Pandas, część 2

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

Indeksowanie i wybieranie danych

Nawiasy kwadratowe
Zaawansowane metody
- loc
- iloc

Dostęp do kolumn [ ]

         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["country"]

BR          Brazil
RU          Russia
IN           India
CH           China
SA    South Africa
Name: country, dtype: object

Dostęp do kolumn [ ]

         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

type(brics["country"])

pandas.core.series.Series

Jednowymiarowa tablica z etykietami

Dostęp do kolumn [ ]

         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[["country"]]

         country
BR        Brazil
RU        Russia
IN         India
CH         China
SA  South Africa

Dostęp do kolumn [ ]

         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

type(brics[["country"]])

pandas.core.frame.DataFrame

Dostęp do kolumn [ ]

         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[["country", "capital"]]

         country    capital
BR        Brazil   Brasilia
RU        Russia     Moscow
IN         India  New Delhi
CH         China    Beijing
SA  South Africa   Pretoria

Dostęp do wierszy [ ]

         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[1:4]

   country    capital    area  population
RU  Russia     Moscow  17.100       143.5
IN   India  New Delhi   3.286      1252.0
CH   China    Beijing   9.597      1357.0

Dostęp do wierszy [ ]

         country    capital    area  population 
BR        Brazil   Brasilia   8.516      200.40    * 0 *
RU        Russia     Moscow  17.100      143.50    * 1 *
IN         India  New Delhi   3.286     1252.00    * 2 *
CH         China    Beijing   9.597     1357.00    * 3 *
SA  South Africa   Pretoria   1.221       52.98    * 4 *

brics[1:4]

   country    capital    area  population
RU  Russia     Moscow  17.100       143.5
IN   India  New Delhi   3.286      1252.0
CH   China    Beijing   9.597      1357.0

Omówienie [ ]

Nawiasy kwadratowe: ograniczona funkcjonalność
Lepsze podejście
- Tablice 2D NumPy
- my_array[rows, columns]
pandas
- loc (oparty na etykietach)
- iloc (oparty na pozycji)

Dostęp do wierszy loc

         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.loc["RU"]

country       Russia
capital       Moscow
area            17.1
population     143.5
Name: RU, dtype: object

Wiersz jako pandas Series

Dostęp do wierszy loc

         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.loc[["RU"]]

   country capital  area  population
RU  Russia  Moscow  17.1       143.5

DataFrame

Dostęp do wierszy loc

         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.loc[["RU", "IN", "CH"]]

   country    capital    area  population
RU  Russia     Moscow  17.100       143.5
IN   India  New Delhi   3.286      1252.0
CH   China    Beijing   9.597      1357.0

Wiersze i kolumny loc

         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.loc[["RU", "IN", "CH"], ["country", "capital"]]

   country    capital
RU  Russia     Moscow
IN   India  New Delhi
CH   China    Beijing

Wiersze i kolumny loc

         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.loc[:, ["country", "capital"]]

         country    capital
BR        Brazil   Brasilia
RU        Russia     Moscow
IN         India  New Delhi
CH         China    Beijing
SA  South Africa   Pretoria

Podsumowanie

Nawiasy kwadratowe
- Dostęp do kolumn brics[["country", "capital"]]
- Dostęp do wierszy: tylko przez wycinanie brics[1:4]
loc (oparty na etykietach)
- Dostęp do wierszy brics.loc[["RU", "IN", "CH"]]
- Dostęp do kolumn brics.loc[:, ["country", "capital"]]
- Dostęp do wierszy i kolumn
```
brics.loc[
["RU", "IN", "CH"], 
["country", "capital"]
]
```

Dostęp do wierszy iloc

         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.loc[["RU"]]

   country capital  area  population
RU  Russia  Moscow  17.1       143.5

brics.iloc[[1]]

   country capital  area  population
RU  Russia  Moscow  17.1       143.5

Dostęp do wierszy iloc

         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.loc[["RU", "IN", "CH"]]

   country    capital    area  population
RU  Russia     Moscow  17.100       143.5
IN   India  New Delhi   3.286      1252.0
CH   China    Beijing   9.597      1357.0

Dostęp do wierszy iloc

         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.iloc[[1,2,3]]

   country    capital    area  population
RU  Russia     Moscow  17.100       143.5
IN   India  New Delhi   3.286      1252.0
CH   China    Beijing   9.597      1357.0

Wiersze i kolumny iloc

         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.loc[["RU", "IN", "CH"], ["country", "capital"]]

   country    capital
RU  Russia     Moscow
IN   India  New Delhi
CH   China    Beijing

Wiersze i kolumny iloc

         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.iloc[[1,2,3], [0, 1]]

   country    capital
RU  Russia     Moscow
IN   India  New Delhi
CH   China    Beijing

Wiersze i kolumny iloc

         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.loc[:, ["country", "capital"]]

         country    capital
BR        Brazil   Brasilia
RU        Russia     Moscow
IN         India  New Delhi
CH         China    Beijing
SA  South Africa   Pretoria

Wiersze i kolumny iloc

         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.iloc[:, [0,1]]

         country    capital
BR        Brazil   Brasilia
RU        Russia     Moscow
IN         India  New Delhi
CH         China    Beijing
SA  South Africa   Pretoria

Czas na ćwiczenia!

Python średnio zaawansowany