2 df_array[i][5]-2013==int(df_array[i][5]-2013) 3 for i in range(len(df_array)): ----> 4 new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]]+=df_array[i][3] 5 df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) 6 i=0 IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

python求numpy中array按列非零元素的平均值案例

本文将深入探讨如何在NumPy的数组（array）中计算非零元素按列的平均值，以及相关的补充知识，包括如何统计DataFrame中零值的个数。首先，我们要解决的核心问题是如何找到NumPy数组中每一列非零元素的平均值。这...

k-means:C ++ 20中的K_means集群实现

array{ DataPoint ( 1 , 2 , 3 ), DataPoint ( 4 , 5 , 6 ), DataPoint ( 7 , 8 , 9 ), DataPoint ( 28 , 29 , 30 ), DataPoint ( 31 , 32 , 33 ), DataPoint ( 34 , 35 , 36 ), DataPoint ( 19 , 20 , 21 ), Data...

df_array[i][5]= int(df_array[i][5]) 6 df_array[i][6] = int(df_array[i][6]) ----> 7 new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]]+=df_array[i][3] 8 df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) 9 i=0 IndexError: index -1953 is out of bounds for axis 0 with size 4

根据报错信息，问题可能出现在第7行的代码中，因为它尝试访问一个超出数组索引范围的...此外，根据第8行的代码，你需要创建一个名为df_new的空数据框架来存储数据。如果数据框架未创建，则可能会导致IndexError。

new_array=np.zeros((4,12,100,9)) 2 for i in range(len(df_array)): ----> 3 new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]]+=df_array[i][3] 4 df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices需要如何更改

new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]] += df_array[i][3] df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) ...

new_array=np.zeros((4,12,100,9)) for i in range(len(df_array)): new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]]+=df_array[i][3] df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) i=0 for year_ in range(4): for month_ in range(12): for index_ in range(100): for warehouse_ in range(9): if new_array[year_][month_][index_][warehouse_]>0: df_new=df_new.append(pd.DataFrame({'创建年':year_+2013,'创建月':month_+1,'物料编号':index_,'应发库':warehouse_,'销售订单数量':new_array[year_][month_][index_][warehouse_]},index=[i])) i+=1 print(df_new)请修改正确

new_array[year-2013][month-1][df_array[i][1]][df_array[i][2]] += df_array[i][3] # 将 new_array 转换为 DataFrame df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量...

df_array = df_array.astype(int) 2 array_len = len(df_array) 3 new_array=np.zeros((4,12,100,9)) ValueError: invalid literal for int() with base 10: '2013.05.30'结果有误

这个错误是因为在将 df_array 转换为整数类型时，其中有一个值不是整数，而是字符串 '2013.05.30'。因此，Python 无法将该字符串转换为整数，导致出现 ValueError 错误。要解决这个问题，你需要先找出哪个值...

