# -----------------------------------------
# DO NOT EDIT THIS CELL
# -----------------------------------------
# The only exception is if you need to run "!pip install" to install a package prior to importing
# This cell loads the required Python packages

# Import geospatial libraries
import geodatasets
import geopandas as gpd
from shapely.geometry import Point, LineString, Polygon

# Import visualization and utility libraries
import matplotlib.pyplot as plt
import numpy as np

c:\Users\joshu\anaconda3\Lib\site-packages\paramiko\pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.
  "cipher": algorithms.TripleDES,
c:\Users\joshu\anaconda3\Lib\site-packages\paramiko\transport.py:219: CryptographyDeprecationWarning: Blowfish has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.Blowfish and will be removed from this module in 45.0.0.
  "class": algorithms.Blowfish,
c:\Users\joshu\anaconda3\Lib\site-packages\paramiko\transport.py:243: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.
  "class": algorithms.TripleDES,

# -----------------------------------------
# DO NOT EDIT THIS CELL
# -----------------------------------------
# This cell shows available geospatial datasets.
# Scroll through the output and locate the one named "natregimes".
# You will use it in the next step.

geodatasets.data

# -----------------------------------------
# INSTRUCTIONS: TASK 1
# -----------------------------------------
# Use the "read_file" and "get_path" functions appropriately 
# to extract the "natregimes" data into an object named "counties"

counties = gpd.read_file(geodatasets.get_path("geoda.natregimes"))

# -----------------------------------------
# INSTRUCTIONS: TASK 2
# -----------------------------------------
# Print out the first few observations and the columns of "counties"



print(counties.head(), list(counties.columns))

   REGIONS  NOSOUTH  POLY_ID               NAME  STATE_NAME STATE_FIPS  \
0      1.0      1.0        1  Lake of the Woods   Minnesota         27   
1      2.0      1.0        2              Ferry  Washington         53   
2      2.0      1.0        3            Stevens  Washington         53   
3      2.0      1.0        4           Okanogan  Washington         53   
4      2.0      1.0        5       Pend Oreille  Washington         53   

  CNTY_FIPS   FIPS  STFIPS  COFIPS  ...      GI59      GI69      GI79  \
0       077  27077      27      77  ...  0.285235  0.372336  0.342104   
1       019  53019      53      19  ...  0.256158  0.360665  0.361928   
2       065  53065      53      65  ...  0.283999  0.394083  0.357566   
3       047  53047      53      47  ...  0.258540  0.371218  0.381240   
4       051  53051      53      51  ...  0.243263  0.365614  0.358706   

       GI89       FH60  FH70       FH80       FH90  West  \
0  0.336455  11.279621   5.4   5.663881   9.515860     0   
1  0.360640  10.053476   2.6  10.079576  11.397059     1   
2  0.369942   9.258437   5.6   6.812127  10.352015     1   
3  0.394519   9.039900   8.1  10.084926  12.840340     1   
4  0.387848   8.243930   4.1   7.557643  10.313002     1   

                                            geometry  
0  POLYGON ((-95.34258 48.5467, -95.34081 48.7151...  
1  POLYGON ((-118.8505 47.94969, -118.84732 48.47...  
2  POLYGON ((-117.43777 48.04422, -117.54113 48.0...  
3  POLYGON ((-118.97096 47.93928, -118.97293 47.9...  
4  POLYGON ((-117.4375 49, -117.03098 49, -117.02...  

[5 rows x 74 columns] ['REGIONS', 'NOSOUTH', 'POLY_ID', 'NAME', 'STATE_NAME', 'STATE_FIPS', 'CNTY_FIPS', 'FIPS', 'STFIPS', 'COFIPS', 'FIPSNO', 'SOUTH', 'HR60', 'HR70', 'HR80', 'HR90', 'HC60', 'HC70', 'HC80', 'HC90', 'PO60', 'PO70', 'PO80', 'PO90', 'RD60', 'RD70', 'RD80', 'RD90', 'PS60', 'PS70', 'PS80', 'PS90', 'UE60', 'UE70', 'UE80', 'UE90', 'DV60', 'DV70', 'DV80', 'DV90', 'MA60', 'MA70', 'MA80', 'MA90', 'POL60', 'POL70', 'POL80', 'POL90', 'DNL60', 'DNL70', 'DNL80', 'DNL90', 'MFIL59', 'MFIL69', 'MFIL79', 'MFIL89', 'FP59', 'FP69', 'FP79', 'FP89', 'BLK60', 'BLK70', 'BLK80', 'BLK90', 'GI59', 'GI69', 'GI79', 'GI89', 'FH60', 'FH70', 'FH80', 'FH90', 'West', 'geometry']

