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102 lines
3.1 KiB
Markdown
102 lines
3.1 KiB
Markdown
# 直方图
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演示如何使用matplotlib绘制直方图。
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import colors
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from matplotlib.ticker import PercentFormatter
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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```
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## 生成数据并绘制简单的直方图
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要生成一维直方图，我们只需要一个数字矢量。对于二维直方图，我们需要第二个矢量。我们将在下面生成两者，并显示每个向量的直方图。
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```python
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N_points = 100000
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n_bins = 20
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# Generate a normal distribution, center at x=0 and y=5
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x = np.random.randn(N_points)
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y = .4 * x + np.random.randn(100000) + 5
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fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
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# We can set the number of bins with the `bins` kwarg
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axs[0].hist(x, bins=n_bins)
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axs[1].hist(y, bins=n_bins)
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```
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## 更新直方图颜色
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直方图方法(除其他外)返回一个修补程序对象。这使我们可以访问所绘制对象的特性。使用这个，我们可以根据自己的喜好编辑直方图。让我们根据每个条的y值更改其颜色。
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```python
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fig, axs = plt.subplots(1, 2, tight_layout=True)
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# N is the count in each bin, bins is the lower-limit of the bin
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N, bins, patches = axs[0].hist(x, bins=n_bins)
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# We'll color code by height, but you could use any scalar
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fracs = N / N.max()
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# we need to normalize the data to 0..1 for the full range of the colormap
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norm = colors.Normalize(fracs.min(), fracs.max())
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# Now, we'll loop through our objects and set the color of each accordingly
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for thisfrac, thispatch in zip(fracs, patches):
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color = plt.cm.viridis(norm(thisfrac))
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thispatch.set_facecolor(color)
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# We can also normalize our inputs by the total number of counts
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axs[1].hist(x, bins=n_bins, density=True)
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# Now we format the y-axis to display percentage
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axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
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```
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## 绘制二维直方图
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要绘制二维直方图，只需两个长度相同的向量，对应于直方图的每个轴。
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```python
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fig, ax = plt.subplots(tight_layout=True)
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hist = ax.hist2d(x, y)
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```
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## 自定义直方图
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自定义2D直方图类似于1D情况，您可以控制可视组件，如存储箱大小或颜色规格化。
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```python
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fig, axs = plt.subplots(3, 1, figsize=(5, 15), sharex=True, sharey=True,
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tight_layout=True)
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# We can increase the number of bins on each axis
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axs[0].hist2d(x, y, bins=40)
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# As well as define normalization of the colors
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axs[1].hist2d(x, y, bins=40, norm=colors.LogNorm())
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# We can also define custom numbers of bins for each axis
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axs[2].hist2d(x, y, bins=(80, 10), norm=colors.LogNorm())
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plt.show()
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```
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## 下载这个示例
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- [下载python源码: hist.py](https://matplotlib.org/_downloads/hist.py)
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- [下载Jupyter notebook: hist.ipynb](https://matplotlib.org/_downloads/hist.ipynb) |