python绘制单列多色柱状图

import pandas as pddfA = pd.read_excel('E:/画图/WashingMachine.xlsx')dtdA = pd.to_datetime(dfA['价格'], infer_datetime_format=True)
month1 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(0, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(1000, infer_datetime_format=True))]
month2 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(1000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(2000, infer_datetime_format=True))]
month3 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(2000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(3000, infer_datetime_format=True))]
month4 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(3000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(4000, infer_datetime_format=True))]
month5 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(4000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(5000, infer_datetime_format=True))]
month6 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(5000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(6000, infer_datetime_format=True))]
month7 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(6000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(7000, infer_datetime_format=True))]
month8 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(7000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(8000, infer_datetime_format=True))]
month9 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(8000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(9000, infer_datetime_format=True))]
month10 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(9000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(10000, infer_datetime_format=True))]
month11 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(10000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(11000, infer_datetime_format=True))]
month12 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(11000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(12000, infer_datetime_format=True))]
month13 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(12000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(13000, infer_datetime_format=True))]
month14 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(13000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(14000, infer_datetime_format=True))]
month15 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(14000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(15000, infer_datetime_format=True))]
month16 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(15000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(16000, infer_datetime_format=True))]
month17 = dfA[(pd.to_datetime(dfA['价格'], infer_datetime_format=True) >= pd.to_datetime(16000, infer_datetime_format=True))& (pd.to_datetime(dfA['价格'], infer_datetime_format=True) <= pd.to_datetime(17000, infer_datetime_format=True))]print(month1['评论数'].sum()/len(month1['评论数']))
print(month2['评论数'].sum()/len(month2['评论数']))
print(month3['评论数'].sum()/len(month3['评论数']))
print(month4['评论数'].sum()/len(month4['评论数']))
print(month5['评论数'].sum()/len(month5['评论数']))
print(month6['评论数'].sum()/len(month6['评论数']))
print(month7['评论数'].sum()/len(month7['评论数']))
print(month8['评论数'].sum()/len(month8['评论数']))
print(month9['评论数'].sum()/len(month9['评论数']))
print(month10['评论数'].sum()/len(month10['评论数']))
print(month11['评论数'].sum()/len(month11['评论数']))
print(month12['评论数'].sum()/len(month12['评论数']))
print(month13['评论数'].sum()/len(month13['评论数']))
print(month14['评论数'].sum()/len(month14['评论数']))
print(month15['评论数'].sum()/len(month15['评论数']))
print(month16['评论数'].sum()/len(month16['评论数']))
print(month17['评论数'].sum()/len(month17['评论数']))import numpy as np
import matplotlib.pyplot as pltplt.rcParams['font.sans-serif']=['SimHei']   # 用黑体显示中文
plt.rcParams['axes.unicode_minus']=False     # 正常显示负号
plt.figure(figsize=(12,8))
x = np.arange(17) # x值取默认值
y = np.array([8642.764089121887, 4157.99251497006, 4452.4393700787405, 2851.2515151515154, 8345.589371980675, 2608.723577235772, 3505.988888888889, 755.4571428571429,1190.423076923077, 938.5714285714286, 315.7142857142857, 78.11111111111111, 403.0, 400.0, 67.5, 400.0, 205.0])
plt.xticks((0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16),('[0-1000]','[1000-2000]','[2000-3000]','[3000-4000]','[4000-5000]','[5000-6000]','[6000-7000]','[7000-8000]','[8000-9000]','[9000-10000]','[10000-11000]','[11000-12000]','[12000-13000]','[13000-14000]','[14000-15000]','[15000-16000]','[16000-17000]'), fontsize=9, rotation=60)# #定义函数来显示柱状上的数值
# def autolabel(rects):
#     for rect in rects:
#         height = rect.get_height()
#         plt.text(rect.get_x()+rect.get_width()/2.-0.25, 1.01*height, '%s' % int(height))a = plt.bar(np.arange(len(x)),y,color=['lightcoral','red','orangered', 'chocolate', 'orange', 'yellow','chartreuse', 'palegreen', 'lime', 'cyan', 'deepskyblue', 'dodgerblue','blue', 'blueviolet', 'fuchsia', 'deeppink', 'lightpink'])
# autolabel(a)
#plt.tick_params(labelsize=14)
ax=plt.gca()#获得坐标轴的句柄
ax.spines['bottom'].set_linewidth(1.5)###设置底部坐标轴的粗细
ax.spines['left'].set_linewidth(1.5)####设置左边坐标轴的粗细
ax.spines['right'].set_linewidth(1.5)###设置右边坐标轴的粗细
ax.spines['top'].set_linewidth(1.5)###设置右边坐标轴的粗细
plt.title('价格区间和平均销量',fontsize=20)
plt.ylabel('平均销量',fontsize=17)
#plt.xlabel('词语', fontsize=12)plt.show()

运行结果

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