NumPy: Compute the line graph of a set of data

NumPy: Array Object Exercise-117 with Solution

Generate Line Graph of Data

Write a NumPy program to compute the line graph of a set of data.

Sample Solution:

Python Code:

# Importing the NumPy library and aliasing it as 'np'
import numpy as np

# Importing the matplotlib.pyplot module and aliasing it as 'plt'
import matplotlib.pyplot as plt

# Generating a NumPy array 'arr' with 10 random integers between 1 and 50 (excluding 50)
arr = np.random.randint(1, 50, 10)

# Creating a histogram with np.histogram()
# 'y' stores the frequencies, 'x' stores the bin edges using np.arange(51) (from 0 to 50)
y, x = np.histogram(arr, bins=np.arange(51))

# Creating a figure and axis using plt.subplots()
fig, ax = plt.subplots()

# Plotting the histogram by plotting the bin edges against frequencies
ax.plot(x[:-1], y)

# Displaying the figure
fig.show()

Sample Output:

Explanation:

In the above code –

arr = np.random.randint(1, 50, 10): This line generates an array of 10 random integers between 1 and 49 (inclusive) using np.random.randint() function.

y, x = np.histogram(arr, bins=np.arange(51)): This line computes the histogram of the random integers using np.histogram() function with bins from 0 to 50 (51 bins in total). y will contain the frequency counts for each bin, and x will contain the bin edges.

fig, ax = plt.subplots(): Create a Matplotlib figure and axis object using the plt.subplots() function. This will be used to create and customize the line plot.

ax.plot(x[:-1], y): Plot the histogram on the axis object ax using the ax.plot() function. Since x contains the bin edges, we use x[:-1] to exclude the last bin edge and match the length of y.

Finally fig.show() function displays the line plot of the histogram with the bins on the x-axis and their corresponding counts on the y-axis.

Python-Numpy Code Editor:

Previous: Write a NumPy program to compute the histogram of a set of data.
Next: Write a NumPy program to find the position of the index of a specified value greater than existing value in numpy array.