diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 287c1e4f23e5..8bb04656bca3 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -5810,10 +5810,10 @@ def table(self, **kwargs):
#### Data analysis
@_preprocess_data(replace_names=["x", 'weights'], label_namer="x")
- def hist(self, x, bins=None, range=None, normed=False, weights=None,
+ def hist(self, x, bins=None, range=None, density=None, weights=None,
cumulative=False, bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False,
- color=None, label=None, stacked=False,
+ color=None, label=None, stacked=False, normed=None,
**kwargs):
"""
Plot a histogram.
@@ -5858,7 +5858,10 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
Default is ``None``
- normed : boolean, optional
+ normed, density : boolean, optional
+ Either the 'normed' or the 'density' arg can be set to
+ accomplish this behavior:
+
If `True`, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
the area (or integral) under the histogram will sum to 1.
@@ -5867,24 +5870,31 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
of observations. If `stacked` is also `True`, the sum of the
histograms is normalized to 1.
- Default is ``False``
+ Default is ``None`` for both 'normed' and 'density.' If either is
+ set, then that value will be used. If neither are set, then the args
+ will be treated as 'False.'
+
+ If both are set to different things, the hist function raises an
+ error.
weights : (n, ) array_like or None, optional
An array of weights, of the same shape as `x`. Each value in `x`
only contributes its associated weight towards the bin count
- (instead of 1). If `normed` is True, the weights are normalized,
- so that the integral of the density over the range remains 1.
+ (instead of 1). If `normed` or 'density' is True,
+ the weights are normalized, so that the integral of the density
+ over the range remains 1.
Default is ``None``
cumulative : boolean, optional
If `True`, then a histogram is computed where each bin gives the
counts in that bin plus all bins for smaller values. The last bin
- gives the total number of datapoints. If `normed` is also `True`
- then the histogram is normalized such that the last bin equals 1.
- If `cumulative` evaluates to less than 0 (e.g., -1), the direction
- of accumulation is reversed. In this case, if `normed` is also
- `True`, then the histogram is normalized such that the first bin
+ gives the total number of datapoints. If `normed` or 'density'
+ is also `True` then the histogram is normalized such that the last
+ bin equals 1. If `cumulative` evaluates to less than 0 (e.g., -1),
+ the direction of accumulation is reversed. In this case, if
+ `normed` and/or 'density' is also `True`, then the histogram is
+ normalized such that the first bin
equals 1.
Default is ``False``
@@ -5967,12 +5977,13 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
Returns
-------
n : array or list of arrays
- The values of the histogram bins. See **normed** and **weights**
- for a description of the possible semantics. If input **x** is an
- array, then this is an array of length **nbins**. If input is a
- sequence arrays ``[data1, data2,..]``, then this is a list of
- arrays with the values of the histograms for each of the arrays
- in the same order.
+ The values of the histogram bins. See **normed or density**
+ and **weights** for a description of the possible semantics.
+ If input **x** is an array, then this is an array of length
+ **nbins**. If input is a sequence arrays
+ ``[data1, data2,..]``, then this is a list of arrays with
+ the values of the histograms for each of the arrays in the
+ same order.
bins : array
The edges of the bins. Length nbins + 1 (nbins left edges and right
@@ -5999,6 +6010,51 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
earlier numpy versions.
"""
+
+ # Sets the density variable, if necessary, to its predecessor, 'normed.'
+ if density is not None and normed is not None:
+ raise ValueError('The density and normed arguments represent the '
+ 'same concept. Please set only one of them.')
+ elif normed is not None and density is None:
+ density = normed
+ elif normed is None and density is None:
+ density = False
+
+ def _normalize_input(inp, ename='input'):
+ """Normalize 1 or 2d input into list of np.ndarray or
+ a single 2D np.ndarray.
+
+ Parameters
+ ----------
+ inp : iterable
+ ename : str, optional
+ Name to use in ValueError if `inp` can not be normalized
+
+ """
+ if (isinstance(x, np.ndarray) or
+ not iterable(cbook.safe_first_element(inp))):
+ # TODO: support masked arrays;
+ inp = np.asarray(inp)
+ if inp.ndim == 2:
+ # 2-D input with columns as datasets; switch to rows
+ inp = inp.T
+ elif inp.ndim == 1:
+ # new view, single row
+ inp = inp.reshape(1, inp.shape[0])
+ else:
+ raise ValueError(
+ "{ename} must be 1D or 2D".format(ename=ename))
+ if inp.shape[1] < inp.shape[0]:
+ warnings.warn(
+ '2D hist input should be nsamples x nvariables;\n '
+ 'this looks transposed '
+ '(shape is %d x %d)' % inp.shape[::-1])
+ else:
+ # multiple hist with data of different length
+ inp = [np.asarray(xi) for xi in inp]
+
+ return inp
+
# Avoid shadowing the builtin.
bin_range = range
del range
@@ -6093,7 +6149,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
m = m.astype(float) # causes problems later if it's an int
if mlast is None:
mlast = np.zeros(len(bins)-1, m.dtype)
- if normed and not stacked:
+ if density and not stacked:
db = np.diff(bins)
m = (m.astype(float) / db) / m.sum()
if stacked:
@@ -6103,7 +6159,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
mlast[:] = m
n.append(m)
- if stacked and normed:
+ if stacked and density:
db = np.diff(bins)
for m in n:
m[:] = (m.astype(float) / db) / n[-1].sum()
@@ -6112,7 +6168,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
if cbook.is_numlike(cumulative) and cumulative < 0:
slc = slice(None, None, -1)
- if normed:
+ if density:
n = [(m * np.diff(bins))[slc].cumsum()[slc] for m in n]
else:
n = [m[slc].cumsum()[slc] for m in n]
@@ -6199,8 +6255,9 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
# Setting a minimum of 0 results in problems for log plots
if np.min(bottom) > 0:
minimum = np.min(bottom)
- elif normed or weights is not None:
- # For normed data, set to minimum data value / logbase
+ elif density or weights is not None:
+ # For data that is normed to form a probability density,
+ # set to minimum data value / logbase
# (gives 1 full tick-label unit for the lowest filled bin)
ndata = np.array(n)
minimum = (np.min(ndata[ndata > 0])) / logbase
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index f1cc67fde7a4..98e4ec74bf80 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -2775,6 +2775,17 @@ def test_hist_stacked_normed():
ax.hist((d1, d2), stacked=True, normed=True)
+def test_hist_stacked_density():
+ # make some data
+ d1 = np.linspace(1, 3, 20)
+ d2 = np.linspace(0, 10, 50)
+ fig, ax = plt.subplots()
+ ax.hist((d1, d2), stacked=True, density=True)
+
+ with pytest.raises(ValueError):
+ ax.hist((d1, d2), stacked=True, density=True, normed=False)
+
+
@image_comparison(baseline_images=['hist_step_bottom'], extensions=['png'],
remove_text=True)
def test_hist_step_bottom():
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