[英]How to create dataframe from numpy array
I have the following numpy
array: 我有以下numpy
数组:
numpy_x.shape
(9982, 26)
numpy_x
have 9982 records/observations and 26 columns index. numpy_x
具有9982个记录/观测值和26列索引。 Is this right really? 真的对吗?
numpy_x[:]
array([[0.00000000e+00, 9.60000000e-01, 1.00000000e+00, ...,
1.20000000e+00, 6.90000000e-01, 1.17000000e+00],
[1.00000000e+00, 9.60000000e-01, 1.00000000e+00, ...,
1.20000000e+00, 7.00000000e-01, 1.17000000e+00],
[2.00000000e+00, 9.60000000e-01, 1.00000000e+00, ...,
1.20000000e+00, 7.00000000e-01, 1.17000000e+00],
...,
[9.97900000e+03, 6.10920994e-01, 7.58135980e-01, ...,
1.08704204e+00, 7.88187535e-01, 1.23021669e+00],
[9.98000000e+03, 6.10920994e-01, 7.58135980e-01, ...,
1.08704204e+00, 7.88187535e-01, 1.23021669e+00],
[9.98100000e+03, 6.10920994e-01, 7.58135980e-01, ...,
1.08704204e+00, 7.88187535e-01, 1.23021669e+00]])
I want generate a dataframe with numpy_x data, index and columns (index and columns are the same really?), then I proceed to perform the following: 我想用numpy_x数据,索引和列生成数据框(索引和列真的相同吗?),然后继续执行以下操作:
import pandas as pd
pd.DataFrame(data=numpy_x[:], # I want pass the entire numpy array content
index=numpy_x[1:26],
columns=numpy_x[9982:26])
But I get the following error: 但是我收到以下错误:
/.conda/envs/x/lib/python3.6/site-packages/pandas/core/internals.py in construction_error(tot_items, block_shape, axes, e)
4606 raise ValueError("Empty data passed with indices specified.")
4607 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 4608 passed, implied))
4609
4610
ValueError: Shape of passed values is (26, 9982), indices imply (0, 25)
How to can I understand what parameters pass on index
and columns
attributes? 如何理解index
和columns
属性上传递的参数?
Use - 采用 -
numpy_x=np.random.random((100,10))
df=pd.DataFrame(numpy_x)
Output 产量
0 1 2 3 4 5 6 \
0 0.204839 0.837503 0.696896 0.235414 0.594766 0.521302 0.841167
1 0.041490 0.679537 0.657314 0.656672 0.524983 0.936918 0.482802
2 0.318928 0.423196 0.218037 0.515017 0.107851 0.564404 0.218297
3 0.644913 0.433771 0.297033 0.011239 0.346021 0.353749 0.587631
4 0.127949 0.517230 0.969399 0.743442 0.268566 0.415327 0.567572
7 8 9
0 0.882685 0.211414 0.659820
1 0.752496 0.047198 0.775250
2 0.521580 0.655942 0.178753
3 0.123761 0.483601 0.157191
4 0.849218 0.098588 0.754402
I want generate a dataframe with numpy_x data, index and columns (index and columns are the same really?) 我想用numpy_x数据,索引和列生成一个数据框(索引和列真的一样吗?)
Yes and no. 是的,没有。 Index
is simply the axis labelling information in pandas
. Index
只是pandas
的轴标记信息。 Depending upon the axis, Index can either mean row indexing or column indexing. 根据轴,索引可以表示行索引或列索引。
The axis labeling information in pandas objects serves many purposes: pandas对象中的轴标签信息有许多用途:
It can also be a simple single integer index or it can also be Multi-Index
它也可以是简单的单整数索引,也可以是Multi-Index
Index
and Columns
Parameter Index
和Columns
参数
The columns
parameter is simply the column labels that you want to provide to your dataset, in this case you want to pass 26 names for the 26 columns in your numpy
array. columns
参数只是您要提供给数据集的列标签,在这种情况下,您希望为numpy
数组中的26列传递26个名称。 This will default to np.arange(n)
if no column labels are provided 如果未提供列标签,则默认为np.arange(n)
The index
parameter is simply the Index to use for the resulting frame. index
参数只是用于结果帧的Index。 Will default to np.arange(n)
if no indexing information part of input data and no index provided (which is what is the case in my example) 如果没有输入数据的索引信息部分并且没有提供索引,则默认为np.arange(n)
(在我的示例中就是这种情况)
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