From c9969f62fbbe263571ed1effb055f7c1bc4d1bc8 Mon Sep 17 00:00:00 2001
From: shao1chuan <4448445@qq.com>
Date: Tue, 12 Mar 2024 21:03:05 +0800
Subject: [PATCH] shao

---
 实例学习 nicegui/Plot events.py  |  9 ++++
 实例学习 nicegui/add line.py     | 15 ++++++
 实例学习 nicegui/add point.py    | 27 ++++++++++
 实例学习 nicegui/show table.py   | 19 +++++++
 实例学习 nicegui/登录系统.py | 23 +++++++++
 实例学习plotly/Express line.py   |  9 +++-
 实例学习plotly/go4.ipynb         | 76 ++++++++++++++--------------
 实例学习plotly/go5.ipynb         |  2 +-
 实验 Pandas_速查表/groupby.py   |  2 +-
 9 files changed, 141 insertions(+), 41 deletions(-)
 create mode 100644 实例学习 nicegui/Plot events.py
 create mode 100644 实例学习 nicegui/add line.py
 create mode 100644 实例学习 nicegui/add point.py
 create mode 100644 实例学习 nicegui/show table.py
 create mode 100644 实例学习 nicegui/登录系统.py

diff --git a/实例学习 nicegui/Plot events.py b/实例学习 nicegui/Plot events.py
new file mode 100644
index 0000000..78a03cd
--- /dev/null
+++ b/实例学习 nicegui/Plot events.py	
@@ -0,0 +1,9 @@
+import plotly.graph_objects as go
+from nicegui import ui
+
+fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[1, 2, 3, 2.5]))
+fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
+plot = ui.plotly(fig).classes('w-full h-40')
+plot.on('plotly_click', ui.notify)
+
+ui.run()
\ No newline at end of file
diff --git a/实例学习 nicegui/add line.py b/实例学习 nicegui/add line.py
new file mode 100644
index 0000000..d47bead
--- /dev/null
+++ b/实例学习 nicegui/add line.py	
@@ -0,0 +1,15 @@
+import plotly.graph_objects as go
+from nicegui import ui
+from random import random
+
+fig = go.Figure()
+fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
+plot = ui.plotly(fig).classes('w-full h-40')
+
+def add_trace():
+    fig.add_trace(go.Scatter(x=[1, 2, 3], y=[random(), random(), random()]))
+    plot.update()
+
+ui.button('Add trace', on_click=add_trace)
+
+ui.run()
\ No newline at end of file
diff --git a/实例学习 nicegui/add point.py b/实例学习 nicegui/add point.py
new file mode 100644
index 0000000..d942702
--- /dev/null
+++ b/实例学习 nicegui/add point.py	
@@ -0,0 +1,27 @@
+import plotly.graph_objects as go
+from nicegui import ui
+from random import random
+
+
+ui.label('登录系统')
+x = ui.input(label='x:')
+y = ui.input(label='y:')
+z = ui.input(label='z:')
+
+
+
+fig = go.Figure()
+fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
+plot = ui.plotly(fig).classes('w-full h-40')
+
+def add_trace():
+    fig.add_trace(go.Scatter(x=[1, 2, 3], y=[x.value, y.value, z.value]))
+    plot.update()
+
+ui.button('Add trace', on_click=add_trace)
+
+
+
+
+
+ui.run()
\ No newline at end of file
diff --git a/实例学习 nicegui/show table.py b/实例学习 nicegui/show table.py
new file mode 100644
index 0000000..55c79ef
--- /dev/null
+++ b/实例学习 nicegui/show table.py	
@@ -0,0 +1,19 @@
+from nicegui import ui
+import pandas as pd
+
+# 创建一个简单的DataFrame
+data = {
+    'Name': ['Alice', 'Bob', 'Charlie'],
+    'Age': [25, 30, 35],
+    'City': ['New York', 'Paris', 'London']
+}
+df = pd.DataFrame(data)
+
+
+# 在nicegui中创建并显示表格
+
+ui.label('显示DataFrame')
+ui.table.from_pandas(df).classes('max-h-40')
+
+# 启动应用
+ui.run()
diff --git a/实例学习 nicegui/登录系统.py b/实例学习 nicegui/登录系统.py
new file mode 100644
index 0000000..d458eaa
--- /dev/null
+++ b/实例学习 nicegui/登录系统.py	
@@ -0,0 +1,23 @@
+from nicegui import ui
+
+# 设置正确的用户名和密码，仅用于演示目的
+CORRECT_USERNAME = 'user'
+CORRECT_PASSWORD = 'password'
+
+# 定义登录函数
+def on_login():
+    # 检查用户名和密码是否正确
+    if username_input.value == CORRECT_USERNAME and password_input.value == CORRECT_PASSWORD:
+        login_status.text = '登录成功！'  # 使用 .text 属性更新文本
+    else:
+        login_status.text = '登录失败，请检查您的用户名和密码。'  # 使用 .text 属性更新文本
+
+# 创建UI元素
+ui.label('登录系统')
+username_input = ui.input(label='用户名:')
+password_input = ui.input(label='密码:')  # 使用 type='password' 使密码输入不可见
+ui.button('登录', on_click=on_login)
+login_status = ui.label('请进行登录')  # 初始提示文本
+
+# 启动应用
+ui.run()
diff --git a/实例学习plotly/Express line.py b/实例学习plotly/Express line.py
index ca24f5f..ed87f17 100644
--- a/实例学习plotly/Express line.py	
+++ b/实例学习plotly/Express line.py	
@@ -1,7 +1,14 @@
 import plotly.express as px
 import pandas
 df = px.data.gapminder()
-df1 = df.query("country == 'United States'")
+df1 = df.query("country == 'United States' or continent == 'Asia'")
+
+
+# 根据大陆对数据进行分组，并计算每个大陆的人均GDP平均值
+gdpPercap_summary = df.groupby('continent')[['gdpPercap','lifeExp']].mean().reset_index()
+
+# 显示结果
+print(gdpPercap_summary)
 
