6. Sorting Value in Pandas

Sorting Values in Pandas

Sorting in pandas means arranging data in ascending or descending order. We use the sort_values() function to sort data.

sort_values()

To sort values in pandas, use sort_values(). It has parameters:

• ascending — Default value is True. If you want data in descending order, set ascending=False.

Example setup:

import pandas as pd

df = pd.read_excel('students.xlsx')
df

	id	name	class	section	score
0	101	Aarav Sharma	10	A	88
1	102	Meera Patel	10	B	92
2	103	Rohan Singh	9	A	75
3	104	Ananya Das	9	B	81
4	105	Ishaan Verma	10	A	95
5	106	Priya Nair	8	C	67
6	107	Dev Mehta	8	B	73
7	108	Nisha Rao	9	C	84
8	109	Arjun Gupta	10	B	90
9	110	Tanya Malik	9	A	77
10	111	Karan Yadav	8	A	71
11	112	Sneha Kapoor	10	C	89
12	113	Rahul Menon	9	B	82
13	114	Diya Joshi	8	A	79
14	115	Mohit Jain	10	C	85
15	116	Kritika Singh	9	B	91
16	117	Varun Bhat	8	C	63
17	118	Riya Sharma	9	A	87
18	119	Aman Thakur	10	A	93
19	120	Kavya Pillai	8	B	76

1

Sort Student Records in Ascending Order By Class

cdf = df.sort_values('class')
cdf

	id	name	class	section	score
6	107	Dev Mehta	8	B	73
5	106	Priya Nair	8	C	67
13	114	Diya Joshi	8	A	79
10	111	Karan Yadav	8	A	71
19	120	Kavya Pillai	8	B	76
16	117	Varun Bhat	8	C	63
7	108	Nisha Rao	9	C	84
12	113	Rahul Menon	9	B	82
15	116	Kritika Singh	9	B	91
9	110	Tanya Malik	9	A	77
3	104	Ananya Das	9	B	81
2	103	Rohan Singh	9	A	75
17	118	Riya Sharma	9	A	87
4	105	Ishaan Verma	10	A	95
1	102	Meera Patel	10	B	92
0	101	Aarav Sharma	10	A	88
14	115	Mohit Jain	10	C	85
11	112	Sneha Kapoor	10	C	89
8	109	Arjun Gupta	10	B	90
18	119	Aman Thakur	10	A	93

2

Sort Student Records in Ascending Order By Class and Section

fdf = df.sort_values(['class','section'])
fdf

	id	name	class	section	score
10	111	Karan Yadav	8	A	71
13	114	Diya Joshi	8	A	79
6	107	Dev Mehta	8	B	73
19	120	Kavya Pillai	8	B	76
5	106	Priya Nair	8	C	67
16	117	Varun Bhat	8	C	63
2	103	Rohan Singh	9	A	75
9	110	Tanya Malik	9	A	77
17	118	Riya Sharma	9	A	87
3	104	Ananya Das	9	B	81
12	113	Rahul Menon	9	B	82
15	116	Kritika Singh	9	B	91
7	108	Nisha Rao	9	C	84
0	101	Aarav Sharma	10	A	88
4	105	Ishaan Verma	10	A	95
18	119	Aman Thakur	10	A	93
1	102	Meera Patel	10	B	92
8	109	Arjun Gupta	10	B	90
11	112	Sneha Kapoor	10	C	89
14	115	Mohit Jain	10	C	85

3

Sort Student Records in Ascending Order By Class, Section and Score

mdf = df.sort_values(['class','section','score'])
mdf

	id	name	class	section	score
10	111	Karan Yadav	8	A	71
13	114	Diya Joshi	8	A	79
6	107	Dev Mehta	8	B	73
19	120	Kavya Pillai	8	B	76
16	117	Varun Bhat	8	C	63
5	106	Priya Nair	8	C	67
2	103	Rohan Singh	9	A	75
9	110	Tanya Malik	9	A	77
17	118	Riya Sharma	9	A	87
3	104	Ananya Das	9	B	81
12	113	Rahul Menon	9	B	82
15	116	Kritika Singh	9	B	91
7	108	Nisha Rao	9	C	84
0	101	Aarav Sharma	10	A	88
18	119	Aman Thakur	10	A	93
4	105	Ishaan Verma	10	A	95
8	109	Arjun Gupta	10	B	90
1	102	Meera Patel	10	B	92
14	115	Mohit Jain	10	C	85
11	112	Sneha Kapoor	10	C	89

4

Sort Student Records in Descending Order By Class

ddf = df.sort_values('class', ascending=False)
ddf

	id	name	class	section	score
0	101	Aarav Sharma	10	A	88
1	102	Meera Patel	10	B	92
8	109	Arjun Gupta	10	B	90
4	105	Ishaan Verma	10	A	95
18	119	Aman Thakur	10	A	93
14	115	Mohit Jain	10	C	85
11	112	Sneha Kapoor	10	C	89
2	103	Rohan Singh	9	A	75
3	104	Ananya Das	9	B	81
15	116	Kritika Singh	9	B	91
9	110	Tanya Malik	9	A	77
7	108	Nisha Rao	9	C	84
17	118	Riya Sharma	9	A	87
12	113	Rahul Menon	9	B	82
5	106	Priya Nair	8	C	67
6	107	Dev Mehta	8	B	73
10	111	Karan Yadav	8	A	71
13	114	Diya Joshi	8	A	79
16	117	Varun Bhat	8	C	63
19	120	Kavya Pillai	8	B	76

