Python Pandas-将列转换为Groupby DF上的百分比

Question

我有一个由groupby创建的数据帧：

hmdf = pd.DataFrame(hm01)
new_hm01 = hmdf[['FinancialYear','Month','FirstReceivedDate']]

hm05 = new_hm01.pivot_table(index=['FinancialYear','Month'], aggfunc='count')
vals1 = ['April    ', 'May      ', 'June     ', 'July     ', 'August   ', 'September', 'October  ', 'November ', 'December ', 'January  ', 'February ', 'March    ']

df_hm = new_hm01.groupby(['Month', 'FinancialYear']).size().unstack(fill_value=0).rename(columns=lambda x: '{}'.format(x))
df_hml = df_hm.reindex(vals1)

DF看起来是这样的：

FinancialYear   2014/2015   2015/2016   2016/2017   2017/2018
Month               
April               34          24          22          20
May                 29          26          21          25
June                19          39          22          20
July                23          39          18          20
August              36          30          34           0
September           35          23          41           0
October             36          37          27           0
November            38          31          30           0
December            36          41          23           0
January             34          30          35           0
February            37          26          37           0
March               36          31          33           0

列名来自变量(threeYr,twoYr,oneYr,Yr)，我想转换dataframe，以便数字是每列总数的百分比，但我无法使其工作。

这就是我想要的：

FinancialYear       2014/2015   2015/2016   2016/2017   2017/2018
Month               
April                   9%          6%          6%         24%
May                     7%          7%          6%         29%
June                    5%         10%          6%         24%
July                    6%         10%          5%         24%
August                  9%          8%         10%          0%
September               9%          6%         12%          0%
October                 9%         10%          8%          0%
November               10%          8%          9%          0%
December                9%         11%          7%          0%
January                 9%          8%         10%          0%
February                9%          7%         11%          0%
March                   9%          8%         10%          0%

有人能帮我做这个吗？

编辑：我尝试了此链接上的响应：pandas convert columns to percentages of the totals。。。。。我不能让它为我的数据帧工作+它不能很好地（对我）解释如何使它为任何DF工作。我相信约翰·高尔特的回答比我的意见要好。

Answer 1

有一个办法

In [1371]: (100. * df / df.sum()).round(0)
Out[1371]:
               2014/2015  2015/2016  2016/2017  2017/2018
FinancialYear
April                9.0        6.0        6.0       24.0
May                  7.0        7.0        6.0       29.0
June                 5.0       10.0        6.0       24.0
July                 6.0       10.0        5.0       24.0
August               9.0        8.0       10.0        0.0
September            9.0        6.0       12.0        0.0
October              9.0       10.0        8.0        0.0
November            10.0        8.0        9.0        0.0
December             9.0       11.0        7.0        0.0
January              9.0        8.0       10.0        0.0
February             9.0        7.0       11.0        0.0
March                9.0        8.0       10.0        0.0

而且，如果要将值四舍五入到小数点后1位，并将值作为带“%”的字符串

In [1375]: (100. * df / df.sum()).round(1).astype(str) + '%'
Out[1375]:
              2014/2015 2015/2016 2016/2017 2017/2018
FinancialYear
April              8.7%      6.4%      6.4%     23.5%
May                7.4%      6.9%      6.1%     29.4%
June               4.8%     10.3%      6.4%     23.5%
July               5.9%     10.3%      5.2%     23.5%
August             9.2%      8.0%      9.9%      0.0%
September          8.9%      6.1%     12.0%      0.0%
October            9.2%      9.8%      7.9%      0.0%
November           9.7%      8.2%      8.7%      0.0%
December           9.2%     10.9%      6.7%      0.0%
January            8.7%      8.0%     10.2%      0.0%
February           9.4%      6.9%     10.8%      0.0%
March              9.2%      8.2%      9.6%      0.0%