从Panda Dataframe转换为numpy数组期间出现奇怪的错误 [英] Strange error during conversion from Panda Dataframe to numpy array

问题描述

我有一个熊猫数据框，其中有两列:评论"(文本)和情感"(1/0)

I have a pandas dataframe with two columns: "review"(text) and "sentiment"(1/0)

X_train = df.loc[0:25000, 'review'].values
y_train = df.loc[0:25000, 'sentiment'].values
X_test = df.loc[25000:, 'review'].values
y_test = df.loc[25000:, 'sentiment'].values

但是在转换为numpy数组之后，请使用values()方法.我得到以下形状的numpy数组:

But after conversion to numpy array, using values() method. I obtain numpy arrays of following shape:

print(df.shape)   #(50000, 2)
print(X_train.shape) #(25001,)
print(y_train.shape) #(25001,)
print(X_test.shape) # (25000,)
print(y_test.shape) # (25000,) 

因此，如您所见values()方法，添加了另一行.这真的很奇怪，我无法检测到错误.

So as you can see values() method, added one additional row. This is really strange and I cant detect error.

推荐答案

df.loc基于标签，即包含上限.使用iloc:

The df.loc is label based, i.e. it includes the upper bound. Use iloc:

df.iloc[:25000, 1].values # here 1 is the column of 'review' for example

如果您想要类似NumPy的切片.

if you want NumPy-like slicing.

对于iloc，您需要同时以整数或整数形式提供行和列 片.

With iloc you need to supply both rows and columns as integers or integer slices.

>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
>>> df
   a  b
0  1  4
1  2  5
2  3  6

这是基于标签的，即上限(包括上限):

This is label based, i.e. upper bound inclusive:

>>> df.loc[:1, 'a']
0    1
1    2
Name: a, dtype: int64

这就像在NumPy中切片一样，即上限互斥:

This works like slicing in NumPy, i.e. upper bound exclusive:

>>> df.iloc[:2, 0]
0    1
1    2
Name: a, dtype: int64

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