Group Data By Time
Preliminaries
/* Import required packages */
import pandas as pd
import datetime
import numpy as npNext, 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 Data
/* 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()| datetime | score | |
|---|---|---|
| datetime | ||
| 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()| score | |
|---|---|
| datetime | |
| 2015-06-30 | 513.629630 |
| 2015-07-31 | 561.516129 |
| 2015-08-31 | 448.032258 |
| 2015-09-30 | 548.000000 |
| 2015-10-31 | 480.419355 |
| 2015-11-30 | 487.033333 |
| 2015-12-31 | 499.935484 |
| 2016-01-31 | 429.193548 |
| 2016-02-29 | 520.413793 |
| 2016-03-31 | 349.806452 |
| 2016-04-30 | 395.500000 |
| 2016-05-31 | 503.451613 |
| 2016-06-30 | 510.500000 |
/* Group the data by month, and take the sum for each group (i.e. each month) */
df.resample('M').sum()| score | |
|---|---|
| datetime | |
| 2015-06-30 | 13868 |
| 2015-07-31 | 17407 |
| 2015-08-31 | 13889 |
| 2015-09-30 | 16440 |
| 2015-10-31 | 14893 |
| 2015-11-30 | 14611 |
| 2015-12-31 | 15498 |
| 2016-01-31 | 13305 |
| 2016-02-29 | 15092 |
| 2016-03-31 | 10844 |
| 2016-04-30 | 11865 |
| 2016-05-31 | 15607 |
| 2016-06-30 | 1021 |
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
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- 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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