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Author: Lonch-Wong

.ipynb_checkpoints/Flight-checkpoint.ipynb

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+import plotly.plotly as py
+import plotly.graph_objs as go
+import pandas as pd
+
+df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
+df_airports.head()  #读取机场数据
+
+df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
+df_flight_paths.head() #读取航班数据
+
+
+
+trace0 = go.Scattergeo(  #线的颜色
+   locationmode = 'USA-states',
+        lon = df_airports['long'],
+        lat = df_airports['lat'],
+        hoverinfo = 'text',
+        text = df_airports['airport'],
+        mode = 'lines',
+        marker = dict( 
+            size=2, 
+            color='rgb(255, 0, 0)',
+            line = dict(
+                width=3,
+                color='rgba(68, 68, 68, 0)'
+            )
+        )
+)
+
+data = [trace0]
+
+cluster0 = []  #每个机场的点
+for i in range( len( df_flight_paths ) ):
+    cluster0 .append(
+        dict(
+            type = 'scattergeo',
+            locationmode = 'USA-states',
+            lon = [ df_flight_paths['start_lon'][i], df_flight_paths['end_lon'][i] ],
+            lat = [ df_flight_paths['start_lat'][i], df_flight_paths['end_lat'][i] ],
+            mode = 'lines',
+            line = dict(
+                width = 1,
+                color = 'red',
+            ),
+            opacity = float(df_flight_paths['cnt'][i])/float(df_flight_paths['cnt'].max()),
+        )
+    )
+
+updatemenus = list([ #按钮
+    dict(type="buttons",
+         buttons=list([   
+            dict(label = 'None',
+                 method = 'update'),
+            dict(label = 'Cluster 0',
+                 method = 'update',
+                 args = [{'annotations':cluster0}]),
+        ]),
+    )
+])
+
+layout = dict( title = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)', #布局
+        showlegend = False, 
+        geo = dict(
+            scope='north america',
+            projection=dict( type='azimuthal equal area' ),
+            showland = True,
+            landcolor = 'rgb(243, 243, 243)',
+            countrycolor = 'rgb(204, 204, 204)',
+        ),
+ updatemenus=updatemenus
+ )
+
+fig = dict(data=data, layout=layout) #数据带入
+
+py.plot(fig, filename='relayout_option') #实现

30.py

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+# python-requests_html-with-flightaware #
+
+# 1.开发环境配置 #
+
+
+- 安装Python3.x+
+
+- 下载本教程配套的压缩包
+
+- 安装Anaconda套装，点击这个网址 [link](https://www.anaconda.com/download/)
+
+- 打开套装里的终端 Anaconda Prompt
+
+- 用终端cd命令进入压缩包解压的文件目录 如cd C:\Users\51774\Desktop\python-requests_html-with-flightaware
+
+- 进入终端后执行代码如下
+
+________________________________________
+    >>> pip install requests_html 
+	>>> pip install pandas
+	>>> pip install numpy
+	>>> cd C:\Users\51774\Desktop\python-requests_html-with-flightaware 
+	>>> jupyter notebook
+
+
+# 2.爬取数据 #
+
+
+
+
+- 打开Flight.ipynb文件查看代码
+
+- Shitf+Enter运行代码
+
+
+
+
+## Image:代码 ##
+![](https://i.imgur.com/ewPnTD4.png)
+_________________________________
+
+## Image:制定的表格 ##
+
+![](https://i.imgur.com/vix3MQA.png)
+___________________________________
+	
+## Image:生成的表格 ##
+![](https://i.imgur.com/29twv5J.png)
+___________________________________
+
+
+
+
+
+# 3.可视化数据 #
+- 我们利用plotly可视化工具
+
+- 需要注册账号，获得账户和密钥，点击这个网址 [link](https://plot.ly/accounts/login/?action=login#/)
+
+- 打开cmd终端
+
+- 在终端里调用plotly库
+
+________________________________________
+    >>> import plotly
+    >>> plotly.tools.set_credentials_file(username='xxx',api_key= 'xxx')
+    >>> #xxx是在plotly账户设置-API Keys里,Username,API Keys.
+
+
+- 打开编译器
+
+- 用编译器运行30.py代码
+
+## Image:运行提示 ##
+
+![](https://i.imgur.com/g8vetaz.png) 
+____________________________________
+
+- 运行完后会自动弹出网页
+
+- 网页里是可视化的机场数据
+
+## Image:机场数据 ##
+![](https://i.imgur.com/T5SpoUN.png) 
+
+
+
+
+
+# 4.常见问题 #
+	1. 爬取数据后导出的是.csv的文件不是存储到数据库?
+	
+> 由于Plotly可视化工具支持导入.csv表格文件，所以没有选择导入到数据据做可视化
+	
+	2. 爬取的是什么航班数据?
+> 爬取的是重庆飞往拉萨的3U8633/CSC8633航班数据
+
+	3. 该航班数据有什么亮点?
+> 5月14日，川航3U8633重庆-拉萨航班，驾驶舱右座前风挡玻璃破裂脱落，机组实施紧急下降。机组正确处置，飞机于07：46分安全备降成都双流机场，所有乘客平安落地，有序下机。
+
+	4. 为什么选择该航班?
+> 做一个项目的同时，并不仅仅为人们提供工作，而是让我们整个国家感到自豪。
+
+> 我想把这件事情传递下去，让人们铭记，3U8633机组拯救了几百位乘客。
+
+> 致敬！