这个主要归功于配置的系统环境变量PATH,当我们在命令行中运行程序时,系统会根据PATH配置的路径列表依次查寻是否有可执行文件python(在windows中,省略了后缀.exe),当查寻到该文件时,执行该文件; 如果在所有路径列表中都查找不到,就会报报错:'python' 不是内部或外部命令,也不是可运行的程序或批处理文件。 test.py代码中
new_data=df.copy()# 增加有NaN的布尔列(True/False) cols_with_missing=(colforcolinnew_data.columnsifnew_data[col].isnull().any())forcolincols_with_missing:new_data[col+'_was_NaN']=new_data[col].isnull()print(new_data)# Imputation my_imputer=Imputer()new_data_imputed=my_imputer.fit...
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defmean_change(x):x = np.asarray(x)return(x[-1] - x[0]) / (len(x) -1)iflen(x) >1elsenp.NaN defmean_second_derivative_central(x):x = np.asarray(x)return(x[-1] - x[-2] - x[1] + x[0]) / (2* (len(x) -2))if...
+ 朴素贝叶斯 + Python：输入包含NaN、无穷大或超出dtype('float64')的值在进行数据分割（train_test...
% (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc)) if __name__ == '__main__': num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 256, 256 W1 = torch.tensor(np.random.normal(0, 0.01, size=(num_inputs, num_hiddens1)), dtype=torch.float, ...
df=pd.DataFrame(table_2[1:],columns=table_2[0])# 保存exceltable_df.to_excel('test.xlsx')...
= series.valuesX = X.astype('float32')train_size = int(len(X) * 0.50)train, test = X[0:train_size], X[train_size:]# walk-foward validationhistory = [x for x in train]predictions = list()for i in range(len(test)):# transformtransformed, lam = boxcox(history)if ...
nan]), 'B': pd.date_range('20210123',periods=4), 'C': pd.Series(1, index=list(range(4)), dtype='float32'), 'D': np.array([3] * 4, dtype='int32'), 'E': pd.Categorical(["test", "train", "test", "train"]), 'F': 'foo'}) print(pd_data) 2pandas数据的增删改查...
# 重新划分X = df_model.drop(['customerID', 'Churn'], axis=1)y = df_model['Churn']# 分层抽样X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, stratify=y)print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)#修正索引...