In [10]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
In [11]:
# read in only relevant removal reason data from 'final_merge_minus_abscondence.csv'
# commented code uses column index instead of titles
data_df = pd.read_csv('./Improving Foster Care Placements/final_merge_minus_abscondence.csv', usecols=['CL_ID', 'CLIENT_RMVL', 'Abandonment', 'Alcohol Use/Abuse - Caretaker', 'Alcohol Use/Abuse - Child', 'Death of Parent(s)', 'Domestic Violence', 'Drug Use/Abuse - Caretaker', 'Drug Use/Abuse - Child', 'Incarceration of Parent/Guardian(s)', 'JPO Removal (Child\'s Behavior Problem)', 'Mental/Emotional Injuries', 'Neglect - educational needs', 'Neglect - hygiene/clothing needs', 'Neglect - medical needs', 'Neglect - No/Inadequate Housing', 'Neglect - nutritional needs', 'Neglect - supervision and safety needs', 'Parent\'s inability to cope', 'Parent lacks skills for providing care', 'Parent not seeking BH treatment', 'Parent not seeking BH treatmnt for child', 'Parent/Child Conflict', 'Parent/Guardian lacks skills to provide', 'Physical Abuse', 'Relinquishment', 'Resumption', 'Sexual Abuse', 'Truancy'])
# data_df = pd.read_csv('./Improving Foster Care Placements/final_merge_minus_abscondence.csv', usecols=[0, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])
In [12]:
# Keep only first instance of each child ID, so have one list of removal reasons per child
per_child_df = data_df.drop_duplicates(['CL_ID'])
per_child_df
Out[12]:
CL_ID
CLIENT_RMVL
Abandonment
Alcohol Use/Abuse - Caretaker
Alcohol Use/Abuse - Child
Death of Parent(s)
Domestic Violence
Drug Use/Abuse - Caretaker
Drug Use/Abuse - Child
Incarceration of Parent/Guardian(s)
...
Parent lacks skills for providing care
Parent not seeking BH treatment
Parent not seeking BH treatmnt for child
Parent/Child Conflict
Parent/Guardian lacks skills to provide
Physical Abuse
Relinquishment
Resumption
Sexual Abuse
Truancy
0
800035
80003540517
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
5
800038
80003840772
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
8
800043
80004340319
0
0
0
0
0
0
0
0
...
0
0
0
0
0
1
0
0
0
0
19
800044
80004440312
0
0
0
0
0
0
0
0
...
0
0
0
0
0
1
0
0
0
0
21
800045
80004540319
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
26
800046
80004640319
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
30
800047
80004740319
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
34
800048
80004840319
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
37
800056
80005641302
0
0
0
0
0
1
0
0
...
0
0
0
0
0
0
0
0
0
0
38
800078
80007840184
0
0
0
0
0
1
0
0
...
0
0
0
0
0
0
0
0
0
0
40
800088
80008840149
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
42
800095
80009542012
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
44
800096
80009640707
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
56
800138
80013840238
0
0
0
0
0
0
1
0
...
0
0
0
0
0
0
0
0
0
0
61
800139
80013940238
0
0
0
0
0
1
0
0
...
0
0
0
0
0
0
0
0
0
0
68
800176
80017642520
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
69
800179
80017941078
1
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
73
800183
80018341110
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
74
800184
80018441110
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
75
800194
80019442302
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
77
800213
80021340528
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
78
800215
80021541837
0
0
0
0
0
1
0
0
...
0
0
0
0
0
0
0
0
0
0
79
800216
80021641837
0
0
0
0
0
1
0
0
...
0
0
0
0
0
0
0
0
0
0
80
800239
80023940164
0
1
0
0
0
1
0
0
...
0
0
0
0
0
1
0
0
0
0
82
800240
80024040164
0
0
0
0
0
1
0
0
...
0
0
0
0
0
1
0
0
0
0
84
800261
80026139731
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
89
800330
80033039745
0
0
0
0
0
0
0
0
...
0
0
0
0
0
1
0
0
0
0
91
800375
80037539800
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
95
800409
80040940379
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
106
800420
80042040695
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
47703
1110599
111059942569
0
0
0
0
0
0
0
0
...
0
0
0
1
0
0
0
0
0
0
47704
1110602
111060242569
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
47705
1110606
111060642578
0
0
0
0
0
1
0
0
...
0
0
0
0
0
0
0
0
0
0
47707
1110826
111082642571
1
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
47708
1110882
111088242573
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
1
0
47709
1110985
111098542572
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
47710
1111021
111102142576
0
0
0
0
0
0
0
0
...
0
0
0
0
0
0
0
0
0
0
47712
1111070
111107042577
0
0
0
0
0
0
0
0
...
0
1
0
0
0
1
0
0
0
0
47713
1111123
111112342595
0
0
0
0
1
0
0
0
...
0
0
0
0
0
0
0
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0
47714
1111205
111120542578
0
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...
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1
0
0
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0
47715
1111290
111129042579
0
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47716
1111359
111135942584
0
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...
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47717
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47721
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47722
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1111868
111186842590
0
0
0
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0
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0
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1111895
111189542586
0
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47728
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0
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47730
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111231642592
0
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1
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111245542597
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14425 rows × 29 columns
In [13]:
# save data to csv file called 'removal_reason_per_child'
per_child_df.to_csv('./removal_reason_per_child.csv')
In [ ]:
Content source: mallorybucell/AECF_DataDrive_2016
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