-
-
Notifications
You must be signed in to change notification settings - Fork 17.8k
/
style.py
996 lines (835 loc) · 31.8 KB
/
style.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
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
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
"""
Module for applying conditional formatting to
DataFrames and Series.
"""
from functools import partial
from itertools import product
from contextlib import contextmanager
from uuid import uuid1
import copy
from collections import defaultdict, MutableMapping
try:
from jinja2 import Template
except ImportError:
msg = "pandas.Styler requires jinja2. "\
"Please install with `conda install Jinja2`\n"\
"or `pip install Jinja2`"
raise ImportError(msg)
from pandas.types.common import is_float, is_string_like
import numpy as np
import pandas as pd
from pandas.compat import range
from pandas.core.config import get_option
import pandas.core.common as com
from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice
try:
import matplotlib.pyplot as plt
from matplotlib import colors
has_mpl = True
except ImportError:
has_mpl = False
no_mpl_message = "{0} requires matplotlib."
@contextmanager
def _mpl(func):
if has_mpl:
yield plt, colors
else:
raise ImportError(no_mpl_message.format(func.__name__))
class Styler(object):
"""
Helps style a DataFrame or Series according to the
data with HTML and CSS.
.. versionadded:: 0.17.1
.. warning::
This is a new feature and is under active development.
We'll be adding features and possibly making breaking changes in future
releases.
Parameters
----------
data: Series or DataFrame
precision: int
precision to round floats to, defaults to pd.options.display.precision
table_styles: list-like, default None
list of {selector: (attr, value)} dicts; see Notes
uuid: str, default None
a unique identifier to avoid CSS collisons; generated automatically
caption: str, default None
caption to attach to the table
Attributes
----------
template: Jinja Template
Notes
-----
Most styling will be done by passing style functions into
``Styler.apply`` or ``Styler.applymap``. Style functions should
return values with strings containing CSS ``'attr: value'`` that will
be applied to the indicated cells.
If using in the Jupyter notebook, Styler has defined a ``_repr_html_``
to automatically render itself. Otherwise call Styler.render to get
the genterated HTML.
CSS classes are attached to the generated HTML
* Index and Column names include ``index_name`` and ``level<k>``
where `k` is its level in a MultiIndex
* Index label cells include
* ``row_heading``
* ``row<n>`` where `n` is the numeric position of the row
* ``level<k>`` where `k` is the level in a MultiIndex
* Column label cells include
* ``col_heading``
* ``col<n>`` where `n` is the numeric position of the column
* ``evel<k>`` where `k` is the level in a MultiIndex
* Blank cells include ``blank``
* Data cells include ``data``
See Also
--------
pandas.DataFrame.style
"""
template = Template("""
<style type="text/css" >
{% for s in table_styles %}
#T_{{uuid}} {{s.selector}} {
{% for p,val in s.props %}
{{p}}: {{val}};
{% endfor %}
}
{% endfor %}
{% for s in cellstyle %}
#T_{{uuid}}{{s.selector}} {
{% for p,val in s.props %}
{{p}}: {{val}};
{% endfor %}
}
{% endfor %}
</style>
<table id="T_{{uuid}}" {{ table_attributes }}>
{% if caption %}
<caption>{{caption}}</caption>
{% endif %}
<thead>
{% for r in head %}
<tr>
{% for c in r %}
{% if c.is_visible != False %}
<{{c.type}} class="{{c.class}}" {{ c.attributes|join(" ") }}>
{{c.value}}
{% endif %}
{% endfor %}
</tr>
{% endfor %}
</thead>
<tbody>
{% for r in body %}
<tr>
{% for c in r %}
{% if c.is_visible != False %}
<{{c.type}} id="T_{{uuid}}{{c.id}}"
class="{{c.class}}" {{ c.attributes|join(" ") }}>
{{ c.display_value }}
{% endif %}
{% endfor %}
</tr>
{% endfor %}
</tbody>
</table>
""")
def __init__(self, data, precision=None, table_styles=None, uuid=None,
caption=None, table_attributes=None):
self.ctx = defaultdict(list)
self._todo = []
if not isinstance(data, (pd.Series, pd.DataFrame)):
raise TypeError("``data`` must be a Series or DataFrame")
if data.ndim == 1:
data = data.to_frame()
if not data.index.is_unique or not data.columns.is_unique:
raise ValueError("style is not supported for non-unique indicies.")
