Python 在转换为字符串之前进行四舍五入

我在取整方面有问题，这似乎很常见，但我无法通过谷歌搜索找到答案，所以我决定在这里提问

这是我的数据

        day         reg     log ad      trans   paid
1111    20171005    172     65  39.0    14.0    3.0
1112    20171006    211     90  46.0    17.0    4.0
1113    20171007    155     70  50.0    17.0    1.0
1114    20171008    174     71  42.0    18.0    0.0
1115    20171009    209     63  43.0    21.0    2.0

这就是我所做的，我仍然想在数字上

%

table['% log'] = (table.log / table.reg * 100).astype(str) + '%'
table['% ad'] = (table.ad / table.reg * 100).astype(str) + '%'
table['% trans'] = (table.trans / table.reg* 100).astype(str) + '%'
table['% paid'] = (table.paid / table.reg * 100).astype(str) + '%'

这是我得到的，需要最后一次四舍五入吗

        day         reg     log ad      trans   paid    % log            % ad       % trans     % paid
1111    20171005    172     65  39.0    14.0    3.0     37.7906976744%  22.6744186047%  8.13953488372%  1.74418604651%
1112    20171006    211     90  46.0    17.0    4.0     42.654028436%   21.8009478673%  8.05687203791%  1.89573459716%
1113    20171007    155     70  50.0    17.0    1.0     45.1612903226%  32.2580645161%  10.9677419355%  0.645161290323%
1114    20171008    174     71  42.0    18.0    0.0     40.8045977011%  24.1379310345%  10.3448275862%  0.0%
1115    20171009    209     63  43.0    21.0    2.0     30.1435406699%  20.5741626794%  10.04784689%    0.956937799043%

我想要的是百分比不是太长，只是两位数的四舍五入。

您需要：

更好的解决方案是按子集选择所有列，并将

join

输出到原始

df

：

cols = ['log','ad','trans','paid']
table =(table.join(table[cols].div(table.reg, 0)
                              .mul(100)
                              .round(2)
                              .astype(str)
                              .add('%')
                              .add_prefix('%% ')))
print (table)
           day  reg  log    ad  trans  paid   % log    % ad % trans % paid
1111  20171005  172   65  39.0   14.0   3.0  37.79%  22.67%   8.14%  1.74%
1112  20171006  211   90  46.0   17.0   4.0  42.65%   21.8%   8.06%   1.9%
1113  20171007  155   70  50.0   17.0   1.0  45.16%  32.26%  10.97%  0.65%
1114  20171008  174   71  42.0   18.0   0.0   40.8%  24.14%  10.34%   0.0%
1115  20171009  209   63  43.0   21.0   2.0  30.14%  20.57%  10.05%  0.96%

此外，如果需要更好的输出-添加2位小数的

0

：

table =(table.join(table[cols].div(table.reg, 0)
                              .mul(100)
                              .applymap("{0:.2f}".format)
                              .add('%')
                              .add_prefix('%% ')))
print (table)
           day  reg  log    ad  trans  paid   % log    % ad % trans % paid
1111  20171005  172   65  39.0   14.0   3.0  37.79%  22.67%   8.14%  1.74%
1112  20171006  211   90  46.0   17.0   4.0  42.65%  21.80%   8.06%  1.90%
1113  20171007  155   70  50.0   17.0   1.0  45.16%  32.26%  10.97%  0.65%
1114  20171008  174   71  42.0   18.0   0.0  40.80%  24.14%  10.34%  0.00%
1115  20171009  209   63  43.0   21.0   2.0  30.14%  20.57%  10.05%  0.96%

你需要：

更好的解决方案是按子集选择所有列，并将

join

输出到原始

df

：

cols = ['log','ad','trans','paid']
table =(table.join(table[cols].div(table.reg, 0)
                              .mul(100)
                              .round(2)
                              .astype(str)
                              .add('%')
                              .add_prefix('%% ')))
print (table)
           day  reg  log    ad  trans  paid   % log    % ad % trans % paid
1111  20171005  172   65  39.0   14.0   3.0  37.79%  22.67%   8.14%  1.74%
1112  20171006  211   90  46.0   17.0   4.0  42.65%   21.8%   8.06%   1.9%
1113  20171007  155   70  50.0   17.0   1.0  45.16%  32.26%  10.97%  0.65%
1114  20171008  174   71  42.0   18.0   0.0   40.8%  24.14%  10.34%   0.0%
1115  20171009  209   63  43.0   21.0   2.0  30.14%  20.57%  10.05%  0.96%

此外，如果需要更好的输出-添加2位小数的

0

：

table =(table.join(table[cols].div(table.reg, 0)
                              .mul(100)
                              .applymap("{0:.2f}".format)
                              .add('%')
                              .add_prefix('%% ')))
print (table)
           day  reg  log    ad  trans  paid   % log    % ad % trans % paid
1111  20171005  172   65  39.0   14.0   3.0  37.79%  22.67%   8.14%  1.74%
1112  20171006  211   90  46.0   17.0   4.0  42.65%  21.80%   8.06%  1.90%
1113  20171007  155   70  50.0   17.0   1.0  45.16%  32.26%  10.97%  0.65%
1114  20171008  174   71  42.0   18.0   0.0  40.80%  24.14%  10.34%  0.00%
1115  20171009  209   63  43.0   21.0   2.0  30.14%  20.57%  10.05%  0.96%

---

哦，很简单，很简单