Filtrowanie DataFrames w pandas

Python średnio zaawansowany

Hugo Bowne-Anderson

Data Scientist at DataCamp

brics

import pandas as pd
brics = pd.read_csv("path/to/brics.csv", index_col = 0)
brics

         country    capital    area  population
BR        Brazil   Brasilia   8.516      200.40
RU        Russia     Moscow  17.100      143.50
IN         India  New Delhi   3.286     1252.00
CH         China    Beijing   9.597     1357.00
SA  South Africa   Pretoria   1.221       52.98

Cel

         country    capital    area  population
BR        Brazil   Brasilia   8.516      200.40
RU        Russia     Moscow  17.100      143.50
IN         India  New Delhi   3.286     1252.00
CH         China    Beijing   9.597     1357.00
SA  South Africa   Pretoria   1.221       52.98

Wybierz kraje o powierzchni powyżej 8 mln km2
3 kroki
- Wybierz kolumnę z powierzchnią
- Porównaj wartości w kolumnie
- Użyj wyniku do filtrowania krajów

Krok 1: Pobierz kolumnę

         country    capital    area  population
BR        Brazil   Brasilia   8.516      200.40
RU        Russia     Moscow  17.100      143.50
IN         India  New Delhi   3.286     1252.00
CH         China    Beijing   9.597     1357.00
SA  South Africa   Pretoria   1.221       52.98

brics["area"]

BR     8.516
RU    17.100
IN     3.286
CH     9.597
SA     1.221
Name: area, dtype: float64    # - Need Pandas Series

Alternatywy:

brics.loc[:,"area"]
brics.iloc[:,2]

Krok 2: Porównaj

brics["area"]

BR     8.516
RU    17.100
IN     3.286
CH     9.597
SA     1.221
Name: area, dtype: float64

brics["area"] > 8

BR     True
RU     True
IN    False
CH     True
SA    False
Name: area, dtype: bool

is_huge = brics["area"] > 8

Krok 3: Podzbiór DF

is_huge

BR     True
RU     True
IN    False
CH     True
SA    False
Name: area, dtype: bool

brics[is_huge]

   country   capital    area  population
BR  Brazil  Brasilia   8.516       200.4
RU  Russia    Moscow  17.100       143.5
CH   China   Beijing   9.597      1357.0

Podsumowanie

         country    capital    area  population
BR        Brazil   Brasilia   8.516      200.40
RU        Russia     Moscow  17.100      143.50
IN         India  New Delhi   3.286     1252.00
CH         China    Beijing   9.597     1357.00
SA  South Africa   Pretoria   1.221       52.988

is_huge = brics["area"] > 8
brics[is_huge]

   country   capital    area  population
BR  Brazil  Brasilia   8.516       200.4
RU  Russia    Moscow  17.100       143.5
CH   China   Beijing   9.597      1357.0

brics[brics["area"] > 8]

   country   capital    area  population
BR  Brazil  Brasilia   8.516       200.4
RU  Russia    Moscow  17.100       143.5
CH   China   Beijing   9.597      1357.0

Operatory boolowskie

         country    capital    area  population
BR        Brazil   Brasilia   8.516      200.40
RU        Russia     Moscow  17.100      143.50
IN         India  New Delhi   3.286     1252.00
CH         China    Beijing   9.597     1357.00
SA  South Africa   Pretoria   1.221       52.98

import numpy as np
np.logical_and(brics["area"] > 8, brics["area"] < 10)

BR     True
RU    False
IN    False
CH     True
SA    False
Name: area, dtype: bool

brics[np.logical_and(brics["area"] > 8, brics["area"] < 10)]

   country   capital   area  population
BR  Brazil  Brasilia  8.516       200.4
CH   China   Beijing  9.597      1357.0

Czas na ćwiczenia!

Python średnio zaawansowany