Python抓取網頁資料的終極辦法！你值得擁有！

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假設你在網上搜尋某個項目所需的未經處理資料，但壞訊息是資料存在於網頁中，並且沒有可用於擷取未經處理資料的API。這時，你可以這樣解決——

 

import pandas as pdtables = pd.read_html("https://apps.sandiego.gov/sdfiredispatch/")print(tables[0])

就這麼簡單! Pandas可以在頁面上找到所有重要的html表，並將它們作為一個新的DataFrame對象返回。

輸入表格0行有欄位標題，並要求它將基於文本的日期轉換為時間對象:

import pandas as pdcalls_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

這隻是一行代碼，資料不能作為json記錄可用。

import pandas as pdcalls_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"))

運行下面的代碼你將得到一個漂亮的json輸出(即使有適當的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...]

你甚至可以將資料儲存到CSV或XLS檔案中:

import pandas as pdcalls_df, = pd.read_html("http://apps.sandiego.gov/sdfiredispatch/", header=0, parse_dates=["Call Date"])calls_df.to_csv("calls.csv", index=False)

運行並雙擊calls.csv在試算表中開啟:

 

當然，Pandas還可以更簡單地對資料進行過濾，分類或處理：

>>> calls_df.describe() Call Date Call Type Street Cross Streets Unitcount 69 69 69 64 69unique 29 2 29 27 60top 2017-06-02 16:59:50 Medical CHANNEL WY LA SALLE ST/WESTERN ST E1freq 5 66 5 5 2first 2017-06-02 16:36:46 NaN NaN NaN NaNlast 2017-06-02 17:41:30 NaN NaN NaN NaN>>> calls_df.groupby("Call Type").count() Call Date Street Cross Streets UnitCall TypeMedical 66 66 61 66Traffic 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)

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Python抓取網頁資料的終極辦法！你值得擁有！