(Python Example for Beginners)
Write a Pandas program to construct a series using the MultiIndex levels as the column and index.
Python Code :
import pandas as pd import numpy as np sales_arrays = [['sale1', 'sale1', 'sale2', 'sale2', 'sale3', 'sale3', 'sale4', 'sale4'], ['city1', 'city2', 'city1', 'city2', 'city1', 'city2', 'city1', 'city2']] sales_tuples = list(zip(*sales_arrays)) print("Create a MultiIndex:") sales_index = pd.MultiIndex.from_tuples(sales_tuples, names=['sale', 'city']) print(sales_tuples) print("nConstruct a series using the said MultiIndex levels: ") s = pd.Series(np.random.randn(8), index = sales_index) print(s)
Create a MultiIndex: [('sale1', 'city1'), ('sale1', 'city2'), ('sale2', 'city1'), ('sale2', 'city2'), ('sale3', 'city1'), ('sale3', 'city2'), ('sale4', 'city1'), ('sale4', 'city2')] Construct a series using the said MultiIndex levels: sale city sale1 city1 -1.533805 city2 -1.546815 sale2 city1 0.018307 city2 -0.210834 sale3 city1 0.903430 city2 1.269479 sale4 city1 -0.550486 city2 1.738659 dtype: float64
Pandas Example – Write a Pandas program to construct a series using the MultiIndex levels as the column and index
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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