Python 将 numpy.array 存储在 Pandas.DataFrame 的单元格中

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时间:2020-08-19 17:06:40  来源:igfitidea点击:

Store numpy.array in cells of a Pandas.DataFrame

pythonpandasnumpydataframe

提问by Cedric H.

I have a dataframe in which I would like to store 'raw' numpy.array:

我有一个数据框,我想在其中存储 'raw' numpy.array

df['COL_ARRAY'] = df.apply(lambda r: np.array(do_something_with_r), axis=1)

but it seems that pandastries to 'unpack' the numpy.array.

但似乎pandas试图“解压” numpy.array。

Is there a workaround? Other than using a wrapper (see edit below)?

有解决方法吗?除了使用包装器(见下面的编辑)?

I tried reduce=Falsewith no success.

我试过reduce=False没有成功。

EDIT

编辑

This works, but I have to use the 'dummy' Dataclass to wrap around the array, which is unsatisfactory and not very elegant.

这有效,但我必须使用 'dummy'Data类来环绕数组,这令人不满意且不是很优雅。

class Data:
    def __init__(self, v):
        self.v = v

meas = pd.read_excel(DATA_FILE)
meas['DATA'] = meas.apply(
    lambda r: Data(np.array(pd.read_csv(r['filename'])))),
    axis=1
)

回答by Bharath

Use a wrapper around the numpy array i.e. pass the numpy array as list

在 numpy 数组周围使用包装器,即将 numpy 数组作为列表传递

a = np.array([5, 6, 7, 8])
df = pd.DataFrame({"a": [a]})

Output:

输出:

             a
0  [5, 6, 7, 8]

Or you can use apply(np.array)by creating the tuples i.e. if you have a dataframe

或者您可以apply(np.array)通过创建元组来使用,即如果您有数据框

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'a': ['on', 'on', 'off', 'off'],
                   'b': ['on', 'off', 'on', 'off']})

df['new'] = df.apply(lambda r: tuple(r), axis=1).apply(np.array)

Output :

输出 :

     a    b  id            new
0   on   on   1    [on, on, 1]
1   on  off   2   [on, off, 2]
2  off   on   3   [off, on, 3]
3  off  off   4  [off, off, 4]
df['new'][0]

Output :

输出 :

array(['on', 'on', '1'], dtype='<U2')

回答by user1717828

You can wrap the Data Frame data args in square brackets to maintain the np.arrayin each cell:

您可以将 Data Frame 数据 args 包裹在方括号中以维护np.array每个单元格中的数据:

one_d_array = np.array([1,2,3])
two_d_array = one_d_array*one_d_array[:,np.newaxis]
two_d_array

array([[1, 2, 3],
       [2, 4, 6],
       [3, 6, 9]])


pd.DataFrame([
    [one_d_array],
    [two_d_array] ])

                                   0
0                          [1, 2, 3]
1  [[1, 2, 3], [2, 4, 6], [3, 6, 9]]

回答by yuzhen_3301

Suppose you have a DataFrame dsand it has a column named as 'class'. If ds['class'] contains strings or numbers, and you want to change them with numpy.ndarrays or lists, the following code would help. In the code, class2vectoris a numpy.ndarrayor listand ds_classis a filter condition.

假设您有一个 DataFrameds并且它有一个名为“class”的列。如果ds['class'] 包含字符串或数字,并且您想用numpy.ndarrays 或lists更改它们,则以下代码会有所帮助。在代码中,class2vector是一个numpy.ndarray或,list并且ds_class是一个过滤条件。

ds['class'] = ds['class'].map(lambda x: class2vector if (isinstance(x, str) and (x == ds_class)) else x)

ds['class'] = ds['class'].map(lambda x: class2vector if (isinstance(x, str) and (x == ds_class)) else x)

回答by allenyllee

Just wrap what you want to store in a cell to a listobject through first apply, and extract it by index 0of that listthrough second apply:

只是包装你想在一个单元格中存放了什么list,通过第一个对象apply,并提取它index 0的是list通过第二apply

import pandas as pd
import numpy as np

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'a': ['on', 'on', 'off', 'off'],
                   'b': ['on', 'off', 'on', 'off']})


df['new'] = df.apply(lambda x: [np.array(x)], axis=1).apply(lambda x: x[0])

df

output:

输出:

    id  a       b       new
0   1   on      on      [1, on, on]
1   2   on      off     [2, on, off]
2   3   off     on      [3, off, on]
3   4   off     off     [4, off, off]

回答by David Wasserman

If you first set a column to have type object, you can insert an array without any wrapping:

如果您首先将一列设置为具有 type object,则可以插入一个不带任何换行的数组:

df = pd.DataFrame(columns=[1])
df[1] = df[1].astype(object)
df.loc[1, 1] = np.array([5, 6, 7, 8])
df

Output:

输出:

    1
1   [5, 6, 7, 8]