如何将 Pandas 数据框转换为 NumPy 数组? [英] How to convert pandas data frame to NumPy array?

问题描述

按照我从我之前的问题中得到的建议我'正在将 Pandas 数据框转换为数字 NumPy 数组.为此，我使用了 numpy.asarray.

我的数据框:

DataFrame----------标签向量0 0 1:0.0033524514 2:-0.021896651 3:0.05087798 4:...1 0 1:0.02134219 2:-0.007388343 3:0.06835007 4:0....2 0 1:0.030515702 2:-0.0037591448 3:0.066626 4:0....3 0 1:0.0069114454 2:-0.0149497045 3:0.020777626 ...4 1 1:0.003118149 2:-0.015105667 3:0.040879637 4:...…………19779 0 1:0.0042634667 2:-0.0044222944 3:-0.012995412...19780 1 1:0.013818732 2:-0.010984628 3:0.060777966 4:...19781 0 1:0.00019213723 2:-0.010443398 3:0.01679976 4...19782 0 1:0.010373874 2:0.0043582567 3:-0.0078354385 ...19783 1 1:0.0016790542 2:-0.028346825 3:0.03908631 4:...[19784 行 x 2 列]数据帧数据类型:标签对象矢量对象数据类型:对象

要转换为 Numpy 数组，我正在使用此脚本:

<预><代码>将熊猫导入为 pd从 sklearn.model_selection 导入 train_test_split从 sklearn 导入 svm从 sklearn 导入指标从 sklearn.preprocessing 导入 OneHotEncoder将 numpy 导入为 np导入 matplotlib.pyplot 作为 pltr_filenameTSV = 'TSV/A19784.tsv'tsv_read = pd.read_csv(r_filenameTSV, sep='\t',names=["vector"])df = pd.DataFrame(tsv_read)df = pd.DataFrame(df.vector.str.split(' ',1).tolist(),列 = ['标签','向量'])print('数据帧\n-----------\n', df)print('\nDataFrame 数据类型:\n', df.dtypes)arr = np.asarray(df, dtype=np.float64)print('\nNumpy 数组\n-----------\n', arr)print('\nNumpy 数组数据类型:', arr.dtype)

我在第 nr.22 行出现此错误 arr = np.asarray(df, dtype=np.float64)

 <代码> ValueError异常:无法将字符串转换为浮动:1:0.0033524514 2:-0.021896651 3:0.05087798 4:0.0072637126 5:-0.013740167 6:-0.0014883851 7:0.02230502 8:0.0053563705 9:0.00465044 10:-0.003082654211:0.010156203 12:-0.021754289 13:-0.03744049 14:0.011198468 15:-0.021201309 16:-0.0006497681 17:0.009229079 18:0.04218278 19:0.020572046 20:0.0021593391 ...

我该如何解决这个问题?

问候并感谢您的时间

解决方案

对 DataFrame 使用列表理解和嵌套字典理解:

df = pd.read_csv(r_filenameTSV, sep='\t',names=["vector"])df = pd.DataFrame([dict(y.split(':') for y in x.split()) for x in df['vector']])打印 (df)1 2 3 40 0.0033524514 -0.021896651 0.05087798 01 0.02134219 -0.007388343 0.06835007 02 0.030515702 -0.0037591448 0.066626 03 0.0069114454 -0.0149497045 0.020777626 04 0.003118149 -0.015105667 0.040879637 0.4

然后转换为浮点数和numpy数组:

print (df.astype(float).to_numpy())[[ 0.00335245 -0.02189665 0.05087798 0.][ 0.02134219 -0.00738834 0.06835007 0. ][ 0.0305157 -0.00375914 0.066626 0. ][ 0.00691145 -0.0149497 0.02077763 0. ][ 0.00311815 -0.01510567 0.04087964 0.4 ]]

Following the suggestions I got from my previous question here I'm converting a Pandas data frame to a numeric NumPy array. To do this Im used numpy.asarray.

My data frame:

DataFrame
----------
       label                                             vector
0         0   1:0.0033524514 2:-0.021896651 3:0.05087798 4:...
1         0   1:0.02134219 2:-0.007388343 3:0.06835007 4:0....
2         0   1:0.030515702 2:-0.0037591448 3:0.066626 4:0....
3         0   1:0.0069114454 2:-0.0149497045 3:0.020777626 ...
4         1   1:0.003118149 2:-0.015105667 3:0.040879637 4:...
...     ...                                                ...
19779     0   1:0.0042634667 2:-0.0044222944 3:-0.012995412...
19780     1   1:0.013818732 2:-0.010984628 3:0.060777966 4:...
19781     0   1:0.00019213723 2:-0.010443398 3:0.01679976 4...
19782     0   1:0.010373874 2:0.0043582567 3:-0.0078354385 ...
19783     1   1:0.0016790542 2:-0.028346825 3:0.03908631 4:...

[19784 rows x 2 columns]

DataFrame datatypes :
 label     object
vector    object
dtype: object

To convert into a Numpy Array I'm using this script:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn import metrics
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import matplotlib.pyplot as plt

r_filenameTSV = 'TSV/A19784.tsv'

tsv_read = pd.read_csv(r_filenameTSV, sep='\t',names=["vector"])

df = pd.DataFrame(tsv_read)

df = pd.DataFrame(df.vector.str.split(' ',1).tolist(),
                                   columns = ['label','vector'])


print('DataFrame\n----------\n', df)
print('\nDataFrame datatypes :\n', df.dtypes)

arr = np.asarray(df, dtype=np.float64)

print('\nNumpy Array\n----------\n', arr)
print('\nNumpy Array Datatype :', arr.dtype)

I'm having this error from line nr.22 arr = np.asarray(df, dtype=np.float64)

ValueError: could not convert string to float: ' 1:0.0033524514 2:-0.021896651 3:0.05087798 4:0.0072637126 5:-0.013740167 6:-0.0014883851 7:0.02230502 8:0.0053563705 9:0.00465044 10:-0.0030826542 11:0.010156203 12:-0.021754289 13:-0.03744049 14:0.011198468 15:-0.021201309 16:-0.0006497681 17:0.009229079 18:0.04218278 19:0.020572046 20:0.0021593391 ...

How can I solve this issue?

Regards and thanks for your time

解决方案

Use list comprehension with nested dictionary comprehension for DataFrame:

df = pd.read_csv(r_filenameTSV, sep='\t',names=["vector"])

df = pd.DataFrame([dict(y.split(':') for y in x.split()) for x in df['vector']])
print (df)
              1              2            3    4
0  0.0033524514   -0.021896651   0.05087798    0
1    0.02134219   -0.007388343   0.06835007    0
2   0.030515702  -0.0037591448     0.066626    0
3  0.0069114454  -0.0149497045  0.020777626    0
4   0.003118149   -0.015105667  0.040879637  0.4

And then convert to floats and to numpy array:

print (df.astype(float).to_numpy())
[[ 0.00335245 -0.02189665  0.05087798  0.        ]
 [ 0.02134219 -0.00738834  0.06835007  0.        ]
 [ 0.0305157  -0.00375914  0.066626    0.        ]
 [ 0.00691145 -0.0149497   0.02077763  0.        ]
 [ 0.00311815 -0.01510567  0.04087964  0.4       ]]

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