18  Pandas - indeksowanie

import pandas as pd

data = {'Country': ['Belgium', 'India', 'Brazil'],
        'Capital': ['Brussels', 'New Delhi', 'Brasília'],
        'Population': [11190846, 1303171035, 207847528]}
data2 = pd.DataFrame(data, columns=['Country', 'Population', 'Capital'])
print(data2.iloc[[0], [0]])
print("--")
print(data2.loc[[0], ['Country']])
print("--")
print(data2.loc[2])
print("--")
print(data2.loc[:, 'Capital'])
print("--")
print(data2.loc[1, 'Capital'])
   Country
0  Belgium
--
   Country
0  Belgium
--
Country          Brazil
Population    207847528
Capital       Brasília
Name: 2, dtype: object
--
0     Brussels
1    New Delhi
2    Brasília
Name: Capital, dtype: object
--
New Delhi
  1. loc:
  1. iloc:
import pandas as pd

data = {'Country': ['Belgium', 'India', 'Brazil'],
        'Capital': ['Brussels', 'New Delhi', 'Brasília'],
        'Population': [11190846, 1303171035, 207847528]}
data2 = pd.DataFrame(data, columns=['Country', 'Population', 'Capital'])
print(data2['Population'])
print("--")
print(data2[data2['Population'] > 1200000000])
print("--")
0      11190846
1    1303171035
2     207847528
Name: Population, dtype: int64
--
  Country  Population    Capital
1   India  1303171035  New Delhi
--

Ćwiczenia: (ex9.py)

Poćwicz indeksowanie na poniższej ramce (nie muszą być wszystkie wiersze):

Region Product Sales_Channel Units_Sold Revenue Profit
North Electronics Online 120 60.5 15.2
South Furniture Retail 80 45.0 12.0
East Clothing Online 200 35.0 8.5
West Electronics Wholesale 150 70.0 20.5
North Furniture Retail 90 50.5 13.2
South Clothing Online 300 55.0 10.0
East Electronics Retail 110 62.0 16.0
West Furniture Online 70 30.0 7.5
North Clothing Wholesale 250 40.0 9.0
South Electronics Retail 130 75.0 22.0