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Data Science Interview Challenge

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Challenge 1: Fundamentals of Plotting

Matplotlib, a cornerstone visualization library in Python, provides a vast array of plotting capabilities that are both expressive and efficient. Some compelling advantages of using Matplotlib for your data visualization tasks are:

  • Versatility: Matplotlib supports a diverse range of plots, from basic line plots to more complex visualizations like contour plots.

  • Customization: Every aspect of a plot, from its colors to its labels, can be tailored, providing full control to the user.

  • Integration: It works seamlessly with other libraries, especially Pandas and NumPy, making it a fundamental tool in the data analysis workflow.

For budding data scientists, analysts, or anyone keen on visual representation, Matplotlib's plotting functions act as a bridge between raw data and insights.

Завдання

Swipe to start coding

Plot three foundational graph types using Matplotlib:

  1. Plot a simple line graph.
  2. Create a scatter plot.
  3. Generate a histogram.

Рішення

import matplotlib.pyplot as plt
import numpy as np

# Fix seed
np.random.seed(1)

# 1. Plot a simple line graph.
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

# 2. Create a scatter plot.
x_scatter = np.random.rand(50)
y_scatter = np.random.rand(50)
plt.scatter(x_scatter, y_scatter)
plt.show()

# 3. Generate a histogram.
data = np.random.randn(1000)
plt.hist(data, bins=30)
plt.show()

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Секція 4. Розділ 1
import matplotlib.pyplot as plt
import numpy as np

# Fix seed
np.random.seed(1)

# 1. Plot a simple line graph.
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.___(___, ___)
plt.show()

# 2. Create a scatter plot.
x_scatter = np.random.rand(50)
y_scatter = np.random.rand(50)
plt.___(___, ___)
plt.show()

# 3. Generate a histogram.
data = np.random.randn(1000)
plt.___(___, bins=30)
plt.show()

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