Write a function clipped_hist(df, clip_threshold) that plots a histogram of the data after applying a filter that removes all the data above a maximum value, clip_threshold.The default threshold should be 1.0. The title of the plot should indicate the the number of values clipped and the value of the clipping threshold. Use a list comprehension when manipulating the data. [2] import matplotlib.pyplot as plt import random import numpy as np # For reproducibility np.random.seed (42) # Generate some data with a random normal distribution data = [random.gauss(0, 1) for i in range(1000)] # Plot a histogram of the data plt.hist(data);

Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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Write a function clipped_hist(df, clip_threshold) that plots a histogram of the data after applying a filter that removes all the data above
a maximum value, clip_threshold.The default threshold should be 1.0. The title of the plot should indicate the the number of values clipped
and the value of the clipping threshold. Use a list comprehension when manipulating the data.
[2] import matplotlib.pyplot as plt
import random
import numpy as np
# For reproducibility
np.random.seed(42)
# Generate some data with a random normal distribution
data =
[random.gauss(0, 1) for i in range(1000)]
# Plot a histogram of the data
plt.hist(data);
Transcribed Image Text:Write a function clipped_hist(df, clip_threshold) that plots a histogram of the data after applying a filter that removes all the data above a maximum value, clip_threshold.The default threshold should be 1.0. The title of the plot should indicate the the number of values clipped and the value of the clipping threshold. Use a list comprehension when manipulating the data. [2] import matplotlib.pyplot as plt import random import numpy as np # For reproducibility np.random.seed(42) # Generate some data with a random normal distribution data = [random.gauss(0, 1) for i in range(1000)] # Plot a histogram of the data plt.hist(data);
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