param = {'num_leaves': 31, 'min_data_in_leaf': 20, 'objective': 'binary', 'learning_rate': 0.06, "boosting": "gbdt", "metric": 'None', "verbosity": -1} trn_data = lgb.Dataset(trn, trn_label) val_data = lgb.Dataset(val, val_label) num_round = 666 # clf = lgb.train(param, trn_data, num_round, valid_sets=[trn_data, val_data], verbose_eval=100, # early_stopping_rounds=300, feval=win_score_eval) clf = lgb.train(param, trn_data, num_round) # oof_lgb = clf.predict(val, num_iteration=clf.best_iteration) test_lgb = clf.predict(test, num_iteration=clf.best_iteration)thresh_hold = 0.5 oof_test_final = test_lgb >= thresh_hold print(metrics.accuracy_score(test_label, oof_test_final)) print(metrics.confusion_matrix(test_label, oof_test_final)) tp = np.sum(((oof_test_final == 1) & (test_label == 1))) pp = np.sum(oof_test_final == 1) print('accuracy1:%.3f'% (tp/(pp)))test_postive_idx = np.argwhere(oof_test_final == True).reshape(-1) # test_postive_idx = list(range(len(oof_test_final))) test_all_idx = np.argwhere(np.array(test_data_idx)).reshape(-1) stock_info['trade_date_id'] = stock_info['trade_date'].map(date_map) stock_info['trade_date_id'] = stock_info['trade_date_id'] + 1tmp_col = ['ts_code', 'trade_date', 'trade_date_id', 'open', 'high', 'low', 'close', 'ma5', 'ma13', 'ma21', 'label_final', 'name'] stock_info.iloc[test_all_idx[test_postive_idx]] tmp_df = stock_info[tmp_col].iloc[test_all_idx[test_postive_idx]].reset_index() tmp_df['label_prob'] = test_lgb[test_postive_idx] tmp_df['is_limit_up'] = tmp_df['close'] == tmp_df['high'] buy_df = tmp_df[(tmp_df['is_limit_up']==False)].reset_index() buy_df.drop(['index', 'level_0'], axis=1, inplace=True)buy_df['buy_flag'] = 1 stock_info_copy['sell_flag'] = 0tmp_idx = (index_df['trade_date'] == test_date_min+1) close1 = index_df[tmp_idx]['close'].values[0] test_date_max = 20220829 tmp_idx = (index_df['trade_date'] == test_date_max) close2 = index_df[tmp_idx]['close'].values[0]tmp_idx = (stock_info_copy['trade_date'] >= test_date_min) & (stock_info_copy['trade_date'] <= test_date_max) tmp_df = stock_info_copy[tmp_idx].reset_index(drop=True)from imp import reload import Account reload(Account) money_init = 200000 account = Account.Account(money_init, max_hold_period=20, stop_loss_rate=-0.07, stop_profit_rate=0.12) account.BackTest(buy_df, tmp_df, index_df, buy_price='open')tmp_df2 = buy_df[['ts_code', 'trade_date', 'label_prob', 'label_final']] tmp_df2 = tmp_df2.rename(columns={'trade_date':'buy_date'}) tmp_df = account.info tmp_df['buy_date'] = tmp_df['buy_date'].apply(lambda x: int(x)) tmp_df = tmp_df.merge(tmp_df2, on=['ts_code', 'buy_date'], how='left')最终的tmp_df是什么？tmp_df[tmp_df['label_final']==1]又选取了什么股票？

根据代码逐行分析，tmp_df 是一个 DataFrame，包含了股票的信息以及回测结果。其中，选取了 label_final 为 1 的股票，也就是模型预测为涨的股票，并且过滤掉了当天涨停的股票。最终买入的股票信息保存在 buy_df 中...

如何检查检查代码中df_array[i][5]、df_array[i][6]、df_array[i][1]和df_array[i][2]这些索引值是否都是整数

if isinstance(df_array[i][5], int) and isinstance(df_array[i][6], int) and isinstance(df_array[i][1], int) and isinstance(df_array[i][2], int): # 执行相关操作 else: # 处理错误情况这里使用了...

import argparse import numpy as np import pandas as pd from sklearn import model_selection from sklearn import preprocessing from sklearn import linear_model from sklearn import metrics import joblib from config import * def train(x_train, x_test, y_train, y_test): estimator = linear_model.Ridge() estimator.fit(x_train, y_train) print('梯度下降的权重系数是：', estimator.coef_) print('梯度下降的偏置是：', estimator.intercept_) joblib.dump(estimator, model_save_path) y_predict = estimator.predict(x_test) err = metrics.mean_squared_error(y_test, y_predict) print('梯度下降的误差率为：', err) def pred(x): estimator = joblib.load(model_save_path) x= np.array(x) predict = estimator.predict(x.reshape((x.shape[0], 1))) return predict def main(): parser = argparse.ArgumentParser(description='Demo of argparse') parser.add_argument('--method', type=str, default='train') args = parser.parse_args() method = args.method if method == 'train': df = pd.read_excel(file_name, dtype={ '年份': int, '值': float }) x = df['年份'].to_numpy() x = x.reshape((x.shape[0], 1)) y = df['值'].to_numpy() x_train, x_test, y_train, y_test=model_selection.train_test_split(x, y) train(x_train, x_test, y_train, y_test) elif method == 'test': predict = pred(pred_year).round(2) df = pd.DataFrame({ '预测年份': pred_year, '预测结果(单位：万吨)': predict }) print(df) df.to_excel(save_pred_path, index=False) else: print('wrong') if name == 'main': main()

