写一段python代码，用openpyxl批量对所有工作表第一行的C1到BD1的单元格重新赋值

for sheet in workbook.worksheets:
for row in sheet.iter_rows(min_row=1, max_col=58, max_row=1):
for cell in row:
cell.value = 'New Value'
workbook.save('workbook.xlsx')

相关问题

写一段代码Python，可以爬虫东方财富网的股票数据

以下是一个简单的Python爬虫程序，可以爬取东方财富网的股票数据：

import requests
from bs4 import BeautifulSoup

url = 'http://quote.eastmoney.com/stocklist.html'  # 股票列表页面

# 发送请求获取页面内容
response = requests.get(url)
response.encoding = 'gbk'  # 设置编码格式
html = response.text

# 解析页面内容，获取股票代码和名称
soup = BeautifulSoup(html, 'html.parser')
stock_list = soup.find('div', {'class': 'quotebody'}).find_all('a')
for stock in stock_list:
    code = stock['href'].split('/')[-1].replace('.html', '')
    name = stock.text
    print(code, name)

该程序会输出东方财富网股票列表页面中所有股票的代码和名称。

如果需要爬取股票的实时行情数据，可以使用东方财富网提供的接口，例如：

import requests

url = 'http://push2.eastmoney.com/api/qt/stock/get?secid=1.600519&ut=bd1d9ddb04089700cf9c27f6f7426281&fields=f56,f57,f58,f59,f60,f61,f62,f63,f64,f65,f66,f67,f68,f69,f70,f71,f72,f73,f74,f75,f76,f77,f78,f79,f80,f81,f82,f83,f84,f85,f86,f87,f88,f89,f90,f91,f92,f93,f94,f95,f96,f97,f98,f99,f100,f101,f102,f103,f104,f105,f106,f107,f108,f109,f110,f111,f112,f113,f114,f115,f116,f117,f118,f119,f120,f121,f122,f123,f124,f125,f126,f127,f128,f129,f130,f131,f132,f133,f134,f135,f136,f137,f138,f139,f140,f141,f142,f143,f144,f145,f146,f147,f148,f149,f150,f151,f152,f153,f154,f155,f156,f157,f158,f159,f160,f161,f162,f163,f164,f165,f166,f167,f168,f169,f170,f171,f172,f173,f174,f175,f176,f177,f178,f179,f180,f181,f182,f183,f184,f185,f186,f187,f188,f189,f190,f191,f192,f193,f194,f195,f196,f197,f198,f199,f200,f201,f202,f203,f204,f205,f206,f207,f208,f209,f210,f211,f212,f213,f214,f215,f216,f217,f218,f219,f220,f221,f222,f223,f224,f225,f226,f227,f228,f229,f230,f231,f232,f233,f234,f235,f236,f237,f238,f239,f240,f241,f242,f243,f244,f245,f246,f247,f248,f249,f250,f251,f252,f253,f254,f255,f256,f257,f258,f259,f260,f261,f262,f263,f264,f265,f266,f267,f268,f269,f270,f271,f272,f273,f274,f275,f276,f277,f278,f279,f280,f281,f282,f283,f284,f285,f286,f287,f288,f289,f290,f291,f292,f293,f294,f295,f296,f297,f298,f299,f300,f301,f302,f303,f304,f305,f306,f307,f308,f309,f310,f311,f312,f313,f314,f315,f316,f317,f318,f319,f320,f321,f322,f323,f324,f325,f326,f327,f328,f329,f330,f331,f332,f333,f334,f335,f336,f337,f338,f339,f340,f341,f342,f343,f344,f345,f346,f347,f348,f349,f350,f351,f352,f353,f354,f355,f356,f357,f358,f359,f360,f361,f362,f363,f364,f365,f366,f367,f368,f369,f370,f371,f372,f373,f374,f375,f376,f377,f378,f379,f380,f381,f382,f383,f384,f385,f386,f387,f388,f389,f390,f391,f392,f393,f394,f395,f396,f397,f398,f399,f400,f401,f402,f403,f404,f405,f406,f407,f408,f409,f410,f411,f412,f413,f414,f415,f416,f417,f418,f419,f420,f421,f422,f423,f424,f425,f426,f427,f428,f429,f430,f431,f432,f433,f434,f435,f436,f437,f438,f439,f440,f441,f442,f443,f444,f445,f446,f447,f448,f449,f450,f451,f452,f453,f454,f455,f456,f457,f458,f459,f460,f461,f462,f463,f464,f465,f466,f467,f468,f469,f470,f471,f472'

