Analysis of the current state

In [1]:

    

%matplotlib inline
from __future__ import division
import pandas as pd
import numpy as np
import thinkstats2
import thinkplot


    

In [2]:

    

data = pd.read_csv("zemi.csv")
data = data[data['junior'] > 0]
data.head()


    

    
Out[2]:






  

    

      

      
id
      
name
      
12
      
13
      
14
      
15_1
      
15_2
      
15_3
      
all
      
junior
      
...
      
s7
      
s8
      
s9
      
s10
      
s11
      
s12
      
s13
      
s14
      
s15
      
s16
    
  
  

    

      
0
      
1
      
aoki
      
0
      
0
      
9
      
6
      
5
      
0
      
20
      
11
      
...
      
118
      
1103
      
1119
      
1120
      
2050
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
1
      
2
      
ishihara
      
0
      
0
      
9
      
6
      
8
      
0
      
23
      
14
      
...
      
31
      
42
      
101
      
139
      
171
      
1042
      
1058
      
2044
      
NaN
      
NaN
    
    

      
2
      
3
      
itou
      
0
      
0
      
12
      
13
      
0
      
0
      
25
      
13
      
...
      
174
      
109
      
99
      
96
      
36
      
35
      
34
      
NaN
      
NaN
      
NaN
    
    

      
3
      
4
      
ueda
      
0
      
0
      
10
      
13
      
0
      
0
      
23
      
13
      
...
      
173
      
1075
      
1093
      
2014
      
2029
      
2035
      
2040
      
NaN
      
NaN
      
NaN
    
    

      
4
      
5
      
oohashi
      
0
      
1
      
4
      
2
      
9
      
0
      
16
      
11
      
...
      
1009
      
1034
      
70
      
1109
      
78
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
  

5 rows × 29 columns

In [3]:

    

# summary statistics
data['junior'].describe()


    

    
Out[3]:





count    42.000000
mean      9.166667
std       4.247907
min       1.000000
25%       5.250000
50%      10.000000
75%      13.000000
max      16.000000
Name: junior, dtype: float64

In [4]:

    

# histgram
junior = thinkstats2.Hist(np.asarray(data['junior']))
thinkplot.PrePlot(2)
thinkplot.Hist(junior, label='Juniors')
thinkplot.Config(title='The number of juniors in each seminar', xlabel='The num of juniors', ylabel='Freq', loc=2, axis=[0, 17, 0, 7])


    

In [5]:

    

# separate seminar from lecture
semi = data[data['zemi']==1]
lec = data[data['zemi']==0]


    

In [6]:

    

# summary statistics of 'seminar'
semi['junior'].describe()


    

    
Out[6]:





count    35.000000
mean      9.657143
std       3.925354
min       3.000000
25%       6.000000
50%      10.000000
75%      13.000000
max      16.000000
Name: junior, dtype: float64

In [7]:

    

# histgram of 'seminar'
semi_hist = thinkstats2.Hist(np.asarray(semi['junior']))
thinkplot.PrePlot(2)
thinkplot.Hist(semi_hist, label='seminar')
thinkplot.Config(title='The number of juniors in each seminar', xlabel='The num of juniors', ylabel='Freq', loc=2, axis=[0, 17, 0, 5])


    

In [8]:

    

semi_cmf = thinkstats2.Cdf(np.asarray(semi['junior']))
thinkplot.PrePlot(2)
thinkplot.Cdf(semi_cmf, label='seminar')
thinkplot.Config(title='The number of juniors in each seminar', xlabel='The num of juniors', ylabel='Freq', loc=2, axis=[0, 17, 0, 1])


    

In [9]:

    

junior = []
double = []
single = []
for i in np.asarray(semi[['s1','s2','s3','s4','s5','s6','s7','s8','s9','s10','s11','s12','s13','s14','s15','s16']], dtype=int).reshape(16*35).tolist():
    if i >=0:
        junior.append(i)
hist = thinkstats2.Hist(junior)
for val, freq in hist.Items():
    if freq == 2:
        double.append(val)
    elif freq == 1:
        single.append(val)
    else:
        print('ERROR')
print('Capa' ,str(len(junior)))
print('Single', len(single))
print('Double', len(double))
print('None', 345-len(single)-len(double))
print('All', len(single)+len(double))


    

    




('Capa', '338')
('Single', 236)
('Double', 51)
('None', 58)
('All', 287)