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
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
|
from __future__ import absolute_import, print_function, division
import operator
from collections import OrderedDict
from petl.compat import next, string_types, text_type
import petl.config as config
from petl.errors import ArgumentError
from petl.util.base import Table, expr, rowgroupby, Record
from petl.transform.sorts import sort
def fieldmap(table, mappings=None, failonerror=None, errorvalue=None, trusted=True):
"""
Transform a table, mapping fields arbitrarily between input and output.
E.g.::
>>> import petl as etl
>>> from collections import OrderedDict
>>> table1 = [['id', 'sex', 'age', 'height', 'weight'],
... [1, 'male', 16, 1.45, 62.0],
... [2, 'female', 19, 1.34, 55.4],
... [3, 'female', 17, 1.78, 74.4],
... [4, 'male', 21, 1.33, 45.2],
... [5, '-', 25, 1.65, 51.9]]
>>> mappings = OrderedDict()
>>> # rename a field
... mappings['subject_id'] = 'id'
>>> # translate a field
... mappings['gender'] = 'sex', {'male': 'M', 'female': 'F'}
>>> # apply a calculation to a field
... mappings['age_months'] = 'age', lambda v: v * 12
>>> # apply a calculation to a combination of fields
... mappings['bmi'] = lambda rec: rec['weight'] / rec['height']**2
>>> # transform and inspect the output
... table2 = etl.fieldmap(table1, mappings)
>>> table2
+------------+--------+------------+--------------------+
| subject_id | gender | age_months | bmi |
+============+========+============+====================+
| 1 | 'M' | 192 | 29.48870392390012 |
+------------+--------+------------+--------------------+
| 2 | 'F' | 228 | 30.8531967030519 |
+------------+--------+------------+--------------------+
| 3 | 'F' | 204 | 23.481883600555488 |
+------------+--------+------------+--------------------+
| 4 | 'M' | 252 | 25.55260331279326 |
+------------+--------+------------+--------------------+
| 5 | '-' | 300 | 19.0633608815427 |
+------------+--------+------------+--------------------+
Note also that the mapping value can be an expression string, which will be
converted to a lambda function via :func:`petl.util.base.expr`.
The ``trusted`` keyword argument can be used to specify whether the
expression is trusted. See `:func:`petl.util.base.expr` for details.
The `failonerror` and `errorvalue` keyword arguments are documented
under :func:`petl.config.failonerror`
"""
return FieldMapView(table, mappings=mappings, failonerror=failonerror,
errorvalue=errorvalue, trusted=trusted)
Table.fieldmap = fieldmap
class FieldMapView(Table):
def __init__(self, source, mappings=None, failonerror=None,
errorvalue=None, trusted=True):
self.source = source
if mappings is None:
self.mappings = OrderedDict()
else:
self.mappings = mappings
self.failonerror = (config.failonerror if failonerror is None
else failonerror)
self.errorvalue = errorvalue
self.trusted = trusted
def __setitem__(self, key, value):
self.mappings[key] = value
def __iter__(self):
return iterfieldmap(self.source, self.mappings, self.failonerror,
self.errorvalue, self.trusted)
def iterfieldmap(source, mappings, failonerror, errorvalue, trusted):
it = iter(source)
try:
hdr = next(it)
except StopIteration:
return
flds = list(map(text_type, hdr))
outhdr = mappings.keys()
yield tuple(outhdr)
mapfuns = dict()
for outfld, m in mappings.items():
if m in hdr:
mapfuns[outfld] = operator.itemgetter(m)
elif isinstance(m, int) and m < len(hdr):
mapfuns[outfld] = operator.itemgetter(m)
elif isinstance(m, string_types):
mapfuns[outfld] = expr(m, trusted=trusted)
elif callable(m):
mapfuns[outfld] = m
elif isinstance(m, (tuple, list)) and len(m) == 2:
srcfld = m[0]
fm = m[1]
if callable(fm):
mapfuns[outfld] = composefun(fm, srcfld)
elif isinstance(fm, dict):
mapfuns[outfld] = composedict(fm, srcfld)
else:
raise ArgumentError('expected callable or dict')
else:
raise ArgumentError('invalid mapping %r: %r' % (outfld, m))
# wrap rows as records
it = (Record(row, flds) for row in it)
for row in it:
outrow = list()
for outfld in outhdr:
try:
val = mapfuns[outfld](row)
except Exception as e:
if failonerror == 'inline':
val = e
elif failonerror:
raise e
else:
val = errorvalue
outrow.append(val)
yield tuple(outrow)
def composefun(f, srcfld):
def g(rec):
return f(rec[srcfld])
return g
def composedict(d, srcfld):
def g(rec):
k = rec[srcfld]
if k in d:
return d[k]
else:
return k
return g
def rowmap(table, rowmapper, header, failonerror=None):
"""
Transform rows via an arbitrary function. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'sex', 'age', 'height', 'weight'],
... [1, 'male', 16, 1.45, 62.0],
... [2, 'female', 19, 1.34, 55.4],
... [3, 'female', 17, 1.78, 74.4],
... [4, 'male', 21, 1.33, 45.2],
... [5, '-', 25, 1.65, 51.9]]
>>> def rowmapper(row):
... transmf = {'male': 'M', 'female': 'F'}
... return [row[0],
... transmf[row['sex']] if row['sex'] in transmf else None,
... row.age * 12,
... row.height / row.weight ** 2]
...
