Sales Order Example

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

from hotstepper import Steps

Read the data file into a dataframe so we can filter some of the columns out and parse the key dates.

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

	Order Date	Ship Date	Sales	Quantity	Profit
0	2016-11-08	2016-11-11	261.9600	2	41.9136
1	2016-11-08	2016-11-11	731.9400	3	219.5820
2	2016-06-12	2016-06-16	14.6200	2	6.8714
3	2015-10-11	2015-10-18	957.5775	5	-383.0310
4	2015-10-11	2015-10-18	22.3680	2	2.5164

We can quick visualise the data and apply a smoothing curve to get a feel for the shape and typical nature of the number of open sales orders.

orders_steps = Steps(True).read_dataframe(df_orders,'Order Date','Ship Date')
ax = orders_steps.plot()
ax.set_title('Open Orders')
ax.set_xlabel('Date')
ax.set_ylabel('Order Count')
orders_steps.smooth_plot(ax=ax,linewidth=5)

<AxesSubplot:title={'center':'Open Orders'}, xlabel='Date', ylabel='Order Count'>

orders_steps.summary();

orders_steps.histogram_plot()

<AxesSubplot:title={'center':'Step Values Histogram'}, xlabel='Steps Range'>

We can also generate an Empirical Cummulative Distribution to see how the values count change across the range.

orders_steps.ecdf_plot()

<AxesSubplot:title={'center':'Step Values Empirical Distribution'}, xlabel='Steps Range'>

We can quickly answer questions like, what percentage of the time are there more than 40 unshipped orders. We can filter for the times where there are more than 40 open orders, integrate this and divide by the interal of the total dataset.

((orders_steps> 40).integrate())/(orders_steps.integrate())

0.3865968956974568

We can also generate a quick overview of the key statistics of the dataset using the describe method.

orders_steps.describe()

	Metric	Value
0	Count	1434
1	Mean	27.04
2	Median	24.0
3	Mode	17.0
4	Std	16.14
5	Var	260.63
6	Min	0.0
7	25%	15.0
8	75%	36.0
9	Max	91.0
10	Area	949392.0
11	Start	2014-01-03 00:00:00
12	End	2018-01-05 00:00:00