self.data = data
self.index = data.index
self.columns = data.columns
self.uuid = uuid
self.table_styles = table_styles
self.caption = caption
if precision is None:
precision = get_option('display.precision')
self.precision = precision
self.table_attributes = table_attributes
# display_funcs maps (row, col) -> formatting function
def default_display_func(x):
if is_float(x):
return '{:>.{precision}g}'.format(x, precision=self.precision)
else:
return x
self._display_funcs = defaultdict(lambda: default_display_func)
def _repr_html_(self):
"""Hooks into Jupyter notebook rich display system."""
return self.render()
def _translate(self):
"""
Convert the DataFrame in `self.data` and the attrs from `_build_styles`
into a dictionary of {head, body, uuid, cellstyle}
"""
table_styles = self.table_styles or []
caption = self.caption
ctx = self.ctx
precision = self.precision
uuid = self.uuid or str(uuid1()).replace("-", "_")
ROW_HEADING_CLASS = "row_heading"
COL_HEADING_CLASS = "col_heading"
INDEX_NAME_CLASS = "index_name"
DATA_CLASS = "data"
BLANK_CLASS = "blank"
BLANK_VALUE = ""
def format_attr(pair):
return "{key}={value}".format(**pair)
# for sparsifying a MultiIndex
idx_lengths = _get_level_lengths(self.index)
col_lengths = _get_level_lengths(self.columns)
cell_context = dict()
n_rlvls = self.data.index.nlevels
n_clvls = self.data.columns.nlevels
rlabels = self.data.index.tolist()
clabels = self.data.columns.tolist()
if n_rlvls == 1:
rlabels = [[x] for x in rlabels]
if n_clvls == 1:
clabels = [[x] for x in clabels]
clabels = list(zip(*clabels))
cellstyle = []
head = []
for r in range(n_clvls):
# Blank for Index columns...
row_es = [{"type": "th",
"value": BLANK_VALUE,
"display_value": BLANK_VALUE,
"is_visible": True,
"class": " ".join([BLANK_CLASS])}] * (n_rlvls - 1)
# ... except maybe the last for columns.names
name = self.data.columns.names[r]
cs = [BLANK_CLASS if name is None else INDEX_NAME_CLASS,
"level%s" % r]
name = BLANK_VALUE if name is None else name
row_es.append({"type": "th",
"value": name,
"display_value": name,
"class": " ".join(cs),
"is_visible": True})
for c in range(len(clabels[0])):
cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c]
cs.extend(cell_context.get(
"col_headings", {}).get(r, {}).get(c, []))
value = clabels[r][c]
row_es.append({"type": "th",
"value": value,
"display_value": value,
"class": " ".join(cs),
"is_visible": _is_visible(c, r, col_lengths),
"attributes": [
format_attr({"key": "colspan",
"value": col_lengths.get(
(r, c), 1)})
]})
head.append(row_es)
if self.data.index.names and not all(x is None
for x in self.data.index.names):
index_header_row = []
for c, name in enumerate(self.data.index.names):
cs = [INDEX_NAME_CLASS,
"level%s" % c]
name = '' if name is None else name
index_header_row.append({"type": "th", "value": name,
"class": " ".join(cs)})
index_header_row.extend(
[{"type": "th",
"value": BLANK_VALUE,
"class": " ".join([BLANK_CLASS])
}] * len(clabels[0]))
head.append(index_header_row)
body = []
for r, idx in enumerate(self.data.index):
# cs.extend(
# cell_context.get("row_headings", {}).get(r, {}).get(c, []))
row_es = [{"type": "th",
"is_visible": _is_visible(r, c, idx_lengths),
"attributes": [
format_attr({"key": "rowspan",
"value": idx_lengths.get((c, r), 1)})
],
"value": rlabels[r][c],
"class": " ".join([ROW_HEADING_CLASS, "level%s" % c,
"row%s" % r]),
"display_value": rlabels[r][c]}
for c in range(len(rlabels[r]))]
for c, col in enumerate(self.data.columns):
cs = [DATA_CLASS, "row%s" % r, "col%s" % c]
cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
formatter = self._display_funcs[(r, c)]
value = self.data.iloc[r, c]
row_es.append({
"type": "td",
"value": value,
"class": " ".join(cs),
"id": "_".join(cs[1:]),
"display_value": formatter(value)
})
props = []
for x in ctx[r, c]:
# have to handle empty styles like ['']
if x.count(":"):
props.append(x.split(":"))
else:
props.append(['', ''])
cellstyle.append({'props': props,
'selector': "row%s_col%s" % (r, c)})
body.append(row_es)
return dict(head=head, cellstyle=cellstyle, body=body, uuid=uuid,
precision=precision, table_styles=table_styles,
caption=caption, table_attributes=self.table_attributes)
def format(self, formatter, subset=None):
"""
Format the text display value of cells.
