Kuni
Reputation: 865
Should I convert list of dictionaries to numpy array?
I'm trying to learn numpy. To learn it, I wanted to use it in my own dataset which is a list of dictionaries. Here's a sample of the data:
array([[{'name': 'Male, n (%)', 'exp_g1 (N=6557)': '1580 (61.28%)', 'Rest (N=11462)': '2837 (62.28%)', 'p_value': '0.017'},
{'name': 'Age, years', 'p_value': '0.379', 'ks_score': '0.01', 'diff_means': '-0.25', 'group0_percent_null': '1.66%', 'group1_percent_null': '1.72%', 'exp_g1 (N=6557)': '44.00 [38.00 - 50.00]', 'Rest (N=11462)': '44.00 [38.00 - 50.00]'},
{'name': 'Baby Boomers (1946-1964), n (%)', 'exp_g1 (N=6557)': '4018 (24.10%)', 'Rest (N=11462)': '7189 (25.25%)', 'p_value': '0.003'},
{'name': 'Generation X (1965-1980), n (%)', 'exp_g1 (N=6557)': '4003 (61.05%)', 'Rest (N=11462)': '6883 (59.98%)', 'p_value': '0.012'},
{'name': 'Generation Y/Millennials (1981-1996), n (%)', 'exp_g1 (N=6557)': '858 (13.09%)', 'Rest (N=11462)': '1533 (12.96%)', 'p_value': '0.358'},
{'name': 'Generation Z (1997 and onwards), n (%)', 'exp_g1 (N=6557)': '5 (0.08%)', 'Rest (N=11462)': '11 (0.11%)', 'p_value': '0.225'},
{'name': 'African American, n (%)', 'exp_g1 (N=6557)': '2021 (6.04%)', 'Rest (N=11462)': '3507 (6.14%)', 'p_value': '0.337'},
{'name': 'American Indian or Alaskan Nat, n (%)', 'exp_g1 (N=6557)': '620 (0.23%)', 'Rest (N=11462)': '1039 (0.29%)', 'p_value': '0.134'},
{'name': 'Asian, n (%)', 'exp_g1 (N=6557)': '192 (2.06%)', 'Rest (N=11462)': '317 (2.06%)', 'p_value': '0.509'},
{'name': 'Caucasian, n (%)', 'exp_g1 (N=6557)': '55 (79.24%)', 'Rest (N=11462)': '86 (79.09%)', 'p_value': '0.354'},
{'name': 'Hawaiian or Other Pacific Islander, n (%)', 'exp_g1 (N=6557)': '131 (0.03%)', 'Rest (N=11462)': '207 (0.08%)', 'p_value': '0.063'},
{'name': 'Hispanic, n (%)', 'exp_g1 (N=6557)': '4149 (3.25%)', 'Rest (N=11462)': '7450 (3.26%)', 'p_value': '0.496'},
{'name': 'Multiracial, n (%)', 'exp_g1 (N=6557)': '1401 (2.47%)', 'Rest (N=11462)': '2322 (2.42%)', 'p_value': '0.379'},
{'name': 'Other (Race), n (%)', 'exp_g1 (N=6557)': '938 (1.05%)', 'Rest (N=11462)': '1591 (1.05%)', 'p_value': '0.520'},
{'name': 'Top, n (%)', 'exp_g1 (N=6557)': '162 (2.93%)', 'Rest (N=11462)': '267 (2.95%)', 'p_value': '0.476'},
{'name': 'Executive, n (%)', 'exp_g1 (N=6557)': '5196 (25.42%)', 'Rest (N=11462)': '9072 (25.29%)', 'p_value': '0.383'},
{'name': 'Upper Middle, n(%)', 'exp_g1 (N=6557)': '2 (26.87%)', 'Rest (N=11462)': '9 (27.43%)', 'p_value': '0.101'},
{'name': 'Middle, n (%)', 'exp_g1 (N=6557)': '135 (30.82%)', 'Rest (N=11462)': '247 (30.50%)', 'p_value': '0.238'},
