Interview Questions and Answers

`plt.show()` displays the plot interactively, while `plt.savefig()` saves the current figure to a file without displaying it.

Example:

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.savefig('plot.png')

`plt.plot()` connects data points with lines, while `plt.scatter()` shows individual data points without connecting them.

Example:

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.scatter([1, 2, 3, 4], [10, 20, 25, 30])
plt.show()

`plt.legend()` is used to add a legend to the plot, providing information about the plotted data series.

Example:

plt.plot([1, 2, 3, 4], [10, 20, 25, 30], label='Line 1')
plt.legend()
plt.show()

A subplot is a way to organize multiple plots within a single figure. It is created using `plt.subplot()`.

Example:

plt.subplot(2, 1, 1)
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.subplot(2, 1, 2)
plt.scatter([1, 2, 3, 4], [10, 20, 25, 30])
plt.show()

`plt.xticks()` and `plt.yticks()` are used to customize the tick positions and labels on the x-axis and y-axis, respectively.

Example:

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.xticks([1, 2, 3, 4], ['A', 'B', 'C', 'D'])
plt.show()

`plt.imshow()` is used to display images in Matplotlib plots.

Example:

import matplotlib.image as mpimg
img = mpimg.imread('image.png')
plt.imshow(img)
plt.show()

Use the `plt.text()` function to add text annotations at specified coordinates on the plot.

Example:

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.text(2, 15, 'Annotation')
plt.show()

`plt.pie()` is used to create a pie chart in Matplotlib, representing data in a circular statistical graphic.

Example:

sizes = [20, 30, 40, 10]
labels = ['A', 'B', 'C', 'D']
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.show()

Use the `plt.xscale()` and `plt.yscale()` functions with the argument 'log' to create a logarithmic scale on the x-axis and y-axis, respectively.

Example:

plt.plot([1, 2, 3, 4], [10, 20, 30, 40])
plt.xscale('log')
plt.yscale('log')
plt.show()

Use the `plt.errorbar()` function to create a plot with error bars.

Example:

x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
error = [1, 2, 1, 3]
plt.errorbar(x, y, yerr=error, fmt='o', capsize=5)
plt.show()

Use the `plt.violinplot()` function to create a violin plot, which is used for visualizing the distribution of data.

Example:

data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.violinplot(data, showmedians=True)
plt.show()

Use the `plt.polar()` function to create a polar plot, which is useful for visualizing data in circular coordinates.

Example:

theta = np.linspace(0, 2*np.pi, 100)
r = 1 + np.sin(3*theta)
plt.polar(theta, r)
plt.show()