将2d阵列放入Pandas系列中 [英] Put a 2d Array into a Pandas Series

问题描述

我有一个2D Numpy数组，我想放入一个熊猫系列(不是DataFrame)中:

I have a 2D Numpy array that I would like to put in a pandas Series (not a DataFrame):

>>> import pandas as pd
>>> import numpy as np
>>> a = np.zeros((5, 2))
>>> a
array([[ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.]])

但这会引发错误:

>>> s = pd.Series(a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/miniconda/envs/pyspark/lib/python3.4/site-packages/pandas/core/series.py", line 227, in __init__
    raise_cast_failure=True)
  File "/miniconda/envs/pyspark/lib/python3.4/site-packages/pandas/core/series.py", line 2920, in _sanitize_array
    raise Exception('Data must be 1-dimensional')
Exception: Data must be 1-dimensional

有可能被黑客入侵

>>> s = pd.Series(map(lambda x:[x], a)).apply(lambda x:x[0])
>>> s
0    [0.0, 0.0]
1    [0.0, 0.0]
2    [0.0, 0.0]
3    [0.0, 0.0]
4    [0.0, 0.0]

有更好的方法吗?

推荐答案

那么，您可以使用numpy.ndarray.tolist函数，如下所示:

Well, you can use the numpy.ndarray.tolist function, like so:

>>> a = np.zeros((5,2))
>>> a
array([[ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.]])
>>> a.tolist()
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]
>>> pd.Series(a.tolist())
0    [0.0, 0.0]
1    [0.0, 0.0]
2    [0.0, 0.0]
3    [0.0, 0.0]
4    [0.0, 0.0]
dtype: object

完成类似结果的更快方法是简单地执行pd.Series(list(a)).这将产生一系列的numpy数组，而不是Python列表，因此应该比返回Python列表列表的a.tolist更快.

A faster way to accomplish a similar result is to simply do pd.Series(list(a)). This will make a Series of numpy arrays instead of Python lists, so should be faster than a.tolist which returns a list of Python lists.

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