.. versionadded:: 0.18.0
Parameters
----------
formatter: str, callable, or dict
subset: IndexSlice
An argument to ``DataFrame.loc`` that restricts which elements
``formatter`` is applied to.
Returns
-------
self : Styler
Notes
-----
``formatter`` is either an ``a`` or a dict ``{column name: a}`` where
``a`` is one of
- str: this will be wrapped in: ``a.format(x)``
- callable: called with the value of an individual cell
The default display value for numeric values is the "general" (``g``)
format with ``pd.options.display.precision`` precision.
Examples
--------
>>> df = pd.DataFrame(np.random.randn(4, 2), columns=['a', 'b'])
>>> df.style.format("{:.2%}")
>>> df['c'] = ['a', 'b', 'c', 'd']
>>> df.style.format({'C': str.upper})
"""
if subset is None:
row_locs = range(len(self.data))
col_locs = range(len(self.data.columns))
else:
subset = _non_reducing_slice(subset)
if len(subset) == 1:
subset = subset, self.data.columns
sub_df = self.data.loc[subset]
row_locs = self.data.index.get_indexer_for(sub_df.index)
col_locs = self.data.columns.get_indexer_for(sub_df.columns)
if isinstance(formatter, MutableMapping):
for col, col_formatter in formatter.items():
# formatter must be callable, so '{}' are converted to lambdas
col_formatter = _maybe_wrap_formatter(col_formatter)
col_num = self.data.columns.get_indexer_for([col])[0]
for row_num in row_locs:
self._display_funcs[(row_num, col_num)] = col_formatter
else:
# single scalar to format all cells with
locs = product(*(row_locs, col_locs))
for i, j in locs:
formatter = _maybe_wrap_formatter(formatter)
self._display_funcs[(i, j)] = formatter
return self
def render(self):
"""
Render the built up styles to HTML
.. versionadded:: 0.17.1
Returns
-------
rendered: str
the rendered HTML
Notes
-----
``Styler`` objects have defined the ``_repr_html_`` method
which automatically calls ``self.render()`` when it's the
last item in a Notebook cell. When calling ``Styler.render()``
directly, wrap the result in ``IPython.display.HTML`` to view
the rendered HTML in the notebook.
"""
self._compute()
d = self._translate()
# filter out empty styles, every cell will have a class
# but the list of props may just be [['', '']].
# so we have the neested anys below
trimmed = [x for x in d['cellstyle']
if any(any(y) for y in x['props'])]
d['cellstyle'] = trimmed
return self.template.render(**d)
def _update_ctx(self, attrs):
"""
update the state of the Styler. Collects a mapping
of {index_label: ['<property>: <value>']}
attrs: Series or DataFrame
should contain strings of '<property>: <value>;<prop2>: <val2>'
Whitespace shouldn't matter and the final trailing ';' shouldn't
matter.
"""
for row_label, v in attrs.iterrows():
for col_label, col in v.iteritems():
i = self.index.get_indexer([row_label])[0]
j = self.columns.get_indexer([col_label])[0]
for pair in col.rstrip(";").split(";"):
self.ctx[(i, j)].append(pair)
def _copy(self, deepcopy=False):
styler = Styler(self.data, precision=self.precision,
caption=self.caption, uuid=self.uuid,
table_styles=self.table_styles)
if deepcopy:
styler.ctx = copy.deepcopy(self.ctx)
styler._todo = copy.deepcopy(self._todo)
else:
styler.ctx = self.ctx
styler._todo = self._todo
return styler
def __copy__(self):
"""
Deep copy by default.
