《Pandas数据处理》课件 2.1.1 处理订单数据中的缺失值

处理订单数据中的缺失值主讲人：赵若馨重庆市九龙坡职业教育中心问题描述订单数据表如下表表2.1.1所示。表2.1.1订单数据表order_idquantityitem_namechoice_descriptionitem_price11ChipsandFreshTomatoSalsaNaN$2.3911Izze[Clementine]$3.3911NantucketNectar[Apple]$3.3911ChipsandTomatillo-GreenChiliSalsaNaN$2.39...............18341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$11.2518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Lettu...$8.7518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$8.75问题描述现要求根据订单数据表，统计订单表格中，商品描述(choice_description)中缺失值（为“NaN”）的数量，并将其批量替换为“banana”，并输出替换后的数据表以及缺失值的数量。输出结果表2.1.2替换为banana的订单数据表order_idquantityitem_namechoice_descriptionitem_price11ChipsandFreshTomatoSalsabanana$2.3911Izze[Clementine]$3.3911NantucketNectar[Apple]$3.3911ChipsandFreshTomatoSalsabanana$2.39...............18341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$11.2518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Lettu...$8.7518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$8.75【choice_description】为空值的数量为：1246题前思考表2.1.3问题分析问题描述问题解答怎样读取csv文件中的数据？如何统计数据中的缺失值的数量？

怎样将缺失值替换为指定数据？read_csv()

isnull()，query()，shapefillna()

统计缺失值数量，并替换为指定数据。importpandasaspdorder_data=pd.read_csv(r"D:\pydata\项目二\订单数据表.csv",encoding='utf-8')①num=order_data.query('choice_description.isnull()',engine='python').shape[0]②order_data.fillna({'choice_description':'banana'},inplace=True)③print(order_data)print("【choice_description】为空值的数量为：",num)程序代码程序代码importpandasaspdorder_data=pd.read_csv(r"D:\pydata\项目二\订单数据表.csv",encoding='utf-8')①num=order_data.query('choice_description.isnull()',engine='python').shape[0]order_data.fillna({'choice_description':'banana'},inplace=True)print(order_data)print("【choice_description】为空值的数量为：",num)read_csv()读取外部csv文件中的数据并将数据以数据框的形式保存到变量order_data。程序代码表2.1.4order_data内容order_idquantityitem_namechoice_descriptionitem_price11ChipsandFreshTomatoSalsaNaN$2.3911Izze[Clementine]$3.3911NantucketNectar[Apple]$3.3911ChipsandTomatillo-GreenChiliSalsaNaN$2.39...............18341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$11.2518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Lettu...$8.7518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$8.75程序代码importpandasaspdorder_data=pd.read_csv(r"D:\pydata\项目二\订单数据表.csv",encoding='utf-8')①num=order_data.query('choice_description.isnull()',engine='python').shape[0]order_data.fillna({'choice_description':'banana'},inplace=True)print(order_data)print("【choice_description】为空值的数量为：",num)计算'choice_description'列中空值的个数。将值存入变量num②1246程序代码importpandasaspdorder_data=pd.read_csv(r"D:\pydata\项目二\订单数据表.csv",encoding='utf-8')①num=order_data.query('choice_description.isnull()',engine='python').shape[0]order_data.fillna({'choice_description':'banana'},inplace=True)print(order_data)print("【choice_description】为空值的数量为：",num)isnull()判断”choice_description”列的值是否为空；得到一个与order_data.choice_description结构相同的逻辑值序列，若choice_description中某值为空，则该序列对应位置的值为True，反之为False②程序代码choice_description.isnull()调用结果如下0True1False2False3True4False...4617False4618False4619False4620False4621FalseName:choice_description,Length:4622,dtype:bool程序代码importpandasaspdorder_data=pd.read_csv(r"D:\pydata\项目二\订单数据表.csv",encoding='utf-8')①num=order_data.query('choice_description.isnull()',engine='python').shape[0]order_data.fillna({'choice_description':'banana'},inplace=True)print(order_data)print("【choice_description】为空值的数量为：",num)query()'会选择上述结果中值为True的数据行构成一个数据框，即选择'choice_description'列值为空的行。②程序代码order_idquantityitem_namechoice_descriptionitem_price11ChipsandFreshTomatoSalsaNaN$2.3911ChipsandTomatillo-GreenChiliSalsaNaN$2.3931SideofChipsNaN$1.6951ChipsandGuacamoleNaN$4.45...............18281ChipsandGuacamoleNaN$4.4518311ChipsNaN$2.1518311BottledWaterNaN$1.5018321ChipsandGuacamoleNaN$4.45表2.1.5'choice_description'列值为空的行程序代码importpandasaspdorder_data=pd.read_csv(r"D:\pydata\项目二\订单数据表.csv",encoding='utf-8')①num=order_data.query('choice_description.isnull()',engine='python').shape[0]order_data.fillna({'choice_description':'banana'},inplace=True)print(order_data)print("【choice_description】为空值的数量为：",num)shape[0]表示数据框的行数，也就是'choice_description'列中空值的个数。shape表示数据框的形状，此处的数据框是一个二维表，shape就表示它的行列数，shape[0]表示行数，shape[1]表示列数②程序代码importpandasaspdorder_data=pd.read_csv(r"D:\pydata\项目二\订单数据表.csv",encoding='utf-8')①num=order_data.query('choice_description.isnull()',engine='python').shape[0]order_data.fillna({'choice_description':'banana'},inplace=True)③print(order_data)print("【choice_description】为空值的数量为：",num)②

使用fillna()方法将“choice_description”列的缺失值替换为“banana”；inplace=True,表示在源数据上进行修改。程序代码替换后的数据表order_idquantityitem_namechoice_descriptionitem_price11ChipsandFreshTomatoSalsabanana$2.3911Izze[Clementine]$3.3911NantucketNectar[Apple]$3.3911ChipsandFreshTomatoSalsabanana$2.39...............18341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$11.2518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Lettu...$8.7518341ChickenSaladBowl[FreshTomatoSalsa,[FajitaVegetables,Pinto...$8.75任务小结1.isnull()，找出含有空值（NaN）的数据行2.query()，查询DataFrame的列3.fillna()，填充缺失数据4.replace()，替换为指定值

order_data.fillna({'choice_description':'banana'},inplace=True)

order_data.replace(np.NaN,'bana