10条行之有效的实践途径，将ChatGPT融入开发

import csv

def count_words_in_csv(filename):
    word_count = {}

    with open(filename, mode='r') as file:
        csv_reader = csv.reader(file)
        
        for row in csv_reader:
            for cell in row:
                words = cell.split(" ")
                for word in words:
                    word = word.lower().strip()
                    if word:
                        if word not in word_count:
                            word_count[word] = 1
                        else:
                            word_count[word] += 1

    for word, count in word_count.items():
        print(f"{word}: {count}")

# 使用示例
count_words_in_csv("sample.csv")

import pandas as pd
import re
from collections import defaultdict

def count_words_in_csv(filename):
    try:
        # 使用pandas进行高效和便捷的CSV读取
        df = pd.read_csv(filename, header=None)
    except FileNotFoundError:
        print("Error: File not found.")
        return

    word_count = defaultdict(int)

    for _, row in df.iterrows():
        for cell in row:
            if pd.isna(cell):
                # 跳过NaN单元格
                continue
            # 使用正则表达式分割和清理单词
            words = re.findall(r'bw+b', cell.lower())
            for word in words:
                word_count[word] += 1

    return word_count

# 使用示例
word_counts = count_words_in_csv("sample.csv")

if word_counts:
    for word, count in word_counts.items():
        print(f"{word}: {count}")

import csv

def calculate_average_from_csv(file_name):
    total = 0
    count = 0    
    with open(file_name, 'r') as file:
        csv_content = csv.reader(file, delimiter=',')

        for row in csv_content:
            for item in row:
                total += int(item)
                count += 1
    
    average = total / count
    return average

print(calculate_average_from_csv('numbers.csv'))

import csv

def calculate_average_from_csv(file_name):
    total = 0
    count = 0
    
    with open(file_name, 'r') as file:
        csv_content = csv.reader(file, delimiter=',')
        
        for row in csv_content:
            for item in row:
                try:
                    total += int(item)
                    count += 1
                except ValueError:
                    pass  # 跳过非整数值
        
        if count == 0:
            return "CSV中未找到有效的整数值"
        
        average = total / count
        return average

print(calculate_average_from_csv('numbers.csv'))

import numpy as np

class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        self.weights1 = np.random.rand(input_size, hidden_size)
        self.weights2 = np.random.rand(hidden_size, output_size)
        self.bias1 = np.random.rand(1, hidden_size)
        self.bias2 = np.random.rand(1, output_size)

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def feedforward(self, x):
        self.hidden = self.sigmoid(np.dot(x, self.weights1) + self.bias1)
        self.output = self.sigmoid(np.dot(self.hidden, self.weights2) + self.bias2)
        return self.output

    def backpropagate(self, x, y, learning_rate=0.1):
        output_error = y - self.output
        hidden_error = output_error.dot(self.weights2.T)

        d_weights2 = self.hidden.T.dot(output_error * self.sigmoid_derivative(self.output))
        d_weights1 = x.T.dot(hidden_error * self.sigmoid_derivative(self.hidden))

        self.weights1 += learning_rate * d_weights1
        self.weights2 += learning_rate * d_weights2

# 使用示例：
input_size = 3
hidden_size = 4
output_size = 2
nn = NeuralNetwork(input_size, hidden_size, output_size)