pandas常用预处理方法

淩亂°似流年 2023-06-19 13:25 110阅读 0赞

1.  求均值，表格中含有空值：
    
        #The result of this is that mean_age would be nan. This is because any calculations we do with a null value also result in a null value
        mean_age = sum(titanic_survival["Age"]) / len(titanic_survival["Age"])
        print (mean_age)
    
    运行结果：  
    ![在这里插入图片描述][20191206190101269.png]
2.  正确的均值
    
        age = titanic_survival["Age"]
        # print(age.loc[0:10])
        age_is_null = pd.isnull(age)
        #we have to filter out the missing values before we calculate the mean.
        good_ages = titanic_survival["Age"][age_is_null == False]
        #print good_ages
        correct_mean_age = sum(good_ages) / len(good_ages)
        print (correct_mean_age)
    
    运行结果：  
    ![在这里插入图片描述][2019120619062130.png]
3.  mean()
    
        # missing data is so common that many pandas methods automatically filter for it
        correct_mean_age = titanic_survival["Age"].mean()
        print (correct_mean_age)
    
    运行结果：  
    ![在这里插入图片描述][2019120619090412.png]
4.  计算不同类别的均值
    
        #mean fare for each class
        passenger_classes = [1, 2, 3]
        fares_by_class = { }
        for this_class in passenger_classes:
            pclass_rows = titanic_survival[titanic_survival["Pclass"] == this_class]
            pclass_fares = pclass_rows["Fare"]
            fare_for_class = pclass_fares.mean()
            fares_by_class[this_class] = fare_for_class
        print fares_by_class
    
    运行结果：  
    ![在这里插入图片描述][20191206191313452.png]
5.  数据透视表 获救的比例
    
        #index tells the method which column to group by
        #values is the column that we want to apply the calculation to
        #aggfunc specifies the calculation we want to perform
        passenger_survival = titanic_survival.pivot_table(index="Pclass", values="Survived", aggfunc=np.mean)
        print (passenger_survival)
    
    运行结果：  
    ![在这里插入图片描述][2019120619170991.png]
6.  平均年龄
    
        passenger_age = titanic_survival.pivot_table(index="Pclass", values="Age")
        print(passenger_age)
    
    运行结果：  
    ![在这里插入图片描述][20191206191842656.png]
7.  一个量和两个量之间的关系
    
        port_stats = titanic_survival.pivot_table(index="Embarked", values=["Fare","Survived"], aggfunc=np.sum)
        print(port_stats)
    
    运行结果：  
    ![在这里插入图片描述][20191206192138583.png]
8.  dropna
    
        #specifying axis=1 or axis='columns' will drop any columns that have null values
        drop_na_columns = titanic_survival.dropna(axis=1)
        new_titanic_survival = titanic_survival.dropna(axis=0,subset=["Age", "Sex"])
        #print new_titanic_survival
9.  loc函数
    
        row_index_83_age = titanic_survival.loc[83,"Age"]
        row_index_766_pclass = titanic_survival.loc[766,"Pclass"]
        print (row_index_83_age)
        print (row_index_766_pclass)
    
    运行结果：  
    ![在这里插入图片描述][20191206192719645.png]

[20191206190101269.png]: https://img-blog.csdnimg.cn/20191206190101269.png
[2019120619062130.png]: https://img-blog.csdnimg.cn/2019120619062130.png
[2019120619090412.png]: https://img-blog.csdnimg.cn/2019120619090412.png
[20191206191313452.png]: https://img-blog.csdnimg.cn/20191206191313452.png
[2019120619170991.png]: https://img-blog.csdnimg.cn/2019120619170991.png
[20191206191842656.png]: https://img-blog.csdnimg.cn/20191206191842656.png
[20191206192138583.png]: https://img-blog.csdnimg.cn/20191206192138583.png
[20191206192719645.png]: https://img-blog.csdnimg.cn/20191206192719645.png