Pandas, část 1

Intermediate Python

Hugo Bowne-Anderson

Data Scientist at DataCamp

Příklady tabulkových datových sad

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Příklady tabulkových datových sad

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Příklady tabulkových datových sad

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Datové sady v Pythonu

2D pole NumPy?
- Jeden datový typ

Datové sady v Pythonu

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Datové sady v Pythonu

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pandas!
- Nástroj pro manipulaci s daty na vysoké úrovni
- Wes McKinney
- Postaveno na NumPy
- DataFrame

DataFrame

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

DataFrame ze slovníku

dict = { 
    "country":["Brazil", "Russia", "India", "China", "South Africa"],
    "capital":["Brasilia", "Moscow", "New Delhi", "Beijing", "Pretoria"],
       "area":[8.516, 17.10, 3.286, 9.597, 1.221]
 "population":[200.4, 143.5, 1252, 1357, 52.98] }

klíče (názvy sloupců)
hodnoty (data, sloupec po sloupci)

import pandas as pd

brics = pd.DataFrame(dict)

DataFrame ze slovníku (2)

brics

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

brics.index = ["BR", "RU", "IN", "CH", "SA"]

brics

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

DataFrame ze souboru CSV

brics.csv

,country,capital,area,population
BR,Brazil,Brasilia,8.516,200.4
RU,Russia,Moscow,17.10,143.5
IN,India,New Delhi,3.286,1252
CH,China,Beijing,9.597,1357
SA,South Africa,Pretoria,1.221,52.98

CSV = hodnoty oddělené čárkami

DataFrame ze souboru CSV

brics.csv

,country,capital,area,population
BR,Brazil,Brasilia,8.516,200.4
RU,Russia,Moscow,17.10,143.5
IN,India,New Delhi,3.286,1252
CH,China,Beijing,9.597,1357
SA,South Africa,Pretoria,1.221,52.98

brics = pd.read_csv("path/to/brics.csv")

brics

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

DataFrame ze souboru CSV

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

brics

         country  population      area    capital
BR        Brazil         200   8515767   Brasilia
RU        Russia         144  17098242     Moscow
IN         India        1252   3287590  New Delhi
CH         China        1357   9596961    Beijing
SA  South Africa          55   1221037   Pretoria

Pojďme si procvičit!

Intermediate Python