Python 将数组列表作为列附加到具有相同列索引的 Pandas Data Frame
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Append a list of arrays as column to pandas Data Frame with same column indices
提问by PyLearner
I have a list of arrays (one-dimensional numpy array) (a_) and a list (l_) and want to have a DataFrame with them as its columns. They look like this:
我有一个数组列表（一维 numpy 数组）（a_）和一个列表（l_），并希望将它们作为列的 DataFrame。它们看起来像这样：
a_: [array([381]), array([376]), array([402]), array([400])...]
l_: [1.5,2.34,4.22,...]
I can do it by:
我可以通过以下方式做到：
df_l = pd.DataFrame(l_) df_a = pd.DataFrame(a_) df = pd.concat([df_l, df_a], axis=1)
df_l = pd.DataFrame(l_) df_a = pd.DataFrame(a_) df = pd.concat([df_l, df_a], axis=1)
Is there a shorterway of doing it? I tried to use pd.append:
有没有更短的方法？我尝试使用pd.append：
df_l = pd.DataFrame(l_) df_l = df_l.append(a_)
df_l = pd.DataFrame(l_) df_l = df_l.append(a_)
However, because columns indices are both 0, it adds a_ to the end of the dataframe column, resulting in a single column. Is there something like this:
但是，由于列索引都是 0，因此它将 a_ 添加到数据框列的末尾，从而生成单个列。有没有这样的：
l_ = l_.append(a_).reset(columns)
l_ = l_.append(a_).reset(columns)
that set a new column index for the appended array? well, obviously this does not work!
为附加数组设置新的列索引？好吧，显然这行不通！
the desired output is like:
所需的输出是这样的：
0 0 0 1.50 381 1 2.34 376 2 4.22 402...
0 0 0 1.50 381 1 2.34 376 2 4.22 402...
Thanks.
谢谢。
采纳答案by kennes
Suggestion:
建议：
df_l = pd.DataFrame(l_)
df_1['a_'] = pd.Series(a_list, index=df_1.index)
Example #1:
示例#1：
L = list(data)
A = list(data)
data_frame = pd.DataFrame(L)
data_frame['A'] = pd.Series(A, index=data_frame.index)
Example #2 - Same Series length (create series and set index to the same as existing data frame):
示例 #2 - 相同的系列长度（创建系列并将索引设置为与现有数据框相同）：
In [33]: L = list(item for item in range(10))
In [34]: A = list(item for item in range(10,20))
In [35]: data_frame = pd.DataFrame(L,columns=['L'])
In [36]: data_frame['A'] = pd.Series(A, index=data_frame.index)
In [37]: print data_frame
L A
0 0 10
1 1 11
2 2 12
3 3 13
4 4 14
5 5 15
6 6 16
7 7 17
8 8 18
9 9 19
Example #3 - Different Series lengths (create series and let pandas handle index matching):
示例 #3 - 不同的系列长度（创建系列并让熊猫处理索引匹配）：
In [45]: not_same_length = list(item for item in range(50,55))
In [46]: data_frame['nsl'] = pd.Series(not_same_length)
In [47]: print data_frame
L A nsl
0 0 10 50
1 1 11 51
2 2 12 52
3 3 13 53
4 4 14 54
5 5 15 NaN
6 6 16 NaN
7 7 17 NaN
8 8 18 NaN
9 9 19 NaN
Based on your comments, it looks like you want to join your list of lists.I'm assuming they are in list structure because array()is not a method in python. To do that you would do the following:
根据您的评论，您似乎想加入列表列表。我假设它们在列表结构中，因为array()它不是 Python 中的方法。为此，您将执行以下操作：
In [63]: A = [[381],[376], [402], [400]]
In [64]: A = [inner_item for item in A for inner_item in item]
In [65]: print A
[381, 376, 402, 400]
Then create the Series using the new array and follow the steps above to add to your data frame.
然后使用新数组创建系列并按照上述步骤添加到您的数据框中。
