數據分析3-after2.6 · Python數據分析課堂代碼

## 2.7 課堂活動1——了解你的班級 ``` #任務1 最受歡迎的愛好或生活習慣 import pandas as pd # 讀取Excel文件 df = pd.read_excel('3227.xlsx').dropna() # 假設除了姓名和性別列之外，其他列都是評分數據 # 選擇評分數據列，并轉換為整數類型 scores = df.iloc[:, 2:].astype(int) # 計算每列的總分 total_scores = scores.sum() # 找到總分最高的列名，即最喜愛的項目 most_popular_item = total_scores.idxmax() # 打印結果 print(f"全班最喜愛的項目是：{most_popular_item}") # 任務2：找出生活習慣不太好的同學，并提醒他。 #尋找規則：愛吃火鍋或燒烤且喜愛點外賣且作息不好或愛熬夜 import pandas as pd # 讀取Excel文件 df = pd.read_excel('3227.xlsx').dropna() df1 = df.iloc[:, 2:].astype(int) # 定義篩選條件 condition1 =(df1['火鍋'] > 3) | (df1['燒烤'] > 3) condition2 = df1['點外賣'] > 3 condition3 =(df1['早睡早起']<2) |(df1['熬夜'] > 3) # 組合所有條件 combined_condition = condition1 & condition2 & condition3 # 篩選出滿足條件的同學 filtered_df = df1[combined_condition] # 打印篩選結果 print(filtered_df) df.loc[14] ``` ## 2.7 課堂活動2——尋找與你“相似”的同學 ``` import pandas as pd import numpy as np # 讀取Excel文件，假設文件名為'students_data.xlsx'且位于當前工作目錄 file_path = '3227.xlsx' #讀取時去掉沒填的同學的行 df = pd.read_excel(file_path).dropna() # 初始化一個空字典來存儲整理后的數據 students = {} # 遍歷數據框的每一行，假設學生姓名在第一列 for index, row in df.iterrows(): student_name = row['學生姓名'] # 獲取學生姓名 # 從第二列開始，到最后一列（不包含空列），提取數據并轉換為numpy數組 scores = np.array(row.tolist()[1:]) # 將學生姓名和對應的分數數組添加到字典中 students[student_name] = scores # 打印整理后的數據結構 print(students) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # 計算兩個向量（商品）之間的內積 def inner_product(vec1, vec2): return np.dot(vec1, vec2) #找出相速度最高的菜 def findSimilar(x): # 獲取給定菜品的特征向量 target_vec = students[x] # 初始化最大相似度和最相似菜品 max_similarity = 0.0 most_similar_student = None # 遍歷所有菜品，計算與給定菜品的內積 for student, vec in students.items(): if student != x: # 排除自身 similarity = inner_product(target_vec, vec) # 如果當前內積大于之前的最大相似度，則更新最大相似度和最相似菜品 if similarity > max_similarity: max_similarity = similarity most_similar_student = student # 返回最相似菜品和相似度 return most_similar_student, max_similarity ``` ## 3.1 API接口獲取天氣數據 ```python import requests import json #知心天氣官網： #東軟秘鑰 #eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjY4ZTQ3ZjFiLTRjMmYtNDBiOS1iZmY4LWQ3YTg3MGFiYzcwMiJ9._TrSam5rj-3EGY2GwEg4qzpEgqWC6fFV5BoS1YILYVOGwllPjw8mHG3SO2nhuaiIhT7oBs-hKrkBam03dW159Q def fetch_weather_data(): # 構建請求的URL #url = f"http://124.93.196.45:10001/dev-api/bs-weather-report/weather/getCurrentDayData/上海市" #url = f"https://route.showapi.com/9-2?showapi_appid=1581147&showapi_sign=3a013bb035394181947e42fff8287556&area=南通" url = "https://api.seniverse.com/v3/weather/daily.json?key=SND4L7mkeZRVCghJH&location=nantong&language=zh-Hans&unit=c&start=0&days=5" # 設置請求頭，包含認證參數 -東軟 # headers = { # 'Authorization': f'Bearer eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjY4ZTQ3ZjFiLTRjMmYtNDBiOS1iZmY4LWQ3YTg3MGFiYzcwMiJ9._TrSam5rj-3EGY2GwEg4qzpEgqWC6fFV5BoS1YILYVOGwllPjw8mHG3SO2nhuaiIhT7oBs-hKrkBam03dW159Q' # } # 發送GET請求，包含請求頭 #response = requests.get(url, headers=headers) response = requests.get(url) # 檢查請求是否成功 if response.status_code == 200: # 解析返回的JSON數據 data = response.json() city = data["results"][0]["location"]["name"] list = data["results"][0]["daily"] # print(list) # for a in list: # print(a["date"]) weather_data = { 'city': city, 'weatherList': list, } return weather_data weather_data = fetch_weather_data() weather_data # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ from IPython.display import display,Image print(weather_data["city"]) print("~~~~~~~~~~~~~~~~~~~~~~~華麗分隔線~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") for daily_weather in weather_data["weatherList"]: print("日期:"+daily_weather["date"]) print("天氣："+daily_weather["text_day"]) print("溫度："+daily_weather["low"]+"--"+daily_weather["high"]+"℃") display(Image(f"{daily_weather['text_day']}.png")) # print(file_path) # print(daily_weather) print("~~~~~~~~~~~~~~~~~~~~~~~華麗分隔線~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") ``` ## 3.2 充電樁查詢 ``` #任務1 將所有的充電樁數據轉換為Excel可編輯的文件 import requests import json import pandas as pd def fetch_weather_data(): # 構建請求的URL url = f"http://124.93.196.45:10001/dev-api/bs-smart-charger/pile/alllist" # 設置請求頭，包含認證參數 -東軟 headers = { 'Authorization': f'Bearer eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjY4ZTQ3ZjFiLTRjMmYtNDBiOS1iZmY4LWQ3YTg3MGFiYzcwMiJ9._TrSam5rj-3EGY2GwEg4qzpEgqWC6fFV5BoS1YILYVOGwllPjw8mHG3SO2nhuaiIhT7oBs-hKrkBam03dW159Q' } # 發送GET請求，包含請求頭 response = requests.get(url, headers=headers) #response = requests.get(url) # 檢查請求是否成功 if response.status_code == 200: # 解析返回的JSON數據 data = response.json() pileList = data['data'] print(type(pileList)) df = pd.DataFrame(pileList) print(df) return df df = fetch_weather_data() df.to_excel('output.xlsx') #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #任務2 查詢：目前是空閑狀態且充電速率快的充電樁。 def fetch_weather_data(): # 構建請求的URL url = f"http://124.93.196.45:10001/dev-api/bs-smart-charger/pile/alllist?chargingPileState=2&chargingPileRate=2" # 設置請求頭，包含認證參數 -東軟 headers = { 'Authorization': f'Bearer eyJhbGciOiJIUzUxMiJ9.eyJsb2dpbl91c2VyX2tleSI6IjY4ZTQ3ZjFiLTRjMmYtNDBiOS1iZmY4LWQ3YTg3MGFiYzcwMiJ9._TrSam5rj-3EGY2GwEg4qzpEgqWC6fFV5BoS1YILYVOGwllPjw8mHG3SO2nhuaiIhT7oBs-hKrkBam03dW159Q' } # 發送GET請求，包含請求頭 response = requests.get(url, headers=headers) #response = requests.get(url) # 檢查請求是否成功 if response.status_code == 200: # 解析返回的JSON數據 data = response.json() pileList = data['data'] df = pd.DataFrame(pileList) print(df) return df fetch_weather_data() ```