Question

我有四舍五入的问题，这似乎很常见，但我无法通过Google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

这是我的所作所为，我仍然希望%的数量

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，需要最后的四舍五入

        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。

Answer 1

您需要round：

table['% log'] = (table.log / table.reg * 100).round(2).astype(str) + '%'

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0 2位小数：

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%

在转换为pandas中的字符串之前进行舍入

1 个答案: