在逐行迭代时更新 Pandas 中的数据帧 [英] Update a dataframe in pandas while iterating row by row

问题描述

我有一个看起来像这样的 Pandas 数据框(它非常大)

I have a pandas data frame that looks like this (its a pretty big one)

           date      exer exp     ifor         mat  
1092  2014-03-17  American   M  528.205  2014-04-19 
1093  2014-03-17  American   M  528.205  2014-04-19 
1094  2014-03-17  American   M  528.205  2014-04-19 
1095  2014-03-17  American   M  528.205  2014-04-19    
1096  2014-03-17  American   M  528.205  2014-05-17 

现在我想逐行迭代，当我遍历每一行时，ifor 的值每一行都可以根据某些条件而改变，我需要查找另一个数据框.

now I would like to iterate row by row and as I go through each row, the value of ifor in each row can change depending on some conditions and I need to lookup another dataframe.

现在，我如何在迭代时更新它.试了几样都没有用.

Now, how do I update this as I iterate. Tried a few things none of them worked.

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

    df.ix[i]['ifor'] = x

这些方法似乎都不起作用.我没有看到数据框中更新的值.

None of these approaches seem to work. I don't see the values updated in the dataframe.

推荐答案

您可以使用 df.set_value 在循环中赋值:

You can assign values in the loop using 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)

如果您不需要行值，您可以简单地遍历 df 的索引，但我保留了原始的 for 循环，以防您需要此处未显示的行值.

If you don't need the row values you could simply iterate over the indices of df, but I kept the original for-loop in case you need the row value for something not shown here.

更新

df.set_value() 从 0.21.0 版本开始被弃用你可以使用 df.at() 代替:

df.set_value() has been deprecated since version 0.21.0 you can use df.at() instead:

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

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