如何在Python中从dataframe加载特性和标签？

我正在尝试使用keras和tensorflow来训练网络。我有自己的缅甸语数字数据集。我正在尝试使用python开发使用神经网络的缅甸数字识别。我有train.csv文件和test.csv文件，它们的标题带有格式标签pixel0，…，pixel783。我用熊猫来加载数据帧。但我想将数据框拆分为特性和标签

import pandas as pd
dataframe = pd.read_csv("mmdigitstrain.csv")
dataframe2 = pd.read_csv("mmdigitstest.csv")

(X_train, y_train) = splitfeaturesandlabelfromdataframe
(X_test, y_test) = splitfeaturesandlabelfromdataframe2

如果数据帧包含最后一列作为

标签

列。然后使用以下命令

X_train=dataframe.iloc[：，：-1]
Y_train=dataframe.iloc[：，-1:]

根据下面的评论更新。现在将数据帧w.r.t子集到列名

标签

另一种方法是组合/合并两个数据帧，并尝试使用
您必须将数据放在numpy数组上

import pandas as pd
import numpy as np
df_train = pd.read_csv("mmdigitstrain.csv")
df_test = pd.read_csv("mmdigitstest.csv")

y_train=df_train['label'].to_numpy() 
#check the shape should bd nbofitem x 1 in train dataset
print(y_train.shape)
X_train=df_train.drop(columns=['label']).to_numpy()
check the shape should bd nbofitem x  780 in train dataset
print(X_train.shape)

y_test=df_test['label'].to_numpy() 
#check the shape should bd nbofitem x 1 in test dataset
print(y_test.shape)
X_test=df_test.drop(columns=['label']).to_numpy()
check the shape should bd nbofitem x  780 in test dataset
print(X_test.shape)

请在数据框中提供标签列的名称标签列的名称是label。在csv文件中添加数据片段标签列是数据框中的第一列。
import pandas as pd
import numpy as np
df_train = pd.read_csv("mmdigitstrain.csv")
df_test = pd.read_csv("mmdigitstest.csv")

y_train=df_train['label'].to_numpy() 
#check the shape should bd nbofitem x 1 in train dataset
print(y_train.shape)
X_train=df_train.drop(columns=['label']).to_numpy()
check the shape should bd nbofitem x  780 in train dataset
print(X_train.shape)

y_test=df_test['label'].to_numpy() 
#check the shape should bd nbofitem x 1 in test dataset
print(y_test.shape)
X_test=df_test.drop(columns=['label']).to_numpy()
check the shape should bd nbofitem x  780 in test dataset
print(X_test.shape)