GUI

1: pip3 install streamlit

1: import streamlit as st
2: 
3: st.title('測試python的web玩具')

1: streamlit run main.py

1: !pip install streamlit -q

1: !wget -q -O - ipv4.icanhazip.com

1: %%writefile app.py
2: import streamlit as st
3: st.write('#Streamlit計算機')
4: num1 = st.number_input('數字1')
5: num2 = st.number_input('數字2')
6: num3 = num1 + num2
7: st.write('#答案', num3)
8: 

1: !streamlit run app.py & npx localtunnel --port 8501

 1: import streamlit as st
 2: import pandas as pd
 3: 
 4: st.title('學生成績')
 5: st.write('lalala')
 6: # 數據
 7: df = pd.DataFrame({
 8:     '國文': [98, 76, 100, 38, 40],
 9:     '數學': [80, 46, 80, 67, 66]
10: })
11: 
12: st.sidebar.write('選項1')
13: st.sidebar.markdown('選項2')
14: with st.sidebar:
15:     st.write('選項3')
16: 
17: df

 1: import streamlit as st
 2: import pandas as pd
 3: 
 4: st.title('學生成績')
 5: 
 6: # 數據
 7: df = pd.DataFrame({
 8:     '國文': [98, 76, 100, 38, 40],
 9:     '數學': [80, 46, 80, 67, 66]
10: })
11: 
12: # 側邊欄連結
13: st.sidebar.markdown("[查看數據](?page=table)")
14: st.sidebar.markdown("[畫圖](?page=chart)")
15: 
16: # 獲取當前URL參數
17: query_params = st.query_params
18: page = query_params.get("page", "table")
19: 
20: # 根據URL參數顯示相應的內容
21: if page == 'table':
22:     st.write('學生成績數據表')
23:     st.dataframe(df)
24: elif page == 'chart':
25:     st.write('學生成績折線圖')
26:     st.line_chart(df)

 1: import streamlit as st
 2: import pandas as pd
 3: import matplotlib.pyplot as plt
 4: 
 5: st.title('學生成績')
 6: 
 7: # 數據
 8: df = pd.DataFrame({
 9:     '國文': [98, 76, 100, 38, 40],
10:     '數學': [80, 46, 80, 67, 66]
11: })
12: 
13: # 側邊欄選擇
14: st.sidebar.write('選單')
15: option = st.sidebar.selectbox(
16:     '請選擇操作',
17:     ['查看成績', '畫折線圖']
18: )
19: 
20: # 根據選擇顯示相應的內容
21: if option == '查看成績':
22:     st.write('學生成績表')
23:     st.dataframe(df)
24: elif option == '畫折線圖':
25:     st.write('學生成績折線圖')
26:     fig, ax = plt.subplots()
27:     ax.plot(df['國文'])
28:     ax.plot(df['數學'])
29:     ax.set_xticks(range(5))
30:     ax.set_xticklabels(range(1, len(df) + 1))
31:     st.pyplot(fig)

 1: import streamlit as st
 2: import pandas as pd
 3: 
 4: st.title('雲林智慧教育中心')
 5: 
 6: data = pd.DataFrame({
 7:     'lat': 23.67,
 8:     'lon': 120.39,
 9:     'name': ['雲林智慧教育中心']
10: })
11: 
12: st.map(data)

 1: import streamlit as st
 2: import pandas as pd
 3: 
 4: st.title('雲林')
 5: # 定義不同地點的數據
 6: locs = {
 7:     '雲林智慧教育中心': {'lat': 23.67, 'lon': 120.39},
 8:     '雲林高鐵站': {'lat': 23.738, 'lon': 120.428}
 9: }
10: 
11: data = pd.DataFrame([
12:     {'lat': locs['雲林智慧教育中心']['lat'], 'lon': locs['雲林智慧教育中心']['lon'], 'name': '雲林智慧教育中心'},
13:     {'lat': locs['雲林高鐵站']['lat'], 'lon': locs['雲林高鐵站']['lon'], 'name': '雲林高鐵站'}
14: ])
15: 
16: st.map(data)
17: 

