Pandas merge合并操作的實(shí)現(xiàn)
Pandas 提供的 merge() 函數(shù)能夠進(jìn)行高效的合并操作，這與 SQL 關(guān)系型數(shù)據(jù)庫的 join用法非常相似。從字面意思上不難理解，merge 翻譯為“合并”，指的是將兩個(gè) DataFrame 數(shù)據(jù)表按照指定的規(guī)則進(jìn)行連接，最后拼接成一個(gè)新的 DataFrame 數(shù)據(jù)表。
merge() 函數(shù)的法格式如下：
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,left_index=False, right_index=False, sort=True,suffixes=('_x', '_y'), copy=True)
參數(shù)說明，如下表所示：
| 參數(shù)名稱 | 說明 |
|---|---|
| left/right | 兩個(gè)不同的 DataFrame 對(duì)象。 |
| on | 指定用于連接的鍵（即列標(biāo)簽的名字），該鍵必須同時(shí)存在于左右兩個(gè) DataFrame 中，如果沒有指定，并且其他參數(shù)也未指定， 那么將會(huì)以兩個(gè) DataFrame 的列名交集做為連接鍵。 |
| left_on | 指定左側(cè) DataFrame 中作連接鍵的列名。該參數(shù)在左、右列標(biāo)簽名不相同，但表達(dá)的含義相同時(shí)非常有用。 |
| right_on | 指定左側(cè) DataFrame 中作連接鍵的列名。 |
| left_index | 布爾參數(shù)，默認(rèn)為 False。如果為 True 則使用左側(cè) DataFrame 的行索引作為連接鍵，若 DataFrame 具有多層 索引(MultiIndex)，則層的數(shù)量必須與連接鍵的數(shù)量相等。 |
| right_index | 布爾參數(shù)，默認(rèn)為 False。如果為 True 則使用左側(cè) DataFrame 的行索引作為連接鍵。 |
| how | 要執(zhí)行的合并類型，從 {‘left’, ‘right’, ‘outer’, ‘inner’} 中取值，默認(rèn)為“inner”內(nèi)連接。 |
| sort | 布爾值參數(shù)，默認(rèn)為True，它會(huì)將合并后的數(shù)據(jù)進(jìn)行排序；若設(shè)置為 False，則按照 how 給定的參數(shù)值進(jìn)行排序。 |
| suffixes | 字符串組成的元組。當(dāng)左右 DataFrame 存在相同列名時(shí)，通過該參數(shù)可以在相同的列名后附加后綴名，默認(rèn)為(‘_x’,‘_y’)。 |
| copy | 默認(rèn)為 True，表示對(duì)數(shù)據(jù)進(jìn)行復(fù)制。 |
注意：Pandas 庫的 merge() 支持各種內(nèi)外連接，與其相似的還有 join() 函數(shù)（默認(rèn)為左連接）。
準(zhǔn)備兩個(gè)不同的DataFrame，這里從文件中讀取：
文件empdata.csv:
EMPNO,ENAME,JOB,MGR,HIREDATE,SAL,COMM,DEPTNO
7369,SMITH,CLERK,7902.0,1980-12-17,800,,20
7499,ALLEN,SALESMAN,7698.0,1981-02-20,1600,300.0,30
7521,WARD,SALESMAN,7698.0,1981-02-22,1250,500.0,30
7566,JONES,MANAGER,7839.0,1981-04-02,2975,,20
7654,MARTIN,SALESMAN,7698.0,1981-09-28,1250,1400.0,30
7698,BLAKE,MANAGER,7839.0,1981-05-01,2850,,30
7782,CLARK,MANAGER,7839.0,1981-06-09,2450,,10
7788,SCOTT,ANALYST,7566.0,1987-04-19,3000,,20
7839,KING,PRESIDENT,,1981-11-17,5000,,10
7844,TURNER,SALESMAN,7698.0,1981-09-08,1500,0.0,30
7876,ADAMS,CLERK,7788.0,1987-05-23,1100,,20
7900,JAMES,CLERK,7698.0,1981-12-03,950,,30
7902,FORD,ANALYST,7566.0,1981-12-03,3000,,20
7934,MILLER,CLERK,7782.0,1982-01-23,1300,,10
文件deptdata.csv:
DEPTNO,DNAME,LOC
10,ACCOUNTING,NEW YORK
20,RESEARCH,DALLAS
30,SALES,CHICAGO
40,OPERATIONS,BOSTON
下面創(chuàng)建兩個(gè)不同的 DataFrame，然后對(duì)它們進(jìn)行合并操作：
import pandas as pd
import numpy as np
df_emp = pd.read_csv('C:\\Users\\qwy\Desktop\data\\empdata.csv')
df_dept = pd.read_csv('C:\\Users\\qwy\Desktop\data\\deptdata.csv')
print("員工表中的df數(shù)據(jù)：\n",df_emp)
print("部門表中的df數(shù)據(jù)：\n",df_dept)
輸出如下：
員工表中的df數(shù)據(jù)：
EMPNO ENAME JOB MGR HIREDATE SAL COMM DEPTNO
0 7369 SMITH CLERK 7902.0 1980-12-17 800 NaN 20
1 7499 ALLEN SALESMAN 7698.0 1981-02-20 1600 300.0 30
2 7521 WARD SALESMAN 7698.0 1981-02-22 1250 500.0 30
3 7566 JONES MANAGER 7839.0 1981-04-02 2975 NaN 20
4 7654 MARTIN SALESMAN 7698.0 1981-09-28 1250 1400.0 30
5 7698 BLAKE MANAGER 7839.0 1981-05-01 2850 NaN 30
6 7782 CLARK MANAGER 7839.0 1981-06-09 2450 NaN 10
7 7788 SCOTT ANALYST 7566.0 1987-04-19 3000 NaN 20
8 7839 KING PRESIDENT NaN 1981-11-17 5000 NaN 10
9 7844 TURNER SALESMAN 7698.0 1981-09-08 1500 0.0 30
10 7876 ADAMS CLERK 7788.0 1987-05-23 1100 NaN 20
11 7900 JAMES CLERK 7698.0 1981-12-03 950 NaN 30
12 7902 FORD ANALYST 7566.0 1981-12-03 3000 NaN 20
13 7934 MILLER CLERK 7782.0 1982-01-23 1300 NaN 10
部門表中的df數(shù)據(jù)：
DEPTNO DNAME LOC
0 10 ACCOUNTING NEW YORK
1 20 RESEARCH DALLAS
2 30 SALES CHICAGO
3 40 OPERATIONS BOSTON
1) 在單個(gè)鍵上進(jìn)行合并操作
通過 on 參數(shù)指定一個(gè)連接鍵，然后對(duì)上述 DataFrame 進(jìn)行合并操作：
import pandas as pd
import numpy as np
df_emp_left = pd.read_csv('C:\\Users\\qwy\Desktop\data\\empdata.csv')
df_dept_right = pd.read_csv('C:\\Users\\qwy\Desktop\data\\deptdata.csv')
# 通過on指定合并的主鍵
print(pd.merge(df_emp_left,df_dept_right,on="DEPTNO"))
輸出結(jié)果：
EMPNO ENAME JOB MGR ... COMM DEPTNO DNAME LOC
0 7782 CLARK MANAGER 7839.0 ... NaN 10 ACCOUNTING NEW YORK
1 7839 KING PRESIDENT NaN ... NaN 10 ACCOUNTING NEW YORK
2 7934 MILLER CLERK 7782.0 ... NaN 10 ACCOUNTING NEW YORK
3 7369 SMITH CLERK 7902.0 ... NaN 20 RESEARCH DALLAS
4 7566 JONES MANAGER 7839.0 ... NaN 20 RESEARCH DALLAS
5 7788 SCOTT ANALYST 7566.0 ... NaN 20 RESEARCH DALLAS
6 7876 ADAMS CLERK 7788.0 ... NaN 20 RESEARCH DALLAS
7 7902 FORD ANALYST 7566.0 ... NaN 20 RESEARCH DALLAS
8 7499 ALLEN SALESMAN 7698.0 ... 300.0 30 SALES CHICAGO
9 7521 WARD SALESMAN 7698.0 ... 500.0 30 SALES CHICAGO
10 7654 MARTIN SALESMAN 7698.0 ... 1400.0 30 SALES CHICAGO
11 7698 BLAKE MANAGER 7839.0 ... NaN 30 SALES CHICAGO
12 7844 TURNER SALESMAN 7698.0 ... 0.0 30 SALES CHICAGO
13 7900 JAMES CLERK 7698.0 ... NaN 30 SALES CHICAGO[14 rows x 10 columns]
2) 在多個(gè)鍵上進(jìn)行合并操作
下面示例，指定多個(gè)鍵來合并上述兩個(gè) DataFrame 對(duì)象：(這里使用自定義數(shù)據(jù)了)
import pandas as pd
left = pd.DataFrame({
'id':[1,2,3,4],
