Pandas：在 Dataframe 子集上使用 iterrows
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Pandas: Use iterrows on Dataframe subset
提问by Andy
What is the best way to do iterrows with a subset of a DataFrame?
使用 DataFrame 的子集进行迭代的最佳方法是什么？
Let's take the following simple example:
让我们举一个简单的例子：
import pandas as pd
df = pd.DataFrame({
'Product': list('AAAABBAA'),
'Quantity': [5,2,5,10,1,5,2,3],
'Start' : [
DT.datetime(2013,1,1,9,0),
DT.datetime(2013,1,1,8,5),
DT.datetime(2013,2,5,14,0),
DT.datetime(2013,2,5,16,0),
DT.datetime(2013,2,8,20,0),
DT.datetime(2013,2,8,16,50),
DT.datetime(2013,2,8,7,0),
DT.datetime(2013,7,4,8,0)]})
df = df.set_index(['Start'])
Now I would like to modify a subset of this DataFrame using the itterrows function, e.g.:
现在我想使用 itterrows 函数修改这个 DataFrame 的一个子集，例如：
for i, row_i in df[df.Product == 'A'].iterrows():
row_i['Product'] = 'A1' # actually a more complex calculation
However, the changes do not persist.
然而，变化不会持续。
Is there any possibility (except a manual lookup using the index 'i') to make persistent changes on the original Dataframe ?
是否有可能（使用索引“i”进行手动查找除外）对原始 Dataframe 进行持久更改？
回答by Roman Pekar
Why do you need iterrows() for this? I think it's always preferrable to use vectorized operations in pandas (or numpy):
为什么你需要 iterrows() 呢？我认为在 Pandas（或 numpy）中使用矢量化操作总是更可取的：
df.ix[df['Product'] == 'A', "Product"] = 'A1'
回答by Magellan88
I guess the best way that comes to my mind is to generate a new vector with the desired result, where you can loop all you want and then reassign it back to the column
我想我想到的最好方法是生成一个具有所需结果的新向量，您可以在其中循环所有想要的内容，然后将其重新分配回列
#make a copy of the column
P = df.Product.copy()
#do the operation or loop if you really must
P[ P=="A" ] = "A1"
#reassign to original df
df["Product"] = P