{'name': 'First Level, n (%)', 'exp_g1 (N=6557)': '69 (9.46%)', 'Rest (N=11462)': '129 (9.43%)', 'p_value': '0.469'},
{'name': 'Hourly, n (%)', 'exp_g1 (N=6557)': '213 (0.84%)', 'Rest (N=11462)': '370 (0.83%)', 'p_value': '0.511'},
{'name': 'Other (Organization level), n (%)', 'exp_g1 (N=6557)': '396 (2.00%)', 'Rest (N=11462)': '703 (1.99%)', 'p_value': '0.486'},
{'name': 'Business Sector, n (%)', 'exp_g1 (N=6557)': '15 (63.28%)', 'Rest (N=11462)': '39 (62.61%)', 'p_value': '0.079'},
{'name': 'Nonprofit Sector, n (%)', 'exp_g1 (N=6557)': '546 (14.31%)', 'Rest (N=11462)': '1047 (14.43%)', 'p_value': '0.364'},
{'name': 'Public, n (%)', 'exp_g1 (N=6557)': '917 (21.37%)', 'Rest (N=11462)': '1488 (22.06%)', 'p_value': '0.043'},
{'name': 'Aerospace & Defense, n (%)', 'exp_g1 (N=6557)': '952 (5.35%)', 'Rest (N=11462)': '1746 (5.47%)', 'p_value': '0.306'},
{'name': 'Automotive & Transport Equipment, n (%)', 'exp_g1 (N=6557)': '263 (2.97%)', 'Rest (N=11462)': '412 (2.94%)', 'p_value': '0.435'},
{'name': 'Chemicals, n (%)', 'exp_g1 (N=6557)': '111 (1.10%)', 'Rest (N=11462)': '217 (1.23%)', 'p_value': '0.120'},
{'name': 'Computer Hardware & Software Services, n (%)', 'exp_g1 (N=6557)': '235 (2.17%)', 'Rest (N=11462)': '426 (2.10%)', 'p_value': '0.336'},
{'name': 'Consumer Products, n (%)', 'exp_g1 (N=6557)': '287 (4.01%)', 'Rest (N=11462)': '457 (3.42%)', 'p_value': '<0.001'},
{'name': 'Education, n (%)', 'exp_g1 (N=6557)': '133 (4.38%)', 'Rest (N=11462)': '238 (4.21%)', 'p_value': '0.208'},
{'name': 'Energy, n (%)', 'exp_g1 (N=6557)': '141 (3.36%)', 'Rest (N=11462)': '252 (3.44%)', 'p_value': '0.322'},
{'name': 'Financial Services, n (%)', 'exp_g1 (N=6557)': '351 (8.78%)', 'Rest (N=11462)': '608 (8.37%)', 'p_value': '0.064'},
{'name': 'Food, Beverage, & Tobacco, n (%)', 'exp_g1 (N=6557)': '142 (3.58%)', 'Rest (N=11462)': '261 (3.41%)', 'p_value': '0.168'},
{'name': 'Government, n (%)', 'exp_g1 (N=6557)': '72 (13.99%)', 'Rest (N=11462)': '155 (14.39%)', 'p_value': '0.117'},
{'name': 'Health Products & Services, n (%)', 'exp_g1 (N=6557)': '212 (4.30%)', 'Rest (N=11462)': '384 (4.15%)', 'p_value': '0.231'},
{'name': 'Manufacturing, n (%)', 'exp_g1 (N=6557)': '220 (8.33%)', 'Rest (N=11462)': '412 (8.62%)', 'p_value': '0.143'},
{'name': 'Materials & Construction, n (%)', 'exp_g1 (N=6557)': '246 (1.69%)', 'Rest (N=11462)': '449 (1.81%)', 'p_value': '0.192'},
{'name': 'Media, n (%)', 'exp_g1 (N=6557)': '576 (1.43%)', 'Rest (N=11462)': '963 (1.31%)', 'p_value': '0.153'},
{'name': 'Nonprofit, n (%)', 'exp_g1 (N=6557)': '342 (5.22%)', 'Rest (N=11462)': '604 (5.59%)', 'p_value': '0.050'},
