循环遍历 2 个数据帧的更好方法

Question

I have 2 Dataframes. i am trying to find corresponding row values based on filter values. At the end, i want to add some columns in df2 to df1. The problem with the example below is that it is nor efficient when dealing with large dataset.

selected_rows = pd.Dataframe()
         for i, rowi in df1.iterrows():
              for j, rowj in df2.iterrows():
                if (rowi['Customer'] == rowj['Customer']) & (rowi['CaseID'] == rowj['CaseID']):
                    rowi['Resolution'] = rowj['Resolution']
                    rowi['Diagnostic'] = rowj['Diagnostic']
                    selected_rows = selected_rows.append(rowi)

Answer 1

This helped me: link . I use an approach like this instead of using iterrows()

for i in df.index:
    value = df._get_value(i, column)

which seems to be faster, but I think there are multiple approaches to get even faster.