'Name': ['Smith', 'Maiki', 'Hunter', 'Hilen'],
'subject_id':['sub1','sub2','sub4','sub6']})
right = pd.DataFrame({
'id':[1,2,3,4],
'Name': ['Bill', 'Lucy', 'Jack', 'Mike'],
'subject_id':['sub2','sub4','sub3','sub6']})
print(pd.merge(left,right,on=['id','subject_id']))
輸出結(jié)果：
id Name_x subject_id Name_y
0 4 Hilen sub6 Mike
使用how參數(shù)合并
通過how參數(shù)可以確定 DataFrame 中要包含哪些鍵，如果在左表、右表都不存的鍵，那么合并后該鍵對(duì)應(yīng)的值為 NaN。為了便于大家學(xué)習(xí)，我們將 how 參數(shù)和與其等價(jià)的 SQL 語句做了總結(jié)：
| Merge方法 | 等效 SQL | 描述 |
|---|---|---|
| left | LEFT OUTER JOIN | 使用左側(cè)對(duì)象的key |
| right | RIGHT OUTER JOIN | 使用右側(cè)對(duì)象的key |
| outer | FULL OUTER JOIN | 使用左右兩側(cè)所有key的并集 |
| inner | INNER JOIN | 使用左右兩側(cè)key的交集 |
1) left join
import pandas as pd
df_emp_left = pd.read_csv('C:\\Users\\qwy\Desktop\data\\empdata.csv')
df_dept_right = pd.read_csv('C:\\Users\\qwy\Desktop\data\\deptdata.csv')
# 為了演示，這里將雇員7369的部門刪除了，運(yùn)行如下：
print(pd.merge(df_emp_left,df_dept_right,on="DEPTNO",how="left"))
輸出結(jié)果：能發(fā)現(xiàn)7369的DEPTNO，DNAME,LOC都為NaN
EMPNO ENAME JOB MGR ... COMM DEPTNO DNAME LOC
0 7369 SMITH CLERK 7902.0 ... NaN NaN NaN NaN
1 7499 ALLEN SALESMAN 7698.0 ... 300.0 30.0 SALES CHICAGO
2 7521 WARD SALESMAN 7698.0 ... 500.0 30.0 SALES CHICAGO
3 7566 JONES MANAGER 7839.0 ... NaN 20.0 RESEARCH DALLAS
4 7654 MARTIN SALESMAN 7698.0 ... 1400.0 30.0 SALES CHICAGO
5 7698 BLAKE MANAGER 7839.0 ... NaN 30.0 SALES CHICAGO
6 7782 CLARK MANAGER 7839.0 ... NaN 10.0 ACCOUNTING NEW YORK
7 7788 SCOTT ANALYST 7566.0 ... NaN 20.0 RESEARCH DALLAS
8 7839 KING PRESIDENT NaN ... NaN 10.0 ACCOUNTING NEW YORK
9 7844 TURNER SALESMAN 7698.0 ... 0.0 30.0 SALES CHICAGO
10 7876 ADAMS CLERK 7788.0 ... NaN 20.0 RESEARCH DALLAS
11 7900 JAMES CLERK 7698.0 ... NaN 30.0 SALES CHICAGO
12 7902 FORD ANALYST 7566.0 ... NaN 20.0 RESEARCH DALLAS
13 7934 MILLER CLERK 7782.0 ... NaN 10.0 ACCOUNTING NEW YORK[14 rows x 10 columns]
2) right join
import pandas as pd
df_emp_left = pd.read_csv('C:\\Users\\qwy\Desktop\data\\empdata.csv')
df_dept_right = pd.read_csv('C:\\Users\\qwy\Desktop\data\\deptdata.csv')
# 部門40因?yàn)闆]有員工，所以對(duì)應(yīng)員工的信息全部為NaN
print(pd.merge(df_emp_left,df_dept_right,on="DEPTNO",how="right"))
輸出結(jié)果：(部門40因?yàn)闆]有員工，所以對(duì)應(yīng)員工的信息全部為NaN)
EMPNO ENAME JOB MGR ... COMM DEPTNO DNAME LOC
0 7782.0 CLARK MANAGER 7839.0 ... NaN 10 ACCOUNTING NEW YORK
1 7839.0 KING PRESIDENT NaN ... NaN 10 ACCOUNTING NEW YORK
