df.loc versus df.iloc

import pandas as pd

df = pd.read_csv('rates.csv', parse_dates=['Time'])
df.index = df['Time']
df.head()

	Time	USD	JPY	BGN	CZK	DKK	GBP	CHF
Time
2024-01-17	2024-01-17	1.0877	160.65	1.9558	24.755	7.4586	0.85818	0.9406
2024-01-16	2024-01-16	1.0882	159.64	1.9558	24.710	7.4582	0.86078	0.9361
2024-01-15	2024-01-15	1.0945	159.67	1.9558	24.714	7.4590	0.86075	0.9351
2024-01-12	2024-01-12	1.0942	159.17	1.9558	24.689	7.4565	0.85950	0.9350
2024-01-11	2024-01-11	1.0987	159.71	1.9558	24.659	7.4568	0.86145	0.9338

loc lets us access rows/columns using labels

df.loc[:, ['USD', 'GBP']]

	USD	GBP
Time
2024-01-17	1.0877	0.85818
2024-01-16	1.0882	0.86078
2024-01-15	1.0945	0.86075
2024-01-12	1.0942	0.85950
2024-01-11	1.0987	0.86145
...	...	...
2023-10-26	1.0540	0.87170
2023-10-25	1.0576	0.87240
2023-10-24	1.0632	0.87025
2023-10-23	1.0597	0.87153
2023-10-20	1.0591	0.87213

61 rows × 2 columns

df.loc[["2024-01-17", "2023-10-20"], ['USD', 'GBP']]

	USD	GBP
Time
2024-01-17	1.0877	0.85818
2023-10-20	1.0591	0.87213

df.loc["2023-11", ['USD', 'GBP']]

	USD	GBP
Time
2023-11-30	1.0931	0.86368
2023-11-29	1.0985	0.86525
2023-11-28	1.0949	0.86820
2023-11-27	1.0951	0.86671
2023-11-24	1.0916	0.86818
2023-11-23	1.0900	0.86940
2023-11-22	1.0911	0.87030
2023-11-21	1.0955	0.87326
2023-11-20	1.0928	0.87630
2023-11-17	1.0872	0.87395
2023-11-16	1.0849	0.87520
2023-11-15	1.0868	0.87188
2023-11-14	1.0724	0.87230
2023-11-13	1.0670	0.87155
2023-11-10	1.0683	0.87435
2023-11-09	1.0691	0.87205
2023-11-08	1.0671	0.87015
2023-11-07	1.0686	0.86855
2023-11-06	1.0741	0.86640
2023-11-03	1.0702	0.86983
2023-11-02	1.0661	0.87305
2023-11-01	1.0537	0.86945

df.loc[:, ['USD', 'GBP']] # same as df[['USD', 'GBP']]

	USD	GBP
Time
2024-01-17	1.0877	0.85818
2024-01-16	1.0882	0.86078
2024-01-15	1.0945	0.86075
2024-01-12	1.0942	0.85950
2024-01-11	1.0987	0.86145
...	...	...
2023-10-26	1.0540	0.87170
2023-10-25	1.0576	0.87240
2023-10-24	1.0632	0.87025
2023-10-23	1.0597	0.87153
2023-10-20	1.0591	0.87213

61 rows × 2 columns

df[['USD', 'GBP']]

	USD	GBP
Time
2024-01-17	1.0877	0.85818
2024-01-16	1.0882	0.86078
2024-01-15	1.0945	0.86075
2024-01-12	1.0942	0.85950
2024-01-11	1.0987	0.86145
...	...	...
2023-10-26	1.0540	0.87170
2023-10-25	1.0576	0.87240
2023-10-24	1.0632	0.87025
2023-10-23	1.0597	0.87153
2023-10-20	1.0591	0.87213

61 rows × 2 columns

iloc lets us access rows/columns by integer index (starting at 0)

df.head()

	Time	USD	JPY	BGN	CZK	DKK	GBP	CHF
Time
2024-01-17	2024-01-17	1.0877	160.65	1.9558	24.755	7.4586	0.85818	0.9406
2024-01-16	2024-01-16	1.0882	159.64	1.9558	24.710	7.4582	0.86078	0.9361
2024-01-15	2024-01-15	1.0945	159.67	1.9558	24.714	7.4590	0.86075	0.9351
2024-01-12	2024-01-12	1.0942	159.17	1.9558	24.689	7.4565	0.85950	0.9350
2024-01-11	2024-01-11	1.0987	159.71	1.9558	24.659	7.4568	0.86145	0.9338

df.loc[:, 1:3] # loc expects labels for rows and columns

TypeError: cannot do slice indexing on Index with these indexers [1] of type int

df.iloc[:, 1:3]

	USD	JPY
Time
2024-01-17	1.0877	160.65
2024-01-16	1.0882	159.64
2024-01-15	1.0945	159.67
2024-01-12	1.0942	159.17
2024-01-11	1.0987	159.71
...	...	...
2023-10-26	1.0540	158.48
2023-10-25	1.0576	158.55
2023-10-24	1.0632	159.26
2023-10-23	1.0597	158.91
2023-10-20	1.0591	158.80

61 rows × 2 columns

df.iloc[:, 1:].head() # all columns except 0th one

	USD	JPY	BGN	CZK	DKK	GBP	CHF
Time
2024-01-17	1.0877	160.65	1.9558	24.755	7.4586	0.85818	0.9406
2024-01-16	1.0882	159.64	1.9558	24.710	7.4582	0.86078	0.9361
2024-01-15	1.0945	159.67	1.9558	24.714	7.4590	0.86075	0.9351
2024-01-12	1.0942	159.17	1.9558	24.689	7.4565	0.85950	0.9350
2024-01-11	1.0987	159.71	1.9558	24.659	7.4568	0.86145	0.9338

df.iloc[:10, 1:4].head() # select a few rows, only keep columns 1,2,3

	USD	JPY	BGN
Time
2024-01-17	1.0877	160.65	1.9558
2024-01-16	1.0882	159.64	1.9558
2024-01-15	1.0945	159.67	1.9558
2024-01-12	1.0942	159.17	1.9558
2024-01-11	1.0987	159.71	1.9558