在使用熊猫iterrows（）时追加新行？

Question

我在创建df['var'2]和更改下面的代码df['var1']。执行完这些更改后，我想将newrow（with df['var'2]）附加到数据框，同时保留原始（尽管现在已更改）行（具有df['var1']）。

for i, row in df.iterrows():
    while row['var1'] > 30: 
        newrow = row
        newrow['var2'] = 30
        row['var1'] = row['var1']-30
        df.append(newrow)

我知道使用时iterrows()，行变量是副本而不是视图，这就是为什么更改未在原始数据框中更新的原因。因此，我将如何更改此代码以实际将newrow追加到数据框？

谢谢！

Answer 1

在循环中将行追加到数据框通常效率不高，因为会返回新副本。最好将中间结果存储在列表中，然后将所有内容最后串联在一起。

使用row.loc['var1'] = row['var1'] - 30将对原始数据框进行就地更改。

np.random.seed(0)
df = pd.DataFrame(np.random.randn(5, 2) * 100, columns=['var1', 'var2'])

>>> df
         var1        var2
0  176.405235   40.015721
1   97.873798  224.089320
2  186.755799  -97.727788
3   95.008842  -15.135721
4  -10.321885   41.059850

new_rows = []
for i, row in df.iterrows():
    while row['var1'] > 30: 
        newrow = row
        newrow['var2'] = 30
        row.loc['var1'] = row['var1'] - 30
        new_rows.append(newrow.values)
    df_new = df.append(pd.DataFrame(new_rows, columns=df.columns)).reset_index()

>>> df
    var1      var2
0  26.405235  30.00000
1   7.873798  30.00000
2   6.755799  30.00000
3   5.008842  30.00000
4 -10.321885  41.05985

>>> df_new
         var1      var2
0   26.405235  30.00000
1    7.873798  30.00000
2    6.755799  30.00000
3    5.008842  30.00000
4  -10.321885  41.05985
5   26.405235  30.00000
6   26.405235  30.00000
7   26.405235  30.00000
8   26.405235  30.00000
9   26.405235  30.00000
10   7.873798  30.00000
11   7.873798  30.00000
12   7.873798  30.00000
13   6.755799  30.00000
14   6.755799  30.00000
15   6.755799  30.00000
16   6.755799  30.00000
17   6.755799  30.00000
18   6.755799  30.00000
19   5.008842  30.00000
20   5.008842  30.00000
21   5.008842  30.00000

编辑（根据下面的请求）：

new_rows = []
for i, row in df.iterrows():
    while row['var1'] > 30: 
        row.loc['var1'] = var1 = row['var1'] - 30
        new_rows.append([var1, 30])
    df_new = df.append(pd.DataFrame(new_rows, columns=df.columns)).reset_index()

>>> df_new
    index        var1        var2
0       0   26.405235   40.015721
1       1    7.873798  224.089320
2       2    6.755799  -97.727788
3       3    5.008842  -15.135721
4       4  -10.321885   41.059850
5       0  146.405235   30.000000
6       1  116.405235   30.000000
7       2   86.405235   30.000000
8       3   56.405235   30.000000
9       4   26.405235   30.000000
10      5   67.873798   30.000000
11      6   37.873798   30.000000
12      7    7.873798   30.000000
13      8  156.755799   30.000000
14      9  126.755799   30.000000
15     10   96.755799   30.000000
16     11   66.755799   30.000000
17     12   36.755799   30.000000
18     13    6.755799   30.000000
19     14   65.008842   30.000000
20     15   35.008842   30.000000
21     16    5.008842   30.000000