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提取numpy数组中的特定列

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问题：提取numpy数组中的特定列
回答 0
回答 1
回答 2
回答 3
回答 4
回答 5
回答 6
回答 7
回答 8

问题：提取numpy数组中的特定列

这是一个简单的问题，但要说我有一个MxN矩阵。我要做的就是提取特定的列并将其存储在另一个numpy数组中，但是我得到了无效的语法错误。这是代码：

extractedData = data[[:,1],[:,9]].

似乎上述行就足够了，但我想不是。我环顾四周，但找不到关于此特定场景的任何语法明智的方法。

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This is an easy question but say I have an MxN matrix. All I want to do is extract specific columns and store them in another numpy array but I get invalid syntax errors. Here is the code:

extractedData = data[[:,1],[:,9]].

It seems like the above line should suffice but I guess not. I looked around but couldn’t find anything syntax wise regarding this specific scenario.

回答 0

我假设你想要的列1和9？那是

data[:, [1, 9]]

或带有名称：

data[:, ['Column Name1','Column Name2']]

您可以从data.dtype.names… 获得名字。

点击查看英文原文

I assume you wanted columns 1 and 9?

To select multiple columns at once, use

X = data[:, [1, 9]]

To select one at a time, use

x, y = data[:, 1], data[:, 9]

With names:

data[:, ['Column Name1','Column Name2']]

You can get the names from data.dtype.names…

回答 1

假设您要获取具有该代码段的第1列和第9列，则应为：

extractedData = data[:,[1,9]]
点击查看英文原文

Assuming you want to get columns 1 and 9 with that code snippet, it should be:

extractedData = data[:,[1,9]]

回答 2

如果只想提取一些列：

idx_IN_columns = [1, 9]
extractedData = data[:,idx_IN_columns]

如果要排除特定列：

idx_OUT_columns = [1, 9]
idx_IN_columns = [i for i in xrange(np.shape(data)[1]) if i not in idx_OUT_columns]
extractedData = data[:,idx_IN_columns]
点击查看英文原文

if you want to extract only some columns:

idx_IN_columns = [1, 9]
extractedData = data[:,idx_IN_columns]

if you want to exclude specific columns:

idx_OUT_columns = [1, 9]
idx_IN_columns = [i for i in xrange(np.shape(data)[1]) if i not in idx_OUT_columns]
extractedData = data[:,idx_IN_columns]

回答 3

我想指出的一件事是，如果要提取的列数为1，则生成的矩阵将不是您期望的Mx1矩阵，而是包含所提取列元素的数组。

要将其转换为矩阵，应在结果数组上使用reshape（M，1）方法。

点击查看英文原文

One thing I would like to point out is, if the number of columns you want to extract is 1 the resulting matrix would not be a Mx1 Matrix as you might expect but instead an array containing the elements of the column you extracted.

To convert it to Matrix the reshape(M,1) method should be used on the resulting array.

回答 4

只是：

>>> m = np.matrix(np.random.random((5, 5)))
>>> m
matrix([[0.91074101, 0.65999332, 0.69774588, 0.007355  , 0.33025395],
        [0.11078742, 0.67463754, 0.43158254, 0.95367876, 0.85926405],
        [0.98665185, 0.86431513, 0.12153138, 0.73006437, 0.13404811],
        [0.24602225, 0.66139215, 0.08400288, 0.56769924, 0.47974697],
        [0.25345299, 0.76385882, 0.11002419, 0.2509888 , 0.06312359]])
>>> m[:,[1, 2]]
matrix([[0.65999332, 0.69774588],
        [0.67463754, 0.43158254],
        [0.86431513, 0.12153138],
        [0.66139215, 0.08400288],
        [0.76385882, 0.11002419]])

列不必按顺序排列：

>>> m[:,[2, 1, 3]]
matrix([[0.69774588, 0.65999332, 0.007355  ],
        [0.43158254, 0.67463754, 0.95367876],
        [0.12153138, 0.86431513, 0.73006437],
        [0.08400288, 0.66139215, 0.56769924],
        [0.11002419, 0.76385882, 0.2509888 ]])
点击查看英文原文

Just:

>>> m = np.matrix(np.random.random((5, 5)))
>>> m
matrix([[0.91074101, 0.65999332, 0.69774588, 0.007355  , 0.33025395],
        [0.11078742, 0.67463754, 0.43158254, 0.95367876, 0.85926405],
        [0.98665185, 0.86431513, 0.12153138, 0.73006437, 0.13404811],
        [0.24602225, 0.66139215, 0.08400288, 0.56769924, 0.47974697],
        [0.25345299, 0.76385882, 0.11002419, 0.2509888 , 0.06312359]])
>>> m[:,[1, 2]]
matrix([[0.65999332, 0.69774588],
        [0.67463754, 0.43158254],
        [0.86431513, 0.12153138],
        [0.66139215, 0.08400288],
        [0.76385882, 0.11002419]])

The columns need not to be in order:

>>> m[:,[2, 1, 3]]
matrix([[0.69774588, 0.65999332, 0.007355  ],
        [0.43158254, 0.67463754, 0.95367876],
        [0.12153138, 0.86431513, 0.73006437],
        [0.08400288, 0.66139215, 0.56769924],
        [0.11002419, 0.76385882, 0.2509888 ]])

回答 5

使用类似这样的列表从ND数组中选择列时，您还应该注意一件事：

data[:,:,[1,9]]

如果要删除维度（例如，仅选择一行），则将由于某种原因对结果数组进行置换。所以：

print data.shape            # gives [10,20,30]
selection = data[1,:,[1,9]]
print selection.shape       # gives [2,20] instead of [20,2]!!
点击查看英文原文

One more thing you should pay attention to when selecting columns from N-D array using a list like this:

data[:,:,[1,9]]

If you are removing a dimension (by selecting only one row, for example), the resulting array will be (for some reason) permuted. So:

print data.shape            # gives [10,20,30]
selection = data[1,:,[1,9]]
print selection.shape       # gives [2,20] instead of [20,2]!!

回答 6

您可以使用 ：

extracted_data = data.ix[:,['Column1','Column2']]

点击查看英文原文

You can use the following:

extracted_data = data.ix[:,['Column1','Column2']]

回答 7

我认为这里的解决方案不再适用于python版本的更新，为此使用新的python函数的一种方法是：

extracted_data = data[['Column Name1','Column Name2']].to_numpy()

这将为您提供理想的结果。

您可以在此处找到文档：https : //pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy

点击查看英文原文

I think the solution here is not working with an update of the python version anymore, one way to do it with a new python function for it is:

extracted_data = data[['Column Name1','Column Name2']].to_numpy()

which gives you the desired outcome.

The documentation you can find here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy

回答 8

您还可以使用extractedData = data（[：，1]，[:, 9]）

点击查看英文原文

you can also use extractedData=data([:,1],[:,9])