如何使matplotlib / pandas条形图看起来像直方图？

Question

绘制bar和hist

之间的差异

鉴于pandas.Series，rv中的某些数据，

之间存在差异

1. 直接在要绘制的数据上调用hist

2. 计算直方图结果（使用numpy.histogram），然后使用bar

进行绘图

示例数据生成

%matplotlib inline

import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib
matplotlib.rcParams['figure.figsize'] = (12.0, 8.0)
matplotlib.style.use('ggplot')

# Setup size and distribution
size = 50000
distribution = stats.norm()

# Create random data
rv = pd.Series(distribution.rvs(size=size))
# Get sane start and end points of distribution
start = distribution.ppf(0.01)
end = distribution.ppf(0.99)

# Build PDF and turn into pandas Series
x = np.linspace(start, end, size)
y = distribution.pdf(x)
pdf = pd.Series(y, x)

# Get histogram of random data
y, x = np.histogram(rv, bins=50, normed=True)
# Correct bin edge placement
x = [(a+x[i+1])/2.0 for i,a in enumerate(x[0:-1])]
hist = pd.Series(y, x)

hist()绘图

ax = pdf.plot(lw=2, label='PDF', legend=True)
rv.plot(kind='hist', bins=50, normed=True, alpha=0.5, label='Random Samples', legend=True, ax=ax)



bar()绘图

ax = pdf.plot(lw=2, label='PDF', legend=True)
hist.plot(kind='bar', alpha=0.5, label='Random Samples', legend=True, ax=ax)



如何将bar绘图看起来像hist图？

此用例需要仅保存要使用的直方图数据并稍后绘图（其大小通常小于原始数据）。

Answer 1

条形图差异

获取看起来像bar图的hist图需要对bar的默认行为进行一些操作。

1. 强制bar通过传递x（hist.index）和y（hist.values）来使用实际x数据绘制范围。 The default bar behavior is to plot the y data against an arbitrary range and put the x data as the label。
2. 将width参数设置为与x数据的实际步长相关（默认为0.8）
3. 将align参数设置为'center'。
4. 手动设置轴图例。

这些更改需要通过matplotlib bar()调用轴（ax）而不是调用pandas的bar()来进行数据（hist）。

示例绘图

%matplotlib inline

import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib
matplotlib.rcParams['figure.figsize'] = (12.0, 8.0)
matplotlib.style.use('ggplot')

# Setup size and distribution
size = 50000
distribution = stats.norm()

# Create random data
rv = pd.Series(distribution.rvs(size=size))
# Get sane start and end points of distribution
start = distribution.ppf(0.01)
end = distribution.ppf(0.99)

# Build PDF and turn into pandas Series
x = np.linspace(start, end, size)
y = distribution.pdf(x)
pdf = pd.Series(y, x)

# Get histogram of random data
y, x = np.histogram(rv, bins=50, normed=True)
# Correct bin edge placement
x = [(a+x[i+1])/2.0 for i,a in enumerate(x[0:-1])]
hist = pd.Series(y, x)

# Plot previously histogrammed data
ax = pdf.plot(lw=2, label='PDF', legend=True)
w = abs(hist.index[1]) - abs(hist.index[0])
ax.bar(hist.index, hist.values, width=w, alpha=0.5, align='center')
ax.legend(['PDF', 'Random Samples'])

Answer 2

另一个更简单的解决方案是创建重现相同直方图的假样本，然后简单地使用hist（）。

即，从存储的数据中检索bins和counts后，执行

fake = np.array([])
for i in range(len(counts)):
    a, b = bins[i], bins[i+1]
    sample = a + (b-a)*np.random.rand(counts[i])
    fake = np.append(fake, sample)

plt.hist(fake, bins=bins)