我有一个由groupby创建的数据框：
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看起来像这样：
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)，我想转换数据帧，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%
有人可以帮我这么做吗？
编辑：我尝试了在此链接中找到的响应：pandas convert columns to percentages of the totals .....我无法让我的数据帧工作+它不能很好地解释（对我来说）如何让它工作任何DF。我认为John Galt的反应比回应更好（我的意见）。
答案 0 :(得分：9)
这是单程
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
并且，如果你想要将值四舍五入为小数位，并将值作为字符串＆＃39;％＆＃39;
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%