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In [37]:

    
import pandas as pd
import re



In [136]:

    
mtdata = pd.read_csv('mousetrackercorrected.csv')



In [17]:

    
includelist = pd.read_csv('n=452 subjectID.csv', header = None)
includelist = includelist[0].values



In [137]:

    
mtdata.loc[mtdata['subject'].isin(includelist)]









    Out[137]:






  
    
      
      subject
      trial
      stimfile
      condition
      code
      resp_1
      resp_2
      response
      distractor
      error
      ...
      Y_92
      Y_93
      Y_94
      Y_95
      Y_96
      Y_97
      Y_98
      Y_99
      Y_100
      Y_101
    
  
  
  

0 rows × 226 columns



In [181]:

    
mtdata['RESPONSE1'] = [x for x in mtdata['resp_1'].str.extract(r'..(\d*)_.(\d*)', expand = True).values]
mtdata['RESPONSE2'] = [x for x in mtdata['resp_2'].str.extract(r'..(\d*)_.(\d*)', expand = True).values]









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-181-e417fb0c78b3> in <module>()
      1 mtdata['RESPONSE1'] = [x for x in mtdata['resp_1'].str.extract(r'..(\d*)_.(\d*)', expand = True).values]
      2 mtdata['RESPONSE2'] = [x for x in mtdata['resp_2'].str.extract(r'..(\d*)_.(\d*)', expand = True).values]
----> 3 mtdata['RESPONSE1'] = [int(x) for x in mtdata['RESPONSE1']]
      4 mtdata['RESPONSE2'] = [int(x) for x in mtdata['RESPONSE2']]

TypeError: only length-1 arrays can be converted to Python scalars



In [141]:

    
mtdata['SELFISHCHOICESELFMORE'] = 0
mtdata['SELFISHCHOICEGROUPMORE'] = 0



In [155]:

    
mtdata.head()









    Out[155]:






  
    
      
      subject
      trial
      stimfile
      condition
      code
      resp_1
      resp_2
      response
      distractor
      error
      ...
      Y_96
      Y_97
      Y_98
      Y_99
      Y_100
      Y_101
      RESPONSE1
      RESPONSE2
      SELFISHCHOICESELFMORE
      SELFISHCHOICEGROUPMORE
    
  
  
    
      0
      455806
      1
      NaN
      777
      prac2
      ~S3_O6.jpg
      ~S7_O8.jpg
      1
      1
      1
      ...
      1.1400
      1.1400
      1.1400
      1.1400
      1.1400
      1.1400
      [3, 6]
      [7, 8]
      0
      0
    
    
      1
      455806
      3
      NaN
      777
      prac8
      ~S5_O4.jpg
      ~S10_O11.jpg
      1
      1
      1
      ...
      1.1500
      1.1500
      1.1500
      1.1500
      1.1500
      1.1500
      [5, 4]
      [10, 11]
      0
      0
    
    
      2
      455806
      5
      NaN
      777
      prac4
      ~S5_O4.jpg
      ~S8_O9.jpg
      1
      2
      0
      ...
      1.1250
      1.1250
      1.1250
      1.1250
      1.1250
      1.1250
      [5, 4]
      [8, 9]
      0
      0
    
    
      3
      455806
      7
      NaN
      777
      prac5
      ~S10_O9.jpg
      ~S7_O8.jpg
      1
      1
      1
      ...
      1.2125
      1.2125
      1.2125
      1.2125
      1.2125
      1.2125
      [10, 9]
      [7, 8]
      0
      0
    
    
      4
      455806
      9
      NaN
      777
      smgmvsslgl12
      ~S14_O10.jpg
      ~S9_O12.jpg
      1
      2
      0
      ...
      1.1900
      1.1900
      1.1900
      1.1900
      1.1900
      1.1900
      [14, 10]
      [9, 12]
      0
      0
    
  

5 rows × 230 columns

if error == 0, then selfish, and that's response 1? if 1 then altruistic

ERROR 0 REPSPONSE 0 ERROR 1 RESPONSE 0 ERROR 0 RESPONSE 1 ERROR 1 RESPONSE 1



In [171]:

    
tempdata = pd.DataFrame(columns = ('RESPONSE','ERROR','RESPONSE1','RESPONSE2'))
tempdata['RESPONSE'] = mtdata['response']
tempdata['ERROR'] = mtdata['error']
tempdata['RESPONSE1'] = mtdata['RESPONSE1']
tempdata['RESPONSE2'] = mtdata['RESPONSE2']
tempdata['SELFISHCHOICESELFMORE'] = 0
tempdata['SELFISHCHOICEGROUPMORE'] = 0



In [172]:

    
tempdata.head()









    Out[172]:






