for i, row in df.iterrows():
    if <something>:
        df.at[i, 'ifor'] = x
    else:
        df.at[i, 'ifor'] = y

你应该用df来赋值。ix[i， 'exp']=X或df。loc[i， 'exp']=X而不是df。Ix [i]['ifor'] = x。

否则，你正在处理一个视图，并且应该得到一个温暖:

-c:1: SettingWithCopyWarning:一个值正在试图从一个DataFrame的切片副本上设置。 尝试使用.loc[row_index,col_indexer] = value代替

但可以肯定的是，循环应该被一些矢量化算法所取代，以充分利用@Phillip Cloud所建议的DataFrame。

最好使用df.apply() -来使用lambda函数

df["ifor"] = df.apply(lambda x: {value} if {condition} else x["ifor"], axis=1)

好吧，如果你无论如何都要迭代，为什么不使用最简单的方法df['Column'].values[i]

df['Column'] = ''

for i in range(len(df)):
    df['Column'].values[i] = something/update/new_value

或者如果你想比较新值和旧值或者类似的东西，为什么不把它存储在一个列表中，然后在最后追加。

mylist, df['Column'] = [], ''

for <condition>:
    mylist.append(something/update/new_value)

df['Column'] = mylist

你可以使用df.at:

for i, row in df.iterrows():
    ifor_val = something
    if <condition>:
        ifor_val = something_else
    df.at[i,'ifor'] = ifor_val

对于0.21.0之前的版本，使用df.set_value:

for i, row in df.iterrows():
    ifor_val = something
    if <condition>:
        ifor_val = something_else
    df.set_value(i,'ifor',ifor_val)

如果你不需要行值，你可以简单地遍历df的索引，但我保留了原始的for循环，以防你需要行值来处理这里没有显示的东西。

Pandas DataFrame object should be thought of as a Series of Series. In other words, you should think of it in terms of columns. The reason why this is important is because when you use pd.DataFrame.iterrows you are iterating through rows as Series. But these are not the Series that the data frame is storing and so they are new Series that are created for you while you iterate. That implies that when you attempt to assign tho them, those edits won't end up reflected in the original data frame.

好了，现在问题已经解决了:我们该怎么做?

在这篇文章之前的建议包括:

pd.DataFrame。set_value在Pandas 0.21版已弃用 pd.DataFrame.ix已弃用 pd.DataFrame.loc很好，但可以在数组索引器上工作，你可以做得更好

我的建议 使用pd.DataFrame.at

for i in df.index:
    if <something>:
        df.at[i, 'ifor'] = x
    else:
        df.at[i, 'ifor'] = y

你甚至可以把它改为:

for i in df.index:
    df.at[i, 'ifor'] = x if <something> else y

回应评论

如果我需要使用前一行的值if条件?

for i in range(1, len(df) + 1):
    j = df.columns.get_loc('ifor')
    if <something>:
        df.iat[i - 1, j] = x
    else:
        df.iat[i - 1, j] = y