In [2]:

    

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


    

In [3]:

    

#df = pd.read_csv('/home/tribilium/Downloads/grades.csv', header=None, names=['grades'])
df = pd.read_csv('grades.csv', header=None, names=['grades'])


    

In [5]:

    

df.head()


    

    
Out[5]:







  

    

      

      
grades
    
  
  

    

      
0
      
81
    
    

      
1
      
71
    
    

      
2
      
67
    
    

      
3
      
54
    
    

      
4
      
90
    
  

In [6]:

    

df.describe()


    

    
Out[6]:







  

    

      

      
grades
    
  
  

    

      
count
      
32.000000
    
    

      
mean
      
83.500000
    
    

      
std
      
9.638632
    
    

      
min
      
54.000000
    
    

      
25%
      
81.000000
    
    

      
50%
      
86.000000
    
    

      
75%
      
90.000000
    
    

      
max
      
97.000000
    
  

In [7]:

    

df.columns[0]
grade_mat = df.as_matrix()


    

In [9]:

    

grade_mat


    

    
Out[9]:





array([[81],
       [71],
       [67],
       [54],
       [90],
       [93],
       [90],
       [91],
       [97],
       [94],
       [83],
       [91],
       [90],
       [84],
       [84],
       [83],
       [87],
       [87],
       [88],
       [95],
       [86],
       [92],
       [66],
       [87],
       [77],
       [84],
       [86],
       [82],
       [88],
       [81],
       [69],
       [74]], dtype=int64)

In [10]:

    

grade_mat.shape


    

    
Out[10]:





(32, 1)

In [14]:

    

plt.style.use('fivethirtyeight')
fig, ax = plt.subplots()
plt.hist(grade_mat, bins=[50, 60, 70, 80, 90, 100])
ax.set_xlim([50, 100])
ax.set_xlabel('Grade Range')
ax.set_ylabel('Number of Students')
ax.set_title('Histogram of Exam 1 Grades - ENGR262 Winter 2018')


    

    
Out[14]:





Text(0.5,1,'Histogram of Exam 1 Grades - ENGR262 Winter 2018')






    

In [15]:

    

plt.style.use('fivethirtyeight')
fig, ax = plt.subplots()
plt.hist(df['grades'], bins=[50, 60, 70, 80, 90, 100])
ax.set_xlim([50, 100])
ax.set_xlabel('Grade Range')
ax.set_ylabel('Number of Students')
ax.set_title('Histogram of Exam 1 Grades - ENGR262 Winter 2018')


    

    
Out[15]:





Text(0.5,1,'Histogram of Exam 1 Grades - ENGR262 Winter 2018')






    

In [16]:

    

min_grade = min(df['grades'])


    

In [17]:

    

min_grade


    

    
Out[17]:





54

In [18]:

    

min_grade=min_grade*.1


    

In [19]:

    

min_grade


    

    
Out[19]:





5.4000000000000004

In [23]:

    

min_grade_floor = np.floor(min_grade)


    

In [25]:

    

min_grade = min_grade_floor*10
min_grade = int(min_grade)


    

In [26]:

    

bin_array = np.arange(min_grade,110,10)
bin_array


    

    
Out[26]:





array([ 50,  60,  70,  80,  90, 100])

In [29]:

    

bin_array.tolist()


    

    
Out[29]:





[50, 60, 70, 80, 90, 100]

In [30]:

    

plt.style.use('fivethirtyeight')
fig, ax = plt.subplots()
plt.hist(df['grades'], bins=bin_array)
ax.set_xlim([min_grade, 100])
ax.set_xlabel('Grade Range')
ax.set_ylabel('Number of Students')
ax.set_title('Histogram of Exam 1 Grades - ENGR262 Winter 2018')


    

    
Out[30]:





Text(0.5,1,'Histogram of Exam 1 Grades - ENGR262 Winter 2018')






    

In [ ]: