假设你在网上搜索某个项目所需的原始数据，但坏消息是数据存在于网页中，并且没有可用于获取原始数据的API。
所以现在你必须浪费30分钟写脚本来获取数据（最后花费 2小时）。
这不难但是很浪费时间。
Pandas库有一种内置的方法，可以从名为read_html()的html页面中提取表格数据:
https://pandas.pydata.org/
import pandas as pd
tables = pd.read_html("https://apps.sandiego.gov/sdfiredispatch/")
print(tables[0])
就这么简单! Pandas可以在页面上找到所有重要的html表，并将它们作为一个新的DataFrame对象返回。
https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
输入表格0行有列标题，并要求它将基于文本的日期转换为时间对象:
import pandas as pd
calls_df, = pd.read_html("http://apps.sandiego.gov/sdfiredispatch/", header=0, parse_dates=["Call Date"])
print(calls_df)
得到：
Call Date Call Type Street Cross Streets Unit
2017-06-02 17:27:58 Medical HIGHLAND AV WIGHTMAN ST/UNIVERSITY AV E17
2017-06-02 17:27:58 Medical HIGHLAND AV WIGHTMAN ST/UNIVERSITY AV M34
2017-06-02 17:23
:51 Medical EMERSON ST LOCUST ST/EVERGREEN ST E22
2017-06-02 17:23:51 Medical EMERSON ST LOCUST ST/EVERGREEN ST M47
2017-06-02 17:23:15 Medical MARAUDER WY BARON LN/FROBISHER ST E38
2017-06-02 17:23:15 Medical MARAUDER WY BARON LN/FROBISHER ST M41
是一行代码，数据不能作为json记录可用。
import pandas as pd
calls_df, = pd.read_html("http://apps.sandiego.gov/sdfiredispatch/", header=0, parse_dates=["Call Date"])
print(calls_df.to_json(orient="records", date_format="iso"))
运行下面的代码你将得到一个漂亮的json输出(即使有适当的ISO 8601日期格式):
[
{
"Call Date": "2017-06-02T17:34:00.000Z",
"Call Type": "Medical",
"Street": "ROSECRANS ST",
"Cross Streets": "HANCOCK ST/ALLEY",
"Unit": "M21"
},
{
"Call Date": "2017-06-02T17:34:00.000Z",
"Call Type": "Medical",
"Street": "ROSECRANS ST",
"Cross Streets": "HANCOCK ST/ALLEY",
"Unit": "T20"
},
{
"Call Date": "2017-06-02T17:30:34.000Z",
"Call Type": "Medical",
"Street": "SPORTS ARENA BL",
"Cross Streets": "CAM DEL RIO WEST/EAST DR",
"Unit": "E20"
}
// etc...
]
你甚至可以将数据保存到CSV或XLS文件中:
import pandas as pd
calls_df, = pd.read_html("http://apps.sandiego.gov/sdfiredispatch/", header=0, parse_dates=["Call Date"])
calls_df.to_csv("calls.csv", index=False)
运行并双击calls.csv在电子表格中打开:
当然，Pandas还可以更简单地对数据进行过滤，分类或处理：
>>> calls_df.describe()
Call Date Call Type Street Cross Streets Unit
count 69 69 69 64 69
unique 29 2 29 27 60
top 2017-06-02 16:59:50 Medical CHANNEL WY LA SALLE ST/WESTERN ST E1
freq 5 66 5 5 2
first 2017-06-02
16:36:46 NaN NaN NaN NaN
last 2017-06-02 17:41:30 NaN NaN NaN NaN
>>> calls_df.groupby("Call Type").count()
Call Date Street Cross Streets Unit
Call Type
Medical 66 66 61 66
Traffic Accident (L1) 3 3 3 3
>>> calls_df["Unit"].unique()
array(['E46', 'MR33', 'T40', 'E201', 'M6', 'E34', 'M34', 'E29', 'M30',
'M43', 'M21', 'T20', 'E20', 'M20', 'E26', 'M32', 'SQ55', 'E1',
'M26', 'BLS4', 'E17', 'E22', 'M47', 'E38', 'M41', 'E5', 'M19',
'E28', 'M1', 'E42', 'M42', 'E23'
, 'MR9', 'PD', 'LCCNOT', 'M52',
'E45', 'M12', 'E40', 'MR40', 'M45', 'T1', 'M23', 'E14', 'M2', 'E39',
'M25', 'E8', 'M17', 'E4', 'M22', 'M37', 'E7', 'M31', 'E9', 'M39',
'SQ56', 'E10', 'M44', 'M11'], dtype=object)
