将pandas数据框转换为NumPy数组
本文翻译自：Convert pandas dataframe to NumPy array
I am interested in knowing how to convert a pandas dataframe into a NumPy array. 我对知道如何将熊猫数据框转换为NumPy数组感兴趣。
dataframe: 数据框：
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')
gives 给
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
I would like to convert this to a NumPy array, as so: 我想将其转换为NumPy数组，如下所示：
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]])
How can I do this? 我怎样才能做到这一点？
As a bonus, is it possible to preserve the dtypes, like this? 另外，是否可以像这样保留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', 'or similar? 或类似的？
#1楼
参考：https://stackoom.com/question/tKk6/将pandas数据框转换为NumPy数组
#2楼
You can use the to_records method, but have to play around a bit with the dtypes if they are not what you want from the get go. 您可以使用to_records方法，但是如果dtype不是您一开始就不想要的类型，则必须对其进行一些操作。 In my case, having copied your DF from a string, the index type is string (represented by an object dtype in pandas): 就我而言，从字符串复制了DF，索引类型为字符串（由pandas中的dtype object表示）：
In [102]: df
Out[102]:
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
In [103]: df.index.dtype
Out[103]: dtype('object')
In [104]: df.to_records()
Out[104]:
rec.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=[('index', '|O8'), ('A', 'Converting the recarray dtype does not work for me, but one can do this in Pandas already: 转换recarray dtype对我不起作用，但是已经可以在Pandas中做到这一点：
In [109]: df.index = df.index.astype('i8')
In [111]: df.to_records().view([('ID', ' Note that Pandas does not set the name of the index properly (to ID ) in the exported record array (a bug?), so we profit from the type conversion to also correct for that. 请注意，Pandas在导出的记录数组中没有正确设置索引名称（至ID ）（错误？），因此我们可以从类型转换中受益，也可以对此进行更正。
At the moment Pandas has only 8-byte integers, i8 , and floats, f8 (see this issue ). 目前，Pandas只有8个字节的整数i8和浮点数f8 （请参阅此问题 ）。
#3楼
I would just chain the DataFrame.reset_index() and DataFrame.values functions to get the Numpy representation of the dataframe, including the index: 我只需要链接DataFrame.reset_index（）和DataFrame.values函数来获得数据帧的Numpy表示，包括索引：
In [8]: df
Out[8]:
A B C
0 -0.982726 0.150726 0.691625
1 0.617297 -0.471879 0.505547
2 0.417123 -1.356803 -1.013499
3 -0.166363 -0.957758 1.178659
4 -0.164103 0.074516 -0.674325
5 -0.340169 -0.293698 1.231791
6 -1.062825 0.556273 1.508058
7 0.959610 0.247539 0.091333
[8 rows x 3 columns]
In [9]: df.reset_index().values
Out[9]:
array([[ 0. , -0.98272574, 0.150726 , 0.69162512],
[ 1. , 0.61729734, -0.47187926, 0.50554728],
[ 2. , 0.4171228 , -1.35680324, -1.01349922],
[ 3. , -0.16636303, -0.95775849, 1.17865945],
[ 4. , -0.16410334, 0.0745164 , -0.67432474],
[ 5. , -0.34016865, -0.29369841, 1.23179064],
[ 6. , -1.06282542, 0.55627285, 1.50805754],
[ 7. , 0.95961001, 0.24753911, 0.09133339]])
To get the dtypes we'd need to transform this ndarray into a structured array using view : 为了获得dtypes，我们需要使用view将此ndarray转换为结构化数组：
In [10]: df.reset_index().values.ravel().view(dtype=[('index', int), ('A', float), ('B', float), ('C', float)])
Out[10]:
array([( 0, -0.98272574, 0.150726 , 0.69162512),
( 1, 0.61729734, -0.47187926, 0.50554728),
( 2, 0.4171228 , -1.35680324, -1.01349922),
( 3, -0.16636303, -0.95775849, 1.17865945),
( 4, -0.16410334, 0.0745164 , -0.67432474),
( 5, -0.34016865, -0.29369841, 1.23179064),
( 6, -1.06282542, 0.55627285, 1.50805754),
( 7, 0.95961001, 0.24753911, 0.09133339),
dtype=[('index', '#4楼
Note : The .as_matrix() method used in this answer is deprecated. 注意 ：不.as_matrix()使用此答案中的.as_matrix()方法。 Pandas 0.23.4 warns: 熊猫0.23.4警告：
Method
.as_matrixwill be removed in a future version. 方法.as_matrix将在以后的版本中删除。 Use .values instead. 请改用.values。
Pandas has something built in... 熊猫内置一些东西...
numpy_matrix = df.as_matrix()
gives 给
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]])
#5楼
Here is my approach to making a structure array from a pandas DataFrame. 这是我从pandas DataFrame制作结构数组的方法。
Create the data frame 创建数据框
import pandas as pd
import numpy as np
import six
NaN = float('nan')
ID = [1, 2, 3, 4, 5, 6, 7]
A = [NaN, NaN, NaN, 0.1, 0.1, 0.1, 0.1]
B = [0.2, NaN, 0.2, 0.2, 0.2, NaN, NaN]
C = [NaN, 0.5, 0.5, NaN, 0.5, 0.5, NaN]
columns = {'A':A, 'B':B, 'C':C}
df = pd.DataFrame(columns, index=ID)
df.index.name = 'ID'
print(df)
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
Define function to make a numpy structure array (not a record array) from a pandas DataFrame. 定义函数以从pandas DataFrame中创建一个numpy结构数组（而不是记录数组）。
def df_to_sarray(df):
"""
Convert a pandas DataFrame object to a numpy structured array.
This is functionally equivalent to but more efficient than
np.array(df.to_array())
:param df: the data frame to convert
:return: a numpy structured array representation of df
"""
v = df.values
cols = df.columns
if six.PY2: # python 2 needs .encode() but 3 does not
types = [(cols[i].encode(), df[k].dtype.type) for (i, k) in enumerate(cols)]
else:
types = [(cols[i], df[k].dtype.type) for (i, k) in enumerate(cols)]
dtype = np.dtype(types)
z = np.zeros(v.shape[0], dtype)
for (i, k) in enumerate(z.dtype.names):
z[k] = v[:, i]
return z
Use reset_index to make a new data frame that includes the index as part of its data. 使用reset_index可以创建一个新数据框，其中将索引作为其数据的一部分。 Convert that data frame to a structure array. 将该数据帧转换为结构数组。
sa = df_to_sarray(df.reset_index())
sa
array([(1L, nan, 0.2, nan), (2L, nan, nan, 0.5), (3L, nan, 0.2, 0.5),
(4L, 0.1, 0.2, nan), (5L, 0.1, 0.2, 0.5), (6L, 0.1, nan, 0.5),
(7L, 0.1, nan, nan)],
dtype=[('ID', 'EDIT: Updated df_to_sarray to avoid error calling .encode() with python 3. Thanks to Joseph Garvin and halcyon for their comment and solution. 编辑：更新df_to_sarray以避免错误调用.encode（）与Python 3.感谢约瑟夫·加尔文和宁静为他们的意见和解决方案。
#6楼
Further to meteore's answer, I found the code 除了陨石的答案，我找到了代码
df.index = df.index.astype('i8')
doesn't work for me. 对我不起作用。 So I put my code here for the convenience of others stuck with this issue. 因此，我将代码放在这里，以方便陷入此问题的其他人。
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))