There are two different interfaces in matplotlib. One is MATLAB(functional)-style interface, and the other is object-oriented interface.
The functional interface is stateless, while the object-oriented interface is stateful in matplotlib.
To generate a scatter plot in matplotlib, we can use either plot() or scatter() function.
To draw two figures in the same figure, we can call the plot() function twice.
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plt.plot(x, y, '--sk'), what does the format string '--sk' mean?Draw a dash line with black color and square marker.
Draw a solid line with black color and circle marker.
Draw a dotted line with sky blue color and square marker.
Draw a dash line with pink color and circle marker.
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Each axis in matplotlib contains both major and minor tick marks. As implied by their names, major ticks are generally more noticeable or sizable, while minor ticks tend to be smaller.
The keyword argument density=True in hist() function serves to normalize the histogram, enabling it to be shown on the same axes as the data.
By default, spines are situated at the edges of the axis and act as lines that connect axis tick marks, delineating the data area’s boundaries.
The primary benefit of using plot() instead of scatter() is the capability it offers to create scatter plots, allowing for individual customization of each point’s properties.
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scatter() instead of plot() enjoy the benifit of being more customizable.plt.subplots(3, 2)?A grid with 3 columns and 2 rows and the first subplot is at the top left corner.
A grid with 3 rows and 2 columns and the first subplot is at the bottom left corner.
A grid with 3 rows and 2 columns and the first subplot is at the top left corner.
A grid with 3 columns and 2 rows and the first subplot is at the bottom left corner.
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%matplotlib qt
%matplotlib inline
%matplotlib notebook
%matplotlib ipympl
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import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('seaborn-whitegrid')C:\Users\adm\AppData\Local\Temp\ipykernel_32440\1473361050.py:4: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
plt.style.use('seaborn-whitegrid')# The rose curve:
t = np.linspace(0, 2*np.pi, 1000)
r = 2*np.sin(2*t)
# plot in polar coordinates
plt.axes(projection='polar')
plt.plot(t+(r<0)*np.pi, np.abs(r), '-')
# Set ticks for polar coordinate
plt.xticks([0, np.pi/2, np.pi, 3*np.pi/2], ['0', '$\pi/2$', '$\pi$', '$3\pi/2$']);
plt.yticks([0, 0.5, 1, 1.5, 2]);
Note that you should change the ticks according to the above plot and you can use the trick mentioned in our slides to set the radius of 0 to the origin.
# Sine Functions with Different Frequencies:
x = np.linspace(0, 2 * np.pi, 1000)
# Calculate the sine functions
y1 = np.sin(x)
y2 = np.sin(2 * x)
y3 = np.sin(3 * x)
y4 = np.sin(4 * x)
# Create the figure and subplots
fig, axs = plt.subplots(2, 2, figsize=(10, 10))
# Plot the sine functions in the subplots
axs[0, 0].plot(x, y1)
axs[0, 0].set_title('sin(x)')
axs[1, 0].plot(x, y2)
axs[1, 0].set_title('sin(2x)')
axs[0, 1].plot(x, y3)
axs[0, 1].set_title('sin(3x)')
axs[1, 1].plot(x, y4)
axs[1, 1].set_title('sin(4x)');
Remember to add the title to your plot using set_title() function.
blue and alpha=0.25 as follows:# Example plot:
n = 1000
X = np.linspace(-np.pi, np.pi, n)
Y = np.sin(2 * X)
#plt.axes([0.025, 0.025, 0.95, 0.95])
plt.plot(X, Y, color='blue', alpha=1.00)
plt.fill_between(X, 0, Y, color='blue', alpha=.25)
plt.xlim(-np.pi, np.pi)
#plt.ylim(-2.5, 2.5)
radian_multiples = [-1, -1/2, 0, 1/2, 1]
radians = [n * np.pi for n in radian_multiples]
radian_labels = ['$\pi$', '$-\pi/2$', '0', '$\pi/2$', '$\pi$']
plt.xticks(radians, radian_labels);
You can use the following code to set the ticks:
np.random.rand() to generate 10000 data points and plot the histogram of the data points with 50 bins and normalize it so that the area under the histogram integrates to 1. Check out the documentation of np.random.rand() on the official website. Does the plot look like the distribution mentioned on the official website?Yes, it looks like the data are random samples from a uniform distribution over [0, 1).
plot() function and set the limit of x between \([-2\pi, 2\pi]\), we will get the following figure:x = np.linspace(-2 * np.pi, 2 * np.pi, 1000)
y = np.tan(x)
plt.xlabel("x")
plt.ylabel("$tan(x)$")
plt.xlim(-2 * np.pi, 2 * np.pi)
plt.title("$y = tan(x)$")
plt.plot(x, y)
radian_multiples = [-2, -3/2, -1, -1/2, 0, 1/2, 1, 3/2, 2]
radians = [n * np.pi for n in radian_multiples]
radian_labels = ['$-2\pi$', '$-3\pi/2$', '$\pi$', '$-\pi/2$', '0', '$\pi/2$', '$\pi$', '$3\pi/2$', '$2\pi$']
plt.xticks(radians, radian_labels);
Try to explain why the plot is not as expected and solve the problem to obtain the following plot:
# The fix tan(x) plot
x = np.linspace(-2 * np.pi, 2 * np.pi, 1000)
y = np.tan(x)
#y[:-1][np.diff(y) < 0] = np.nan
y[np.isclose(x, 3*np.pi/2, rtol=1e-02)] = np.nan
y[np.isclose(x, np.pi/2, rtol=1e-02)] = np.nan
y[np.isclose(x, -np.pi/2, rtol=1e-02)] = np.nan
y[np.isclose(x, -3*np.pi/2, rtol=1e-02)] = np.nan
plt.xlabel("x")
plt.ylabel("$tan(x)$")
plt.xlim(-2 * np.pi, 2 * np.pi)
plt.ylim(-5, 5)
plt.title("$y = tan(x)$")
plt.plot(x, y)
radian_multiples = [-2, -3/2, -1, -1/2, 0, 1/2, 1, 3/2, 2]
radians = [n * np.pi for n in radian_multiples]
radian_labels = ['$-2\pi$', '$-3\pi/2$', '$\pi$', '$-\pi/2$', '0', '$\pi/2$', '$\pi$', '$3\pi/2$', '$2\pi$']
plt.xticks(radians, radian_labels);
Hint: You can set the point at the discontinuity to np.nan so that the point won’t be plotted in the figure.