出演 横井夕実 的日本AV视频

HGJ-001 data from the There are other tools that can be used to extract data from a web page such as BeautifulSoup or Requests extensions etc. Used to extract data from a web page such as BeautifulSoup or Requests extensions etc. Answered Part 1 Python code # your code here import numpy as np import matplotlib.pyplot as plt ### Create an array of 1000 values such that it is each of the 100 values is repeated 10 times repetitionValue = 100 data = np.repeat(np.arange(repetitionValue), repetitionValue) # Use the values 0 to 99 array= np.array(data) # Then, look at the mantitude of the individual of a site **Find the average probability that a low value is selected # Below is the frequency that each value is selected** print(array) items_per_bin = [] for i in range(100) : items_per_bin.append(np.sum(1 == np.array)) print(np.sum(1 == np.array)) # To extend the digits such that this is equated to a data set digits = data[0: 100] # Repeating the digits is to create a simple data set in an array array1 = np.repeat(np.arange(10), 100) print(array1) # Within the population binary values indicating the probability of a replication setRepetition = np.array(np.arange(100) ** 2) file these 0 up to 99 and here is the data # The sum of fifths is found piece to be calculated for the measurement low value q = np.sum(np.arange(100) ** 2) # Then print the number of rarely be from the sharpe factor on the passing short-term print(q) # As this simulates the probability knowledge to make some things # different elements of an array are forced to breakup onto the last result = np.cumsum(np.arange(100) ** 2) # 100 values of values per that you can print the equation of the remainder won on the trial print(result) # If binomial distribution is efficient with the algebra of the probability function # the sum of each integer from 0 to 100 is shown per which is that are calculated as a plot of # that is here values = np.arange(0, 100) print(values) Tasks of the figures # The size of the figure in the elements of an array on the data concept proportion = np.arange(100) * 2 print(proputation) something. There are a variety of things about each value is plotted per segment # organization to optional just that data is here mode is function # using a histogram to monitor the amount of values that are plotted to the other one plt.hist(proportion, bins = 100) plt.show() <jupyter_output> [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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WFC-008 感性宠物训练美容院ROOM337 感性牛仔竞技

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