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ENH: Allow elementwise coloring in background_gradient with axis=None p…
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soxofaan committed May 31, 2018
1 parent bc9241d commit 5c05bf1
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Showing 3 changed files with 43 additions and 8 deletions.
2 changes: 2 additions & 0 deletions doc/source/whatsnew/v0.24.0.txt
Expand Up @@ -14,6 +14,8 @@ Other Enhancements
^^^^^^^^^^^^^^^^^^
- :func:`to_datetime` now supports the ``%Z`` and ``%z`` directive when passed into ``format`` (:issue:`13486`)
- :func:`to_csv` now supports ``compression`` keyword when a file handle is passed. (:issue:`21227`)
- Allow elementwise coloring in ``style.background_gradient`` with ``axis=None`` (:issue:`15204`)
-
-

.. _whatsnew_0240.api_breaking:
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21 changes: 13 additions & 8 deletions pandas/io/formats/style.py
Expand Up @@ -901,16 +901,21 @@ def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
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()
smin = s.values.min()
smax = s.values.max()
rng = smax - smin
# extend lower / upper bounds, compresses color range
norm = colors.Normalize(s.min() - (rng * low),
s.max() + (rng * high))
# matplotlib modifies inplace?
norm = colors.Normalize(smin - (rng * low), smax + (rng * high))
# matplotlib colors.Normalize 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: {color}'.format(color=color)
for color in c]
rgbs = plt.cm.get_cmap(cmap)(norm(s.values))

css = lambda x: 'background-color: {c}'.format(c=colors.rgb2hex(x))
if s.ndim == 1:
return [css(x) for x in rgbs]
else:
return pd.DataFrame([[css(x) for x in row] for row in rgbs],
index=s.index, columns=s.columns)

def set_properties(self, subset=None, **kwargs):
"""
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28 changes: 28 additions & 0 deletions pandas/tests/io/formats/test_style.py
Expand Up @@ -1033,6 +1033,34 @@ def test_background_gradient(self):
subset=pd.IndexSlice[1, 'A'])._compute().ctx
assert result[(1, 0)] == ['background-color: #fff7fb']

@td.skip_if_no_mpl
def test_background_gradient_axis(self):
df = pd.DataFrame([[1, 2], [2, 4]], columns=['A', 'B'])

low = ['background-color: #f7fbff']
high = ['background-color: #08306b']
mid = ['background-color: #abd0e6']
result = df.style.background_gradient(cmap='Blues',
axis=0)._compute().ctx
assert result[(0, 0)] == low
assert result[(0, 1)] == low
assert result[(1, 0)] == high
assert result[(1, 1)] == high

result = df.style.background_gradient(cmap='Blues',
axis=1)._compute().ctx
assert result[(0, 0)] == low
assert result[(0, 1)] == high
assert result[(1, 0)] == low
assert result[(1, 1)] == high

result = df.style.background_gradient(cmap='Blues',
axis=None)._compute().ctx
assert result[(0, 0)] == low
assert result[(0, 1)] == mid
assert result[(1, 0)] == mid
assert result[(1, 1)] == high


def test_block_names():
# catch accidental removal of a block
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