jiobu1

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jiobu1

def wrangle(X):
  # Wrangle all df files to merge later
  # Create copy
  X = X.copy()

  # Need this to create states column
  k = X.iloc[0][0]

  # Make sure that k has no extra characters
  k = re.sub('[^a-zA-Z]', '', k)

jiobu1

combined_csv['Crime Rating'] = pd.qcut(
                                    combined_csv['Crime Rate (per 1000 residents)'], 
                                    q=3, 
                                    labels=['Low', 'Medium', 'High'])

jiobu1

pollution_df = pollution_df
                          .pipe(start_pipe)
                          .pipe(levels_of_concern)
                          .pipe(split_city_state)
                          .pipe(explode_str, col='City')
                          .pipe(explode_str, col='State')
                          .pipe(drop_columns)

jiobu1

# Nearest Neighbor

import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline

# Select columns that will be used to calculate nearest neighbors
X = merged[[
       'TotalPop', 'Men', 'Women', 'Hispanic', 'White',

jiobu1

print(df['Grades'].nunique())
df['Grades'].unique()

302
array(['9-12', '6-8', 'K-4', '5-8', '4-5', 'K-5', '4-6', '7-12', 'K-6','4-8', 'K-8', '1-6', 'PK-3', '6-12', 'K-3', 'PK-K', 'PK', 'PK-8',
       'PK-6', '4-12', 'PK-6 & Ungraded', '1-8', 'K', 'PK-5', 'PK-12','7-11', '3-6', 'K-12', '3-8', '2-10', 'K-1, 5-8', 'PK-4',
       'Ungraded', '1-12', '2-5', '3-5', '10-12', 'PK-1 & Ungraded','K-11', 'K-2', 'K-1', '9-10', 'K-7', '1-5', 'PK-1', 'PK-K, 2',
       'PK-2', '7-8', 'PK-11', '9', 'K-9', '2-11', '2-12', '2-9', '8-12','K-10', 'PK & Ungraded', '7-9', '6', '5-6', '2, 5-6, 8-9, 11-12',
       '11-12', '3-12', 'K-1, 3-4, 6-7, 9, 11', '1-11', '5-12', '6-10','11', '3-7', '7-10', 'PK-10', 'PK-12 & Ungraded', 'PK-9', '6-9',
       '4-9', '9-11', '6-7', '5-12 & Ungraded', '8-11', '2-8', '3, 5, 7-11', 'PK, 8', 'PK-7', '6, 9-12', '1-3', 'K-3, 5-10, 12',

jiobu1

import unittest

class TestParseGrades(unittest.TestCase):
    def test_join_string_grades_success(self):
        actual = parse_grades('2, 4, 6, 8, 10, 12')
        expected = ['2','4','6','8','10','12']
        self.assertEqual(actual, expected)

    def test_parse_grades_success(self):
        actual = parse_grades('K-4')

jiobu1

# Parse combineed grades
def parse_grades(grades_string):
    GRADES = ['PK', 'K', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', 'Ungraded']

    # Remove & for grades list
    grades_string = grades_string.replace(' &', ',')

    # Grades list - will add to separated grade string to grades
    grades = []

jiobu1

def parse_grades(grades_string):
    GRADES = ['PK', 'K', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', 'Ungraded']

    # Remove & for grades list
    grades_string = grades_string.replace(' &', ',')

    # Grades list - will add to separated grade string to grades
    grades = []

    # split strings based on ','

jiobu1

import requests
from bs4 import BeautifulSoup

url = "https://www.greatschools.org/new-york/new-york/schools/?tableView=Overview&view=table"
page_response = requests.get(url)
content = BeautifulSoup(page_response.text,"html.parser")

table=content.find_all('table')
table