In [1]:

    

ls -lah ../data


    

    




total 92M
drwxrwxr-x 2 ilya ilya 4.0K Sep 23 15:58 ./
drwxrwxr-x 5 ilya ilya 4.0K Sep 16 13:23 ../
-rw-rw-r-- 1 ilya ilya  91M Sep 16 09:02 BJ-HSR1.pe.fastq.gz
-rw-rw-r-- 1 ilya ilya 500K Sep 16 09:03 contigs.fasta
-rw-r----- 1 ilya ilya  644 Sep 16 09:00 dHSR1.fa
-rw-rw-r-- 1 ilya ilya 5.4K Sep 23 15:58 gradtimes.txt
-rw-rw-r-- 1 ilya ilya  445 Sep 16 09:00 hHSR-435.fa
-rw-rw-r-- 1 ilya ilya  611 Sep 16 09:00 hHSR.fa
-rw-rw-r-- 1 ilya ilya  126 Sep 16 09:04 rose.fa

In [1]:

    

import csv
import pandas as pd


    

In [2]:

    

from __future__ import print_function


    

In [3]:

    

results = []
with open('../data/gradtimes.txt', 'r') as fi:
    reader = csv.DictReader(fi, fieldnames=['program', 'grad_time'], delimiter='\t')
    for rec in reader:
        results.append(rec)
        
print(len(results))


    

    




525

In [4]:

    

df = pd.DataFrame.from_records(results)
df['grad_time'] = df['grad_time'].map(float)


    

In [5]:

    

df


    

    
Out[5]:






  

    

      

      
grad_time
      
program
    
  
  

    

      
0
      
7.59
      
NEURO
    
    

      
1
      
5.92
      
MICRO
    
    

      
2
      
9.01
      
MICRO
    
    

      
3
      
5.17
      
CMB
    
    

      
4
      
4.17
      
CMB
    
    

      
5
      
4.83
      
DG
    
    

      
6
      
3.08
      
CMB
    
    

      
7
      
7.42
      
CMB
    
    

      
8
      
6.59
      
NEURO
    
    

      
9
      
7.50
      
PHARM
    
    

      
10
      
7.75
      
PHARM
    
    

      
11
      
17.43
      
MOTI
    
    

      
12
      
5.92
      
CMB
    
    

      
13
      
7.34
      
MICRO
    
    

      
14
      
6.75
      
MICRO
    
    

      
15
      
4.25
      
MICRO
    
    

      
16
      
2.83
      
CMB
    
    

      
17
      
4.00
      
MICRO
    
    

      
18
      
4.50
      
MOTI
    
    

      
19
      
5.50
      
PHARM
    
    

      
20
      
10.25
      
NEURO
    
    

      
21
      
3.17
      
DG
    
    

      
22
      
6.25
      
DG
    
    

      
23
      
6.92
      
DG
    
    

      
24
      
5.92
      
MICRO
    
    

      
25
      
5.58
      
MOTI
    
    

      
26
      
5.42
      
NEURO
    
    

      
27
      
4.17
      
MOTI
    
    

      
28
      
5.75
      
NEURO
    
    

      
29
      
3.08
      
CMB
    
    

      
...
      
...
      
...
    
    

      
495
      
5.58
      
PATHOBIOLOGY
    
    

      
496
      
3.92
      
NEURO
    
    

      
497
      
5.25
      
NEURO
    
    

      
498
      
5.58
      
MICRO
    
    

      
499
      
4.50
      
COB
    
    

      
500
      
5.92
      
CMB
    
    

      
501
      
5.58
      
STEM
    
    

      
502
      
5.25
      
PATHOBIOLOGY
    
    

      
503
      
3.58
      
NEURO
    
    

      
504
      
4.00
      
CMB
    
    

      
505
      
2.67
      
PHARM
    
    

      
506
      
3.92
      
MOTI
    
    

      
507
      
4.92
      
NEURO
    
    