2 7934.0 MILLER CLERK 7782.0 ... NaN 10 ACCOUNTING NEW YORK
3 7369.0 SMITH CLERK 7902.0 ... NaN 20 RESEARCH DALLAS
4 7566.0 JONES MANAGER 7839.0 ... NaN 20 RESEARCH DALLAS
5 7788.0 SCOTT ANALYST 7566.0 ... NaN 20 RESEARCH DALLAS
6 7876.0 ADAMS CLERK 7788.0 ... NaN 20 RESEARCH DALLAS
7 7902.0 FORD ANALYST 7566.0 ... NaN 20 RESEARCH DALLAS
8 7499.0 ALLEN SALESMAN 7698.0 ... 300.0 30 SALES CHICAGO
9 7521.0 WARD SALESMAN 7698.0 ... 500.0 30 SALES CHICAGO
10 7654.0 MARTIN SALESMAN 7698.0 ... 1400.0 30 SALES CHICAGO
11 7698.0 BLAKE MANAGER 7839.0 ... NaN 30 SALES CHICAGO
12 7844.0 TURNER SALESMAN 7698.0 ... 0.0 30 SALES CHICAGO
13 7900.0 JAMES CLERK 7698.0 ... NaN 30 SALES CHICAGO
14 NaN NaN NaN NaN ... NaN 40 OPERATIONS BOSTON[15 rows x 10 columns]
3) outer join(并集)
import pandas as pd
df_emp_left = pd.read_csv('C:\\Users\\qwy\Desktop\data\\empdata.csv')
df_dept_right = pd.read_csv('C:\\Users\\qwy\Desktop\data\\deptdata.csv')
# 部門40因?yàn)闆]有員工，所以對(duì)應(yīng)員工的信息全部為NaN,又因?yàn)?369沒對(duì)應(yīng)的部門，所以部門信息全部為Nan
print(pd.merge(df_emp_left,df_dept_right,on="DEPTNO",how="outer"))
輸出結(jié)果：
EMPNO ENAME JOB MGR ... COMM DEPTNO DNAME LOC
0 7369.0 SMITH CLERK 7902.0 ... NaN NaN NaN NaN
1 7499.0 ALLEN SALESMAN 7698.0 ... 300.0 30.0 SALES CHICAGO
2 7521.0 WARD SALESMAN 7698.0 ... 500.0 30.0 SALES CHICAGO
3 7654.0 MARTIN SALESMAN 7698.0 ... 1400.0 30.0 SALES CHICAGO
4 7698.0 BLAKE MANAGER 7839.0 ... NaN 30.0 SALES CHICAGO
5 7844.0 TURNER SALESMAN 7698.0 ... 0.0 30.0 SALES CHICAGO
6 7900.0 JAMES CLERK 7698.0 ... NaN 30.0 SALES CHICAGO
7 7566.0 JONES MANAGER 7839.0 ... NaN 20.0 RESEARCH DALLAS
8 7788.0 SCOTT ANALYST 7566.0 ... NaN 20.0 RESEARCH DALLAS
9 7876.0 ADAMS CLERK 7788.0 ... NaN 20.0 RESEARCH DALLAS
10 7902.0 FORD ANALYST 7566.0 ... NaN 20.0 RESEARCH DALLAS
11 7782.0 CLARK MANAGER 7839.0 ... NaN 10.0 ACCOUNTING NEW YORK
12 7839.0 KING PRESIDENT NaN ... NaN 10.0 ACCOUNTING NEW YORK
13 7934.0 MILLER CLERK 7782.0 ... NaN 10.0 ACCOUNTING NEW YORK
14 NaN NaN NaN NaN ... NaN 40.0 OPERATIONS BOSTON[15 rows x 10 columns]
4) inner join(交集)
import pandas as pd
df_emp_left = pd.read_csv('C:\\Users\\qwy\Desktop\data\\empdata.csv')
df_dept_right = pd.read_csv('C:\\Users\\qwy\Desktop\data\\deptdata.csv')
# 部門40因?yàn)闆]有員工，,又因?yàn)?369沒對(duì)應(yīng)的部門，所以部門40和員工7369沒顯示
print(pd.merge(df_emp_left,df_dept_right,on="DEPTNO",how="inner"))
輸出結(jié)果：
EMPNO ENAME JOB MGR ... COMM DEPTNO DNAME LOC
