Question

我在以下代码中创建df['var'2]并更改df['var1']。执行这些更改后，我想将newrow（带df['var'2]）附加到数据框，同时保留原始（虽然现在已更改）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)

我理解在使用iterrows()时，行变量是副本而不是视图，这就是原始数据框中未更新更改的原因。那么，我如何更改此代码以实际将新内容附加到数据框？

谢谢！

Answer 1

将行追加到循环中的数据框通常是低效的，因为返回了新副本。最好将中间结果存储在列表中，然后在最后将所有内容连接在一起。

使用row.loc['var1'] = row['var1'] - 30将对原始数据框进行原位更改。

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（根据以下要求）：

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

使用pandas iterrows（）时追加新行？

1 个答案: