如何拆分单列的数据以成对分隔列

Question

I am using a csv file with dataframe name content . I am trying this code but it not providing expected output

new_content1 = content['Value1','Value2','Value3','Value4','Value5','Value6']
def get_pairs(x):
    arr = x.split(' ')
    return list(map(list, zip(arr, arr[1:])))

new_content1['pairs'] = new_content1.applymap(get_pairs)
new_content1

Value1 Value2 .... pairs(single column)
0 3 2 2 4 2 2 [[3, 2], [2, 2], [2, 4], [4, 2], [2, 2]] 1 1 2 3 4 5 6 [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]]

Answer 1

IIUC, you can simply use:

new_content1['pairs'] = new_content1['ColName'].str.split(" ", n = 1, expand = True)

where ColName is column you want to split, it will return a values as list in pairs column

Alternatively, if you want the output to make new columns based on splits, eg two columns, you can use the above as:

new_content1[['newCol1', 'newCol2']] = new_content1['ColName'].str.split(" ", n = 1, expand = True)

As per your comment let say you have df column like this one:

Value
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6

if your column are separate then join them using:

df['Value']=df['value1']+' '+df['value2']+' '+df['value3']+' '+df['value4']+' '+df['value5']+' '+df['value6']

then you can do this

df['Value']=df['Value'].astype('str')

df['new']=df['Value'].str.split(' ').apply(lambda x:list((zip(x[:], x[1:]))))

Output looks like this

+--------+--------------+------------------------------------------+
| Value  |     new      |                pairs                     |
+--------+--------------+------------------------------------------+
|     0  | 1 2 3 4 5 6  | [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] |
|     1  | 1 2 3 4 5 6  | [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] |
|     2  | 1 2 3 4 5 6  | [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] |
|     3  | 1 2 3 4 5 6  | [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] |
|     4  | 1 2 3 4 5 6  | [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] |
|     5  | 1 2 3 4 5 6  | [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] |
|     6  | 1 2 3 4 5 6  | [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] |
+--------+--------------+------------------------------------------+