Today small for everyone to share the use of a python on the plt.hist Detailed parameters, a good reference value, we want to help. Xiao Bian together to follow up to see it
as follows:
matplotlib.pyplot.hist(
x, bins=10, range=None, normed=False,
weights=None, cumulative=False, bottom=None,
histtype=u'bar', align=u'mid', orientation=u'vertical',
rwidth=None, log=False, color=None, label=None, stacked=False,
hold=None, **kwargs)
x : (n,) array or sequence of (n,) arrays
This parameter is specified for each bin (bin) of data distribution, corresponding to the x-axis
bins : integer or array_like, optional
This parameter specifies the number of bin (box), i.e. a total of a few bar chart
normed : boolean, optional
If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e.,n/(len(x)`dbin)
This parameter specifies the density, that is, each bar graph proportion ratio, the default is 1
color : color or array_like of colors or None, optional
The specified color bar chart
We draw a bar chart the distribution of 10,000 data, a total of 50 parts, based on the statistical distribution of 10,000 points
"""
Demo of the histogram (hist) function with a few features.
In addition to the basic histogram, this demo shows a few optional features:
* Setting the number of data bins
* The ``normed`` flag, which normalizes bin heights so that the integral of
the histogram is 1. The resulting histogram is a probability density.
* Setting the face color of the bars
* Setting the opacity (alpha value).
"""
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(10000)
num_bins = 50
# the histogram of the data
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.5)
# add a 'best fit' line
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--')
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
# Tweak spacing to prevent clipping of ylabel
plt.subplots_adjust(left=0.15)
plt.show()
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