Python绘制频率分布直方图的示例

#-*- encoding=utf-8 -*-import datetimeimport numpy as npimport matplotlib.pyplot as pltimport matplotlibzhfont1 = matplotlib.font_manager.FontProperties(fname='C:/Windows/Fonts/simsun.ttc')# 按照固定区间长度绘制频率分布直方图# bins_interval 区间的长度# margin    设定的左边和右边空留的大小def probability_distribution(data, bins_interval=1, margin=1):  bins = range(min(data), max(data) + bins_interval - 1, bins_interval)  print(len(bins))  for i in range(0, len(bins)):    print(bins[i])  plt.xlim(min(data) - margin, max(data) + margin)  plt.title("probability-distribution")  plt.xlabel('Interval')  plt.ylabel('Probability')  plt.hist(x=data, bins=bins, histtype='bar', color=['r'])  plt.show()

#-*- encoding=utf-8 -*-import datetimeimport numpy as npimport matplotlib.pyplot as pltimport matplotlibzhfont1 = matplotlib.font_manager.FontProperties(fname='C:/Windows/Fonts/simsun.ttc'# 自己给定区间，小于区间左端点和大于区间右端点的统一做处理，对于数据分布不均很的情况处理较友好# bins   自己设定的区间数值列表# margin  设定的左边和右边空留的大小# label   右上方显示的图例文字"""e  import numpy as np  data = np.random.normal(0, 1, 1000)  bins = np.arange(-5, 5, 0.1)  probability_distribution_extend(data=data, bins=bins)"""def probability_distribution_extend(data, bins, margin=1, label='Distribution'):  bins = sorted(bins)  length = len(bins)  intervals = np.zeros(length+1)  for value in data:    i = 0    while i < length and value >= bins[i]:      i += 1    intervals[i] += 1  intervals = intervals / float(len(data))  plt.xlim(min(bins) - margin, max(bins) + margin)  bins.insert(0, -999)  plt.title("probability-distribution")  plt.xlabel('Interval')  plt.ylabel('Probability')  plt.bar(bins, intervals, color=['r'], label=label)  plt.legend()  plt.show()

if __name__ == '__main__':  data = [1,4,6,7,8,9,11,11,12,12,13,13,16,17,18,22,25]  probability_distribution(data=data, bins_interval=5,margin=0)