5, 0],
[4.9, 3.0, 1],
[4.7, 3.2, 2],
# ... Add more data here ...
])
# Unique flower types
flower_types = np.unique(data[:, 2])
# Scatter plot with distinct marker color for each flower type
for flower_type in flower_types:
x = data[data[:, 2] == flower_type][:, 0]
y = data[data[:, 2] == flower_type][:, 1]
plt.scatter(x, y, label=f'Flower Type {flower_type}')
plt.title('Scatter plot of the first two columns')
plt.xlabel('Column 0')
plt.ylabel('Column 1')
plt.legend()
plt.show()
c) Make a quiver plot, representing sepal data by position, petal data by arrows, and flower type by arrow color:
python
import matplotlib.pyplot as plt
import numpy as np
# Assuming you have data for the quiver plot in the form of a NumPy array called 'data'
# Columns 0 and 1 represent the x and y coordinates of sepal data respectively,
# Columns 2 and 3 represent the x and y components of petal data respectively,
# and column 4 represents the flower type.
# Sample data
data = np.array([
[5.1, 3.5, 1.4, 0.2, 0],
[4.9, 3.0, 1.4, 0.2, 1],
[4.7, 3.2, 1.3, 0.2, 2],
# ... Add more data here ...
])
# Separate data for sepal and petal
sepal_data = data[:, :2]
petal_data = data[:, 2:4]
flower_types = data[:, 4]
# Quiver plot
plt.quiver(sepal_data[:, 0], sepal_data[:, 1], petal_data[:, 0], petal_data[:, 1], flower_types, pivot='mid', cmap='viridis')
plt.title('Quiver plot with flower type by arrow color')
plt.xlabel('Sepal x')
plt.ylabel('Sepal y')
plt.show()
```
d) Make a 3D scatter plot of the sepal and petal data, with the 4th column represented by marker size:
python
import matplotlib.pyplot as plt
import numpy as np
# Assuming you have data for the 3D scatter plot in the form of a NumPy array called 'data'
# Columns 0, 1, and 2 represent the x, y, and z coordinates respectively, and column 3 represents the marker size.
# Sample data
data = np.array([
[5.1, 3.5, 1.4, 10],
[4.9, 3.0, 1.4, 15],
[4.7, 3.2, 1.3, 5],
# ... Add more data here ...
])
x = data[:, 0]
y = data[:, 1]
z = data[:, 2]
marker_size = data[:, 3]
# 3D scatter plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z, s=marker_size)
ax.set_xlabel('Sepal x')
ax.set_ylabel('Sepal y')
ax.set_zlabel('Petal z')
plt.title('3D Scatter plot with marker size representing the 4th column')
plt.show()
learn more about scatter plot-
https://brainly.com/question/17029728
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