Please Help me write this function. Provide comments to the code so I understand. It needs to pass the test cases. Thank you

import pytest from datetime import datetime import numpy as np # Your task is to write the group adjustment method below. There are some # unimplemented unit_tests at the bottom which also need implementation. # Your solution can be pure python, pure NumPy, pure Pandas # or any combination of the three. There are multiple ways of solving this # problem, be creative, use comments to explain your code. # Group Adjust Method # The algorithm needs to do the following: # 1.) For each group-list provided, calculate the means of the values for each # unique group. # # For example: # vals = [ 1 , 2 , 3 ] # ctry_grp = ['USA', 'USA', 'USA'] # state_grp = ['MA' , 'MA' , 'CT' ] # # There is only 1 country in the ctry_grp list. So to get the means: # USA_mean == mean(vals) == 2 # ctry_means = [2, 2, 2] # There are 2 states, so to get the means for each state: # MA_mean == mean(vals[0], vals[1]) == 1.5 # CT_mean == mean(vals[2]) == 3 # state_means = [1.5, 1.5, 3] # # 2.) Using the weights, calculate a weighted average of those group means # Continuing from our example: # weights = [.35, .65] # 35% weighted on country, 65% weighted on state # ctry_means = [2 , 2 , 2] # state_means = [1.5, 1.5, 3] # weighted_means = [2*.35 + .65*1.5, 2*.35 + .65*1.5, 2*.35 + .65*3] # # 3.) Subtract the weighted average group means from each original value # Continuing from our example: # val[0] = 1 # ctry[0] = 'USA' --> 'USA' mean == 2, ctry weight = .35 # state[0] = 'MA' --> 'MA' mean == 1.5, state weight = .65 # weighted_mean = 2*.35 + .65*1.5 = 1.675 # demeaned = 1 - 1.675 = -0.675 # Do this for all values in the original list. # # 4.) Return the demeaned values # Hint: See the test cases below for how the calculation should work. def group_adjust(vals, groups, weights): """ Calculate a group adjustment (demean). Parameters ---------- vals : List of floats/ints The original values to adjust groups : List of Lists A list of groups. Each group will be a list of ints weights : List of floats A list of weights for the groupings. Returns ------- A list-like demeaned version of the input values """ raise NotImplementedError def test_three_groups(): vals = [1, 2, 3, 8, 5] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'MA', 'MA', 'RI', 'RI'] grps_3 = ['WEYMOUTH', 'BOSTON', 'BOSTON', 'PROVIDENCE', 'PROVIDENCE'] weights = [.15, .35, .5] adj_vals = group_adjust(vals, [grps_1, grps_2, grps_3], weights) # 1 - (USA_mean*.15 + MA_mean * .35 + WEYMOUTH_mean * .5) # 2 - (USA_mean*.15 + MA_mean * .35 + BOSTON_mean * .5) # 3 - (USA_mean*.15 + MA_mean * .35 + BOSTON_mean * .5) # etc ... # Plug in the numbers ... # 1 - (.15 * 3.8 + .35 * 2.0 + .5 * 1.0) = -0.770 # 2 - (.15 * 3.8 + .35 * 2.0 + .5 * 2.5) = -0.520 # 3 - (.15 * 3.8 + .35 * 2.0 + .5 * 2.5) = 0.480 # etc... answer = [-0.770, -0.520, 0.480, 1.905, -1.095] for ans, res in zip(answer, adj_vals): assert abs(ans - res) < 1e-5 def test_two_groups(): vals = [1, 2, 3, 8, 5] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'RI', 'CT', 'CT', 'CT'] weights = [.65, .35] adj_vals = group_adjust(vals, [grps_1, grps_2], weights) # 1 - (.65 * 3.8 + .35 * 1.0) = -1.82 # 2 - (.65 * 3.8 + .35 * 2.0) = -1.17 # 3 - (.65 * 3.8 + .35 * 5.33333) = -1.33666 answer = [-1.82, -1.17, -1.33666, 3.66333, 0.66333] for ans, res in zip(answer, adj_vals): assert abs(ans - res) < 1e-5 def test_missing_vals(): # If you're using NumPy or Pandas, use np.NaN # If you're writing pyton, use None vals = [1, np.NaN, 3, 5, 8, 7] # vals = [1, None, 3, 5, 8, 7] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'RI', 'RI', 'CT', 'CT', 'CT'] weights = [.65, .35] adj_vals = group_adjust(vals, [grps_1, grps_2], weights) # This should be None or np.NaN depending on your implementation # please feel free to change this line to match yours answer = [-2.47, np.NaN, -1.170, -0.4533333, 2.54666666, 1.54666666] # answer = [-2.47, None, -1.170, -0.4533333, 2.54666666, 1.54666666] for ans, res in zip(answer, adj_vals): if ans is None: assert res is None elif np.isnan(ans): assert np.isnan(res) else: assert abs(ans - res) < 1e-5 def test_weights_len_equals_group_len(): # Need to have 1 weight for each group # vals = [1, np.NaN, 3, 5, 8, 7] vals = [1, None, 3, 5, 8, 7] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'RI', 'RI', 'CT', 'CT', 'CT'] weights = [.65] with pytest.raises(ValueError): group_adjust(vals, [grps_1, grps_2], weights) def test_group_len_equals_vals_len(): # The groups need to be same shape as vals vals = [1, None, 3, 5, 8, 7] grps_1 = ['USA'] grps_2 = ['MA', 'RI', 'RI', 'CT', 'CT', 'CT'] weights = [.65] with pytest.raises(ValueError): group_adjust(vals, [grps_1, grps_2], weights) def test_performance(): # vals = 1000000*[1, None, 3, 5, 8, 7] # If you're doing numpy, use the np.NaN instead vals = 1000000 * [1, np.NaN, 3, 5, 8, 7] grps_1 = 1000000 * [1, 1, 1, 1, 1, 1] grps_2 = 1000000 * [1, 1, 1, 1, 2, 2] grps_3 = 1000000 * [1, 2, 2, 3, 4, 5] weights = [.20, .30, .50] start = datetime.now() group_adjust(vals, [grps_1, grps_2, grps_3], weights) end = datetime.now() diff = end - start

