Python 如何修复 AttributeError: 'Series' 对象没有属性 'to_numpy'
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How to fix AttributeError: 'Series' object has no attribute 'to_numpy'
提问by Nguyen Thuan
My output:
我的输出：
def load_data(self):
"""
Load data from list of paths
:return: 3D-array X and 2D-array y
"""
X = None
y = None
df = pd.read_excel('data/Data.xlsx', header=None)
for i in range(len(df.columns)):
sentences_ = df[i].to_numpy().tolist()
label_vec = [0.0 for _ in range(0, self.n_class)]
label_vec[i] = 1.0
labels_ = [label_vec for _ in range(0, len(sentences_))]
if X is None:
X = sentences_
y = labels_
else:
X += sentences_
y += labels_
X, max_length = self.tokenize_sentences(X)
X = self.word_embed_sentences(X, max_length=self.max_length)
return np.array(X), np.array(y)
This is my code with pandas library as pd. When I run in Google Colab I get the following error:
这是我使用 Pandas 库作为 pd 的代码。当我在 Google Colab 中运行时，出现以下错误：
AttributeError: 'Series' object has no attribute 'to_numpy'
回答by Javide
Check the version of your pandas library:
检查您的熊猫库的版本：
import pandas
print(pandas.__version__)
If your version is less than 0.24.1:
如果您的版本低于 0.24.1：
pip install --upgrade pandas
回答by Mike T
If you need your code to work with all versions of pandas, here's a simple way to convert a Series into a NumPy array:
如果您需要您的代码与所有版本的 Pandas 一起使用，这里有一种将 Series 转换为 NumPy 数组的简单方法：
import pandas as pd
import numpy as np
s = pd.Series([1.1, 2.3])
a = np.array(s)
print(a) # [1.1 2.3]
On an advanced note, if your Series has missing values (as NaN values), these can be converted to a masked array:
在高级说明中，如果您的系列缺少值（作为 NaN 值），则可以将这些值转换为掩码数组：
s = pd.Series([1.1, np.nan])
a = np.ma.masked_invalid(s)
print(a) # [1.1 --]
