/* Import required packages */ import pandas as pd import datetime import numpy as np
Next, let’s create some sample data that we can group by time as an sample. In this example I am creating a dataframe with two columns with 365 rows. One column is a date, the second column is a numeric value.
/* Create a datetime variable for today */ base = datetime.datetime.today() /* Create a list variable that creates 365 days of rows of datetime values */ date_list = [base - datetime.timedelta(days=x) for x in range(0, 365)]
/* Create a list variable of 365 numeric values */ score_list = list(np.random.randint(low=1, high=1000, size=365))
/* Create an empty dataframe */ df = pd.DataFrame() /* Create a column from the datetime variable */ df['datetime'] = date_list /* Convert that column into a datetime datatype */ df['datetime'] = pd.to_datetime(df['datetime']) /* Set the datetime column as the index */ df.index = df['datetime'] /* Create a column from the numeric score variable */ df['score'] = score_list
/* Let's take a took at the data */ df.head()
|2016-06-02 09:57:54.793972||2016-06-02 09:57:54.793972||900|
|2016-06-01 09:57:54.793972||2016-06-01 09:57:54.793972||121|
|2016-05-31 09:57:54.793972||2016-05-31 09:57:54.793972||547|
|2016-05-30 09:57:54.793972||2016-05-30 09:57:54.793972||504|
|2016-05-29 09:57:54.793972||2016-05-29 09:57:54.793972||304|
Group Data By Date
In pandas, the most common way to group by time is to use the .resample() function. In v0.18.0 this function is two-stage. This means that ‘df.resample(’M’)’ creates an object to which we can apply other functions (‘mean’, ‘count’, ‘sum’, etc.)
/* Group the data by month, and take the mean for each group (i.e. each month) */ df.resample('M').mean()
/* Group the data by month, and take the sum for each group (i.e. each month) */ df.resample('M').sum()
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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