pandas常用预处理方法

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：
   

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：
   

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：
   

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：
   

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：
   

6. 平均年龄

   passenger_age = titanic_survival.pivot_table(index="Pclass", values="Age")
   print(passenger_age)
   

   运行结果：
   

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：
   

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：