Importing Data, Plotting, and Changing CRS¶
In this notebook, we'll walk through some basics of Pandas, Geopandas, and displaying your data with MatPlotLib.
Start by importing pandas, geopandas, and matplotlib:
>>> 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
It is important that we reset our working directory to the home folder. Use os.chdir() to move back one directory.
Important: only run this once or you'll wind up in the wrong directory.¶
>>> os.chdir('../')
>>> os.getcwd()In [5]:
os.chdir('../')
os.getcwd()
Out[5]:
You can import a file using gpd.read_file()...¶
>>> gpd.read_file('workshopdata/Boulder_Co.shp')In [6]:
gpd.read_file('workshopdata/Boulder_Co.shp')
Out[6]:
Create a variable out of the Boulder County shapefile:¶
>>> county = gpd.read_file('workshopata/Boulder_Co.shp')In [7]:
county = gpd.read_file('workshopdata/Boulder_Co.shp')
Now, you can just call the variable and if you want to view it:¶
>>> countyIn [8]:
county
Out[8]:
Now, we'll use the MatPlotLib plot function to display the geometry of the shapefile:¶
>>> county.plot()In [9]:
county.plot()
Out[9]:
You can enter parameters between the parentheses. Let's change the color of Boulder County to green:¶
>>> county.plot(color='green')In [10]:
county.plot(color='green')
Out[10]:
You can change more than color...¶
>>> county.plot(color='green', edgecolor='black', alpha=0.5, figsize=(10,10))In [11]:
county.plot(color='green', edgecolor='black', alpha=0.5, figsize=(10,10))
Out[11]:
In [12]:
county.crs
Out[12]:
Pro tip: go to https://spatialreference.org/ to check all the different epsg codes.¶
Now, let's read in another file, the Boulder City Limits shapefile:¶
>>> city = gpd.read_file('workshopdata/City_Limites.shp')
>>> cityIn [13]:
city = gpd.read_file('workshopdata/City_Limits.shp')
city
Out[13]:
There are five polygons representing the city limits.
Use len to check how many rows there are in a Pandas/GeoPandas dataframe:
>>> len(city)In [14]:
len(city)
Out[14]:
Plot it with the .plot() function:¶
>>> city.plot()In [15]:
city.plot()
Out[15]:
Let's take a minute to understand indexing in Python. Type in the following and view the different results.¶
>>> city[0:5]
>>> city[0:]
>>> city[1:3]
>>> city[:5]
>>> city[:-2]In [16]:
city[0:5]
Out[16]:
You can use index for a lot of things, including plotting:¶
>>> city[1:3].plot()In [17]:
city[1:3].plot()
Out[17]:
Now let's plot both layers on top of one another.¶
Start by making the county variable the plot "axis", then plot city using county as the axis...
>>> ax = county.plot()
>>> city.plot(ax=ax)In [18]:
ax = county.plot()
city.plot(ax=ax)
Out[18]:
In [19]:
city.crs
Out[19]:
Ah ha. Not in the same projection as the county layer. But we can easily reset it to the county variable's crs:¶
>>> city = city.to_crs(county.crs)
>>> city.crsIn [20]:
city = city.to_crs(county.crs)
city.crs
Out[20]:
Okay! Now that both layers are in the same crs, we can try plotting again:¶
>>> ax = county.plot()
>>> city.plot(ax=ax)In [22]:
ax = county.plot()
city.plot(ax=ax)
Out[22]:
It worked, but they're all the same color. So, let's work on changing the styling parameters:¶
>>> ax = county.plot(color='green', edgecolor='black', alpha=0.5, figsize=(10,10))
>>> city.plot(ax=ax, color='none', edgecolor='white')In [23]:
ax = county.plot(color='green', edgecolor='black', alpha=0.5, figsize=(10,10))
city.plot(ax=ax, color='none', edgecolor='white')
Out[23]:
Nice job!¶
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