我有三个充满数据的列表,我想将它们连接起来以创建一个数据帧
type of data_activationsLV : list
type of data_activationsF : list
type of data_activationsPC : list
三个列表的数据结构:
data_activationsLV data_activationsF data_activationsPC
index a index b index c
14468 7.8 14468 7.2 14468 7.6
14469 7.8 14469 7.1 14469 7.0
14470 7.9 14470 7.9 14470 8.1
14471 8.2 14471 9.5 14471 9.9
..
我将它们转换为系列并确认它们:
df15LV = pd.Series(data_activationsLV)
df15F = pd.Series(data_activationsF)
df15PC = pd.Series(data_activationsPC)
dfnew2=pd.concat([df15LV,df15F,df15PC], ignore_index=True, axis=1)
通过缺点,我在这里有一个问题,在每一列中,它都考虑旧列的名称及其带有值的索引
index 0 1 2
0 a14468 7.8 b14468 7.2 c14468 7.6
1 a14469 7.8 b14469 7.1 c14469 7.0
2 a14470 7.9 b14470 7.9 c14470 8.1
3 a14471 8.2 b14471 9.5 c14471 9.9
所以我测试了拆分函数:
dfnew2['a'] = dfnew2[2].split(' ')
但是它不起作用,当我尝试拆分这些列时,就会发生这种情况:
AttributeError: 'Series' object has no attribute 'split'
是否可以只包含每列的值:
index df15LV df15F df15PC
0 7.8 7.2 7.6
1 7.8 7.1 7.0
2 7.9 7.9 8.1
3 8.2 9.5 9.9
我认为您需要使用 str.split
拆分apply
并使用str[1]
进行选择:
print (data_activationsLV)
['14468 7.8', '14469 7.8']
print (data_activationsF)
['14468 7.2', '14469 7.1', '14470 7.9', '14471 9.5']
print (data_activationsPC)
['14468 7.6', '14470 8.1', '14471 9.9']
df15LV = pd.Series(data_activationsLV)
df15F = pd.Series(data_activationsF)
df15PC = pd.Series(data_activationsPC)
dfnew2=pd.concat([df15LV,df15F,df15PC], axis=1)
dfnew2 = dfnew2.apply(lambda x: x.str.split().str[1])
#if necessary convert to float
dfnew2 = dfnew2.astype(float)
print (dfnew2)
0 1 2
0 7.8 7.2 7.6
1 7.8 7.1 8.1
2 NaN 7.9 9.9
3 NaN 9.5 NaN
另一种解决方案是使用 list comprehension
进行拆分:
print (data_activationsLV)
['7.8', '7.8']
print (data_activationsF)
['7.2', '7.1', '7.9', '9.5']
print (data_activationsPC)
['7.6', '8.1', '9.9']
df15LV = pd.Series(data_activationsLV)
df15F = pd.Series(data_activationsF)
df15PC = pd.Series(data_activationsPC)
dfnew2=pd.concat([df15LV,df15F,df15PC], axis=1)
#if necessary convert to float
dfnew2 = dfnew2.astype(float)
print (dfnew2)
0 1 2
0 7.8 7.2 7.6
1 7.8 7.1 8.1
2 NaN 7.9 9.9
3 NaN 9.5 NaN
如果您有相同长度的列表,则可以创建一个空的数据帧并填充它:
data_activationsLV = [7.8,7.8,7.9,8.2]
data_activationsF = [7.2,7.1,7.9,9.5]
# create an empty dataframe
columns = ['LV', 'F']
index = np.arange(len(data_activationsLV)) # array of numbers for the number of rows
df = pd.DataFrame(columns=columns, index = index)
df['LV'] = data_activationsLV
df['F'] = data_activationsF
df