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In [10]:

    
import pandas as pd
data = pd.read_csv('/Users/markhoughton/Documents/code_projects/py_projects/euroscipy_2015/stats/examples/brain_size.csv', sep=';', na_values=".")
data



In [11]:

    
import numpy as np
t = np.linspace(-6, 6, 20)
sin_t = np.sin(t)
cos_t = np.cos(t)



In [12]:

    
pd.DataFrame({'t':t, 'sin':sin_t, 'cos':cos_t})



In [13]:

    
data.shape









    Out[13]:





(40, 8)



In [14]:

    
data.columns









    Out[14]:





Index([u'Unnamed: 0', u'Gender', u'FSIQ', u'VIQ', u'PIQ', u'Weight', u'Height',
       u'MRI_Count'],
      dtype='object')



In [15]:

    
data['Gender']









    Out[15]:





0     Female
1       Male
2       Male
3       Male
4     Female
5     Female
6     Female
7     Female
8       Male
9       Male
10    Female
11      Male
12      Male
13    Female
14    Female
15    Female
16    Female
17      Male
18    Female
19      Male
20      Male
21      Male
22    Female
23      Male
24    Female
25      Male
26    Female
27      Male
28    Female
29    Female
30    Female
31      Male
32      Male
33      Male
34    Female
35    Female
36      Male
37    Female
38      Male
39      Male
Name: Gender, dtype: object



In [16]:

    
# Simpler selector
data[data['Gender'] == 'Female']['VIQ'].mean()









    Out[16]:





109.45



In [21]:

    
groupby_gender = data.groupby('Gender')
for gender, value in groupby_gender['VIQ']:
    print gender,value.mean()









    



Female 109.45
Male 115.25



In [22]:

    
groupby_gender.mean()



In [25]:

    
from pandas.tools import plotting
plotting.scatter_matrix(data[['Weight','Height','MRI_Count']])
show()



In [28]:

    
plotting.scatter_matrix(data[['PIQ','VIQ','FSIQ']])
show()



In [32]:

    
from scipy import stats
stats.ttest_1samp(data['VIQ'], 0)









    Out[32]:





(30.088099970849328, 1.3289196468728067e-28)



In [ ]:

	Unnamed: 0	Gender	FSIQ	VIQ	PIQ	Weight	Height	MRI_Count
0	1	Female	133	132	124	118	64.5	816932
1	2	Male	140	150	124	NaN	72.5	1001121
2	3	Male	139	123	150	143	73.3	1038437
3	4	Male	133	129	128	172	68.8	965353
4	5	Female	137	132	134	147	65.0	951545
5	6	Female	99	90	110	146	69.0	928799
6	7	Female	138	136	131	138	64.5	991305
7	8	Female	92	90	98	175	66.0	854258
8	9	Male	89	93	84	134	66.3	904858
9	10	Male	133	114	147	172	68.8	955466
10	11	Female	132	129	124	118	64.5	833868
11	12	Male	141	150	128	151	70.0	1079549
12	13	Male	135	129	124	155	69.0	924059
13	14	Female	140	120	147	155	70.5	856472
14	15	Female	96	100	90	146	66.0	878897
15	16	Female	83	71	96	135	68.0	865363
16	17	Female	132	132	120	127	68.5	852244
17	18	Male	100	96	102	178	73.5	945088
18	19	Female	101	112	84	136	66.3	808020
19	20	Male	80	77	86	180	70.0	889083
20	21	Male	83	83	86	NaN	NaN	892420
21	22	Male	97	107	84	186	76.5	905940
22	23	Female	135	129	134	122	62.0	790619
23	24	Male	139	145	128	132	68.0	955003
24	25	Female	91	86	102	114	63.0	831772
25	26	Male	141	145	131	171	72.0	935494
26	27	Female	85	90	84	140	68.0	798612
27	28	Male	103	96	110	187	77.0	1062462
28	29	Female	77	83	72	106	63.0	793549
29	30	Female	130	126	124	159	66.5	866662
30	31	Female	133	126	132	127	62.5	857782
31	32	Male	144	145	137	191	67.0	949589
32	33	Male	103	96	110	192	75.5	997925
33	34	Male	90	96	86	181	69.0	879987
34	35	Female	83	90	81	143	66.5	834344
35	36	Female	133	129	128	153	66.5	948066
36	37	Male	140	150	124	144	70.5	949395
37	38	Female	88	86	94	139	64.5	893983
38	39	Male	81	90	74	148	74.0	930016
39	40	Male	89	91	89	179	75.5	935863

	cos	sin	t
0	0.960170	0.279415	-6.000000
1	0.609977	0.792419	-5.368421
2	0.024451	0.999701	-4.736842
3	-0.570509	0.821291	-4.105263
4	-0.945363	0.326021	-3.473684
5	-0.955488	-0.295030	-2.842105
6	-0.596979	-0.802257	-2.210526
7	-0.008151	-0.999967	-1.578947
8	0.583822	-0.811882	-0.947368
9	0.950551	-0.310567	-0.315789
10	0.950551	0.310567	0.315789
11	0.583822	0.811882	0.947368
12	-0.008151	0.999967	1.578947
13	-0.596979	0.802257	2.210526
14	-0.955488	0.295030	2.842105
15	-0.945363	-0.326021	3.473684
16	-0.570509	-0.821291	4.105263
17	0.024451	-0.999701	4.736842
18	0.609977	-0.792419	5.368421
19	0.960170	-0.279415	6.000000

	Unnamed: 0	FSIQ	VIQ	PIQ	Weight	Height	MRI_Count
Gender
Female	19.65	111.9	109.45	110.45	137.200000	65.765000	862654.6
Male	21.35	115.0	115.25	111.60	166.444444	71.431579	954855.4