Date	Latitude	Longitude	Depth	Type
01/02/1965	19.246	145.616	131.6	Earthquake
01/04/1965	1.863	127.352	80.0	Earthquake
01/05/1965	-20.579	-173.972	20.0	Earthquake
01/08/1965	-59.076	-23.557	15.0	Earthquake
01/09/1965	11.938	126.427	15.0	Earthquake

#1、分組統(tǒng)計組內個數(shù)
df.groupby(['date','Type'])
	.size()

#2、分組統(tǒng)計計算‘Depth'的基本數(shù)據(jù)分布
df.groupby(['date','type'])['Depth']
	.describe()
	
#3、分組統(tǒng)計計算‘Latitude'的均值	
df.groupby(['date','type'])
	.agg({'Latitude':'mean'})

#分組統(tǒng)計計算兩列數(shù)據(jù)的均值
df.groupby(['date','type'])
	.agg(la_mean = ('Latitude','mean'),
 		 lo_mean = ('Longitude','mean'),
 		 la_range = ('depth',lambda x:x.max() - x.min()))

#4、分組然后對每組去重，然后匯總去重后的個數(shù)
df.groupby('date')
	.apply(lambda x: x.drop_duplicates('Type')['Type']
		.count())

#5、分組，然后按照‘Latitude'降序，并取出每組的前3個樣本。
df.groupby(['date'])
	.apply(lambda x: x.sort_values('Latitude',ascending = False)
			.head(3))

#6、分組，然后匯總計算兩列數(shù)據(jù),再進行分組，取出每一組‘la_sum'的最大值
df.groupby(['date','type'])
	.agg(la_sum = ('Latitude','sum'),
		 lo_sum = ('Longitude','sum'))
		 	.groupby(['date'])
				.apply(lambda x:x.sort_values('la_sum ',ascending = False).head(1))

#分組，然后按照type排序，再分組，取出前10匯總和后10匯總，然后再計算兩者的比率
df.groupby('date')
	.apply(lambda x:x.sort_values('type'))
		.reset_index(drop = True).groupby('date')
			.agg(tail10 = ('depth',lambda x:x.head(10).sum()),
				 top10 = ('depth',lambda x:x.tail(10).sum()))
					.apply(lambda x:x['top10']/x['tail10'],axis = 1)