 1: import streamlit as st
 2: import pandas as pd
 3: 
 4: st.title('雲林智慧教育中心')
 5: # 定義不同地點的數據
 6: locations = {
 7:     '雲林智慧教育中心': {'lat': 23.67, 'lon': 120.39},
 8:     '雲林高鐵站': {'lat': 23.738, 'lon': 120.428}
 9: }
10: 
11: # 在側邊欄中選擇地點
12: st.sidebar.write('選單')
13: option = st.sidebar.selectbox(
14:     '請選擇地點',
15:     ['雲林智慧教育中心', '雲林高鐵站'])
16: 
17: # 根據選擇的地點更新地圖數據
18: selected_location = locations[option]
19: data = pd.DataFrame({
20:     'lat': [selected_location['lat']],
21:     'lon': [selected_location['lon']],
22:     'name': [option]
23: })
24: selected_location['lat']
25: selected_location['lon']
26: option
27: 
28: st.map(data)

1: pip3 install pandas
2: pip3 install streamlit
3: pip3 install plotly
4: pip3 install scikit-learn

 1: import pandas as pd
 2: import streamlit as st
 3: import plotly.express as px
 4: from sklearn.cluster import KMeans
 5: 
 6: # 讀取資料
 7: test = pd.read_csv("https://letranger.github.io/working/scores.csv")
 8: 
 9: # Streamlit 應用程式標題
10: st.title("分數分佈")
11: 
12: # 設定側邊欄選單
13: st.sidebar.write('選單')
14: 
15: # 科目選擇下拉選單
16: option = st.sidebar.selectbox(
17:     '請選擇科目',
18:     ['國文', '英文', '數學', '物理']
19: )
20: 
21: # 顯示資料框
22: st.write("### 資料", test)
23: 
24: # 將分數依每10分分一組
25: test['binned'] = pd.cut(test[option], bins=range(0, 101, 10))
26: 
27: # 計算每個區間的頻率
28: frequency = test['binned'].value_counts().sort_index()
29: # 轉換為資料框格式，方便 Streamlit 顯示
30: frequency_df = pd.DataFrame({'分數區間': frequency.index.astype(str), '頻率': frequency.values})
31: 
32: # 使用 Streamlit 顯示長條圖
33: st.bar_chart(frequency_df.set_index('分數區間'))
34: 
35: # KMeans 聚類
36: # 使用滑桿選擇 k 值（聚類數量）
37: k = st.slider("選擇聚類的數量 k", 2, 10, 3)
38: kmeans = KMeans(n_clusters=k)  # 預設分為 k 群
39: kmeans.fit(test[['國文', '英文']])  # 使用 '國文' 和 '英文' 進行聚類
40: clusters = kmeans.predict(test[['國文', '英文']])
41: 
42: # 將聚類結果添加到資料框
43: test['聚類'] = clusters
44: 
45: # 使用 Plotly 繪製聚類結果的散點圖
46: fig2 = px.scatter(test, x='國文', y='英文', color='聚類', title='國文與英文分數的聚類結果')
47: st.plotly_chart(fig2)

 1: # Flask網站前後端互動 09 - 超連結與圖片
 2: # 載入Flask、Request、render_template
 3: from flask import Flask, request, render_template
 4: 
 5: app = Flask(__name__, template_folder='templates',
 6:             static_folder="public",
 7:             static_url_path="/")
 8: 
 9: # 處理路徑 / 的對應函式
10: @app.route("/")
11: 
12: def main():
13:     return render_template("index.html")
14: 
15: @app.route("/page")
16: 
17: def page():
18:     #從main.html頁面中讀取姓名，存在name中
19:     name = request.args.get("nameis")
20:     # 將name的資料轉給page.html中的變數namepage
21:     return render_template("page.html", namepage=name)
22: # 啟動Server
23: 
24: if __name__ == '__main__':
25:     app.debug = True
26:     app.run()

1: brew install python-tk@3.9
2: pip install tk

 1: import tkinter as tk
 2: from tkinter import messagebox
 3: 
 4: def button_event():
 5:     #print(var.get())
 6:     if var.get() == '':
 7:         tk.messagebox.showerror('message', '未輸入答案')
 8:     elif var.get() == '2':
 9:         tk.messagebox.showinfo('message', '答對了！')
10:     else:
11:         tk.messagebox.showerror('message', '答錯')
12: 
13: root = tk.Tk()
14: root.title('my window')
15: 
16: # label
17: mylabel = tk.Label(root, text='1+1=')
18: mylabel.grid(row=0, column=0)
19: 
20: var = tk.StringVar()
21: myentry = tk.Entry(root, textvariable=var)
22: myentry.grid(row=0, column=1)
23: 
24: mybutton = tk.Button(root, text='完成', command=button_event)
25: mybutton.grid(row=1, column=1)
26: 
27: root.mainloop()