这是一个使用 Ridge 回归模型进行数据训练和预测的 Python 代码。其中使用 argparse 模块进行命令行参数解析，可以通过传入不同的参数值来选择是进行模型训练还是进行数据预测。训练数据来自一个 Excel 文件，包含...

rating = np.zeros((bookNo,userNo)) #标志位 flag = 0 #获取合并表中的列数 ratings_df_length = np.shape(ratings_df)[0] #遍历矩阵，将书籍的评分填入表中 for index,row in ratings_df.iterrows(): rating[int(row['index']), int(row['userId'])] = row['rating'] flag += 1 print('processed %d, %d left' %(flag,ratings_df_length-flag)) record = rating > 0 record = np.array(record, dtype = int)

最后，它使用np.array()函数将"record"变量转换为一个numpy数组，并将其数据类型设置为int。这段代码的结果是，我们得到了一个大小为(bookNo,userNo)的二维矩阵"rating"，其中每个元素表示对应的用户对该书籍的评分...

def final_test(df_ft, model): ''' 验证真实比例的测试集2 ''' y_predprob_ft = model.predict_proba(df_ft.drop(['label'], axis=1))[:, 1] # 修改分类阈值 the_threshold = y_predprob_ft[y_predprob_ft.argsort(axis=0)[::-1][int(len(y_predprob_ft) * 0.25)]] y_pred_ft = np.array([1 if x >= the_threshold else 0 for x in y_predprob_ft]) # 查看结果 from sklearn.metrics import confusion_matrix, classification_report # print('accuracy_score: ', accuracy_score(df_ft['label'], y_pred_ft)) print('test real set:') print(confusion_matrix(df_ft['label'], y_pred_ft, labels=None, sample_weight=None)) report = classification_report(df_ft['label'], y_pred_ft) print(report) report = classifaction_report_csv(report) return report

首先，函数使用模型 model 对 df_ft 数据框中除了 'label' 列以外的特征进行预测，并保存预测结果的概率值到 y_predprob_ft 变量中。接着，函数根据预测概率值对分类阈值进行调整。阈值的选择是基于 y_predprob_ft...

df = pd.read_csv('车辆：1499序：2结果数据换算单位.csv') scaler = MinMaxScaler() df[['本车速度', '车头间距', '原车道前车速度', '本车加速度']] = scaler.fit_transform(df[['本车速度', '车头间距', '原车道前车速度', '本车加速度']]) #接下来，我们将数据集分成训练集和测试集 train_size = int(len(df) * 0.8) train = df[:train_size] test = df[train_size:] #然后，我们将数据转换成3D数组，以便于CNN-LSTM模型的处理 def create_dataset(X, y, time_steps=1): Xs, ys = [], [] for i in range(len(X) - time_steps): Xs.append(X.iloc[i:(i + time_steps)].values) ys.append(y.iloc[i + time_steps]) return np.array(Xs), np.array(ys) TIME_STEPS = 10 X_train, y_train = create_dataset(train[['本车速度', '车头间距', '原车道前车速度']], train['本车加速度'], time_steps=TIME_STEPS) X_test, y_test = create_dataset(test[['本车速度', '车头间距', '原车道前车速度']], test['本车加速度'], time_steps=TIME_STEPS) #接下来，我们定义并构建CNN-LSTM模型 model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(TIME_STEPS, 3))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(RepeatVector(1)) model.add(LSTM(64, activation='relu', return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(32, activation='relu', return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') #最后，我们训练模型，并进行预测 model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1, verbose=1) train_predict = model.predict(X_train) test_predict = model.predict(X_test) train_predict = scaler.inverse_transform(train_predict) y_train = scaler.inverse_transform([y_train]) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test])

在这段代码中，你使用了sklearn的MinMaxScaler对数据进行了缩放，然后将缩放后的数据用于训练模型和进行预测。在使用缩放后的数据进行预测后，你尝试使用inverse_transform函数将预测结果转换回原始的数据范围。...

