使用 pandas iterrows() 时追加新行？
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Append new row when using pandas iterrows()?
提问by jam
I have the following code where I create df['var'2]and alter df['var1']. After performing these changes, I would like to append the newrow(with df['var'2]) to the dataframe while keeping the original (though now altered) row (which has df['var1']).
我有以下代码在其中创建df['var'2]和更改df['var1']. 执行这些更改后，我想将newrow(with df['var'2])附加到数据帧，同时保留原始（尽管现在已更改）行（其中有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)
I understand that when using iterrows(), row variables are copies instead of views which is why the changes are not being updated in the original dataframe. So, how would I alter this code to actually append newrow to the dataframe?
我知道在使用时iterrows()，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，我将如何更改此代码以实际将 newrow 附加到数据帧？
Thank you!
谢谢！
回答by Alexander
It is generally inefficient to append rows to a dataframe in a loop because a new copy is returned. You are better off storing the intermediate results in a list and then concatenating everything together at the end.
在循环中将行附加到数据帧通常效率低下，因为返回了一个新副本。您最好将中间结果存储在一个列表中，然后在最后将所有内容连接在一起。
Using row.loc['var1'] = row['var1'] - 30will make an inplace change to the original dataframe.
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
EDIT(per request below):
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
