np.append(x, [df1], axis=0)

这行代码使用了Numpy库中的append函数来将数据添加到之前创建的空数组x中。其中，第一个参数x指定了要添加数据的目标数组，第二个参数[df1]是要添加的数据，它是一个列表，列表中只包含一个元素df1，表示要添加的数据是一个二维的DataFrame类型。第三个参数axis=0表示按行的方向添加数据，即将df1添加为x的新的一行。最终，这行代码的返回值是一个新的numpy数组，它是将df1添加到x数组后得到的结果。需要注意的是，这种方式虽然可以动态添加数据，但是每次添加数据都需要重新创建一个新的数组，因此在处理大量数据时可能会导致性能问题。

相关问题

import matplotlib.pyplot as plt import pandas as pd from keras.models import Sequential from keras import layers from keras import regularizers import os import keras import keras.backend as K import numpy as np from keras.callbacks import LearningRateScheduler data = "data.csv" df = pd.read_csv(data, header=0, index_col=0) df1 = df.drop(["y"], axis=1) lbls = df["y"].values - 1 wave = np.zeros((11500, 178)) z = 0 for index, row in df1.iterrows(): wave[z, :] = row z+=1 mean = wave.mean(axis=0) wave -= mean std = wave.std(axis=0) wave /= std def one_hot(y): lbl = np.zeros(5) lbl[y] = 1 return lbl target = [] for value in lbls: target.append(one_hot(value)) target = np.array(target) wave = np.expand_dims(wave, axis=-1) model = Sequential() model.add(layers.Conv1D(64, 15, strides=2, input_shape=(178, 1), use_bias=False)) model.add(layers.ReLU()) model.add(layers.Conv1D(64, 3)) model.add(layers.Conv1D(64, 3, strides=2)) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.5)) model.add(layers.Conv1D(64, 3)) model.add(layers.Conv1D(64, 3, strides=2)) model.add(layers.BatchNormalization()) model.add(layers.LSTM(64, dropout=0.5, return_sequences=True)) model.add(layers.LSTM(64, dropout=0.5, return_sequences=True)) model.add(layers.LSTM(32)) model.add(layers.Dropout(0.5)) model.add(layers.Dense(5, activation="softmax")) model.summary() save_path = './keras_model3.h5' if os.path.isfile(save_path): model.load_weights(save_path) print('reloaded.') adam = keras.optimizers.adam() model.compile(optimizer=adam, loss="categorical_crossentropy", metrics=["acc"]) # 计算学习率 def lr_scheduler(epoch): # 每隔100个epoch，学习率减小为原来的0.5 if epoch % 100 == 0 and epoch != 0: lr = K.get_value(model.optimizer.lr) K.set_value(model.optimizer.lr, lr * 0.5) print("lr changed to {}".format(lr * 0.5)) return K.get_value(model.optimizer.lr) lrate = LearningRateScheduler(lr_scheduler) history = model.fit(wave, target, epochs=400, batch_size=128, validation_split=0.2, verbose=2, callbacks=[lrate]) model.save_weights(save_path) print(history.history.keys()) # summarize history for accuracy plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() # summarize history for loss plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show()

这段代码是用来训练一个深度学习模型的代码，使用了Keras和Tensorflow进行模型的搭建、训练和保存。该模型包含了卷积层、批量归一化层、Dropout层、LSTM层和全连接层，并且最终使用softmax激活函数得到预测的类别。通过对数据进行处理，将输入数据按照一定的规范进行规范化，将标签转换成了一个one-hot编码的形式。最后，该代码通过在数据上进行训练，得到一个可以用来做预测的深度学习模型。

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)

代码中有以下问题：

1. 在执行 `temp2.append(df['销售订单数量'][i].replace(',',''))` 时，应该替换的是 `df['发货数量'][i]` 而不是 `df['销售订单数量'][i]`。

2. 在执行 `df['销售订单数量']=temp1` 和 `df['销售订单数量']=temp2` 时，应该将 `df['销售订单数量']` 和 `df['发货数量']` 两列分别赋值为 `temp1` 和 `temp2`。

3. 在执行 `x=pd.factorize(df['物料编号'])` 和 `y=pd.factorize(df['应发库'])` 时，应该使用 df1 而不是 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。

以下是修改后的代码：

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['发货数量'][i].replace(',', ''))
df1['销售订单数量'] = temp1
df1['发货数量'] = temp2

x = pd.factorize(df1['物料编号'])
y = pd.factorize(df1['应发库'])
df1['物料编号'] = x[0]
df1['应发库'] = y[0]

year = []
month = []
for i in range(len(df1)):
    year.append(int(df1['创建日期'][i][0:4]))
    month.append(int(df1['创建日期'][i][5:7]))
df1['创建年'] = year
df1['创建月'] = month

df_array = np.array(df1)