FOLLOWING ANSWER WAS ALREADY PROVIDED BY AN EXPERT ON CHEGG BUT THAT SEEMS TO HAVE ERRORS:

ANSWER:

import pytest from datetime import datetime import numpy as np import pandas as pd

# Your task is to write the group adjustment method below. There are multiple # unit tests below, your job is to make sure they all pass. Feel free # to add more tests. # Your solution can be pure python, pure NumPy, pure Pandas # or any combination of the three. There are multiple ways of solving this # problem, be creative, use comments to explain your code.

# Group Adjust Method # The algorithm needs to do the following: # 1.) For each group-list provided, calculate the means of the values for each # unique group. # # For example: # vals = [ 1 , 2 , 3 ] # ctry_grp = ['USA', 'USA', 'USA'] # state_grp = ['MA' , 'MA' , 'CT' ] # # There is only 1 country in the ctry_grp list. So to get the means: # USA_mean == mean(vals) == 2 # ctry_means = [2, 2, 2] # There are 2 states, so to get the means for each state: # MA_mean == mean(vals[0], vals[1]) == 1.5 # CT_mean == mean(vals[2]) == 3 # state_means = [1.5, 1.5, 3] # # 2.) Using the weights, calculate a weighted average of those group means # Continuing from our example: # weights = [.35, .65] # 35% weighted on country, 65% weighted on state # ctry_means = [2 , 2 , 2] # state_means = [1.5, 1.5, 3] # weighted_means = [2*.35 + .65*1.5, 2*.35 + .65*1.5, 2*.35 + .65*3] # # 3.) Subtract the weighted average group means from each original value # Continuing from our example: # val[0] = 1 # ctry[0] = 'USA' --> 'USA' mean == 2, ctry weight = .35 # state[0] = 'MA' --> 'MA' mean == 1.5, state weight = .65 # weighted_mean = 2*.35 + .65*1.5 = 1.675 # demeaned = 1 - 1.675 = -0.675 # Do this for all values in the original list. # # 4.) Return the demeaned values

# Hint: See the test cases below for how the calculation should work.

def group_adjust(vals, groups, weights): """ Calculate a group adjustment (demean).

Parameters ----------

vals : List of floats/ints

The original values to adjust

groups : List of Lists

A list of groups. Each group will be a list of ints

weights : List of floats

A list of weights for the groupings.

Returns -------

A list-like demeaned version of the input values """ # pass groupslen = len(groups) weightslen = len(weights) valslen = len(vals) if (weightslen != groupslen): raise ValueError('length of weights {} isnt length of groups {}'.format(weightslen, groupslen))

for itr in range(0, groupslen): group = groups[itr] grouplen = len(group) if (valslen != grouplen): raise ValueError('length of vals {} isnt length of group {}'.format(valslen, group))

headings = ['quant'] groupnames = [] groupmeannames = [] for itr in range(0, groupslen): name = 'column' + str(itr) headings += [name] groupnames += [name] groupmeannames += [name + 'mean']

table = [vals] + groups matrix = np.matrix(table) matrix = matrix.transpose() df = pd.DataFrame(data=matrix, columns=headings) df['quant'] = pd.to_numeric(df['quant'], errors='coerce') # just in case python has some obscure copy issues like other languages I know df0 = pd.DataFrame(data=matrix, columns=headings) df0['quant'] = pd.to_numeric(df0['quant'], errors='coerce') tableToConcat = df0 for itr in range(0, groupslen): pt = pd.pivot_table(df, index=[groupnames[itr]], values=['quant'], aggfunc=[np.mean]) pt = pt.reset_index() pt.columns = [groupnames[itr], groupnames[itr] + 'mean'] tableToConcat = tableToConcat.merge(pt, on=groupnames[itr])

