/* Import libraries */ import pandas as pd import numpy as np
/* Create a time series of 2000 elements, one very five minutes starting on 1/1/2000 */ time = pd.date_range('1/1/2000', periods=2000, freq='5min') /* Create a pandas series with a random values between 0 and 100, using 'time' as the index */ series = pd.Series(np.random.randint(100, size=2000), index=time)
/* View the first few rows of the data */ series[0:10]
2000-01-01 00:00:00 40 2000-01-01 00:05:00 13 2000-01-01 00:10:00 99 2000-01-01 00:15:00 72 2000-01-01 00:20:00 4 2000-01-01 00:25:00 36 2000-01-01 00:30:00 24 2000-01-01 00:35:00 20 2000-01-01 00:40:00 83 2000-01-01 00:45:00 44 Freq: 5T, dtype: int64
Group Data By Time Of The Day
/* Group the data by the index's hour value, then aggregate by the average */ series.groupby(series.index.hour).mean()
0 50.380952 1 49.380952 2 49.904762 3 53.273810 4 47.178571 5 46.095238 6 49.047619 7 44.297619 8 53.119048 9 48.261905 10 45.166667 11 54.214286 12 50.714286 13 56.130952 14 50.916667 15 42.428571 16 46.880952 17 56.892857 18 54.071429 19 47.607143 20 50.940476 21 50.511905 22 44.550000 23 50.250000 dtype: float64
Python Example for Beginners
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.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
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