>>> table2 = etl.rowmap(table1, rowmapper,
... header=['subject_id', 'gender', 'age_months',
... 'bmi'])
>>> table2
+------------+--------+------------+-----------------------+
| subject_id | gender | age_months | bmi |
+============+========+============+=======================+
| 1 | 'M' | 192 | 0.0003772112382934443 |
+------------+--------+------------+-----------------------+
| 2 | 'F' | 228 | 0.0004366015456998006 |
+------------+--------+------------+-----------------------+
| 3 | 'F' | 204 | 0.0003215689675106949 |
+------------+--------+------------+-----------------------+
| 4 | 'M' | 252 | 0.0006509906805544679 |
+------------+--------+------------+-----------------------+
| 5 | None | 300 | 0.0006125608384287258 |
+------------+--------+------------+-----------------------+
The `rowmapper` function should accept a single row and return a single
row (list or tuple).
The `failonerror` keyword argument is documented under
:func:`petl.config.failonerror`
"""
return RowMapView(table, rowmapper, header, failonerror=failonerror)
Table.rowmap = rowmap
class RowMapView(Table):
def __init__(self, source, rowmapper, header, failonerror=None):
self.source = source
self.rowmapper = rowmapper
self.header = header
self.failonerror = (config.failonerror if failonerror is None
else failonerror)
def __iter__(self):
return iterrowmap(self.source, self.rowmapper, self.header,
self.failonerror)
def iterrowmap(source, rowmapper, header, failonerror):
it = iter(source)
try:
hdr = next(it)
except StopIteration:
return
flds = list(map(text_type, hdr))
yield tuple(header)
it = (Record(row, flds) for row in it)
for row in it:
try:
outrow = rowmapper(row)
yield tuple(outrow)
except Exception as e:
if failonerror == 'inline':
yield tuple([e])
elif failonerror:
raise e
def rowmapmany(table, rowgenerator, header, failonerror=None):
"""
Map each input row to any number of output rows via an arbitrary
function. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'sex', 'age', 'height', 'weight'],
... [1, 'male', 16, 1.45, 62.0],
... [2, 'female', 19, 1.34, 55.4],
... [3, '-', 17, 1.78, 74.4],
... [4, 'male', 21, 1.33]]
>>> def rowgenerator(row):
... transmf = {'male': 'M', 'female': 'F'}
... yield [row[0], 'gender',
... transmf[row['sex']] if row['sex'] in transmf else None]
... yield [row[0], 'age_months', row.age * 12]
... yield [row[0], 'bmi', row.height / row.weight ** 2]
...
>>> table2 = etl.rowmapmany(table1, rowgenerator,
... header=['subject_id', 'variable', 'value'])
>>> table2.lookall()
+------------+--------------+-----------------------+
| subject_id | variable | value |
+============+==============+=======================+
| 1 | 'gender' | 'M' |
+------------+--------------+-----------------------+
| 1 | 'age_months' | 192 |
+------------+--------------+-----------------------+
| 1 | 'bmi' | 0.0003772112382934443 |
+------------+--------------+-----------------------+
| 2 | 'gender' | 'F' |
+------------+--------------+-----------------------+
| 2 | 'age_months' | 228 |
+------------+--------------+-----------------------+
| 2 | 'bmi' | 0.0004366015456998006 |
+------------+--------------+-----------------------+
| 3 | 'gender' | None |
+------------+--------------+-----------------------+
| 3 | 'age_months' | 204 |
+------------+--------------+-----------------------+
| 3 | 'bmi' | 0.0003215689675106949 |
+------------+--------------+-----------------------+
| 4 | 'gender' | 'M' |
+------------+--------------+-----------------------+
| 4 | 'age_months' | 252 |
+------------+--------------+-----------------------+
The `rowgenerator` function should accept a single row and yield zero or
more rows (lists or tuples).
The `failonerror` keyword argument is documented under
:func:`petl.config.failonerror`
See also the :func:`petl.transform.reshape.melt` function.
"""
return RowMapManyView(table, rowgenerator, header, failonerror=failonerror)
Table.rowmapmany = rowmapmany
class RowMapManyView(Table):
def __init__(self, source, rowgenerator, header, failonerror=None):
self.source = source
self.rowgenerator = rowgenerator
self.header = header
self.failonerror = (config.failonerror if failonerror is None
else failonerror)
def __iter__(self):
return iterrowmapmany(self.source, self.rowgenerator, self.header,
self.failonerror)
def iterrowmapmany(source, rowgenerator, header, failonerror):
it = iter(source)
try:
hdr = next(it)
except StopIteration:
return
flds = list(map(text_type, hdr))
yield tuple(header)
it = (Record(row, flds) for row in it)
for row in it:
try:
for outrow in rowgenerator(row):
yield tuple(outrow)
except Exception as e:
if failonerror == 'inline':
yield tuple([e])
elif failonerror:
raise e
else:
pass
def rowgroupmap(table, key, mapper, header=None, presorted=False,
buffersize=None, tempdir=None, cache=True):
"""
Group rows under the given key then apply `mapper` to yield zero or more
output rows for each input group of rows.
"""
return RowGroupMapView(table, key, mapper, header=header,
presorted=presorted,
buffersize=buffersize, tempdir=tempdir, cache=cache)
Table.rowgroupmap = rowgroupmap
class RowGroupMapView(Table):
def __init__(self, source, key, mapper, header=None,
presorted=False, buffersize=None, tempdir=None, cache=True):
if presorted:
self.source = source
else:
self.source = sort(source, key, buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.key = key
self.header = header
self.mapper = mapper
def __iter__(self):
return iterrowgroupmap(self.source, self.key, self.mapper, self.header)
def iterrowgroupmap(source, key, mapper, header):
yield tuple(header)
for key, rows in rowgroupby(source, key):
for row in mapper(key, rows):
yield row
|