"""
return self._copy(deepcopy=False)
def __deepcopy__(self, memo):
return self._copy(deepcopy=True)
def clear(self):
""""Reset" the styler, removing any previously applied styles.
Returns None.
"""
self.ctx.clear()
self._todo = []
def _compute(self):
"""
Execute the style functions built up in `self._todo`.
Relies on the conventions that all style functions go through
.apply or .applymap. The append styles to apply as tuples of
(application method, *args, **kwargs)
"""
r = self
for func, args, kwargs in self._todo:
r = func(self)(*args, **kwargs)
return r
def _apply(self, func, axis=0, subset=None, **kwargs):
subset = slice(None) if subset is None else subset
subset = _non_reducing_slice(subset)
data = self.data.loc[subset]
if axis is not None:
result = data.apply(func, axis=axis, **kwargs)
else:
result = func(data, **kwargs)
if not isinstance(result, pd.DataFrame):
raise TypeError(
"Function {!r} must return a DataFrame when "
"passed to `Styler.apply` with axis=None".format(func))
if not (result.index.equals(data.index) and
result.columns.equals(data.columns)):
msg = ('Result of {!r} must have identical index and columns '
'as the input'.format(func))
raise ValueError(msg)
result_shape = result.shape
expected_shape = self.data.loc[subset].shape
if result_shape != expected_shape:
msg = ("Function {!r} returned the wrong shape.\n"
"Result has shape: {}\n"
"Expected shape: {}".format(func,
result.shape,
expected_shape))
raise ValueError(msg)
self._update_ctx(result)
return self
def apply(self, func, axis=0, subset=None, **kwargs):
"""
Apply a function column-wise, row-wise, or table-wase,
updating the HTML representation with the result.
.. versionadded:: 0.17.1
Parameters
----------
func : function
``func`` should take a Series or DataFrame (depending
on ``axis``), and return an object with the same shape.
Must return a DataFrame with identical index and
column labels when ``axis=None``
axis : int, str or None
apply to each column (``axis=0`` or ``'index'``)
or to each row (``axis=1`` or ``'columns'``) or
to the entire DataFrame at once with ``axis=None``
subset : IndexSlice
a valid indexer to limit ``data`` to *before* applying the
function. Consider using a pandas.IndexSlice
kwargs : dict
pass along to ``func``
Returns
-------
self : Styler
Notes
-----
The output shape of ``func`` should match the input, i.e. if
``x`` is the input row, column, or table (depending on ``axis``),
then ``func(x.shape) == x.shape`` should be true.
This is similar to ``DataFrame.apply``, except that ``axis=None``
applies the function to the entire DataFrame at once,
rather than column-wise or row-wise.
Examples
--------
>>> def highlight_max(x):
... return ['background-color: yellow' if v == x.max() else ''
for v in x]
...
>>> df = pd.DataFrame(np.random.randn(5, 2))
>>> df.style.apply(highlight_max)
"""
self._todo.append((lambda instance: getattr(instance, '_apply'),
(func, axis, subset), kwargs))
return self
def _applymap(self, func, subset=None, **kwargs):
func = partial(func, **kwargs) # applymap doesn't take kwargs?
if subset is None:
subset = pd.IndexSlice[:]
subset = _non_reducing_slice(subset)
result = self.data.loc[subset].applymap(func)
self._update_ctx(result)
return self
def applymap(self, func, subset=None, **kwargs):
"""
Apply a function elementwise, updating the HTML
representation with the result.
.. versionadded:: 0.17.1
Parameters
----------
func : function
``func`` should take a scalar and return a scalar
subset : IndexSlice
a valid indexer to limit ``data`` to *before* applying the
function. Consider using a pandas.IndexSlice
kwargs : dict
pass along to ``func``
Returns
-------
self : Styler
"""
self._todo.append((lambda instance: getattr(instance, '_applymap'),
(func, subset), kwargs))
return self
def set_precision(self, precision):
"""
Set the precision used to render.
.. versionadded:: 0.17.1
Parameters
----------
precision: int
Returns
-------
self : Styler
"""
self.precision = precision
return self
def set_table_attributes(self, attributes):
"""
Set the table attributes. These are the items
that show up in the opening ``<table>`` tag in addition
to to automatic (by default) id.
