Visualize all walks

all_walks is a list of lists: every sub-list represents a single random walk. If you convert this list of lists to a Numpy array, you can start making interesting plots! matplotlib.pyplot is already imported as plt.

The nested for loop is already coded for you - don't worry about it. For now, focus on the code that comes after this for loop.

This exercise is part of the course

Intermediate Python for Data Science

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Exercise instructions

Use np.array() to convert all_walks to a Numpy array, np_aw.
Try to use plt.plot() on np_aw. Also include plt.show(). Does it work out of the box?
Transpose np_aw by calling np.transpose() on np_aw. Call the result np_aw_t. Now every row in np_all_walks represents the position after 1 throw for the 10 random walks.
Use plt.plot() to plot np_aw_t; also include a plt.show(). Does it look better this time?

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123)
all_walks = []
for i in range(10) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)  
        random_walk.append(step)
    all_walks.append(random_walk)

# Convert all_walks to Numpy array: np_aw


# Plot np_aw and show


# Clear the figure
plt.clf()

# Transpose np_aw: np_aw_t


# Plot np_aw_t and show

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