Pandas, भाग 2

इंटरमीडिएट Python

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
इंटरमीडिएट Python

इंडेक्स करें और डेटा चुनें

  • Square brackets
  • उन्नत मेथड्स
    • loc
    • iloc
इंटरमीडिएट Python

कॉलम एक्सेस [ ]

         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
इंटरमीडिएट Python

कॉलम एक्सेस [ ]

         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
  • 1D लेबल्ड ऐरे
इंटरमीडिएट Python

कॉलम एक्सेस [ ]

         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
इंटरमीडिएट Python

कॉलम एक्सेस [ ]

         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
इंटरमीडिएट Python

कॉलम एक्सेस [ ]

         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
इंटरमीडिएट Python

रो एक्सेस [ ]

         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
इंटरमीडिएट Python

रो एक्सेस [ ]

         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
इंटरमीडिएट Python

चर्चा [ ]

  • Square brackets: सीमित फंक्शनैलिटी
  • आदर्श रूप से
    • 2D NumPy arrays
    • my_array[rows, columns]
  • pandas
    • loc (लेबल-आधारित)
    • iloc (इंटीजर पोजीशन-आधारित)
इंटरमीडिएट Python

रो एक्सेस 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
  • रो को pandas Series के रूप में
इंटरमीडिएट Python

रो एक्सेस 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
इंटरमीडिएट Python

रो एक्सेस 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
इंटरमीडिएट Python

रो और कॉलम 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
इंटरमीडिएट Python

रो और कॉलम 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
इंटरमीडिएट Python

पुनरावृत्ति

  • Square brackets
    • कॉलम एक्सेस brics[["country", "capital"]]
    • रो एक्सेस: सिर्फ स्लाइसिंग से brics[1:4]
  • loc (लेबल-आधारित)
    • रो एक्सेस brics.loc[["RU", "IN", "CH"]]
    • कॉलम एक्सेस brics.loc[:, ["country", "capital"]]
    • रो और कॉलम एक्सेस
      brics.loc[
      ["RU", "IN", "CH"], 
      ["country", "capital"]
      ]
      
इंटरमीडिएट Python

रो एक्सेस 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
इंटरमीडिएट Python

रो एक्सेस 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
इंटरमीडिएट Python

रो एक्सेस 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
इंटरमीडिएट Python

रो और कॉलम 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
इंटरमीडिएट Python

रो और कॉलम 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
इंटरमीडिएट Python

रो और कॉलम 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
इंटरमीडिएट Python

रो और कॉलम 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
इंटरमीडिएट Python

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इंटरमीडिएट Python

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