0 7499 ALLEN SALESMAN 7698.0 ... 300.0 30.0 SALES CHICAGO
1 7521 WARD SALESMAN 7698.0 ... 500.0 30.0 SALES CHICAGO
2 7654 MARTIN SALESMAN 7698.0 ... 1400.0 30.0 SALES CHICAGO
3 7698 BLAKE MANAGER 7839.0 ... NaN 30.0 SALES CHICAGO
4 7844 TURNER SALESMAN 7698.0 ... 0.0 30.0 SALES CHICAGO
5 7900 JAMES CLERK 7698.0 ... NaN 30.0 SALES CHICAGO
6 7566 JONES MANAGER 7839.0 ... NaN 20.0 RESEARCH DALLAS
7 7788 SCOTT ANALYST 7566.0 ... NaN 20.0 RESEARCH DALLAS
8 7876 ADAMS CLERK 7788.0 ... NaN 20.0 RESEARCH DALLAS
9 7902 FORD ANALYST 7566.0 ... NaN 20.0 RESEARCH DALLAS
10 7782 CLARK MANAGER 7839.0 ... NaN 10.0 ACCOUNTING NEW YORK
11 7839 KING PRESIDENT NaN ... NaN 10.0 ACCOUNTING NEW YORK
12 7934 MILLER CLERK 7782.0 ... NaN 10.0 ACCOUNTING NEW YORK
注意：當(dāng) a 與 b 進(jìn)行內(nèi)連操作時(shí) a.join(b) 不等于 b.join(a)。
5)使用join函數(shù)
如果左右兩個(gè)表的主鍵名相同，可以使用join函數(shù)。
import pandas as pd
df_emp_left = pd.read_csv('C:\\Users\\qwy\Desktop\data\\empdata.csv')
df_dept_right = pd.read_csv('C:\\Users\\qwy\Desktop\data\\deptdata.csv')
print(df_emp_left.join(df_dept_right,how="outer",rsuffix='1'))運(yùn)行結(jié)果：
EMPNO ENAME JOB MGR ... DEPTNO DEPTNO1 DNAME LOC
0 7369 SMITH CLERK 7902.0 ... NaN 10.0 ACCOUNTING NEW YORK
1 7499 ALLEN SALESMAN 7698.0 ... 30.0 20.0 RESEARCH DALLAS
2 7521 WARD SALESMAN 7698.0 ... 30.0 30.0 SALES CHICAGO
3 7566 JONES MANAGER 7839.0 ... 20.0 40.0 OPERATIONS BOSTON
4 7654 MARTIN SALESMAN 7698.0 ... 30.0 NaN NaN NaN
5 7698 BLAKE MANAGER 7839.0 ... 30.0 NaN NaN NaN
6 7782 CLARK MANAGER 7839.0 ... 10.0 NaN NaN NaN
7 7788 SCOTT ANALYST 7566.0 ... 20.0 NaN NaN NaN
8 7839 KING PRESIDENT NaN ... 10.0 NaN NaN NaN
9 7844 TURNER SALESMAN 7698.0 ... 30.0 NaN NaN NaN
10 7876 ADAMS CLERK 7788.0 ... 20.0 NaN NaN NaN
11 7900 JAMES CLERK 7698.0 ... 30.0 NaN NaN NaN
12 7902 FORD ANALYST 7566.0 ... 20.0 NaN NaN NaN
13 7934 MILLER CLERK 7782.0 ... 10.0 NaN NaN NaN
到此這篇關(guān)于Pandas merge合并操作的實(shí)現(xiàn)的文章就介紹到這了,更多相關(guān)Pandas merge合并內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
- pandas數(shù)據(jù)合并與重塑之merge詳解
- Pandas數(shù)據(jù)集的合并與連接merge()方法
- Python?pandas數(shù)據(jù)合并merge函數(shù)用法詳解
- Pandas merge合并兩個(gè)DataFram的實(shí)現(xiàn)
- Python?Pandas數(shù)據(jù)合并pd.merge用法詳解
- pandas中DataFrame數(shù)據(jù)合并連接（merge、join、concat)
- Pandas 連接合并函數(shù)merge()詳解
- 在Pandas中DataFrame數(shù)據(jù)合并,連接(concat,merge,join)的實(shí)例
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