Locate rows: .iloc[] and .loc[]

Writing Efficient Code with pandas

Leonidas Souliotis

PhD Candidate

The poker dataset

	S1	R1	S2	R2	S3	R3	S4	R4	S5	R5
1	♦	10	♣	Jack	♣	King	♠	4	♥	Ace
2	♦	Jack	♦	King	♦	10	♦	Queen	♦	Ace
3	♣	Queen	♣	Jack	♣	King	♣	10	♣	Ace

	S1	R1	S2	R2	S3	R3	S4	R4	S5	R5
1	2	10	3	11	3	13	4	4	1	1
2	2	11	2	13	2	10	2	12	2	1
3	3	12	3	11	3	13	3	10	3	1

Sn: symbol of the n-th card

1 (Hearts), 2 (Diamonds), 3 (Clubs), 4 (Spades)

Rn: rank of the n-th card

1 (Ace), 2-10, 11 (Jack), 12 (Queen), 13 (King)

Locate targeted rows

.loc[] — index name locator

# Specify the range of rows to select 
rows = range(0, 500)
# Time selecting rows using .loc[]
loc_start_time = time.time()
data.loc[rows]    
loc_end_time = time.time()

print("Time using .loc[] : {} sec".format(
            loc_end_time - loc_start_time))

Time using .loc[]: 0.001951932 seconds

.iloc[] — index number locator

# Specify the range of rows to select 
rows = range(0, 500)
# Time selecting rows using .iloc[]
iloc_start_time = time.time()
data.iloc[rows]    
iloc_end_time = time.time()

print("Time using .iloc[]: {} sec".format(
            iloc_end_time - iloc_start_time)

Time using .iloc[] : 0.0007140636 sec

Difference in speed: 173.355592654%

Locate targeted columns

.iloc[] — index number locator

iloc_start_time = time.time()
data.iloc[:,:3]
iloc_end_time = time.time()
print("Time using .iloc[]: {} sec".format(
            iloc_end_time - iloc_start_time))

Time using .iloc[]: 0.00125193595886 sec

Locating columns by names

names_start_time = time.time()
data[['S1', 'R1', 'S2']]
names_end_time = time.time()
print("Time using selection by name: {} sec".format(
            names_end_time - names_start_time))

Time using selection by name: 0.000964879989624 sec

Difference in speed: 29.7504324188%

Let's do it!

Writing Efficient Code with pandas