title: Using bootstrapping random.choice
body: <p>I am trying to use bootstrapping to make 1000 replications of the sons (np.random.choice) for resampling with replacement, so that i can calculate the mean for each replication. Then I would compare the standard deviation of these mean values ​​with standard.</p>
  <p>However I don't get bootstrapping part right, how to fix that?</p>
  <pre><code>import numpy as np
  import pandas as pd
  from matplotlib import pyplot as plt
  import seaborn as sns
  from scipy import stats
  
  df = pd.read_csv('http://www.math.uah.edu/stat/data/Pearson.txt',
                   delim_whitespace=True)
  df.head()
  y = df['Son'].values
  
  Replications = np.random.choice(y, 1000, replace = True)
  print(&quot;Replications: &quot; , Replications)
  print(&quot;&quot;)
  Mean = np.mean(Replications)
  
  print(&quot;Mean: &quot; , Mean)
  
  sem = stats.sem(y)
  print (&quot;The SEM : &quot;, sem)
  </code></pre>