Pandas, Part 1

Intermediate Python

Hugo Bowne-Anderson

Data Scientist at DataCamp

Tabular dataset examples

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Tabular dataset examples

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Tabular dataset examples

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Datasets in Python

2D NumPy array?
- One data type

Datasets in Python

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Datasets in Python

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pandas!
- High level data manipulation tool
- Wes McKinney
- Built on 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 from Dictionary

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] }

keys (column labels)
values (data, column by column)

import pandas as pd

brics = pd.DataFrame(dict)

DataFrame from Dictionary (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 from CSV file

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 = comma-separated values

DataFrame from CSV file

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 from CSV file

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

Let's practice!

Intermediate Python