Question

我们假设我有这样的DataFrame：

np.linalg.norm

我想使用Type Vector distance1 distance2 A [0.2340, 0.5463, 0.5652, 0.3243, 0.3243] A-B A-C distance3 A-D来计算这些向量的距离。我想得到的是

df['vector'] = df['vector'].apply(lambda x: np.array(x)) 
print(type(df['vector'].iloc[0]))

作为新列。编辑：我也这样做了：

<class 'numpy.ndarray'>

结果是：

print(np.linalg.norm(df['vector'].iloc[0] -df['vector'].iloc[1]))

当我简单地说：

ValueError: Wrong number of items passed 544, placement implies 1

我得到一个浮动值

但是我迭代了我得到的行：

{{1}}

我怎么能解决它？注意：矢量确实长544个字符

Answer 1

如果您正在使用pickle，请使用pandas pickle-import：

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由于pandas是在numpy上构建的，现在你可以将你想要的列作为numpy数组：

import pandas as pd

df = pd.read_pickle('your_file_name')

请注意你的矢量 - 它们的大小不一样！例如，C和D的长度为6.我假设您的逗号是第一个值的点。

编辑：

一个完整的例子是：

import numpy as np:

np.linalg.norm(x = df['your column'])

编辑2（与我的评论相关）：

我建议您使用所需的值生成列表或字典，因为将所有内容附加到表可能会导致表格非常大。然后代码如下：

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A':[0.2340, 0.5463, 0.5652, 0.3243, 0.3243],
    'B':[0.3244, 0.5566, 0.2344, 0.1213, 0.9821],
    'C':[0.5652,  0.3453, 0.3454, 0.5656, 0.6766],
    'D':[0.5125,  0.3345, 0.1112, 0.4545, 0.6324]
})

df_distances = df.transpose()           #Transpose columns to rows

for col in df:
    for col2 in df:
        df_distances["{}_{}".format(col, col2)] = np.linalg.norm(df[col] - df[col2])

将列转换为numpy数组

1 个答案: