今天我們繼續來講一下Pandas和SQL之間的聯用，我們其實也可以在Pandas當中使用SQL語句來篩選數據，通過Pandasql模塊來實現該想法，首先我們來安裝一下該模塊

pip?install?pandasql

要是你目前正在使用jupyter notebook，也可以這么來下載

!pip?install?pandasql

導入數據

我們首先導入數據

import?pandas?as?pd from?pandasql?import?sqldf df?=?pd.read_csv("Dummy_Sales_Data_v1.csv",?sep=",") df.head()

output

我們先對導入的數據集做一個初步的探索性分析，

df.info()

output

<class?'pandas.core.frame.DataFrame'> RangeIndex:?9999?entries,?0?to?9998 Data?columns?(total?12?columns):#???Column???????????????Non-Null?Count??Dtype?? ---??------???????????????--------------??-----??0???OrderID??????????????9999?non-null???int64??1???Quantity?????????????9999?non-null???int64??2???UnitPrice(USD)???????9999?non-null???int64??3???Status???????????????9999?non-null???object?4???OrderDate????????????9999?non-null???object?5???Product_Category?????9963?non-null???object?6???Sales_Manager????????9999?non-null???object?7???Shipping_Cost(USD)???9999?non-null???int64??8???Delivery_Time(Days)??9948?non-null???float649???Shipping_Address?????9999?non-null???object?10??Product_Code?????????9999?non-null???object?11??OrderCode????????????9999?non-null???int64?? dtypes:?float64(1),?int64(5),?object(6) memory?usage:?937.5+?KB

再開始進一步的數據篩選之前，我們再對數據集的列名做一個轉換，代碼如下

df.rename(columns={"Shipping_Cost(USD)":"ShippingCost_USD","UnitPrice(USD)":"UnitPrice_USD","Delivery_Time(Days)":"Delivery_Time_Days"},inplace=True) df.info()

output

<class?'pandas.core.frame.DataFrame'> RangeIndex:?9999?entries,?0?to?9998 Data?columns?(total?12?columns):#???Column??????????????Non-Null?Count??Dtype?? ---??------??????????????--------------??-----??0???OrderID?????????????9999?non-null???int64??1???Quantity????????????9999?non-null???int64??2???UnitPrice_USD???????9999?non-null???int64??3???Status??????????????9999?non-null???object?4???OrderDate???????????9999?non-null???object?5???Product_Category????9963?non-null???object?6???Sales_Manager???????9999?non-null???object?7???ShippingCost_USD????9999?non-null???int64??8???Delivery_Time_Days??9948?non-null???float649???Shipping_Address????9999?non-null???object?10??Product_Code????????9999?non-null???object?11??OrderCode???????????9999?non-null???int64?? dtypes:?float64(1),?int64(5),?object(6) memory?usage:?937.5+?KB

用SQL篩選出若干列來

我們先嘗試篩選出OrderID、Quantity、Sales_Manager、Status等若干列數據，用SQL語句應該是這么來寫的

SELECT?OrderID,?Quantity,?Sales_Manager,?\ Status,?Shipping_Address,?ShippingCost_USD?\ FROM?df

與Pandas模塊聯用的時候就這么來寫

query?=?"SELECT?OrderID,?Quantity,?Sales_Manager,\ Status,?Shipping_Address,?ShippingCost_USD?\ FROM?df"df_orders?=?sqldf(query) df_orders.head()

output

SQL中帶WHERE條件篩選

我們在SQL語句當中添加指定的條件進而來篩選數據，代碼如下

query?=?"SELECT?*?\FROM?df_orders?\WHERE?Shipping_Address?=?'Kenya'"df_kenya?=?sqldf(query) df_kenya.head()

output

而要是條件不止一個，則用AND來連接各個條件，代碼如下

query?=?"SELECT?*?\FROM?df_orders?\WHERE?Shipping_Address?=?'Kenya'?\AND?Quantity?<?40?\AND?Status?IN?('Shipped',?'Delivered')" df_kenya?=?sqldf(query) df_kenya.head()

output

分組

同理我們可以調用SQL當中的GROUP BY來對篩選出來的數據進行分組，代碼如下

query?=?"SELECT?Shipping_Address,?\COUNT(OrderID)?AS?Orders?\FROM?df_orders?\GROUP?BY?Shipping_Address"df_group?=?sqldf(query) df_group.head(10)

output

排序

而排序在SQL當中則是用ORDER BY，代碼如下

query?=?"SELECT?Shipping_Address,?\COUNT(OrderID)?AS?Orders?\FROM?df_orders?\GROUP?BY?Shipping_Address?\ORDER?BY?Orders"df_group?=?sqldf(query) df_group.head(10)

output

數據合并

我們先創建一個數據集，用于后面兩個數據集之間的合并，代碼如下

query?=?"SELECT?OrderID,\Quantity,?\Product_Code,?\Product_Category,?\UnitPrice_USD?\FROM?df" df_products?=?sqldf(query) df_products.head()

output

我們這里采用的兩個數據集之間的交集，因此是INNER JOIN，代碼如下

query?=?"SELECT?T1.OrderID,?\T1.Shipping_Address,?\T2.Product_Category?\FROM?df_orders?T1\INNER?JOIN?df_products?T2\ON?T1.OrderID?=?T2.OrderID"df_combined?=?sqldf(query) df_combined.head()

output

與LIMIT之間的聯用

在SQL當中的LIMIT是用于限制查詢結果返回的數量的，我們想看查詢結果的前10個，代碼如下

query?=?"SELECT?OrderID,?Quantity,?Sales_Manager,?\? Status,?Shipping_Address,?\ ShippingCost_USD?FROM?df?LIMIT?10"df_orders_limit?=?sqldf(query) df_orders_limit

output

END

推薦閱讀牛逼！Python常用數據類型的基本操作（長文系列第①篇） 牛逼！Python的判斷、循環和各種表達式（長文系列第②篇）牛逼！Python函數和文件操作（長文系列第③篇）牛逼！Python錯誤、異常和模塊（長文系列第④篇）

總結

以上是生活随笔為你收集整理的厉害了，在Pandas中用SQL来查询数据，效率超高的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。