如何将一系列元组转换成Pandas数据帧？

Question

假设下面的pandas系列是在groupby之后对数据帧应用apply函数得到的。在

<class 'pandas.core.series.Series'>
0        (1, 0, [0.2, 0.2, 0.2], [0.2, 0.2, 0.2])
1     (2, 1000, [0.6, 0.7, 0.5], [0.1, 0.3, 0.1])
2        (1, 0, [0.4, 0.4, 0.4], [0.4, 0.4, 0.4])
3        (1, 0, [0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
4    (3, 14000, [0.8, 0.8, 0.8], [0.6, 0.6, 0.6])
dtype: object

当给定sigList=['sig1'，'sig2'，'sig3']时，我们能把它转换成一个数据帧吗？在

^{pr2}$

提前谢谢

Answer 1

您可以展平每个元素，然后将每个元素转换为序列本身。将每个元素转换为一个序列将主序列（在下面的示例中为s）转换为一个数据帧。然后根据需要设置列名。在

例如：

import pandas as pd

# load in your data
s = pd.Series([
    (1, 0, [0.2, 0.2, 0.2], [0.2, 0.2, 0.2]),
    (2, 1000, [0.6, 0.7, 0.5], [0.1, 0.3, 0.1]),
    (1, 0, [0.4, 0.4, 0.4], [0.4, 0.4, 0.4]),
    (1, 0, [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
    (3, 14000, [0.8, 0.8, 0.8], [0.6, 0.6, 0.6]),
])

def flatten(x):
    # note this is not very robust, but works for this case
    return [x[0], x[1], *x[2], *x[3]]

df = s.apply(flatten).apply(pd.Series)
df.columns = [
    "Length", "Distance", "sig1Max", "sig2Max", "sig3Max", "sig1Min", "sig2Min", "sig3Min"
]

那么df为：

^{pr2}$

Answer 2

用老式（快速）的方式，使用列表理解：

columns = ("Length Distance sig1Max sig2Max" 
           "sig3Max sig1Min sig2Min sig3Min").split()
df = pd.DataFrame([[a, b, *c, *d] for a,b,c,d in series.values], columns=columns)
print(df)
   Length  Distance  sig1Max  sig2Max  sig3Max  sig1Min  sig2Min  sig3Min
0       1         0      0.2      0.2      0.2      0.2      0.2      0.2
1       2      1000      0.6      0.7      0.5      0.1      0.3      0.1
2       1         0      0.4      0.4      0.4      0.4      0.4      0.4
3       1         0      0.5      0.5      0.5      0.5      0.5      0.5
4       3     14000      0.8      0.8      0.8      0.6      0.6      0.6

---

或者，也许你是说，做得更有活力一点

^{pr2}$

Answer 3

你可以查一下

newdf=pd.DataFrame(s.tolist())
newdf=pd.concat([newdf[[0,1]],pd.DataFrame(newdf[2].tolist()),pd.DataFrame(newdf[3].tolist())],1)
newdf.columns = [
    "Length", "Distance", "sig1Max", "sig2Max", "sig3Max", "sig1Min", "sig2Min", "sig3Min"
]
newdf
Out[163]: 
   Length  Distance  sig1Max   ...     sig1Min  sig2Min  sig3Min
0       1         0      0.2   ...         0.2      0.2      0.2
1       2      1000      0.6   ...         0.1      0.3      0.1
2       1         0      0.4   ...         0.4      0.4      0.4
3       1         0      0.5   ...         0.5      0.5      0.5
4       3     14000      0.8   ...         0.6      0.6      0.6
[5 rows x 8 columns]