demeanlist = [] for index, row in tableToConcat.iterrows(): weightedmean = 0 for itr in range(0, groupslen): weightedmean += (weights[itr] * row[groupmeannames[itr]]) demeanlist += [row['quant'] - weightedmean]

return demeanlist

def test_three_groups(): vals = [1, 2, 3, 8, 5] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'MA', 'MA', 'RI', 'RI'] grps_3 = ['WEYMOUTH', 'BOSTON', 'BOSTON', 'PROVIDENCE', 'PROVIDENCE'] weights = [.15, .35, .5]

adj_vals = group_adjust(vals, [grps_1, grps_2, grps_3], weights) # 1 - (USA_mean*.15 + MA_mean * .35 + WEYMOUTH_mean * .5) # 2 - (USA_mean*.15 + MA_mean * .35 + BOSTON_mean * .5) # 3 - (USA_mean*.15 + MA_mean * .35 + BOSTON_mean * .5) # etc ... # Plug in the numbers ... # 1 - (.15*2 + .35*2 + .5*1) # -0.5 # 2 - (.15*2 + .35*2 + .5*2.5) # -.25 # 3 - (.15*2 + .35*2 + .5*2.5) # 0.75 # etc...

answer = [-0.770, -0.520, 0.480, 1.905, -1.095] for ans, res in zip(answer, adj_vals): assert abs(ans - res) < 1e-5

def test_two_groups(): vals = [1, 2, 3, 8, 5] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'RI', 'CT', 'CT', 'CT'] weights = [.65, .35]

adj_vals = group_adjust(vals, [grps_1, grps_2], weights) # 1 - (.65*2 + .35*1) # -0.65 # 2 - (.65*2 + .35*2.5) # -.175 # 3 - (.65*2 + .35*2.5) # -.825 answer = [-1.81999, -1.16999, -1.33666, 3.66333, 0.66333] for ans, res in zip(answer, adj_vals): assert abs(ans - res) < 1e-5

def test_missing_vals(): # If you're using NumPy or Pandas, use np.NaN # If you're writing pyton, use None vals = [1, np.NaN, 3, 5, 8, 7] # vals = [1, None, 3, 5, 8, 7] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'RI', 'RI', 'CT', 'CT', 'CT'] weights = [.65, .35]

adj_vals = group_adjust(vals, [grps_1, grps_2], weights)

# This should be None or np.NaN depending on your implementation # please feel free to change this line to match yours answer = [-2.47, np.NaN, -1.170, -0.4533333, 2.54666666, 1.54666666] # answer = [-2.47, None, -1.170, -0.4533333, 2.54666666, 1.54666666]

for ans, res in zip(answer, adj_vals): if ans is None: assert res is None elif np.isnan(ans): assert np.isnan(res) else: assert abs(ans - res) < 1e-5

def test_weights_len_equals_group_len(): # Need to have 1 weight for each group

# vals = [1, np.NaN, 3, 5, 8, 7] vals = [1, None, 3, 5, 8, 7] grps_1 = ['USA', 'USA', 'USA', 'USA', 'USA', 'USA'] grps_2 = ['MA', 'RI', 'RI', 'CT', 'CT', 'CT'] weights = [.65]

with pytest.raises(ValueError): group_adjust(vals, [grps_1, grps_2], weights)

def test_group_len_equals_vals_len(): # The groups need to be same shape as vals vals = [1, None, 3, 5, 8, 7] grps_1 = ['USA'] grps_2 = ['MA', 'RI', 'RI', 'CT', 'CT', 'CT'] weights = [.65]

with pytest.raises(ValueError): group_adjust(vals, [grps_1, grps_2], weights)

def test_performance(): # vals = *[1, None, 3, 5, 8, 7] # If you're doing numpy, use the np.NaN instead vals = 1000000 * [1, np.NaN, 3, 5, 8, 7] grps_1 = 1000000 * [1, 1, 1, 1, 1, 1] grps_2 = 1000000 * [1, 1, 1, 1, 2, 2] grps_3 = 1000000 * [1, 2, 2, 3, 4, 5] weights = [.20, .30, .50]

start = datetime.now() group_adjust(vals, [grps_1, grps_2, grps_3], weights) end = datetime.now() diff = end - start print('Total performance test time: {}'.format(diff.total_seconds()))

if __name__ == "__main__": test_three_groups() test_two_groups() test_missing_vals() test_weights_len_equals_group_len() test_group_len_equals_vals_len() test_performance()