pandas 如何在另一个列的基础上派生新列的值 [英] pandas how to derived values for a new column base on another column

问题描述

我有一个dataframe，其中有一个列，每个值都是一个列表，现在我想派生一个新列，该列仅考虑大小大于1的列表，并为对应的行分配一个唯一的整数作为id . 样本dataframe就像

I have a dataframe that has a column that each value is a list, now I want to derive a new column which only considers list whose size is greater than 1, and assigns a unique integer to the corresponding row as id. A sample dataframe is like,

document_no_list    cluster_id
[1,2,3]             1
[4,5,6,7]           2
[8]                 nan
[9,10]              3 

列cluster_id仅考虑第一行，第二行和第四行，每行的大小都大于1，并为其列中的相应单元格分配一个唯一的整数id.

column cluster_id only considers the 1st, 2nd and 4th row, each of which has a size greater than 1, and assigns a unique integer id to its corresponding cell in the column.

我想知道如何在pandas中做到这一点.

I am wondering how to do that in pandas.

推荐答案

我们可以将np.random.choice用于具有.loc的唯一随机值，即

We can use np.random.choice for unique random values with .loc for assignment i.e

df = pd.DataFrame({'document_no_list' :[[1,2,3],[4,5,6,7],[8],[9,10]]})

x = df['document_no_list'].apply(len) > 1 

df.loc[x,'Cluster'] =  np.random.choice(range(len(df)),x.sum(),replace=False)

输出:

 document_no_list  Cluster
0        [1, 2, 3]      2.0
1     [4, 5, 6, 7]      1.0
2              [8]      NaN
3          [9, 10]      3.0

如果要连续数字，则可以使用

If you want continuous numbers then you can use

df.loc[x,'Cluster'] =  np.arange(x.sum())+1

 document_no_list  Cluster
0        [1, 2, 3]      1.0
1     [4, 5, 6, 7]      2.0
2              [8]      NaN
3          [9, 10]      3.0

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