5

Sort Student Records in Descending Order By Class and Section

ddf = df.sort_values(['class','section'], ascending=False)
ddf

	id	name	class	section	score
11	112	Sneha Kapoor	10	C	89
14	115	Mohit Jain	10	C	85
1	102	Meera Patel	10	B	92
8	109	Arjun Gupta	10	B	90
0	101	Aarav Sharma	10	A	88
4	105	Ishaan Verma	10	A	95
18	119	Aman Thakur	10	A	93
7	108	Nisha Rao	9	C	84
3	104	Ananya Das	9	B	81
12	113	Rahul Menon	9	B	82
15	116	Kritika Singh	9	B	91
2	103	Rohan Singh	9	A	75
9	110	Tanya Malik	9	A	77
17	118	Riya Sharma	9	A	87
5	106	Priya Nair	8	C	67
16	117	Varun Bhat	8	C	63
6	107	Dev Mehta	8	B	73
19	120	Kavya Pillai	8	B	76
10	111	Karan Yadav	8	A	71
13	114	Diya Joshi	8	A	79

6

Sort Student Records in Descending Order By Class, Section and Score

ddf = df.sort_values(['class','section','score'], ascending=False)
ddf

	id	name	class	section	score
11	112	Sneha Kapoor	10	C	89
14	115	Mohit Jain	10	C	85
1	102	Meera Patel	10	B	92
8	109	Arjun Gupta	10	B	90
4	105	Ishaan Verma	10	A	95
18	119	Aman Thakur	10	A	93
0	101	Aarav Sharma	10	A	88
7	108	Nisha Rao	9	C	84
15	116	Kritika Singh	9	B	91
12	113	Rahul Menon	9	B	82
3	104	Ananya Das	9	B	81
17	118	Riya Sharma	9	A	87
9	110	Tanya Malik	9	A	77
2	103	Rohan Singh	9	A	75
5	106	Priya Nair	8	C	67
16	117	Varun Bhat	8	C	63
19	120	Kavya Pillai	8	B	76
6	107	Dev Mehta	8	B	73
13	114	Diya Joshi	8	A	79
10	111	Karan Yadav	8	A	71

7

Sort Student Records in Ascending Order By Class and Section, Descending Order By Score

ddf = df.sort_values(['class','section','score'], ascending=[True, True, False])
ddf

	id	name	class	section	score
13	114	Diya Joshi	8	A	79
10	111	Karan Yadav	8	A	71
19	120	Kavya Pillai	8	B	76
6	107	Dev Mehta	8	B	73
5	106	Priya Nair	8	C	67
16	117	Varun Bhat	8	C	63
17	118	Riya Sharma	9	A	87
9	110	Tanya Malik	9	A	77
2	103	Rohan Singh	9	A	75
15	116	Kritika Singh	9	B	91
12	113	Rahul Menon	9	B	82
3	104	Ananya Das	9	B	81
7	108	Nisha Rao	9	C	84
4	105	Ishaan Verma	10	A	95
18	119	Aman Thakur	10	A	93
0	101	Aarav Sharma	10	A	88
1	102	Meera Patel	10	B	92
8	109	Arjun Gupta	10	B	90
11	112	Sneha Kapoor	10	C	89
14	115	Mohit Jain	10	C	85

Assignments:

20 + Sorting Assignments

Single-column Sorting

1. Sort the dataset by Units Sold in ascending order.

2. Sort the dataset by Units Sold in descending order.

3. Sort the dataset by Price per Unit (lowest to highest).

4. Sort the dataset by Price per Unit (highest to lowest).

5. Sort all records by Invoice Date in ascending (chronological) order.

6. Sort all records by Invoice Date in descending (latest first) order.

7. Sort by Product alphabetically (A–Z).

8. Sort by Product in reverse alphabetical order (Z–A).

9. Sort by Sales Method alphabetically.

10. Sort by City and observe if all rows belong to the same city.

Multi-column Sorting

1. Sort by Product ascending, then by Units Sold descending.

2. Sort by Region, then by State, both ascending.

3. Sort by Sales Method, then Price per Unit descending.

4. Sort by Retailer ascending and Invoice Date descending.

5. Sort by City, Product, and Units Sold.

6. Sort by State ascending and Price per Unit descending.

7. Sort by Region, Product, and Sales Method.

8. Sort by Product, then within each product by highest Units Sold.

9. Sort by Product, then Price per Unit, then Invoice Date.

10. Calculate a new column Total Sale = Price per Unit * Units Sold and sort descending.

11. Filter rows where Sales Method == 'In-store' and sort by Units Sold.

12. Filter rows where Sales Method == 'Outlet' and sort by Price per Unit.

13. Sort only rows where Units Sold > 1000 by Product.