Uber Trips

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

from hotstepper import Steps

import warnings
warnings.filterwarnings("ignore")

Read the data from a csv into a dataframe so we can have a quick look at what we have.

df_uber = pd.read_csv(r'https://raw.githubusercontent.com/TangleSpace/hotstepper-data/master/data/taxi%20data.csv',parse_dates=['tpep_pickup_datetime','tpep_dropoff_datetime'])
df_uber.head()

	VendorID	tpep_pickup_datetime	tpep_dropoff_datetime	passenger_count	trip_distance	pickup_longitude	pickup_latitude	dropoff_longitude	dropoff_latitude	fare_amount	tip_amount	total_amount
0	2	2015-01-15 19:05:39+00:00	2015-01-15 19:23:42+00:00	1	1.59	-73.993896	40.750111	-73.974785	40.750618	12.0	3.25	17.05
1	2	2015-01-15 19:05:39+00:00	2015-01-15 19:32:00+00:00	1	2.38	-73.976425	40.739811	-73.983978	40.757889	16.5	4.38	22.68
2	2	2015-01-15 19:05:40+00:00	2015-01-15 19:21:00+00:00	5	2.83	-73.968704	40.754246	-73.955124	40.786858	12.5	0.00	14.30
3	2	2015-01-15 19:05:40+00:00	2015-01-15 19:28:18+00:00	5	8.33	-73.863060	40.769581	-73.952713	40.785782	26.0	8.08	41.21
4	2	2015-01-15 19:05:41+00:00	2015-01-15 19:20:36+00:00	1	2.37	-73.945541	40.779423	-73.980850	40.786083	11.5	0.00	13.30

uber_steps = Steps.read_dataframe(df_uber,'tpep_pickup_datetime','tpep_dropoff_datetime','passenger_count')
ax = uber_steps.plot()
uber_steps.smooth_plot(ax=ax,color='g',linewidth=3)

<AxesSubplot:>

uber_steps.summary();

df_start = df_uber.loc[df_uber.tpep_pickup_datetime==df_uber.tpep_pickup_datetime.min()]
print(f"Pandas direct {df_start['passenger_count'].sum()}")
print(f"Step function at first {uber_steps(uber_steps.first())[0]}")

Pandas direct 8
Step function at first 8.0

uber_steps.pacf_plot()

<AxesSubplot:title={'center':'Steps Partial Autocorrelation for Lags = 10'}, xlabel='Lag'>