建立 ETL 與 ELT 管線

使用 Python 的 ETL 與 ELT

Jake Roach

Data Engineer

從 CSV 檔擷取資料

import pandas as pd

# Read in the CSV file to a DataFrame
data_frame = pd.read_csv("raw_data.csv")

# Output the first few rows
data_frame.head()

   name         num_firms   total_income
0  Advertising     58         3892.41
1  Apparel         39         5422.69
...
49 Trucking        35         17324.36

read_csv()

接受檔案路徑，回傳 DataFrame
delimiter、header、engine

.head()

輸出 DataFrame 最前面的 n 列

篩選 DataFrame

   name         num_firms   total_income
0  Advertising     58         3892.41
1  Apparel         39         5422.69
...
49 Trucking        35         17324.36


         name         num_firms
      1  Apparel         39
      37 Apparel         61

# First, by rows
data_frame.loc[data_frame["name"] == "Apparel", :]

# Then, by columns
data_frame.loc[:, ["name", "num_firms"]]

.loc

篩選 DataFrame
: 代表「全部」

將 DataFrame 寫出為 CSV 檔

# Write a DataFrame to a .csv file
data_frame.to_csv("cleaned_data.csv")

.to_csv()

接受 path，將 DataFrame 寫出到該 path
可搭配其他參數自訂輸出

其他選項，例如：

.to_json()、.to_excel()、.to_sql()

執行 SQL 查詢

data_warehouse.execute(  # Use Python clients or other tools to run SQL queries
    """
    CREATE TABLE total_sales AS 
        SELECT
            ds,
            SUM(sales)
        FROM raw_sales_data
        GROUP BY ds;
    """
)

使用如 .execute() 的工具來執行 SQL 查詢

整合所有步驟！

# Define extract(), transform(), and load() functions
...

def transform(data_frame, value):
    return data_frame.loc[data_frame["name"] == value, ["name", "num_firms"]]

# First, extract data from a .csv
extracted_data = extract(file_name="raw_data.csv")

# Then, transform the `extracted_data`
transformed_data = transform(data_frame=extracted_data, value="Apparel")

# Finally, load the `transformed_data`
load(data_frame=transformed_data, file_name="cleaned_data.csv")

一起來練習吧！

使用 Python 的 ETL 與 ELT