26. US State Ranking#

  1. http://www.businessinsider.com/us-news-best-states-quality-of-life-ranked-2018-2

  2. https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_population

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix

plt.style.use('ggplot')

mcdfraw = pd.read_csv("businessinsider.csv")
popdfraw = pd.read_csv("population.csv")

mc_temp = []
mc_rank = []
mc_state = []
mc_state_natural = []
mc_state_social = []

for elem in mcdfraw['State']:
    mc_temp.append(elem.split(". "))
mc_rank = np.transpose(mc_temp)[0]
mc_rank = [int(elem) for elem in mc_rank]
mc_state = np.transpose(mc_temp)[1]

mc_temp = []
for elem in mcdfraw['Natural']:
    mc_temp.append(elem.split(": "))
mc_state_natural = np.transpose(mc_temp)[1]
mc_state_natural = [int(elem) for elem in mc_state_natural]


mc_temp = []
for elem in mcdfraw['Social']:
    mc_temp.append(elem.split(": "))
mc_state_social = np.transpose(mc_temp)[1]
mc_state_social = [int(elem) for elem in mc_state_social] 

pop_temp = []
for elem in popdfraw['State']:
    pop_temp.append(elem.strip())

popdfraw['State'] = pop_temp

for elem in popdfraw['State']:
    popdfraw['State'].replace([elem], elem.strip())

mcdf_dict = {"State": mc_state, "Rank": mc_rank, "Natural": mc_state_natural, "Social": mc_state_social}
mcdf = pd.DataFrame(data=mcdf_dict)

mcdf = mcdf.set_index('State')
popdf = popdfraw.set_index('State')

popdf[popdf['PopRank']==47].index

Index(['North Dakota'], dtype='object', name='State')

df = pd.concat([mcdf,popdf], axis=1)

df.sort_values(by=['Rank'])
Rank Natural Social PopRank Population2017 HouseSeats PerHouseSeat Percent
State
North Dakota 1 2 3 47 755393 1 755393 0.0024
Minnesota 2 12 4 22 5576606 8 697076 0.0171
Wisconsin 3 20 5 20 5795483 8 724435 0.0179
New Hampshire 4 13 7 41 1342795 2 671398 0.0041
South Dakota 5 6 14 46 869666 1 869666 0.0027
Mississippi 6 9 17 34 2984100 4 746025 0.0092
Arkansas 7 5 21 32 3004279 4 751070 0.0093
New Mexico 8 17 11 36 2088070 3 696023 0.0064
Iowa 9 18 10 30 3145711 4 786428 0.0097
Colorado 10 8 20 21 5607154 7 801022 0.0172
Wyoming 11 11 19 50 579315 1 579315 0.0018
Maine 12 25 8 42 1335907 2 667954 0.0041
Montana 13 33 2 44 1050493 1 1050493 0.0032
Nebraska 14 21 13 37 1920076 3 640025 0.0059
Missouri 15 14 18 18 6113532 8 764192 0.0189
Kansas 16 16 16 35 2913123 4 728281 0.0090
Oklahoma 17 28 9 28 3930864 5 786173 0.0121
Oregon 18 19 15 27 4142776 5 828555 0.0127
Alaska 19 42 1 48 739795 1 739795 0.0023
Idaho 20 26 12 39 1716943 2 858472 0.0052
Washington 21 7 24 13 7405743 10 740574 0.0226
Utah 22 41 6 31 3101833 4 775458 0.0095
Rhode Island 23 4 36 43 1059639 2 529820 0.0033
Delaware 24 23 30 45 961939 1 961939 0.0030
Massachusetts 25 3 42 15 6859819 9 762202 0.0211
Vermont 26 27 28 49 623657 1 623657 0.0019
Kentucky 27 34 22 26 4454189 6 742365 0.0137
Tennessee 28 24 32 16 6715984 9 746220 0.0206
Michigan 29 35 23 10 9962311 14 711594 0.0307
South Carolina 30 22 33 23 5024369 7 717767 0.0154
Maryland 31 29 26 19 6052177 8 756522 0.0186
Georgia 32 15 38 8 10429379 14 744956 0.0319
Florida 33 1 45 3 20984400 27 777200 0.0638
North Carolina 34 37 25 9 10273419 13 790263 0.0314
Alabama 35 43 27 24 4874747 7 696392 0.0151
Hawaii 36 31 37 40 1427538 2 713769 0.0044
New York 37 10 48 4 19849399 27 735163 0.0611
Connecticut 38 36 39 29 3588184 5 717637 0.0111
Arizona 39 40 34 14 7016270 9 779586 0.0215
Ohio 40 48 29 7 11658609 16 728663 0.0359
Virginia 41 39 44 12 8470020 11 770002 0.0260
Louisiana 42 49 31 25 4684333 6 780722 0.0145
Nevada 43 30 50 33 2998039 4 749510 0.0092
Pennsylvania 44 45 41 5 12805537 18 711419 0.0396
West Virginia 45 47 40 38 1815857 3 605285 0.0056
Texas 46 38 46 2 28304596 36 786238 0.0862
Illinois 47 50 35 6 12802023 18 711224 0.0396
Indiana 48 46 43 17 6666818 9 740758 0.0205
New Jersey 49 32 49 11 9005644 12 750470 0.0277
California 50 44 47 1 39536653 53 745974 0.1214

26.1. Visualization#

df[['Rank','PopRank','Natural','Social']].plot.area();
../../../../_images/rank-data_12_0.png 
plt.figure(figsize=(17,15))
plt.scatter(x=df['Rank'], y=df.index.values, s=df['PopRank']*10);
plt.xlabel("Mckinsey Rank")
plt.show()
../../../../_images/rank-data_13_0.png