Python Pandas - 在 Groupby DF 上将列转换为百分比
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Python Pandas - Convert column to percentage on Groupby DF
提问by ScoutEU
I have a dataframe that I created by a groupby:
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)
The DF looks like this:
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
The column names are from variables (threeYr,twoYr,oneYr,Yr), and I want to convert the dataframe so that the numbers are percentages of the total for each column, but I cant get it to work.
列名来自 variables (threeYr,twoYr,oneYr,Yr)，我想转换数据框，以便数字是每列总数的百分比，但我无法让它工作。
This is what I want:
这就是我要的：
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%
Could anyone help me with doing this?
有人可以帮我做这件事吗？
Edit: I tried the response found at this link: pandas convert columns to percentages of the totals..... I could not get that to work for my dataframe + it does not explain well (to me) how to make it work for any DF. The response from John Galt I believe is better than that response (my opinion).
编辑：我尝试了在此链接中找到的响应：pandas 将列转换为总数的百分比..... 我无法让它适用于我的数据框 + 它没有很好地解释（对我而言）如何使其工作任何 DF。我相信 John Galt 的回应比那个回应要好（我的意见）。
回答by Zero
Here's one way
这是一种方法
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
And, if you want to rounded to 1 decimal place with value as strings with '%'
而且，如果你想四舍五入到小数点后 1 位，值作为带有 '%' 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%