# 发送请求获取实时行情数据
response = requests.get(url)
data = response.json()

# 解析数据
print(data['data']['f58'])  # 当前股价
print(data['data']['f170'])  # 涨跌幅

以上代码中的`secid`参数可以通过股票代码获取，例如：

code = '600519'  # 股票代码
url = f'http://push2.eastmoney.com/api/qt/stock/get?secid=1.{code}&ut=bd1d9ddb04089700cf9c27f6f7426281&fields=f56,f57,f58,f59,f60,f61,f62,f63,f64,f65,f66,f67,f68,f69,f70,f71,f72,f73,f74,f75,f76,f77,f78,f79,f80,f81,f82,f83,f84,f85,f86,f87,f88,f89,f90,f91,f92,f93,f94,f95,f96,f97,f98,f99,f100,f101,f102,f103,f104,f105,f106,f107,f108,f109,f110,f111,f112,f113,f114,f115,f116,f117,f118,f119,f120,f121,f122,f123,f124,f125,f126,f127,f128,f129,f130,f131,f132,f133,f134,f135,f136,f137,f138,f139,f140,f141,f142,f143,f144,f145,f146,f147,f148,f149,f150,f151,f152,f153,f154,f155,f156,f157,f158,f159,f160,f161,f162,f163,f164,f165,f166,f167,f168,f169,f170,f171,f172,f173,f174,f175,f176,f177,f178,f179,f180,f181,f182,f183,f184,f185,f186,f187,f188,f189,f190,f191,f192,f193,f194,f195,f196,f197,f198,f199,f200,f201,f202,f203,f204,f205,f206,f207,f208,f209,f210,f211,f212,f213,f214,f215,f216,f217,f218,f219,f220,f221,f222,f223,f224,f225,f226,f227,f228,f229,f230,f231,f232,f233,f234,f235,f236,f237,f238,f239,f240,f241,f242,f243,f244,f245,f246,f247,f248,f249,f250,f251,f252,f253,f254,f255,f256,f257,f258,f259,f260,f261,f262,f263,f264,f265,f266,f267,f268,f269,f270,f271,f272,f273,f274,f275,f276,f277,f278,f279,f280,f281,f282,f283,f284,f285,f286,f287,f288,f289,f290,f291,f292,f293,f294,f295,f296,f297,f298,f299,f300,f301,f302,f303,f304,f305,f306,f307,f308,f309,f310,f311,f312,f313,f314,f315,f316,f317,f318,f319,f320,f321,f322,f323,f324,f325,f326,f327,f328,f329,f330,f331,f332,f333,f334,f335,f336,f337,f338,f339,f340,f341,f342,f343,f344,f345,f346,f347,f348,f349,f350,f351,f352,f353,f354,f355,f356,f357,f358,f359,f360,f361,f362,f363,f364,f365,f366,f367,f368,f369,f370,f371,f372,f373,f374,f375,f376,f377,f378,f379,f380,f381,f382,f383,f384,f385,f386,f387,f388,f389,f390,f391,f392,f393,f394,f395,f396,f397,f398,f399,f400,f401,f402,f403,f404,f405,f406,f407,f408,f409,f410,f411,f412,f413,f414,f415,f416,f417,f418,f419,f420,f421,f422,f423,f424,f425,f426,f427,f428,f429,f430,f431,f432,f433,f434,f435,f436,f437,f438,f439,f440,f441,f442,f443,f444,f445,f446,f447,f448,f449,f450,f451,f452,f453,f454,f455,f456,f457,f458,f459,f460,f461,f462,f463,f464,f465,f466,f467,f468,f469,f470,f471,f472'

帮我写一段用tensorflow搭建cnn实现手写数字识别的python

好的，以下是一个用TensorFlow搭建CNN实现手写数字识别的Python代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 导入MNIST数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义超参数
learning_rate = 0.001
training_iters = 20000
batch_size = 128
display_step = 10

# 定义网络参数
n_input = 784  # MNIST数据集中每张图片的大小为28x28
n_classes = 10  # MNIST数据集中共有10个数字类别
dropout = 0.75  # Dropout的概率，防止过拟合

# 定义输入占位符
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)

# 定义卷积层和池化层的函数
def conv2d(name, x, W, b, strides=1):
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x, name=name)

def maxpool2d(name, x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)

# 定义卷积神经网络模型
def conv_net(x, weights, biases, dropout):
    # 将输入的图片转化为4D张量
    x = tf.reshape(x, shape=[-1, 28, 28, 1])

    # 第一层卷积层
    conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1'])
    conv1 = maxpool2d('maxpool1', conv1, k=2)

    # 第二层卷积层
    conv2 = conv2d('conv2', conv1, weights['wc2'], biases['bc2'])
    conv2 = maxpool2d('maxpool2', conv2, k=2)

    # 将卷积层的输出转化为全连接层的输入
    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    fc1 = tf.nn.dropout(fc1, dropout)

    # 输出层
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out

# 定义权重参数和偏置参数
weights = {
    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
    'out': tf.Variable(tf.random_normal([1024, n_classes]))
}

biases = {
    'bc1': tf.Variable(tf.random_normal([32])),
    'bc2': tf.Variable(tf.random_normal([64])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# 构建模型
pred = conv_net(x, weights, biases, keep_prob)

# 定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 定义评估模型的准确率的函数
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 初始化变量
init = tf.global_variables_initializer()

# 开始训练模型
with tf.Session() as sess:
    sess.run(init)
    step = 1
    while step * batch_size < training_iters:
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
        if step % display_step == 0:
            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))
        step += 1
    print("Optimization Finished!")
    print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))

这个代码实现了一个两层卷积神经网络，通过对MNIST数据集进行训练，实现了手写数字识别的功能。