如何转换每个列的每一行中包含列表的数据

Question

我有下面的dataframe，它是for循环的输出之一。

df = pd.DataFrame()

df['Score'] = [['0-0','1-1','2-2'],['0-0','1-1','2-2']]
df ['value'] =[[0.08,0.1,0.15],[0.07,0.12,0.06]]
df ['Team'] = ['A','B']

我希望将每一行列表中的每个元素转换为列的每个元素。以下是预期的输出。

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有人能帮我把它改造吗？

谢谢,

泽普

Answer 1

使用np.concatenate：

import pandas as pd 
import numpy as np 

x = [['0-0','1-1','2-2'],['0-0','1-1','2-2']]
y = [[0.08,0.1,0.15],[0.07,0.12,0.06]]
z = ['A','B']
df = pd.DataFrame()

df['Score'] = np.concatenate(x)
df ['value'] = np.concatenate(y)
df['Team'] = np.repeat(z, len(df)/len(z))
print(df)

输出：

  Score  value Team                                                                                                                          
0   0-0   0.08    A                                                                                                                          
1   1-1   0.10    A                                                                                                                          
2   2-2   0.15    A                                                                                                                          
3   0-0   0.07    B                                                                                                                          
4   1-1   0.12    B                                                                                                                          
5   2-2   0.06    B   

Answer 2

你首先需要把专家们弄平，你可以使用itertools.chain

from itertools import chain
score = list(chain(*[['0-0','1-1','2-2'],['0-0','1-1','2-2']]))
value = list(chain(*[[0.08,0.1,0.15],[0.07,0.12,0.06]]))

pd.DataFrame({'score':score, 'value':value})

Score  value
0   0-0   0.08
1   1-1   0.10
2   2-2   0.15
3   0-0   0.07
4   1-1   0.12
5   2-2   0.06

Answer 3

您可以使用可迭代来扁平输入：

from itertools import chain

import pandas as pd

data = [['0-0','1-1','2-2'],['0-0','1-1','2-2']]
values = [[0.08,0.1,0.15],[0.07,0.12,0.06]]

df = pd.DataFrame(data=list(zip(chain.from_iterable(data), chain.from_iterable(values))), columns=['score', 'value'])
print(df)

输出

  score  value
0   0-0   0.08
1   1-1   0.10
2   2-2   0.15
3   0-0   0.07
4   1-1   0.12
5   2-2   0.06

作为另一种选择，您可以使用np.ravel

import numpy as np
import pandas as pd

data = [['0-0', '1-1', '2-2'], ['0-0', '1-1', '2-2']]
values = [[0.08, 0.1, 0.15], [0.07, 0.12, 0.06]]

df = pd.DataFrame({'score': np.array(data).ravel(), 'value': np.array(values).ravel()})
print(df)