  
    
      
      RESPONSE
      ERROR
      RESPONSE1
      RESPONSE2
      SELFISHCHOICESELFMORE
      SELFISHCHOICEGROUPMORE
    
  
  
    
      0
      1
      1
      [3, 6]
      [7, 8]
      0
      0
    
    
      1
      1
      1
      [5, 4]
      [10, 11]
      0
      0
    
    
      2
      1
      0
      [5, 4]
      [8, 9]
      0
      0
    
    
      3
      1
      1
      [10, 9]
      [7, 8]
      0
      0
    
    
      4
      1
      0
      [14, 10]
      [9, 12]
      0
      0



In [204]:

    
SELFISHCHOICESELFMORE = []
SELFISHCHOICEGROUPMORE = []

for row in tempdata.iterrows():
    if (row[1][0] == 1) & (row[1][1] == 0) | ((row[1][0] == 2) & (row[1][1] == 1)):
        try:
            SELFISHCHOICESELFMORE.append(int(row[1][2][0]) - int(row[1][3][0]))
            SELFISHCHOICEGROUPMORE.append((int(row[1][2][0]) + int(row[1][2][1])) - (int(row[1][3][0]) + int(row[1][3][1])))
        except:
            SELFISHCHOICESELFMORE.append(None)
            SELFISHCHOICEGROUPMORE.append(None)
    elif ((row[1][0] == 2) & (row[1][1] == 0)) | ((row[1][0] == 1) & (row[1][1] == 1)):
        try:
            SELFISHCHOICESELFMORE.append(int(row[1][3][0]) - int(row[1][2][0]))
            SELFISHCHOICEGROUPMORE.append((int(row[1][3][0]) + int(row[1][3][1])) - (int(row[1][2][0]) + int(row[1][2][1])))
        except:
            SELFISHCHOICESELFMORE.append(None)
            SELFISHCHOICEGROUPMORE.append(None)









    



  File "<ipython-input-204-a20c7951e5a0>", line 9
    except:
         ^
SyntaxError: invalid syntax



In [202]:

    
tempdata['SELFISHCHOICESELFMORE'] = SELFISHCHOICESELFMORE



In [203]:

    
tempdata









    Out[203]:






  
    
      
      RESPONSE
      ERROR
      RESPONSE1
      RESPONSE2
      SELFISHCHOICESELFMORE
      SELFISHCHOICEGROUPMORE
    
  
  
    
      0
      1
      1
      [3, 6]
      [7, 8]
      4.0
      0
    
    
      1
      1
      1
      [5, 4]
      [10, 11]
      5.0
      0
    
    
      2
      1
      0
      [5, 4]
      [8, 9]
      -3.0
      0
    
    
      3
      1
      1
      [10, 9]
      [7, 8]
      -3.0
      0
    
    
      4
      1
      0
      [14, 10]
      [9, 12]
      5.0
      0
    
    
      5
      2
      0
      [8, 10]
      [11, 7]
      3.0
      0
    
    
      6
      2
      0
      [10, 11]
      [12, 9]
      2.0
      0
    
    
      7
      1
      0
      [11, 7]
      [5, 9]
      6.0
      0
    
    
      8
      1
      1
      [7, 8]
      [10, 5]
      3.0
      0
    
    
      9
      1
      1
      [6, 8]
      [8, 5]
      2.0
      0
    
    
      10
      2
      1
      [11, 6]
      [7, 10]
      4.0
      0
    
    
      11
      1
      1
      [6, 11]
      [13, 10]
      7.0
      0
    
    
      12
      2
      1
      [12, 8]
      [9, 11]
      3.0
      0
    
    
      13
      1
      0
      [11, 6]
      [8, 12]
      3.0
      0
    
    
      14
      1
      0
      [nan, nan]
      [nan, nan]
      NaN
      0
    
    
      15
      1
      1
      [6, 9]
      [10, 5]
      4.0
      0
    
    
      16
      1
      1
      [9, 12]
      [10, 5]
      1.0
      0
    
    
      17
      1
      0
      [12, 8]
      [7, 9]
      5.0
      0
    
    
      18
      1
      0
      [12, 8]
      [7, 11]
      5.0
      0
    
    
      19
      1
      0
      [12, 8]
      [9, 12]
      3.0
      0
    
    
      20
      2
      1
      [11, 6]
      [8, 9]
      3.0
      0
    
    
      21
      1
      1
      [6, 8]
      [9, 5]
      3.0
      0
    
    
      22
      1
      0
      [14, 10]
      [6, 11]
      8.0
      0
    
    
      23
      2
      0
      [9, 10]
      [11, 8]
      2.0
      0
    
    
      24
      1
      0
      [10, 6]
      [5, 9]
      5.0
      0
    
    
      25
      1
      0
      [9, 6]
      [7, 8]
      2.0
      0
    
    
      26
      1
      0
      [13, 8]
      [10, 13]
      3.0
      0
    
    
      27
      2
      0
      [7, 8]
      [10, 5]
      3.0
      0
    
    
      28
      2
      0
      [8, 11]
      [12, 7]
      4.0
      0
    
    
      29
      1
      0
      [15, 10]
      [9, 14]
      6.0
      0
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      875
      1
      0
      [12, 8]
      [9, 12]
      3.0
      0
    
