一.准备知识
pandas.isnull(Series对象) 返回bool型的Series对象
同 Series对象.isnull()
DataFrame和Series都可以通过bool型的Series取值
bool型的Series和True/False进行比较，可用于数据过滤
NAN和任何值做计算时，结果都为NAN，所以在对某一列做求和，求均值等操作时，需先过滤掉缺失值
*.mean()方法可以自动过滤缺失值，再求平均
使用DataFrame的pivot_table()实现分组 聚合处理：
my_dataframe.pivot_table(index="列名1", values="列名2"/["列名2","列名3",...], aggfunc=np.聚合函数)
备注：index指定分组依据的列；values指定对哪几个列做聚合统计运算；aggfunc指定处理的numpy函数
my_dataframe.dropna(axis=1) 或 my_dataframe.dropna(axis='column') 删除存在空值的列
my_dataframe.dropna(axis=0, subset=['column1','column2',...]) 删除指定列存在空值的记录
备注：返回一个新的DataFrame,原DataFrame不会发生改变
my_dataframe.loc[行索引,列索引] 获取指定值
my_dataframe.sortvalues('列名', inplace=False, ascending=True) #默认：返回一个新的升序dataframe
my_dataframe.reset_index(drop=True) #用于排序后重置索引
二.代码示例（运行环境：python2.7）
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78import pandas as pd import numpy as np titanic_survival = pd.read_csv('titanic_train.csv') # ============================================================================= # age = titanic_survival["Age"] # # age_is_null = pd.isnull(age) # age_is_null = age.isnull() # # # bool型Series统计缺失值数量 # age_null_true = age[age_is_null] # print(len(age_null_true)) # # # 过滤缺失值 # age_null_false = age[age_is_null == False] # print(age_null_false) # print(titanic_survival.loc[age_is_null == False]) # ============================================================================= # ============================================================================= # # 计算年龄的均值 # age_null = titanic_survival["Age"].isnull() # age = titanic_survival["Age"][age_null == False] # mean_age = sum(age) / len(age) # print(mean_age) # # mean_age = titanic_survival["Age"].mean() # print(mean_age) # ============================================================================= # ============================================================================= # # 旅客类型 # passenger_classes = [1, 2, 3] # # 计算每类旅客的平均票价：分组（按照旅客类型分组）聚合（求每组的平均值） # 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) # # # 使用DataFrame的pivot_table()实现数据透视表 # fares_by_class = titanic_survival.pivot_table(index="Pclass", values="Fare", aggfunc=np.mean) # # 返回的类型为DataFrame # print(fares_by_class) # # # 对多个列做同一个聚合操作 # port_stats = titanic_survival.pivot_table(index="Embarked", values=["Survived","Fare"], aggfunc=np.sum) # print(port_stats) # ============================================================================= # ============================================================================= # drop_na_colums = titanic_survival.dropna(axis=1) # print(drop_na_colums.shape) # print(titanic_survival.shape) # # 返回一个新的DataFrame,原DataFrame不会发生改变 # new_titanic_survival = titanic_survival.dropna(axis=0, subset=["Age","Sex"]) # print(new_titanic_survival.shape) # print(titanic_survival.shape) # ============================================================================= # ============================================================================= # print(titanic_survival.loc[0,"Age"]) # print(titanic_survival.loc[100,"Pclass"]) # ============================================================================= # ============================================================================= # # sort_values()默认返回一个新的DataFrame;inplace=True时，就地修改 # new_titanic_survival = titanic_survival.sort_values("Age", ascending=False) # print(new_titanic_survival.iloc[0:3]) # reindex_titanic = new_titanic_survival.reset_index(drop=True) # print(reindex_titanic[0:3]) # ============================================================================= def hundredth_row(column): hundredth_item = column.iloc[99] return hundredth_item # 对DataFrame的每一列使用hundredth_row()函数处理 hundredth_row = titanic_survival.apply(hundredth_row) print(hundredth_row)
CSV文件网盘下载链接：https://pan.baidu.com/s/1jAOOXCobDSeBZc3h3qGybQ
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