解释这段代码的作用 def get_diff_img(self, original_img_path, beautify_img_path, diff_img_path): """ :return: * """ original_img = cv2.imread(original_img_path) beautify_img = cv2.imread(beautify_img_path) difference = cv2.absdiff(original_img, beautify_img) Conv_hsv_Gray = cv2.cvtColor(difference, cv2.COLOR_BGR2GRAY) max_thresh = 50 threshs = list(range(0, max_thresh, int(max_thresh / 10))) colors = ['f1ea09', 'efd60a', 'edc20b', 'eaae0d', 'e89a0e', 'e6850f', 'e47110', 'e15d12', 'df4913', 'dd3514'] diff_image = np.zeros(original_img.shape, dtype=np.uint8) for idx, thresh in enumerate(threshs): ret, mask = cv2.threshold(Conv_hsv_Gray, thresh, 255, cv2.THRESH_BINARY_INV) color = colors[idx] diff_image[mask != 255] = np.array(list(int(color[i:i + 2], 16) for i in (0, 2, 4))) diff_image = cv2.cvtColor(diff_image, cv2.COLOR_RGBA2BGRA) cv2.imwrite(diff_img_path, diff_image)

具体来说，它读取了两张图片的路径，使用OpenCV库的函数cv2.imread()读取这两张图片，接着使用cv2.absdiff()计算出两张图片的差异，将结果转换为灰度图像并存储在Conv_hsv_Gray中。然后，它定义了一个阈值列表...

将冒号后面的代码改写成一个nn.module类：data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, ...

将冒号后面的代码改写成一个nn.module类：import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, LSTM data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

for i, (inputs, labels) in enumerate(zip(trainX, trainY)): inputs = torch.from_numpy(inputs).float().to(device) labels = torch.from_numpy(labels).float().to(device) outputs = model(inputs) loss ...

import numpy as np import pandas as pd array=["Python","C","Scala","Java","GO","Scala","SQL","PHP","Python"] language=pd.Series(data=array) print(language) score=pd.Series(np.random.randint(low=0,high=100,size=9)) print(score) df_series1=language print(pd.DataFrame(df_series1)) df_series2=score print(pd.DataFrame(df_series2)) integrate=pd.concat([pd.DataFrame(df_series1),pd.DataFrame(df_series2)],axis=1,ignore_index=True) print(integrate) print(integrate.head(4))完成下面的题目：(5) 输出language字段为Python的行(6) 按照score字段升序排序(7) 统计每种编程语言出现的次数

(5) 输出language字段为Python的行： python print(integrate[integrate[0]=="Python"]) 输出： 0 1 0 Python 24 8 Python 60 (6) 按照score字段升序排序： python print(integrate.sort_...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap from scipy.spatial.distance import cdist from ant_colony import solve_tsp # 读取城市数据 df = pd.read_excel('world_coordinate.xlsx', index_col=0, dtype=str) # 提取城市和经纬度数据 countrys = df.index.values countrys_coords = np.array(df['[longitude, latitude]'].apply(eval).tolist()) # 计算城市间的距离矩阵 dist_matrix = cdist(countrys_coords, countrys_coords, metric='euclidean') # 创建蚁群算法实例 num_ants = 50 num_iterations = 500 alpha = 1 beta = 2 rho = 0.5 acs = solve_tsp(dist_matrix, num_ants=num_ants, num_iterations=num_iterations, alpha=alpha, beta=beta, rho=rho) # 输出访问完所有城市的最短路径的距离和城市序列 best_path = acs.get_best_path() best_distance = acs.best_cost visited_cities = [countrys[i] for i in best_path] print("最短路径距离：", best_distance) print("访问城市序列：", visited_cities) # 数据可视化 fig = plt.figure(figsize=(12, 8)) map = Basemap(projection='robin', lat_0=0, lon_0=0, resolution='l') map.drawcoastlines(color='gray') map.drawcountries(color='gray') x, y = map(countrys_coords[:, 0], countrys_coords[:, 1]) map.scatter(x, y, c='b', marker='o') path_coords = countrys_coords[best_path] path_x, path_y = map(path_coords[:, 0], path_coords[:, 1]) map.plot(path_x, path_y, c='r', marker='o') for i in range(len(countrys)): x, y = map(countrys_coords[i, 1], countrys_coords[i, 0]) plt.text(x, y, countrys[i], fontproperties='SimHei', color='black', fontsize=8, ha='center', va='center') plt.title("全球首都最短路径规划") plt.show()改成现在都有调用蚁群算法库的代码

countrys_coords = np.array(df['[longitude, latitude]'].apply(eval).tolist()) # 创建蚁群算法实例 num_ants = 50 num_iterations = 500 alpha = 1 beta = 2 rho = 0.5 optimizer = AntColonyOptimizer(num_ants...

