使用 python/pandas 将数据标准化并绘制为堆积条形图

问题描述：

我有以下数据框:

I have the following data frame:

    land_cover  canopy_cat  count  tc_density_cor
0           20           1     56       35.760967
1           20           2     28       35.760967
2           20           3     11       35.760967
3           20           4      9       35.760967
4           20           5      4       35.760967
5           20           6      3       35.760967
6           20           7      3       35.760967
7           20           8      1       35.760967
8           20           9      4       35.760967
9           20          10      6       35.760967
10          20          11      2       35.760967
11          30           1    194       17.408260
12          30           2     86       17.408260
13          30           3     55       17.408260
14          30           4     36       17.408260
15          30           5     21       17.408260
16          30           6     15       17.408260
17          30           7      9       17.408260
18          30           8      6       17.408260
19          30           9     19       17.408260
20          30          10     14       17.408260
21          30          11      9       17.408260
22          40           1    106       17.458283
23          40           2     45       17.458283
24          40           3     19       17.458283
25          40           4     14       17.458283
26          40           5      9       17.458283
27          40           6      8       17.458283
28          40           7      5       17.458283
29          40           8      5       17.458283
30          40           9      8       17.458283
31          40          10     12       17.458283
32          40          11      3       17.458283

我想将我的数据绘制为堆积条形图:
x轴= land_cover
y 轴 = 每个 canopy_cat 的数量

我认为枢纽功能是我想要的.但是，在我想要相对于"tc_density_cor"相对于每个land_cover的计数"列进行规范化之前.
例如，land_cover=20 = 127 的计数"总和.
127/35.76 = 56/x->新值应为:15.76

我怎样才能做到这一点??:)

and I want to plot my data as a stacked bar plot:
x-axis = land_cover
y-axis = count per canopy_cat

I think that the pivot function is what I am looking for. However before I want to normalize the "count" column for each land_cover relative to "tc_density_cor".
for example, the sum of "counts" for land_cover=20 = 127.
127/35.76 = 56/x --> new value would be: 15.76

How can I do that?? :)

答

我认为您需要:

df['Count Per Canopy Cat'] = (df['count'] * df['tc_density_cor'] / 
                              df.groupby('land_cover')['count'].transform(sum))

df.pivot('land_cover',
         'canopy_cat',
         'Count Per Canopy Cat')\
  .plot.bar(stacked=True, figsize=(15,8))

图表:

使用 python/pandas 将数据标准化并绘制为堆积条形图

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