将熊猫数据帧转换为NumPy数组

如何将熊猫数据帧转换为NumPy数组?

DataFrame:

import numpy as np
import pandas as pd

index = [1, 2, 3, 4, 5, 6, 7]
a = [np.nan, np.nan, np.nan, 0.1, 0.1, 0.1, 0.1]
b = [0.2, np.nan, 0.2, 0.2, 0.2, np.nan, np.nan]
c = [np.nan, 0.5, 0.5, np.nan, 0.5, 0.5, np.nan]
df = pd.DataFrame({'A': a, 'B': b, 'C': c}, index=index)
df = df.rename_axis('ID')

给了

label   A    B    C
ID                                 
1   NaN  0.2  NaN
2   NaN  NaN  0.5
3   NaN  0.2  0.5
4   0.1  0.2  NaN
5   0.1  0.2  0.5
6   0.1  NaN  0.5
7   0.1  NaN  NaN

我想把它转换成一个NumPy数组，像这样:

array([[ nan,  0.2,  nan],
       [ nan,  nan,  0.5],
       [ nan,  0.2,  0.5],
       [ 0.1,  0.2,  nan],
       [ 0.1,  0.2,  0.5],
       [ 0.1,  nan,  0.5],
       [ 0.1,  nan,  nan]])

另外，是否可以像这样保存dtype ?

array([[ 1, nan,  0.2,  nan],
       [ 2, nan,  nan,  0.5],
       [ 3, nan,  0.2,  0.5],
       [ 4, 0.1,  0.2,  nan],
       [ 5, 0.1,  0.2,  0.5],
       [ 6, 0.1,  nan,  0.5],
       [ 7, 0.1,  nan,  nan]],
     dtype=[('ID', '<i4'), ('A', '<f8'), ('B', '<f8'), ('B', '<f8')])

当前回答

除了meteore的答案，我还找到了密码

df.index = df.index.astype('i8')

对我没用。所以我把我的代码放在这里，以方便其他被这个问题困扰的人。

city_cluster_df = pd.read_csv(text_filepath, encoding='utf-8')
# the field 'city_en' is a string, when converted to Numpy array, it will be an object
city_cluster_arr = city_cluster_df[['city_en','lat','lon','cluster','cluster_filtered']].to_records()
descr=city_cluster_arr.dtype.descr
# change the field 'city_en' to string type (the index for 'city_en' here is 1 because before the field is the row index of dataframe)
descr[1]=(descr[1][0], "S20")
newArr=city_cluster_arr.astype(np.dtype(descr))

2016-03-13 15:16:01

其他回答

一个简单的方法将数据帧转换为numpy数组:

import pandas as pd
df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
df_to_array = df.to_numpy()
array([[1, 3],
   [2, 4]])

为了保持一致性，建议使用to_numpy。

参考: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html

2019-11-21 05:13:25

注意:这个答案中使用的.as_matrix()方法已弃用。熊猫0.23.4警告:

方法.as_matrix将在未来的版本中被删除。请改用.values。

熊猫有某种内在的东西……

numpy_matrix = df.as_matrix()

给了

array([[nan, 0.2, nan],
       [nan, nan, 0.5],
       [nan, 0.2, 0.5],
       [0.1, 0.2, nan],
       [0.1, 0.2, 0.5],
       [0.1, nan, 0.5],
       [0.1, nan, nan]])

2014-07-17 01:13:50

除了meteore的答案，我还找到了密码

df.index = df.index.astype('i8')

对我没用。所以我把我的代码放在这里，以方便其他被这个问题困扰的人。

city_cluster_df = pd.read_csv(text_filepath, encoding='utf-8')
# the field 'city_en' is a string, when converted to Numpy array, it will be an object
city_cluster_arr = city_cluster_df[['city_en','lat','lon','cluster','cluster_filtered']].to_records()
descr=city_cluster_arr.dtype.descr
# change the field 'city_en' to string type (the index for 'city_en' here is 1 because before the field is the row index of dataframe)
descr[1]=(descr[1][0], "S20")
newArr=city_cluster_arr.astype(np.dtype(descr))

2016-03-13 15:16:01

DataFrame的一个更简单的例子:

df

         gbm       nnet        reg
0  12.097439  12.047437  12.100953
1  12.109811  12.070209  12.095288
2  11.720734  11.622139  11.740523
3  11.824557  11.926414  11.926527
4  11.800868  11.727730  11.729737
5  12.490984  12.502440  12.530894

USE:

np.array(df.to_records().view(type=np.matrix))

GET:

array([[(0, 12.097439  , 12.047437, 12.10095324),
        (1, 12.10981081, 12.070209, 12.09528824),
        (2, 11.72073428, 11.622139, 11.74052253),
        (3, 11.82455653, 11.926414, 11.92652727),
        (4, 11.80086775, 11.72773 , 11.72973699),
        (5, 12.49098389, 12.50244 , 12.53089367)]],
dtype=(numpy.record, [('index', '<i8'), ('gbm', '<f8'), ('nnet', '<f4'),
       ('reg', '<f8')]))

2018-07-13 16:21:15

将数据帧转换为numpy数组表示的两种方法。

mah_np_array = df.as_matrix(columns=None) Mah_np_array = df.values

医生:https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.as_matrix.html

2017-12-29 10:02:05

将熊猫数据帧转换为NumPy数组

推荐文章

最新文章

标签