厉害了，在Pandas中用SQL来查询数据，效率超高

作者：俊欣

来源：关于数据分析与可视化

今天我们继续来讲一下Pandas和SQL之间的联用，我们其实也可以在Pandas当中使用SQL语句来筛选数据，通过Pandasql模块来实现该想法，首先我们来安装一下该模块

pip install pandasql

要是你目前正在使用jupyter notebook，也可以这么来下载

!pip install pandasql

导入数据

我们首先导入数据

import pandas as pdfrom pandasql import sqldfdf = pd.read_csv('Dummy_Sales_Data_v1.csv', sep=',')df.head()

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我们先对导入的数据集做一个初步的探索性分析，

df.info()

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<class 'pandas.core.frame.DataFrame'>RangeIndex: 9999 entries, 0 to 9998Data 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 float64 9 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

再开始进一步的数据筛选之前，我们再对数据集的列名做一个转换，代码如下

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

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<class 'pandas.core.frame.DataFrame'>RangeIndex: 9999 entries, 0 to 9998Data 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 float64 9 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筛选出若干列来

我们先尝试筛选出OrderID、Quantity、Sales_Manager、Status等若干列数据，用SQL语句应该是这么来写的

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

与Pandas模块联用的时候就这么来写

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

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SQL中带WHERE条件筛选

我们在SQL语句当中添加指定的条件进而来筛选数据，代码如下

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

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而要是条件不止一个，则用AND来连接各个条件，代码如下

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

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分组

同理我们可以调用SQL当中的GROUP BY来对筛选出来的数据进行分组，代码如下

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

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排序

而排序在SQL当中则是用ORDER BY，代码如下

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)

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数据合并

我们先创建一个数据集，用于后面两个数据集之间的合并，代码如下

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

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我们这里采用的两个数据集之间的交集，因此是INNER JOIN，代码如下

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()

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与LIMIT之间的联用

在SQL当中的LIMIT是用于限制查询结果返回的数量的，我们想看查询结果的前10个，代码如下

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

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