    
      876
      1
      1
      [8, 10]
      [11, 7]
      3.0
      0
    
    
      877
      1
      1
      [6, 8]
      [8, 5]
      2.0
      0
    
    
      878
      1
      0
      [13, 10]
      [10, 14]
      3.0
      0
    
    
      879
      1
      0
      [11, 6]
      [8, 11]
      3.0
      0
    
    
      880
      1
      0
      [14, 10]
      [9, 12]
      5.0
      0
    
    
      881
      1
      0
      [14, 9]
      [9, 13]
      5.0
      0
    
    
      882
      2
      0
      [6, 7]
      [8, 5]
      2.0
      0
    
    
      883
      2
      0
      [7, 8]
      [9, 6]
      2.0
      0
    
    
      884
      2
      1
      [10, 5]
      [7, 8]
      3.0
      0
    
    
      885
      2
      1
      [12, 7]
      [8, 11]
      4.0
      0
    
    
      886
      2
      0
      [7, 10]
      [11, 6]
      4.0
      0
    
    
      887
      1
      0
      [nan, nan]
      [nan, nan]
      NaN
      0
    
    
      888
      2
      1
      [10, 7]
      [8, 9]
      2.0
      0
    
    
      889
      2
      0
      [6, 9]
      [10, 5]
      4.0
      0
    
    
      890
      2
      1
      [11, 7]
      [8, 10]
      3.0
      0
    
    
      891
      2
      0
      [7, 9]
      [10, 6]
      3.0
      0
    
    
      892
      2
      0
      [9, 10]
      [12, 7]
      3.0
      0
    
    
      893
      2
      0
      [9, 10]
      [11, 8]
      2.0
      0
    
    
      894
      2
      1
      [13, 8]
      [9, 12]
      4.0
      0
    
    
      895
      2
      0
      [6, 8]
      [9, 5]
      3.0
      0
    
    
      896
      2
      0
      [6, 7]
      [8, 5]
      2.0
      0
    
    
      897
      2
      1
      [12, 9]
      [10, 11]
      2.0
      0
    
    
      898
      2
      0
      [10, 11]
      [13, 8]
      3.0
      0
    
    
      899
      1
      0
      [nan, nan]
      [nan, nan]
      NaN
      0
    
    
      900
      2
      1
      [10, 5]
      [7, 8]
      3.0
      0
    
    
      901
      2
      1
      [11, 6]
      [8, 9]
      3.0
      0
    
    
      902
      2
      0
      [9, 11]
      [12, 8]
      3.0
      0
    
    
      903
      2
      1
      [12, 8]
      [9, 11]
      3.0
      0
    
    
      904
      2
      1
      [9, 5]
      [6, 8]
      3.0
      0
    
  

905 rows × 6 columns

	subject	trial	stimfile	condition	code	resp_1	resp_2	response	distractor	error	...	Y_96	Y_97	Y_98	Y_99	Y_100	Y_101	RESPONSE1	RESPONSE2
0	455806	1	NaN	777	prac2	~S3_O6.jpg	~S7_O8.jpg	1	1	1	...	1.1400	1.1400	1.1400	1.1400	1.1400	1.1400	[3, 6]	[7, 8]
1	455806	3	NaN	777	prac8	~S5_O4.jpg	~S10_O11.jpg	1	1	1	...	1.1500	1.1500	1.1500	1.1500	1.1500	1.1500	[5, 4]	[10, 11]
2	455806	5	NaN	777	prac4	~S5_O4.jpg	~S8_O9.jpg	1	2	0	...	1.1250	1.1250	1.1250	1.1250	1.1250	1.1250	[5, 4]	[8, 9]
3	455806	7	NaN	777	prac5	~S10_O9.jpg	~S7_O8.jpg	1	1	1	...	1.2125	1.2125	1.2125	1.2125	1.2125	1.2125	[10, 9]	[7, 8]
4	455806	9	NaN	777	smgmvsslgl12	~S14_O10.jpg	~S9_O12.jpg	1	2	0	...	1.1900	1.1900	1.1900	1.1900	1.1900	1.1900	[14, 10]	[9, 12]