.. versionadded:: 0.17.1
Parameters
----------
precision: int
Returns
-------
self : Styler
"""
self.table_attributes = attributes
return self
def export(self):
"""
Export the styles to applied to the current Styler.
Can be applied to a second style with ``Styler.use``.
.. versionadded:: 0.17.1
Returns
-------
styles: list
See Also
--------
Styler.use
"""
return self._todo
def use(self, styles):
"""
Set the styles on the current Styler, possibly using styles
from ``Styler.export``.
.. versionadded:: 0.17.1
Parameters
----------
styles: list
list of style functions
Returns
-------
self : Styler
See Also
--------
Styler.export
"""
self._todo.extend(styles)
return self
def set_uuid(self, uuid):
"""
Set the uuid for a Styler.
.. versionadded:: 0.17.1
Parameters
----------
uuid: str
Returns
-------
self : Styler
"""
self.uuid = uuid
return self
def set_caption(self, caption):
"""
Se the caption on a Styler
.. versionadded:: 0.17.1
Parameters
----------
caption: str
Returns
-------
self : Styler
"""
self.caption = caption
return self
def set_table_styles(self, table_styles):
"""
Set the table styles on a Styler. These are placed in a
``<style>`` tag before the generated HTML table.
.. versionadded:: 0.17.1
Parameters
----------
table_styles: list
Each individual table_style should be a dictionary with
``selector`` and ``props`` keys. ``selector`` should be a CSS
selector that the style will be applied to (automatically
prefixed by the table's UUID) and ``props`` should be a list of
tuples with ``(attribute, value)``.
Returns
-------
self : Styler
Examples
--------
>>> df = pd.DataFrame(np.random.randn(10, 4))
>>> df.style.set_table_styles(
... [{'selector': 'tr:hover',
... 'props': [('background-color', 'yellow')]}]
... )
"""
self.table_styles = table_styles
return self
# -----------------------------------------------------------------------
# A collection of "builtin" styles
# -----------------------------------------------------------------------
@staticmethod
def _highlight_null(v, null_color):
return 'background-color: %s' % null_color if pd.isnull(v) else ''
def highlight_null(self, null_color='red'):
"""
Shade the background ``null_color`` for missing values.
.. versionadded:: 0.17.1
Parameters
----------
null_color: str
Returns
-------
self : Styler
"""
self.applymap(self._highlight_null, null_color=null_color)
return self
def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
subset=None):
"""
Color the background in a gradient according to
the data in each column (optionally row).
Requires matplotlib.
.. versionadded:: 0.17.1
Parameters
----------
cmap: str or colormap
matplotlib colormap
low, high: float
compress the range by these values.
axis: int or str
1 or 'columns' for colunwise, 0 or 'index' for rowwise
subset: IndexSlice
a valid slice for ``data`` to limit the style application to
Returns
-------
self : Styler
Notes
-----
Tune ``low`` and ``high`` to keep the text legible by
not using the entire range of the color map. These extend
the range of the data by ``low * (x.max() - x.min())``
and ``high * (x.max() - x.min())`` before normalizing.
"""
subset = _maybe_numeric_slice(self.data, subset)
subset = _non_reducing_slice(subset)
self.apply(self._background_gradient, cmap=cmap, subset=subset,
axis=axis, low=low, high=high)
return self
@staticmethod
def _background_gradient(s, cmap='PuBu', low=0, high=0):
"""Color background in a range according to the data."""
with _mpl(Styler.background_gradient) as (plt, colors):
rng = s.max() - s.min()
# extend lower / upper bounds, compresses color range
norm = colors.Normalize(s.min() - (rng * low),
s.max() + (rng * high))
# matplotlib modifies inplace?
# https://github.com/matplotlib/matplotlib/issues/5427
normed = norm(s.values)
c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
return ['background-color: %s' % color for color in c]
def set_properties(self, subset=None, **kwargs):
"""
Convience method for setting one or more non-data dependent
properties or each cell.