      
508
      
4.83
      
PATHOBIOLOGY
    
    

      
509
      
4.92
      
CMB
    
    

      
510
      
5.00
      
MICRO
    
    

      
511
      
5.25
      
STEM
    
    

      
512
      
4.50
      
CMB
    
    

      
513
      
4.92
      
CMB
    
    

      
514
      
5.08
      
NEURO
    
    

      
515
      
5.17
      
NEURO
    
    

      
516
      
4.00
      
NEURO
    
    

      
517
      
3.00
      
BIOMEDICAL
    
    

      
518
      
4.58
      
PATHOBIOLOGY
    
    

      
519
      
4.92
      
PATHOBIOLOGY
    
    

      
520
      
2.34
      
BIOMEDICAL
    
    

      
521
      
3.50
      
COB
    
    

      
522
      
3.25
      
IMMUNOLOGY
    
    

      
523
      
2.83
      
CMB
    
    

      
524
      
2.42
      
PATHOBIOLOGY
    
  

525 rows × 2 columns

In [6]:

    

df.describe()


    

    
Out[6]:






  

    

      

      
grad_time
    
  
  

    

      
count
      
525.000000
    
    

      
mean
      
5.428076
    
    

      
std
      
1.657043
    
    

      
min
      
1.670000
    
    

      
25%
      
4.580000
    
    

      
50%
      
5.250000
    
    

      
75%
      
6.010000
    
    

      
max
      
17.430000
    
  

In [7]:

    

df[df['program'] == 'NEURO'].describe()


    

    
Out[7]:






  

    

      

      
grad_time
    
  
  

    

      
count
      
84.000000
    
    

      
mean
      
5.932738
    
    

      
std
      
1.750239
    
    

      
min
      
3.580000
    
    

      
25%
      
4.980000
    
    

      
50%
      
5.670000
    
    

      
75%
      
6.670000
    
    

      
max
      
16.760000
    
  

In [8]:

    

df[df['program'] <> 'NEURO'].describe()


    

    
Out[8]:






  

    

      

      
grad_time
    
  
  

    

      
count
      
441.000000
    
    

      
mean
      
5.331950
    
    

      
std
      
1.623037
    
    

      
min
      
1.670000
    
    

      
25%
      
4.420000
    
    

      
50%
      
5.250000
    
    

      
75%
      
5.920000
    
    

      
max
      
17.430000
    
  

In [9]:

    

df[df['program'] == 'MBP-GPP'].describe()


    

    
Out[9]:






  

    

      

      
grad_time
    
  
  

    

      
count
      
7.000000
    
    

      
mean
      
5.168571
    
    

      
std
      
1.134481
    
    

      
min
      
3.670000
    
    

      
25%
      
4.420000
    
    

      
50%
      
4.920000
    
    

      
75%
      
6.000000
    
    

      
max
      
6.750000
    
  

In [10]:

    

set(df['program'])


    

    
Out[10]:





{'BIOMEDICAL',
 'CMB',
 'COB',
 'DG',
 'IMMUNOLOGY',
 'MBP',
 'MBP-GPP',
 'MICRO',
 'MOTI',
 'NEURO',
 'PATHOBIOLOGY',
 'PHARM',
 'STEM'}

In [11]:

    

from ggplot import *


    

In [12]:

    

%matplotlib inline


    

In [13]:

    

g = ggplot(aes(x='grad_time'), data=df) \
    + geom_density(fill='#586e75', alpha=0.4)

print(g)


    

    













    




<ggplot: (8765787373389)>

In [14]:

    

import random


    

In [29]:

    

hits = []
nndf = df[df['program'] <> 'NEURO'].copy()
for i in range(100000):
    mean = nndf.sample(n=84)['grad_time'].mean()
    if mean >= 5.9327:
        hits.append(mean)


    

In [30]:

    

len(hits)


    

    
Out[30]:





19

In [ ]: