如何做购物券网站,wordpress文字摘要,nginx与WordPress,网站百科怎么做1、Pandas 连接
Pandas 连接的操作实例 Pandas具有与SQL等关系数据库非常相似的功能齐全的高性能内存中连接操作。 Pandas提供单个功能merge作为DataFrame对象之间所有标准数据库联接操作的入口点
pd.merge(left, right, howinner, onNone, left_onNone, right_onNone,left_i…1、Pandas 连接
Pandas 连接的操作实例 Pandas具有与SQL等关系数据库非常相似的功能齐全的高性能内存中连接操作。 Pandas提供单个功能merge作为DataFrame对象之间所有标准数据库联接操作的入口点
pd.merge(left, right, howinner, onNone, left_onNone, right_onNone,left_indexFalse, right_indexFalse, sortTrue)在这里我们使用了以下参数 left − 一个DataFrame对象。 right − 另一个DataFrame对象。 on − 列名加入上。必须在左右DataFrame对象中都找到。 left_on − 左侧DataFrame中的列用作键。可以是列名也可以是长度等于DataFrame长度的数组。 right_on − 右侧DataFrame中的列用作键。可以是列名也可以是长度等于DataFrame长度的数组。 left_index − 如果为True则使用左侧DataFrame的索引行标签作为其连接键。如果DataFrame具有MultiIndex分层则级别数必须与右侧DataFrame中的连接键数匹配。 right_index − 相同的使用作为left_index为正确的数据帧。 how − “左”“右”“外”“内”之一。默认为内部。每种方法已在下面描述。 sort − 排序的结果数据框中加入字典顺序按键。默认情况下为True在许多情况下设置为False将大大提高性能。 现在让我们创建两个不同的DataFrame并对其执行合并操作。
import pandas as pd
left pd.DataFrame({id:[1,2,3,4,5],Name: [Alex, Amy, Allen, Alice, Ayoung],subject_id:[sub1,sub2,sub4,sub6,sub5]})
right pd.DataFrame({id:[1,2,3,4,5],Name: [Billy, Brian, Bran, Bryce, Betty],subject_id:[sub2,sub4,sub3,sub6,sub5]})
print(left)
print(right)运行结果 id Name subject_id
0 1 Alex sub1
1 2 Amy sub2
2 3 Allen sub4
3 4 Alice sub6
4 5 Ayoung sub5id Name subject_id
0 1 Billy sub2
1 2 Brian sub4
2 3 Bran sub3
3 4 Bryce sub6
4 5 Betty sub51.1、在一个键上合并两个数据框
import pandas as pd
left pd.DataFrame({id: [1, 2, 3, 4, 5],Name: [Alex, Amy, Allen, Alice, Ayoung],subject_id: [sub1, sub2, sub4, sub6, sub5]})
right pd.DataFrame({id: [1, 2, 3, 4, 5],Name: [Billy, Brian, Bran, Bryce, Betty],subject_id: [sub2, sub4, sub3, sub6, sub5]})
print(pd.merge(left, right, onid))运行结果 id Name_x subject_id_x Name_y subject_id_y
0 1 Alex sub1 Billy sub2
1 2 Amy sub2 Brian sub4
2 3 Allen sub4 Bran sub3
3 4 Alice sub6 Bryce sub6
4 5 Ayoung sub5 Betty sub51.2、在多个键上合并两个数据框
import pandas as pd
left pd.DataFrame({id:[1,2,3,4,5],Name: [Alex, Amy, Allen, Alice, Ayoung],subject_id:[sub1,sub2,sub4,sub6,sub5]})
right pd.DataFrame({id:[1,2,3,4,5],Name: [Billy, Brian, Bran, Bryce, Betty],subject_id:[sub2,sub4,sub3,sub6,sub5]})
print(pd.merge(left,right,on[id,subject_id]))运行结果 id Name_x subject_id Name_y
0 4 Alice sub6 Bryce
1 5 Ayoung sub5 Betty1.3、合并使用“how”参数
合并的how参数指定如何确定要在结果表中包括哪些键。如果左侧或右侧表中均未出现组合键则联接表中的值为NA。 这里的一个总结如何选择和他们的SQL等价的名字:
1.4、左连接
import pandas as pd
left pd.DataFrame({id:[1,2,3,4,5],Name: [Alex, Amy, Allen, Alice, Ayoung],subject_id:[sub1,sub2,sub4,sub6,sub5]})
right pd.DataFrame({id:[1,2,3,4,5],Name: [Billy, Brian, Bran, Bryce, Betty],subject_id:[sub2,sub4,sub3,sub6,sub5]})
print(pd.merge(left, right, onsubject_id, howleft))运行结果 id_x Name_x subject_id id_y Name_y
0 1 Alex sub1 NaN NaN
1 2 Amy sub2 1.0 Billy
2 3 Allen sub4 2.0 Brian
3 4 Alice sub6 4.0 Bryce
4 5 Ayoung sub5 5.0 Betty1.5、右连接
import pandas as pd
left pd.DataFrame({id:[1,2,3,4,5],Name: [Alex, Amy, Allen, Alice, Ayoung],subject_id:[sub1,sub2,sub4,sub6,sub5]})
right pd.DataFrame({id:[1,2,3,4,5],Name: [Billy, Brian, Bran, Bryce, Betty],subject_id:[sub2,sub4,sub3,sub6,sub5]})
print(pd.merge(left, right, onsubject_id, howright))运行结果 id_x Name_x subject_id id_y Name_y
0 2.0 Amy sub2 1 Billy
1 3.0 Allen sub4 2 Brian
2 NaN NaN sub3 3 Bran
3 4.0 Alice sub6 4 Bryce
4 5.0 Ayoung sub5 5 Betty1.6、外连接
import pandas as pdleft pd.DataFrame({id:[1,2,3,4,5],Name: [Alex, Amy, Allen, Alice, Ayoung],subject_id:[sub1,sub2,sub4,sub6,sub5]})right pd.DataFrame({id:[1,2,3,4,5],Name: [Billy, Brian, Bran, Bryce, Betty],subject_id:[sub2,sub4,sub3,sub6,sub5]})print(pd.merge(left, right, howouter, onsubject_id))运行结果 id_x Name_x subject_id id_y Name_y
0 1.0 Alex sub1 NaN NaN
1 2.0 Amy sub2 1.0 Billy
2 NaN NaN sub3 3.0 Bran
3 3.0 Allen sub4 2.0 Brian
4 5.0 Ayoung sub5 5.0 Betty
5 4.0 Alice sub6 4.0 Bryce1.7、内连接
连接将在索引上执行。联接操作接受调用它的对象。因此a.join(b)不等于b.join(a)。
import pandas as pdleft pd.DataFrame({id:[1,2,3,4,5],Name: [Alex, Amy, Allen, Alice, Ayoung],subject_id:[sub1,sub2,sub4,sub6,sub5]})right pd.DataFrame({id:[1,2,3,4,5],Name: [Billy, Brian, Bran, Bryce, Betty],subject_id:[sub2,sub4,sub3,sub6,sub5]})print(pd.merge(left, right, onsubject_id, howinner))运行结果 id_x Name_x subject_id id_y Name_y
0 2 Amy sub2 1 Billy
1 3 Allen sub4 2 Brian
2 4 Alice sub6 4 Bryce
3 5 Ayoung sub5 5 Betty