以下是一个简单的 Python 代码，用于从呼吸信号数据集中估计呼吸率：复制 import numpy as np import pandas as pd # 读取数据集 data = [] for i in range(201, 206): filename = 'ResWave_{}.xlsx'.format(i) df = pd.read_excel(filename, header=None) data.append(df.values) # 数据预处理 X = [] y = [] for d in data: num = d[:, 0] times = d[:, 1] signal = d[:, 2:] rr = np.diff(times) rr = np.insert(rr, 0, rr[0]) # 补充第一个值 X.append(signal) y.append(rr) # 将数据转换为 NumPy 数组 X = np.array(X) y = np.array(y) # 将数据分为训练集和测试集 train_ratio = 0.8 train_size = int(train_ratio * len(X)) train_X, test_X = X[:train_size], X[train_size:] train_y, test_y = y[:train_size], y[train_size:] # 计算平均呼吸率 mean_rr = np.mean(train_y) # 计算测试集的预测呼吸率 pred_y = np.full_like(test_y, mean_rr) mae = np.mean(np.abs(pred_y - test_y)) print('平均呼吸率：', mean_rr) print('测试集 MAE：', mae) 现输出的结果为平均呼吸率： -0.022605119356429053 测试集 MAE： 0.0862699542011055 但结果中平均呼吸率为负，结果有问题。修改代码，使输出的平均呼吸率为正常值

for i in range(201, 206): filename = 'ResWave_{}.xlsx'.format(i) df = pd.read_excel(filename, header=None) data.append(df.values) # 数据预处理 X = [] y = [] for d in data: num = d[:, 0] times = ...

找出以下代码的错误并改正import pandas as pd df=pd.read_csv("D:/qcbj/sj/sales_details.csv") df1=df.dropna() df1.to_csv('D:/qcbj/sj/sales_dropna.csv',index=None) count=0 for i in range(len(df)): if (df['应发库'][i]!="sz" and df['应发库'][i]!="cs" and df['应发库'][i]!="sy" and df['应发库'][i]!="sh" and df['应发库'][i]!="cd" and df['应发库'][i]!="xa" and df['应发库'][i] !="km" and df['应发库'][i]!="jn" and df['应发库'][i]!="bj"): count+=1 print (count) droplist=[] for i in range(len(df)): if (df['应发库'][i]!="sz"and df['应发库'][i]!="cs"and df['应发库'][i]!="sy"and df['应发库'][i]!="sh"and df['应发库'][i]!="cd"and df['应发库'][i]!="xa"and df['应发库'][i]!="km"and df['应发库'][i]!="jn"and df['应发库'][i]!="bj"): droplist.append(i) df2=df1.drop(labels=droplist,axis=0) temp1=[] temp2=[] for i in range(len(df)): temp1.append(df['销售订单数量'][i].replace(',','')) temp2.append(df['销售订单数量'][i].replace(',','')) df['销售订单数量']=temp1 df['销售订单数量']=temp2 x=pd.factorize(df['物料编号']) y=pd.factorize(df['应发库']) df['物料编号']=x[0] df['应发库']=y[0] year=[] month=[] for i in range(len(df)): year.append(int(df['创建日期'][i][0:4])) month.append(int(df['创建日期'][i][5:7])) df['创建年']=year df['创建月']=month df import numpy as np df_array=np.array(df)

4. 在执行 year.append(int(df['创建日期'][i][0:4])) 和 month.append(int(df['创建日期'][i][5:7])) 时，应该使用 df1 而不是 df。 5. 在执行 df_array=np.array(df) 时，应该使用 df1 而不是 df。以下是...

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