-
Notifications
You must be signed in to change notification settings - Fork 0
/
pain_region.py
188 lines (140 loc) · 7.11 KB
/
pain_region.py
1
2
3
4
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
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
import pandas as pd
import json
import fingertips_py as ftp
from math import ceil
data = pd.read_csv('region/302_0to99.csv')
utla_to_region = pd.read_csv('region/Upper_Tier_Local_Authority_to_Region_(December_2020)_Lookup_in_England.csv')
def get_data_batches(indicator_list, area_type_id, batch_size, folder_name):
replace_comma = '%2C'
batch_size = 100
number_of_batches = ceil(len(indicator_list)/batch_size)
# max 100 indicators can be retrieved at once so need to do this in batches
for i in range(number_of_batches):
start_index = i*batch_size
end_index = (i+1)*batch_size
batch_indicator_list = indicator_list[start_index: end_index]
ids_as_str = replace_comma.join([str(i) for i in batch_indicator_list])
values = ftp.retrieve_data.get_data_by_indicator_ids(indicator_ids=ids_as_str, # [Maximum 100]
area_type_id=area_type_id,
# parent_area_type_id=15,
# profile_id=None,
include_sortable_time_periods=True,
is_test=False)
filename = (folder_name + '/' +
str(area_type_id) + '_' +
str(start_index) + 'to' +
str(end_index-1) + '.csv'
)
values.to_csv(filename, index=False)
print(filename + ' successfully saved')
# # concat all of the separate files for 6
# file_names = ['region/6_0to99.csv',
# 'region/6_100to199.csv',
# 'region/6_200to299.csv',
# 'region/6_300to399.csv',
# 'region/6_400to499.csv']
# data = pd.DataFrame()
# for file in file_names:
# file_data = pd.read_csv(file)
# data = pd.concat([data, file_data])
# # save the file
# data.to_csv('region/6.csv', index=False)
# file_name = 'region/302_0to99.csv'
# # drop england
# data = pd.read_csv(file_name)
# data = data.loc[data['Area Name']!='England']
# # # save the file
# data.to_csv(file_name, index=False)
# # filter data to only include max time period only
# max_time_period = pd.DataFrame(data.groupby(['Indicator ID', 'Sex', 'Age'])['Time period Sortable'].max()).reset_index().drop_duplicates()
# data_max_time = pd.merge(left=max_time_period,
# right=data,
# how='inner')
# data_max_time.to_csv(file_name, index=False)
# len(data_max_time)
# len(data)
# # number of sex values for indicators
# data.loc[:,['Indicator ID', 'Sex']].groupby('Indicator ID').nunique()
# # number of age values for indicators
# data.loc[:,['Indicator ID', 'Age']].groupby('Indicator ID').nunique().describe()
# # shape file
# shape_file_name = 'region/9_region_shapes.json'
# with open(shape_file_name, 'r') as contents:
# shape_json = json.loads(contents)
# shape_json
# # retrieve value data are different areas
# raw_available_indicator_at_area = pd.read_json('https://fingertips.phe.org.uk/api/available_data').fillna(value='null')
# area_ref = pd.read_json('https://fingertips.phe.org.uk/api/area_types').fillna(value='null')
# ids = [90285,90812,90284,90832,92758,93114,93219,93224,93372,342,384,93086,93090,93375]
# ids = (raw_available_indicator_at_area.loc[(raw_available_indicator_at_area['IndicatorId'].isin(ids))
# & (raw_available_indicator_at_area['AreaTypeId']==6)]
# ['IndicatorId'].values
# )
# pain_ind_area = raw_available_indicator_at_area.loc[(raw_available_indicator_at_area['IndicatorId'].isin(ids))]
# pd.merge(left = pain_ind_area.pivot_table(values='IndicatorId',
# index='AreaTypeId',
# aggfunc='count')
# ,left_on = 'AreaTypeId'
# ,right = area_ref
# ,right_on = 'Id'
# ).sort_values('IndicatorId', ascending=False)
# # use func to save data
# get_data_batches(ids, 302, 100, 'region')
## calculations for each region based on inner utlas
data['indicator_region_dataset_id'] = (data.loc[:,["Indicator ID", "Time period Sortable", "Sex", "Age"]].apply(lambda x: '_'.join(x.astype(str).values.tolist()), axis=1) + '_6')
utla_to_region = utla_to_region.loc[:,['UTLA20CD', 'RGN20CD']]
print(data.columns, utla_to_region.columns)
region_utla_calcs = pd.merge(left=data,
right=utla_to_region,
left_on='Area Code',
right_on='UTLA20CD',
how='left',
indicator=True)
region_utla_calcs.groupby('_merge')['Area Code'].count()
def calc_utla_for_regions(calc_method):
if calc_method == 'min' or calc_method == 'max':
df = (region_utla_calcs.pivot_table(values='Value',
index=['indicator_region_dataset_id', 'RGN20CD'],
aggfunc=calc_method
)
.rename({'Value': 'utla_value_' + calc_method}, axis=1)
)
if calc_method == 'range':
df = (pd.DataFrame(region_utla_calcs
.groupby(['indicator_region_dataset_id', 'RGN20CD'])
.apply(lambda x: x['Value'].max() - x['Value'].min())
)
.rename({0: 'utla_value_' + calc_method}, axis=1)
)
df['utla_value_' + calc_method + '_rank'] = (df.groupby(['indicator_region_dataset_id'])
.rank(axis=0, method='min', na_option='keep', ascending=False)
)
df.index = df.index.rename(['indicator_dataset_id','Area Code'])
return df
utla_summary_for_regions = pd.DataFrame()
for i in ['range', 'min', 'max']:
utla_summary_for_regions = pd.concat([utla_summary_for_regions,
calc_utla_for_regions(i)],
axis=1
)
utla_summary_for_regions.reset_index()
# utla_summary_for_regions.to_csv('region/utla_summary_for_regions.csv')
region = pd.read_csv('region/6.csv')
utla_summary_for_regions = pd.read_csv('region/utla_summary_for_regions.csv')
data = pd.merge(left=region, right=utla_summary_for_regions, on=['indicator_dataset_id', 'Area Code'])
# data.to_csv('region/6.csv', index=False)
## standardise indicators minmaxscaler
from sklearn.preprocessing import MinMaxScaler
import numpy as np
def scale(X):
X_ = np.atleast_2d(X)
scaler=MinMaxScaler()
return pd.DataFrame(scaler.fit_transform(X_), X.index)
cols = 'Value'
data[cols + '_scale'] = (data.groupby(['indicator_dataset_id','Area Code'])
[cols].apply(scale))
data
(data.groupby(['indicator_dataset_id','Area Code'])
[cols].apply(scale))
data.loc[:,, 'Value']].dropna().groupby(['indicator_dataset_id','Area Code']).apply(lambda x: (x['Value']-x['Value'].min())/(x['Value'].max() - x['Value'].min()))
data.groupby(['indicator_dataset_id','Area Code']).apply(lambda x: (x['Value'].max()-x['Value'].min()))