Table of Contents

• 1  Dealing with taxi data
  • 1.1  Read data
  • 1.2  Transfer to shapefile
  • 1.3  Only deal with travel within austin

Dealing with taxi data¶

Read data¶

https://data.world/andytryba/rideaustin

In [1]:

import geopandas as gpd
import pandas as pd

import geopandas as gpd import pandas as pd

In [2]:

csv_name = '../Data/RideAustin_Weather.csv'
df = pd.read_csv(csv_name, encoding= 'unicode_escape')
df.head()

csv_name = '../Data/RideAustin_Weather.csv' df = pd.read_csv(csv_name, encoding= 'unicode_escape') df.head()

E:\Anaconda\envs\gpd\lib\site-packages\IPython\core\interactiveshell.py:3146: DtypeWarning: Columns (1,7) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

Out[2]:

	completed_on	distance_travelled	end_location_lat	end_location_long	started_on	driver_rating	rider_rating	start_zip_code	end_zip_code	charity_id	...	11/24/2016 8:35	9879	30.31	-97.74	11/24/2016 8:21	5	5.1	78701	78756	Thunder
0	11/14/2016 19:16	2300	30.26	-97.76	11/14/2016 19:11	5.0	5.0	78701	78704	NaN	...	4.955556	11/14/2016	0.0	82.0	56.0	2.0	8.9	1.0	1.0	0.0
1	11/14/2016 19:17	2977	30.29	-97.73	11/14/2016 19:08	5.0	5.0	78701	78712	NaN	...	4.955556	11/14/2016	0.0	82.0	56.0	2.0	8.9	1.0	1.0	0.0
2	11/14/2016 19:19	1298	30.27	-97.75	11/14/2016 19:13	5.0	5.0	78701	78703	5.0	...	5.000000	11/14/2016	0.0	82.0	56.0	2.0	8.9	1.0	1.0	0.0
3	11/14/2016 19:19	3442	30.26	-97.76	11/14/2016 19:13	5.0	5.0	78704	78704	NaN	...	5.000000	11/14/2016	0.0	82.0	56.0	2.0	8.9	1.0	1.0	0.0
4	11/14/2016 19:21	6635	30.32	-97.71	11/14/2016 19:12	5.0	5.0	78701	78723	NaN	...	5.000000	11/14/2016	0.0	82.0	56.0	2.0	8.9	1.0	1.0	0.0

5 rows × 29 columns

Transfer to shapefile¶

In [3]:

%%time
from shapely.geometry import Point
import fiona

start_loc = [Point(xy) for xy in zip(df.start_location_long,df.start_location_lat)]
end_loc = [Point(xy) for xy in zip(df.end_location_long,df.end_location_lat)]

%%time from shapely.geometry import Point import fiona start_loc = [Point(xy) for xy in zip(df.start_location_long,df.start_location_lat)] end_loc = [Point(xy) for xy in zip(df.end_location_long,df.end_location_lat)]

Wall time: 5.12 s

Only deal with travel within austin¶

In [4]:

Shapefile_path = '../Data/austin/Austin2018.shp'
austin = gpd.read_file(Shapefile_path)

Shapefile_path = '../Data/austin/Austin2018.shp' austin = gpd.read_file(Shapefile_path)

In [5]:

austin.plot()

austin.plot()

Out[5]:

<AxesSubplot:>

In [6]:

%%time
remove_id = []

for index,pt in enumerate(start_loc):
    if not austin.contains(pt)[0]:
        remove_id.append(index)

for index,pt in enumerate(end_loc):
    if not austin.contains(pt)[0]:
        remove_id.append(index)

remove_id = list(set(remove_id))
len(remove_id)

%%time remove_id = [] for index,pt in enumerate(start_loc): if not austin.contains(pt)[0]: remove_id.append(index) for index,pt in enumerate(end_loc): if not austin.contains(pt)[0]: remove_id.append(index) remove_id = list(set(remove_id)) len(remove_id)

Wall time: 29min 35s

Out[6]:

8739

In [9]:

df_new = df.drop(remove_id)
df_new.to_csv('../Data/RideAustin_Weather_new.csv')

df_new = df.drop(remove_id) df_new.to_csv('../Data/RideAustin_Weather_new.csv')