我得到了一个数据帧df5,下面是我从read_csv中读到的表,
Week_Days,Category,Total_Products_Sold,Total_Profit
0.Monday,A,3221,9999.53
0.Monday,B,1038,26070.33
0.Monday,C,699,13779.56
0.Monday,E,3055,18157.26
0.Monday,F,47569,215868.15
0.Monday,G,2348,23695.25
0.Monday,H,6,57
0.Monday,I,14033,64594.24
0.Monday,J,13876,47890.91
0.Monday,K,3878,14119.74
0.Monday,L,243,2649.6
0.Monday,M,2992,16757.38
1.Tuesday,A,2839,8864.78
1.Tuesday,B,1013,26254.69
1.Tuesday,C,656,13206.98
1.Tuesday,E,2696,15872.45
1.Tuesday,F,43039,197621.18
1.Tuesday,G,2107,21048.72
1.Tuesday,H,3,17
1.Tuesday,I,12297,56942.99
1.Tuesday,J,12095,40724.2
1.Tuesday,K,3418,12551.26
1.Tuesday,L,243,2520.3
1.Tuesday,M,2375,13268.28
2.Wednesday,A,2936,9119.93
2.Wednesday,B,1061,26927.86
2.Wednesday,C,634,10424.05
2.Wednesday,E,2835,16627.35
2.Wednesday,F,46128,218014.59
2.Wednesday,G,1986,19173.64
4.Friday,H,24,233
4.Friday,I,17576,81648.75
4.Friday,J,16468,55820.9
4.Friday,K,4294,16603.39
4.Friday,L,440,4258.51
4.Friday,M,3600,20142.44
5.Saturday,A,4658,15051.13
5.Saturday,B,1492,38236.07
5.Saturday,C,1057,15449.7
5.Saturday,E,5335,29904.96
5.Saturday,F,79925,362120.61
5.Saturday,G,4324,44088.79
5.Saturday,H,26,933
5.Saturday,I,22688,106313.86
5.Saturday,J,21882,74725.11
5.Saturday,K,5402,20875.84
5.Saturday,L,458,4692.84
5.Saturday,M,4896,27769.68
6.Sunday,A,3429,11310.1
6.Sunday,B,1104,27282.99
6.Sunday,C,1051,11567.08
6.Sunday,E,3913,22740.63
6.Sunday,F,56048,259105.03
6.Sunday,G,3224,32528.39
6.Sunday,H,21,749
6.Sunday,I,15853,74876.77
6.Sunday,J,16072,55259.76
6.Sunday,K,4383,16058.36
6.Sunday,L,327,3348.82
6.Sunday,M,3551,20814.05
我想绘制2个100%堆积条形图,每个销售总产品和总利润,其中x轴是周日,标签是不同的类别。
我销售的总产品代码是
df5 = df5.set_index(['Week_Days', 'Category'])
df5 = df5.div(df5.sum(1), axis=0)
ax = df5[['Total_Products_Sold']].plot(kind='bar', stacked=True, width = 0.3, figsize=(20, 10), colormap="RdBu")
patches, labels = ax.get_legend_handles_labels()
ax.legend(bbox_to_anchor=(1.1, 1.0))
ax.set_xlabel('Week Days')
ax.set_ylabel('Products Sold')
我得到的图表看起来没什么我需要的。它不是100堆叠而且图例是已售出的总产品,而不是类别中的不同类别。
有人可以帮忙吗?感谢。
此致 Lobbie