Labels: Numpy Arrays, DataFrames

Numpy Arrays [1]

# Import the numpy package as np
import numpy as np

• Element-wise calculations: fast and computationally efficient
• subset: quickly find out a subset according to the conditions

list = [1,2,3]
np_list = np.array(list)   # Construct
sub_np_list = np_list[np_list>2]   # Find all elements larger than 2

Pandas DataFrames [2]

• Store and manipulate tabular data in rows of observations and columns of variables.

dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
       "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
       "area": [8.516, 17.10, 3.286, 9.597, 1.221],
       "population": [200.4, 143.5, 1252, 1357, 52.98] }
import pandas as pd
brics = pd.DataFrame(dict)
print(brics)

Results shown in DataFrame format

• Assign the value of index.

# Set the index for brics
brics.index = ["BR", "RU", "IN", "CH", "SA"]

• Create a DataFrame is by importing a csv file using Pandas.

# Import the cars.csv data: cars
cars = pd.read_csv('cars.csv')

• Indexing DataFrames
  We can use square brackets to select one column of the DataFrame:
• The single bracket with output a Pandas Series.
  It can also been used to access observations (rows) from a DataFrame.

# Print out country column as Pandas Series
print(cars['cars_per_cap'])

Pandas Series

• The double bracket will output a Pandas DataFrame.

# Print out country column as Pandas DataFrame
print(cars[['cars_per_cap']])

Pandas DataFrame

• Data selection
  loc is label-based, which specifies rows and columns based on their row and column labels.
  iloc is integer index based, which specifies rows and columns by their integer index.

# Print out observation for Japan
print(cars.iloc[2])
# Print out observations for Australia and Egypt
print(cars.loc[['AUS', 'EG']])

Reference

[1] Python Numpy Arrays https://www.learnpython.org/en/Numpy_Arrays
[2] Python Pandas DataForms https://www.learnpython.org/en/Pandas_Basics