.. versionadded:: 0.17.1
Parameters
----------
subset: IndexSlice
a valid slice for ``data`` to limit the style application to
kwargs: dict
property: value pairs to be set for each cell
Returns
-------
self : Styler
Examples
--------
>>> df = pd.DataFrame(np.random.randn(10, 4))
>>> df.style.set_properties(color="white", align="right")
>>> df.style.set_properties(**{'background-color': 'yellow'})
"""
values = ';'.join('{p}: {v}'.format(p=p, v=v)
for p, v in kwargs.items())
f = lambda x: values
return self.applymap(f, subset=subset)
@staticmethod
def _bar(s, color, width):
normed = width * (s - s.min()) / (s.max() - s.min())
base = 'width: 10em; height: 80%;'
attrs = (base + 'background: linear-gradient(90deg,{c} {w}%, '
'transparent 0%)')
return [attrs.format(c=color, w=x) if x != 0 else base for x in normed]
def bar(self, subset=None, axis=0, color='#d65f5f', width=100):
"""
Color the background ``color`` proptional to the values in each column.
Excludes non-numeric data by default.
.. versionadded:: 0.17.1
Parameters
----------
subset: IndexSlice, default None
a valid slice for ``data`` to limit the style application to
axis: int
color: str
width: float
A number between 0 or 100. The largest value will cover ``width``
percent of the cell's width
Returns
-------
self : Styler
"""
subset = _maybe_numeric_slice(self.data, subset)
subset = _non_reducing_slice(subset)
self.apply(self._bar, subset=subset, axis=axis, color=color,
width=width)
return self
def highlight_max(self, subset=None, color='yellow', axis=0):
"""
Highlight the maximum by shading the background
.. versionadded:: 0.17.1
Parameters
----------
subset: IndexSlice, default None
a valid slice for ``data`` to limit the style application to
color: str, default 'yellow'
axis: int, str, or None; default None
0 or 'index' for columnwise, 1 or 'columns' for rowwise
or ``None`` for tablewise (the default)
Returns
-------
self : Styler
"""
return self._highlight_handler(subset=subset, color=color, axis=axis,
max_=True)
def highlight_min(self, subset=None, color='yellow', axis=0):
"""
Highlight the minimum by shading the background
.. versionadded:: 0.17.1
Parameters
----------
subset: IndexSlice, default None
a valid slice for ``data`` to limit the style application to
color: str, default 'yellow'
axis: int, str, or None; default None
0 or 'index' for columnwise, 1 or 'columns' for rowwise
or ``None`` for tablewise (the default)
Returns
-------
self : Styler
"""
return self._highlight_handler(subset=subset, color=color, axis=axis,
max_=False)
def _highlight_handler(self, subset=None, color='yellow', axis=None,
max_=True):
subset = _non_reducing_slice(_maybe_numeric_slice(self.data, subset))
self.apply(self._highlight_extrema, color=color, axis=axis,
subset=subset, max_=max_)
return self
@staticmethod
def _highlight_extrema(data, color='yellow', max_=True):
"""Highlight the min or max in a Series or DataFrame"""
attr = 'background-color: {0}'.format(color)
if data.ndim == 1: # Series from .apply
if max_:
extrema = data == data.max()
else:
extrema = data == data.min()
return [attr if v else '' for v in extrema]
else: # DataFrame from .tee
if max_:
extrema = data == data.max().max()
else:
extrema = data == data.min().min()
return pd.DataFrame(np.where(extrema, attr, ''),
index=data.index, columns=data.columns)
def _is_visible(idx_row, idx_col, lengths):
"""
Index -> {(idx_row, idx_col): bool})
"""
return (idx_col, idx_row) in lengths
def _get_level_lengths(index):
"""
Given an index, find the level lenght for each element.
Result is a dictionary of (level, inital_position): span
"""
sentinel = com.sentinel_factory()
levels = index.format(sparsify=sentinel, adjoin=False, names=False)
if index.nlevels == 1:
return {(0, i): 1 for i, value in enumerate(levels)}
lengths = {}
for i, lvl in enumerate(levels):
for j, row in enumerate(lvl):
if not get_option('display.multi_sparse'):
lengths[(i, j)] = 1
elif row != sentinel:
last_label = j
lengths[(i, last_label)] = 1
else:
lengths[(i, last_label)] += 1
return lengths
def _maybe_wrap_formatter(formatter):
if is_string_like(formatter):
return lambda x: formatter.format(x)
elif callable(formatter):
return formatter
else:
msg = "Expected a template string or callable, got {} instead".format(
formatter)
raise TypeError(msg)