Load some data

import hotstepper as hs
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df_data = pd.read_excel('https://raw.githubusercontent.com/TangleSpace/hotstepper-data/master/data/superstore.xls',
                            parse_dates=['Order Date','Ship Date'])
df_data.head()

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
0	1	CA-2016-152156	2016-11-08	2016-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600	2	0.00	41.9136
1	2	CA-2016-152156	2016-11-08	2016-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400	3	0.00	219.5820
2	3	CA-2016-138688	2016-06-12	2016-06-16	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	...	90036	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200	2	0.00	6.8714
3	4	US-2015-108966	2015-10-11	2015-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775	5	0.45	-383.0310
4	5	US-2015-108966	2015-10-11	2015-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680	2	0.20	2.5164

5 rows × 21 columns

All we need as a start is to usually have the data in a Pandas Dataframe. Fun fact, if you set the field type of a datetime field within the dataframe, either with the parse_dates option when reading a file or explicitly using apply pd.to_datetime directly on the field, when you tell HotStepper to use that field as either a start or end step key, Hotstepper will automatically detect the type and set the use_datetime=True flag for you.

For example,

sales_orders = hs.Steps.read_dataframe(
        df_data, #the dataframe we will read from
        start='Order Date', #field name of the start keys
        end='Ship Date', #field name of the end keys
        weight='Profit') #field name of the weight or value of each step

sales_orders.using_datetime()

True

ax = sales_orders.plot()
sales_orders.describe()

	Metric	Value
0	Count	1434
1	Mean	761.85
2	Median	513.1
3	Mode	-6927.35
4	Std	1363.45
5	Var	1859001.89
6	Min	-6927.35
7	25%	140.24
8	75%	1195.82
9	Max	11642.96
10	Area	26750137.57
11	Start	2014-01-03 00:00:00
12	End	2018-01-05 00:00:00

We can also load data into a Steps object a few more ways, let’s go with a raw example and use the read_array method.

sales_orders_arr = hs.Steps.read_array(
        start=df_data['Order Date'], #array of values for the start keys
        end=df_data['Ship Date'],    #array of values for the end keys
        weight=df_data['Profit'])    #array of values for the step weights or values

ax = sales_orders_arr.plot()
ax.axhline(0,color='r');