似乎无法使用使用 pandas to_csv和read_csv来正确读取numpy数组 [英] Can't seem to use use pandas to_csv and read_csv to properly read numpy array

问题描述

该问题似乎源于当我尝试在nparray上执行操作时，我在读取带有csv的read_csv时遇到类型问题.以下是一个最小的工作示例.

The problem seems to stem from when I read in the csv with read_csv having a type issue when I try to perform operations on the nparray. The following is a minimum working example.

x = np.array([0.83151197,0.00444986])
df = pd.DataFrame({'numpy': [x]})
np.array(df['numpy']).mean()

Out[151]: array([ 0.83151197,  0.00444986])

这是我所期望的.但是，如果我将结果写入文件，然后将数据读回pandas DataFrame，则类型会损坏.

Which is what I would expect. However, if I write the result to a file and then read the data back into a pandas DataFrame the types are broken.

x = np.array([0.83151197,0.00444986])
df = pd.DataFrame({'numpy': [x]})
df.to_csv('C:/temp/test5.csv')
df5 = pd.read_csv('C:/temp/test5.csv', dtype={'numpy': object})
np.array(df5['numpy']).mean()

TypeError:/的不支持的操作数类型:"str"和"long"

TypeError: unsupported operand type(s) for /: 'str' and 'long'

以下是"df5"对象的输出

The following is the output of "df5" object

df5 
Out[186]:     
    Unnamed: 0                      numpy 
 0           0    [0.83151197  0.00444986]

以下是文件内容:

,numpy
0,[ 0.83151197  0.00444986]

我弄清楚如何使它起作用的唯一方法是读取数据并手动转换类型，这似乎很愚蠢且缓慢.

The only way I have figured out how to get this to work is to read the data and manually convert the type, which seems silly and slow.

[float(num) for num in df5['numpy'][0][1:-1].split()]

总有办法避免上述情况吗?

Is there anyway to avoid the above?

推荐答案

pd.DataFrame({'col_name': data})希望一维数组与对象data类似:

pd.DataFrame({'col_name': data}) expects a 1D array alike objects as data:

In [63]: pd.DataFrame({'numpy': [0.83151197,0.00444986]})
Out[63]:
      numpy
0  0.831512
1  0.004450

In [64]: pd.DataFrame({'numpy': np.array([0.83151197,0.00444986])})
Out[64]:
      numpy
0  0.831512
1  0.004450

您已经用[]包裹了numpy数组，因此您传递了一个numpy数组列表:

you've wrapped numpy array with [] so you passed a list of numpy arrays:

In [65]: pd.DataFrame({'numpy': [np.array([0.83151197,0.00444986])]})
Out[65]:
                      numpy
0  [0.83151197, 0.00444986]

将df = pd.DataFrame({'numpy': [x]})替换为df = pd.DataFrame({'numpy': x})

演示:

In [56]: x = np.array([0.83151197,0.00444986])
    ...: df = pd.DataFrame({'numpy': x})
#                                   ^  ^
    ...: df.to_csv('d:/temp/test5.csv', index=False)
    ...:

In [57]: df5 = pd.read_csv('d:/temp/test5.csv')

In [58]: df5
Out[58]:
      numpy
0  0.831512
1  0.004450

In [59]: df5.dtypes
Out[59]:
numpy    float64
dtype: object

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