从Pandas DataFrame中删除重复项，并保留原始条件 [英] Removing duplicates from Pandas dataFrame with condition for retaining original

问题描述

假设我具有以下DataFrame:

Assuming I have the following DataFrame:

 A | B
 1 | Ms
 1 | PhD
 2 | Ms
 2 | Bs

我想删除与A列有关的重复行，并且我想保留B列中值为"PhD"的行作为原始行，如果找不到"PhD"，我想保留B列中带有"Bs"的行.

I want to remove the duplicate rows with respect to column A, and I want to retain the row with value 'PhD' in column B as the original, if I don't find a 'PhD', I want to retain the row with 'Bs' in column B.

我正在尝试使用

 df.drop_duplicates('A') 

有条件

推荐答案

>>> df
    A   B
0   1   Ms
1   1   Ms
2   1   Ms
3   1   Ms
4   1   PhD
5   2   Ms
6   2   Ms
7   2   Bs
8   2   PhD

使用自定义功能对数据框进行排序:

Sorting a dataframe with a custom function:

def sort_df(df, column_idx, key):
    '''Takes a dataframe, a column index and a custom function for sorting, 
    returns a dataframe sorted by that column using that function'''

    col = df.ix[:,column_idx]
    df = df.ix[[i[1] for i in sorted(zip(col,range(len(col))), key=key)]]
    return df

我们的排序功能:

cmp = lambda x:2 if 'PhD' in x else 1 if 'Bs' in x else 0

实际情况:

sort_df(df,'B',cmp).drop_duplicates('A', take_last=True)

    A   B
4   1   PhD
8   2   PhD

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