# -----------------------------------------
# INSTRUCTIONS: TASK 3
# -----------------------------------------
# Construct a map visualization using the entire dataset in "counties"
# Make the title of the visualization "All US Counties"

counties.plot(edgecolor='black', color='orange', figsize=(11, 6))
plt.title("All US Counties")
plt.show()

# -----------------------------------------
# INSTRUCTIONS: TASK 4
# -----------------------------------------
# Create an object named "massachusetts" that only includes Massachusetts's counties from your "counties" object
# Construct a map visualization of Massachusetts's counties and give the visualization an appropriate title

massachusetts = counties[counties['STATE_NAME'] == 'Massachusetts']
massachusetts.plot(edgecolor='black', color='purple', figsize=(12, 6))
plt.title("Massachusetts Counties")
plt.show()

# -----------------------------------------
# INSTRUCTIONS: TASK 5
# -----------------------------------------
# Create an object named "mississippi" that only includes Mississippi's counties from your "counties" object
# Construct a map visualization of Mississippi's counties and give the visualization an appropriate title

mississippi = counties[counties['STATE_NAME'] == 'Mississippi']
mississippi.plot(edgecolor='black', color='coral', figsize=(10, 6))
plt.title("Mississippi Counties")
plt.show()

# -----------------------------------------
# INSTRUCTIONS: TASK 6
# -----------------------------------------
# Refer to counties columns and choose one numeric measure from 1 of the 5 columns: HR90, UE90, DV90, FP89, MA90
    # HR90: Homicide Rate (1990)
    # UE90: Unemployment Rate (1990)
    # DV90: Divorce Rate (1990)
    # FP89: % of Families Below Poverty Level (1989)
    # MA90: Median Age (1990)
# Use your chosen measure to create two map plots:
    # 1. A map of Massachusetts's counties whose color fills correspond to the value of your chosen measure
    # 2. A map of Mississippi's counties whose color fills correspond to the value of your chosen measure
# HINT: Use the "column" argument in your plot function properly

massachusetts.plot(column='FP89', legend=True, edgecolor='darkblue', figsize=(11, 6))
plt.title("Massachusetts - % Families In Poverty (1989)")
plt.show()

mississippi.plot(column='FP89', legend=True, edgecolor='darkblue', figsize=(11, 6))
plt.title("Mississippi - % Families Below Poverty (1989)")
plt.show()

# -----------------------------------------
# INSTRUCTIONS: TASK 7
# -----------------------------------------
# Recreate your prior two plots from the previous cell, except this time make the color fill scale the same across both maps
# HINT: "vmin" and "vmax" arguments in your plot function can help in this regard


min = counties['FP89'].min()
max = counties['FP89'].max()


massachusetts.plot(column='FP89', edgecolor='darkblue', legend=True, figsize=(11, 6), vmin=min, vmax=max)
plt.title("Massachusetts - % Families Below Poverty (1989)")
plt.show()


mississippi.plot(column='FP89', edgecolor='darkblue', legend=True, figsize=(11, 6), vmin=min, vmax=max)
plt.title("Mississippi - % Families Below Poverty (1989)")
plt.show()

# -----------------------------------------
# INSTRUCTIONS: TASK 8
# -----------------------------------------
# Refer to counties columns and choose a SECOND numeric measure from 1 of the 5 columns: HR90, UE90, DV90, FP89, MA90
    # HR90: Homicide Rate (1990)
    # UE90: Unemployment Rate (1990)
    # DV90: Divorce Rate (1990)
    # FP90: % of Families Below Poverty Level (1989)
    # MA90: Median Age (1990)
# Use your second chosen measure to create two map plots:
    # 1. A map of Massachusetts's counties whose color fills correspond to the value of your chosen measure
    # 2. A map of Mississippi's counties whose color fills correspond to the value of your chosen measure
# Also, the two maps should use the same MINIMUM and MAXIMUM for your color scale.


min_age = counties['MA90'].min()
max_age = counties['MA90'].max()

# Massachusetts map for Median Age
massachusetts.plot(column='MA90', edgecolor='darkblue', legend=True, figsize=(11, 6), vmin=min_age, vmax=max_age)
plt.title("Massachusetts - Median Age (1990)")
plt.show()

# Mississippi map for Median Age
mississippi.plot(column='MA90', edgecolor='darkblue', legend=True, figsize=(11, 6), vmin=min_age, vmax=max_age)
plt.title("Mississippi - Median Age (1990)")
plt.show()