pandas: iloc and loc in Pandas#

import pandas as pd

Loading data into Pandas DataFrame#

The dataset is from Kaggle - Pokemon.

data = pd.read_csv('data/Pokemon.csv')

data

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# Name Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed Generation Legendary
0 1 Bulbasaur Grass Poison 318 45 49 49 65 65 45 1 False
1 2 Ivysaur Grass Poison 405 60 62 63 80 80 60 1 False
2 3 Venusaur Grass Poison 525 80 82 83 100 100 80 1 False
3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 122 120 80 1 False
4 4 Charmander Fire NaN 309 39 52 43 60 50 65 1 False
... ... ... ... ... ... ... ... ... ... ... ... ... ...
795 719 Diancie Rock Fairy 600 50 100 150 100 150 50 6 True
796 719 DiancieMega Diancie Rock Fairy 700 50 160 110 160 110 110 6 True
797 720 HoopaHoopa Confined Psychic Ghost 600 80 110 60 150 130 70 6 True
798 720 HoopaHoopa Unbound Psychic Dark 680 80 160 60 170 130 80 6 True
799 721 Volcanion Fire Water 600 80 110 120 130 90 70 6 True

800 rows × 13 columns

iloc#

# Read single row
data.iloc[3]

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#                                 3
Name          VenusaurMega Venusaur
Type 1                        Grass
Type 2                       Poison
Total                           625
HP                               80
Attack                          100
Defense                         123
Sp. Atk                         122
Sp. Def                         120
Speed                            80
Generation                        1
Legendary                     False
Name: 3, dtype: object

# Read multiple rows
data.iloc[[3,6]]

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# Name Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed Generation Legendary
3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 122 120 80 1 False
6 6 Charizard Fire Flying 534 78 84 78 109 85 100 1 False 
# Read a range of rows
data.iloc[3:7]

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# Name Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed Generation Legendary
3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 122 120 80 1 False
4 4 Charmander Fire NaN 309 39 52 43 60 50 65 1 False
5 5 Charmeleon Fire NaN 405 58 64 58 80 65 80 1 False
6 6 Charizard Fire Flying 534 78 84 78 109 85 100 1 False

loc#

The loc property in Pandas is used to access a group of rows and columns by labels or a boolean array. Unlike iloc, which uses integer-based indexing, loc uses the actual labels of the index and columns. This makes it very useful for selecting data based on meaningful row or column names.

data.loc[data['Legendary'] == True].head()

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# Name Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed Generation Legendary
156 144 Articuno Ice Flying 580 90 85 100 95 125 85 1 True
157 145 Zapdos Electric Flying 580 90 90 85 125 90 100 1 True
158 146 Moltres Fire Flying 580 90 100 90 125 85 90 1 True
162 150 Mewtwo Psychic NaN 680 106 110 90 154 90 130 1 True
163 150 MewtwoMega Mewtwo X Psychic Fighting 780 106 190 100 154 100 130 1 True

If you have a condition to filter rows, then you must pass the columns that you want to select after the comma as a sequence. e.g., [condition, ['col1', 'col2']]

data.loc[data['Attack'] == 49, ['Name']]

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Name
0 Bulbasaur
166 Chikorita
506 Finneon