 print(df1.columns)
 fig = px.line(df1,
diff --git a/实例学习plotly/go4.ipynb b/实例学习plotly/go4.ipynb
index 26d88e6..f29f1dc 100644
--- a/实例学习plotly/go4.ipynb
+++ b/实例学习plotly/go4.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -42,7 +42,7 @@
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      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -1571,7 +1571,7 @@
         "plotlyServerURL": "https://plot.ly"
        }
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@@ -1667,7 +1645,7 @@
    "outputs": [
     {
      "data": {
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+      "text/plain": "array([[-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5],\n       [-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5]])"
      },
      "execution_count": 10,
      "metadata": {},
@@ -1675,7 +1653,7 @@
     }
    ],
    "source": [
-    "yv"
+    "xv"
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@@ -1689,13 +1667,35 @@
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     {
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      },
      "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "yv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[50, 41, 34, 29, 26, 25, 26, 29, 34, 41, 50],\n       [41, 32, 25, 20, 17, 16, 17, 20, 25, 32, 41],\n       [34, 25, 18, 13, 10,  9, 10, 13, 18, 25, 34],\n       [29, 20, 13,  8,  5,  4,  5,  8, 13, 20, 29],\n       [26, 17, 10,  5,  2,  1,  2,  5, 10, 17, 26],\n       [25, 16,  9,  4,  1,  0,  1,  4,  9, 16, 25],\n       [26, 17, 10,  5,  2,  1,  2,  5, 10, 17, 26],\n       [29, 20, 13,  8,  5,  4,  5,  8, 13, 20, 29],\n       [34, 25, 18, 13, 10,  9, 10, 13, 18, 25, 34],\n       [41, 32, 25, 20, 17, 16, 17, 20, 25, 32, 41],\n       [50, 41, 34, 29, 26, 25, 26, 29, 34, 41, 50]])"
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "z = xv**2 + yv**2\n",
     "z"
@@ -1703,7 +1703,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
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@@ -2984,7 +2984,7 @@
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diff --git a/实验 Pandas_速查表/groupby.py b/实验 Pandas_速查表/groupby.py
index e1b2f29..b5f753d 100644
--- a/实验 Pandas_速查表/groupby.py	
+++ b/实验 Pandas_速查表/groupby.py	
@@ -9,7 +9,7 @@ data = {
     'Sales': [100, 150, 120, 110, 130, 120]
 }
 df = pd.DataFrame(data)
-grouped_df = df.groupby('Product')['Sales'].sum()
+grouped_df = df.groupby(by = 'Product')['Sales'].sum()
 print(grouped_df)
 # 这个操作将按照Product列进行分组，并计算每个分组中Sales列的总和，从而得到每个产品的总销售额。
 # 这意味着产品A的总销售额为350，而产品B的总销售额为380。通过使用groupby方法，我们能够轻松地按照Product列对数据进行分组，并计算每个分组的Sales列总和，从而得到每个产品在所有日期上的总销售额。这个例子展示了groupby方法在数据分析和处理中的强大功能和灵活性