In [1]:

    

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np


    

In [2]:

    

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
        'pre_score': [4, 24, 31, 2, 3],
        'mid_score': [25, 94, 57, 62, 70],
        'post_score': [5, 43, 23, 23, 51]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'pre_score', 'mid_score', 'post_score'])
df


    

    
Out[2]:






  

    

      

      
first_name
      
pre_score
      
mid_score
      
post_score
    
  
  

    

      
0
      
Jason
      
4
      
25
      
5
    
    

      
1
      
Molly
      
24
      
94
      
43
    
    

      
2
      
Tina
      
31
      
57
      
23
    
    

      
3
      
Jake
      
2
      
62
      
23
    
    

      
4
      
Amy
      
3
      
70
      
51
    
  

In [3]:

    

# Create a list of the mean scores for each variable
mean_values = [df['pre_score'].mean(), df['mid_score'].mean(), df['post_score'].mean()]

# Create a list of variances, which are set at .25 above and below the score
variance = [df['pre_score'].mean() * 0.25, df['pre_score'].mean() * 0.25, df['pre_score'].mean() * 0.25]

# Set the bar labels
bar_labels = ['Pre Score', 'Mid Score', 'Post Score']

# Create the x position of the bars
x_pos = list(range(len(bar_labels)))

# Create the plot bars
# In x position
plt.bar(x_pos,
        # using the data from the mean_values
        mean_values, 
        # with a y-error lines set at variance
        yerr=variance, 
        # aligned in the center
        align='center',
        # with color
        color='#FFC222',
        # alpha 0.5
        alpha=0.5)

# add a grid
plt.grid()

# set height of the y-axis
max_y = max(zip(mean_values, variance)) # returns a tuple, here: (3, 5)
plt.ylim([0, (max_y[0] + max_y[1]) * 1.1])

# set axes labels and title
plt.ylabel('Score')
plt.xticks(x_pos, bar_labels)
plt.title('Mean Scores For Each Test')

plt.show()