Selecting & Filtering by Attributes; More Plotting¶
This time, we'll work a little more on manipulating and exploring the attributes. This is akin to working with the attribute table in desktop GIS software.
Start by importing pandas, geopandas, matplotlib.pyplot, and os:
>>> import pandas as pd
>>> import geopandas as gpd
>>> import matplotlib.pyplot as plt
>>> import osIn [1]:
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import os
Important! Reset your working directory to your home folder, and only run this once!¶
>>> os.chdir('../')
>>> os.getcwd()In [2]:
os.chdir('../')
os.getcwd()
Out[2]:
Import the OSMP_Trails shapefile as trails:¶
>>> trails = gpd.read_file('workshopdata/OSMP_Trails.shp')In [3]:
trails = gpd.read_file('workshopata/OSMP_Trails.shp')
Use .head() to view only the first few rows of the dataframe:¶
>>> trails.head()In [4]:
trails.head()
Out[4]:
You can also view all column headers:¶
>>> trails.columnsIn [5]:
trails.columns
Out[5]:
You can also call up just one column:¶
>>> trails.TRAILTYPEIn [6]:
trails.TRAILTYPE
Out[6]:
Now take a look at the trail data by plotting it:¶
>>> trails.plot()In [7]:
trails.plot()
Out[7]:
You can also filter by attributes and create a new variable from the result:¶
>>> bikeTrails = trails.loc[trails['BICYCLES']=='Yes']
>>> bikeTrailsIn [8]:
bikeTrails = trails.loc[trails['BICYCLES']=='Yes']
bikeTrails
Out[8]:
Nice. You have a new geodataframe that is just bike trails. Plot them:¶
>>> bikeTrails.plot()In [9]:
bikeTrails.plot()
Out[9]:
Now, do the same for hiking trails, then count the results:¶
>>> hikeTrails = trails.loc[trails['TRAILTYPE']=='Hiking Trail']
>>> len(hikeTrails)In [10]:
hikeTrails = trails.loc[trails['TRAILTYPE']=='Hiking Trail']
len(hikeTrails)
Out[10]:
Do the same for horse trails:¶
>>> horseTrails = trails.loc[trails['HORSES']=='Yes']
>>> len(horseTrails)In [11]:
horseTrails = trails.loc[trails['HORSES']=='Yes']
len(horseTrails)
Out[11]:
Now, select all trails whose mileage is greater than 2 miles:¶
>>> trails.loc[trails['MILEAGE']>=2]In [12]:
trails.loc[trails['MILEAGE']>=2]
Out[12]:
In [13]:
trails.TRAILNAME.unique()
Out[13]:
Find out how many unique trail names there are using the same len()¶
>>> len(trails.TRAILNAME.unique())In [14]:
len(trails.TRAILNAME.unique())
Out[14]:
Now, let's take each row, group them by trail name, then sum up the total mileage of each trail by trail name:¶
Sounds hard, but not really!
>>> trailLengths = trails.groupby('TRAILNAME')['MILEAGE'].sum()
>>> trailLengthsIn [16]:
trailLengths = trails.groupby('TRAILNAME')['MILEAGE'].sum()
trailLengths
Out[16]:
Resetting the index will bring it into a Pandas dataframe:¶
>>> trailLengths = trailLengths.reset_index()
>>> trailLengthsIn [17]:
trailLengths = trailLengths.reset_index()
trailLengths
Out[17]:
Now you can export your dataframe to a csv:¶
>>> trailLengths.to_csv('trailLengths.csv')In [18]:
trailLengths.to_csv('trailLengths.csv')
In [19]:
osmp = gpd.read_file('workshopdata/OSMP.shp')
county = gpd.read_file('workshopdata/Boulder_Co.shp')
Reset the county layer's crs to match the osmp layer:¶
>>> county = county.to_crs(osmp.crs)In [20]:
county = county.to_crs(osmp.crs)
To plot more than two layers together, you must use 'fig' and a "blank" axis like so:¶
>>> fig, ax = plt.subplots(1, 1, figsize = (15,15))
>>> county.plot(ax=ax, color = '#F0E68C', edgecolor='black')
>>> osmp.plot(ax=ax, color = 'green', alpha=0.5)
>>> hikeTrails.plot(ax=ax, color='red')
>>> bikeTrails.plot(ax=ax, color='yellow')In [25]:
fig, ax = plt.subplots(1, 1, figsize = (15,15))
county.plot(ax=ax, color = '#F0E68C', edgecolor='black')
osmp.plot(ax=ax, color='green', alpha=0.5)
hikeTrails.plot(ax=ax, color='red')
bikeTrails.plot(ax=ax, color='yellow')
Out[25]:
