Sorting in pandas

How to sort ?

Sort_values() function is used to perform sorting. This function has two parameters, 1. by, 2. ascending or descending. Ascending and descending have two values True or False. In by parameter, write column names by which column you want to sort. If you want to sort according to index then use sort_index() function and the parameters are ascending or descending.

Let's see the dataset

Input
import numpy as np
df=pd.read_csv("practice2.csv")
print(df)
Output

	Id	Name	Group_name	Total_marks	Grade	Ranking
0	01	A	Science	700	A+	01
1	02	B	Commerce	618	B+	02
2	03	A	Science	700	A+	01
3	04	D	Arts	687	A+	01
4	05	E	Commerce	611	B+	02
5	06	F	Arts	599	C+	03
6	07	P	Science	575	C+	03
7	08	F	Arts	600	C	03
8	09	I	Commerce	550	C+	03
9	10	J	Science	650	A+	01
10	11	K	Arts	680	A+	01
11	12	L	Science	570	C+	03
12	13	M	Arts	599	C+	03
13	14	N	Commerce	597	C+	03
14	15	O	Science	697	A+	01
15	16	B	Arts	570	C+	03
16	17	D	Science	588	C+	03
17	18	E	Science	687	A+	01
18	19	C	Commerce	688	A+	01
19	20	P	Arts	588	C+	03
20	21	C	Science	619	B+	02
21	22	M	Commerce	600	B+	02
22	23	P	Arts	700	A+	01

Example 1:

Input
import numpy as np
x=df.sort_values(by="Grade",ascending=True)
y=df.sort_values(by="Rank",ascending=False)
z=df.sort_index(ascending=False)

print("Sort using Grade column")
print(x)
print("Sort using Rank column")
print(y)
print("Sort in descending order")
print(z)
Output

Sort using Grade column

	Id	Name	Group_name	Total_marks	Grade	Ranking
0	01	A	Science	700	A+	01
18	19	C	Commerce	688	A+	01
17	18	E	Science	687	A+	01
14	15	O	Science	697	A+	01
10	11	K	Arts	680	A+	01
9	10	J	Science	650	A+	01
22	23	P	Arts	700	A+	01
3	04	D	Arts	687	A+	01
2	03	A	Science	700	A+	01
4	05	E	Commerce	611	B+	02
21	22	M	Commerce	600	B+	02
1	02	B	Commerce	618	B+	02
20	21	C	Science	619	B+	02
5	06	F	Arts	599	C+	03
12	13	M	Arts	599	C+	03
13	14	N	Commerce	597	C+	03
15	16	B	Arts	570	C+	03
16	17	D	Science	588	C+	03
19	20	P	Arts	588	C+	03
6	07	P	Science	575	C+	03
11	12	L	Science	570	C+	03

Sort using Rank column

	Id	Name	Group_name	Total_marks	Grade	Ranking
11	12	L	Science	570	C+	03
16	17	D	Science	588	C+	03
5	06	F	Arts	599	C+	03
6	07	P	Science	575	C+	03
7	08	F	Arts	600	C	03
8	09	I	Commerce	550	C+	03
19	20	P	Arts	588	C+	03
12	13	M	Arts	599	C+	03
13	14	N	Commerce	597	C+	03
15	16	B	Arts	570	C+	03
21	22	M	Commerce	600	B+	02
20	21	C	Science	619	B+	02
4	05	E	Commerce	611	B+	02
1	02	B	Commerce	618	B+	02
18	19	C	Commerce	688	A+	01
17	18	E	Science	687	A+	01
0	01	A	Science	700	A+	01
14	15	O	Science	697	A+	01
10	11	K	Arts	680	A+	01
9	10	J	Science	650	A+	01
3	04	D	Arts	687	A+	01
2	03	A	Science	700	A+	01
22	23	P	Arts	700	A+	01

Sort in descending order

	Id	Name	Group_name	Total_marks	Grade	Ranking
22	23	P	Arts	700	A+	01
21	22	M	Commerce	600	B+	02
20	21	C	Science	619	B+	02
19	20	P	Arts	588	C+	03
18	19	C	Commerce	688	A+	01
17	18	E	Science	687	A+	01
16	17	D	Science	588	C+	03
15	16	B	Arts	570	C+	03
14	15	O	Science	697	A+	01
13	14	N	Commerce	597	C+	03
12	13	M	Arts	599	C+	03
11	12	L	Science	570	C+	03
10	11	K	Arts	680	A+	01
9	10	J	Science	650	A+	01
8	09	I	Commerce	550	C+	03
7	08	F	Arts	600	C	03
6	07	P	Science	575	C+	03
5	06	F	Arts	599	C+	03
4	05	E	Commerce	611	B+	02
3	04	D	Arts	687	A+	01
2	03	A	Science	700	A+	01
1	02	B	Commerce	618	B+	02
0	01	A	Science	700	A+	01

sorting can be done also depends on multiple columns.

Example 2:

Input
import numpy as np
x=df.sort_values(by=["Total marks","Grade"],ascending=False)
y=df.sort_values(by=["Total marks","Grade"],ascending=[True ,False])
print(x)
print(y)
Output

	Id	Name	Group_name	Total_marks	Grade	Ranking
0	01	A	Science	700	A+	01
2	03	A	Science	700	A+	01
22	23	P	Arts	700	A+	01
14	15	O	Science	697	A+	01
18	19	C	Commerce	688	A+	01
3	04	D	Arts	687	A+	01
17	18	E	Science	687	A+	01
9	10	J	Science	650	A+	01
20	21	C	Science	619	B+	02
1	02	B	Commerce	618	B+	02
4	05	E	Commerce	611	B+	02
7	08	F	Arts	600	C	03
21	22	M	Commerce	600	B+	02
5	06	F	Arts	599	C+	03
12	13	M	Arts	599	C+	03
16	17	D	Science	588	C+	03
19	20	P	Arts	588	C+	03
10	11	K	Arts	680	A+	01
6	07	P	Science	575	C+	03
11	12	L	Science	570	C+	03
15	16	B	Arts	570	C+	03
13	14	N	Commerce	597	C+	03
8	09	I	Commerce	550	C+	03

	Id	Name	Group_name	Total_marks	Grade	Ranking
8	09	I	Commerce	550	C+	03
13	14	N	Commerce	597	C+	03
11	12	L	Science	570	C+	03
15	16	B	Arts	570	C+	03
6	07	P	Science	575	C+	03
10	11	K	Arts	680	A+	01
16	17	D	Science	588	C+	03
19	20	P	Arts	588	C+	03
5	06	F	Arts	599	C+	03
12	13	M	Arts	599	C+	03
7	08	F	Arts	600	C	03
21	22	M	Commerce	600	B+	02
4	05	E	Commerce	611	B+	02
1	02	B	Commerce	618	B+	02
20	21	C	Science	619	B+	02
9	10	J	Science	650	A+	01
3	04	D	Arts	687	A+	01
17	18	E	Science	687	A+	01
18	19	C	Commerce	688	A+	01
14	15	O	Science	697	A+	01
0	01	A	Science	700	A+	01
2	03	A	Science	700	A+	01
22	23	P	Arts	700	A+	01

In example 2, in y variable true is for Total marks and False is for Grade.

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