Course Notes
Use this workspace to take notes, store code snippets, and build your own interactive cheatsheet!
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# Import any packages you want to use here
import matplotlib.pyplot as pltTake Notes
Add your code snippets here Import matplotlib.pyplot import matplotlib.pyplot as plt
##Scatterplot 1 - father heights vs. son heights with darkred square markers
plt.scatter(father_son.fheight, father_son.sheight, c = 'darkred', marker = 's')
##Show your plot plt.show()
Add your notes here
Import matplotlib.pyplot
import matplotlib.pyplot as plt
Scatterplot 2 - yellow markers with darkblue borders
plt.scatter(father_son.fheight, father_son.sheight, c = 'yellow', edgecolor = 'darkblue')
Show the plot
plt.show()
Import matplotlib.pyplot
import matplotlib.pyplot as plt
Scatterplot 3
plt.scatter(father_son.fheight, father_son.sheight, c = 'yellow', edgecolor = 'darkblue') plt.grid() plt.xlabel('father height (inches)') plt.ylabel('son height (inches)') plt.title('Son Height as a Function of Father Height')
Show your plot
plt.show()
A DataFrame named df has been pre-loaded for you. Complete the code to extract longitude and latitude to new, separate columns.
print the first few rows of df
print(df.head())
extract latitude to a new column: lat
df['lat'] = [loc[0] for loc in df.Location]
extract longitude to a new column: lng
df['lng'] = [loc[1] for loc in df.Location]
print the first few rows of df again
print(df.head())
Import pandas and matplotlib.pyplot using their customary aliases
import pandas as pd import matplotlib.pyplot as plt
Load the dataset
chickens = pd.read_csv(chickens_path)
Look at the first few rows of the chickens DataFrame
print(chickens.head())
Plot the locations of all Nashville chicken permits
plt.scatter(x = chickens.lng, y = chickens.lat) (x = долгота, y = широта)
Show the plot
plt.show()
Geometries and shapefiles
Import geopandas
import geopandas as gpd
Read in the services district shapefile and look at the first few rows.
service_district = gpd.read_file(shapefile_path) print(service_district.head())
Print the contents of the service districts geometry in the first row
print(service_district.loc[0, 'geometry'])
Import packages
import geopandas as gpd import matplotlib.pyplot as plt
Plot the Service Districts without any additional arguments
service_district.plot() plt.show()
Plot the Service Districts, color them according to name, and show a legend
service_district.plot(column = 'name', legend = True) plt.show()
Plot the service district shapefile
service_district.plot(column='name', legend=True)
Add the chicken locations
plt.scatter(x=chickens.lng, y=chickens.lat, c='black', edgecolor = 'white')
Add labels and title
plt.title('Nashville Chicken Permits') plt.xlabel('longitude') plt.ylabel('latitude')
Add grid lines and show the plot
plt.grid() plt.show()
Creating and joining GeoDataFrames
GeoJSON and plotting with geopandas
Set legend style
lgnd_kwds = {'title': 'School Districts', 'loc': 'upper left', 'bbox_to_anchor': (1, 1.03), 'ncol': 1}
Plot the school districts using the summer colormap (sequential)
school_districts.plot(column = 'district', cmap = 'summer', legend = True, legend_kwds = lgnd_kwds) plt.xlabel('Longitude') plt.ylabel('Latitude') plt.title('Nashville School Districts') plt.show();
Set legend style
lgnd_kwds = {'title': 'School Districts', 'loc': 'upper left', 'bbox_to_anchor': (1, 1.03), 'ncol': 1}
Plot the school districts using Set3 colormap without the column argument
school_districts.plot(cmap = 'Set3', legend = True, legend_kwds = lgnd_kwds) plt.xlabel('Longitude') plt.ylabel('Latitude') plt.title('Nashville School Districts') plt.show();