{'name': 'Other (Organization Type), n (%)', 'exp_g1 (N=6557)': '282 (14.52%)', 'Rest (N=11462)': '462 (14.98%)', 'p_value': '0.095'},
{'name': 'Pharmaceuticals, n (%)', 'exp_g1 (N=6557)': '99 (3.23%)', 'Rest (N=11462)': '168 (3.12%)', 'p_value': '0.264'},
{'name': 'Real Estate, n (%)', 'exp_g1 (N=6557)': '72 (1.10%)', 'Rest (N=11462)': '124 (1.04%)', 'p_value': '0.316'},
{'name': 'Retail, n (%)', 'exp_g1 (N=6557)': '195 (3.75%)', 'Rest (N=11462)': '342 (3.85%)', 'p_value': '0.309'},
{'name': 'Telecommunications, n (%)', 'exp_g1 (N=6557)': '94 (1.51%)', 'Rest (N=11462)': '148 (1.45%)', 'p_value': '0.311'},
{'name': 'Transportation, n (%)', 'exp_g1 (N=6557)': '1762 (2.03%)', 'Rest (N=11462)': '3181 (2.03%)', 'p_value': '0.507'},
{'name': 'Utilities, n (%)', 'exp_g1 (N=6557)': '1667 (2.15%)', 'Rest (N=11462)': '2942 (2.15%)', 'p_value': '0.514'}],
[{'name': 'Male, n (%)', 'exp_g4 (N=2757)': '725 (64.20%)', 'Rest (N=11462)': '2837 (62.28%)', 'p_value': '0.012'},
{'name': 'Age, years', 'p_value': '0.481', 'ks_score': '0.02', 'diff_means': '0.14', 'group0_percent_null': '1.52%', 'group1_percent_null': '1.73%', 'exp_g4 (N=2757)': '44.00 [38.00 - 50.00]', 'Rest (N=11462)': '44.00 [38.00 - 50.00]'},
{'name': 'Baby Boomers (1946-1964), n (%)', 'exp_g4 (N=2757)': '1770 (26.30%)', 'Rest (N=11462)': '7189 (25.25%)', 'p_value': '0.087'},
{'name': 'Generation X (1965-1980), n (%)', 'exp_g4 (N=2757)': '1633 (59.23%)', 'Rest (N=11462)': '6883 (59.98%)', 'p_value': '0.194'},
{'name': 'Generation Y/Millennials (1981-1996), n (%)', 'exp_g4 (N=2757)': '354 (12.84%)', 'Rest (N=11462)': '1533 (12.96%)', 'p_value': '0.432'},
{'name': 'Generation Z (1997 and onwards), n (%)', 'exp_g4 (N=2757)': '1 (0.04%)', 'Rest (N=11462)': '11 (0.11%)', 'p_value': '0.176'},
{'name': 'African American, n (%)', 'exp_g4 (N=2757)': '846 (5.73%)', 'Rest (N=11462)': '3507 (6.14%)', 'p_value': '0.172'},
{'name': 'American Indian or Alaskan Nat, n (%)', 'exp_g4 (N=2757)': '270 (0.33%)', 'Rest (N=11462)': '1039 (0.29%)', 'p_value': '0.423'},
{'name': 'Asian, n (%)', 'exp_g4 (N=2757)': '56 (1.85%)', 'Rest (N=11462)': '317 (2.06%)', 'p_value': '0.216'},
{'name': 'Caucasian, n (%)', 'exp_g4 (N=2757)': '30 (80.38%)', 'Rest (N=11462)': '86 (79.09%)', 'p_value': '0.036'},
{'name': 'Hawaiian or Other Pacific Islander, n (%)', 'exp_g4 (N=2757)': '42 (0.07%)', 'Rest (N=11462)': '207 (0.08%)', 'p_value': '0.633'},
{'name': 'Hispanic, n (%)', 'exp_g4 (N=2757)': '1773 (2.97%)', 'Rest (N=11462)': '7450 (3.26%)', 'p_value': '0.196'},
{'name': 'Multiracial, n (%)', 'exp_g4 (N=2757)': '628 (2.79%)', 'Rest (N=11462)': '2322 (2.42%)', 'p_value': '0.093'},
{'name': 'Other (Race), n (%)', 'exp_g4 (N=2757)': '334 (0.73%)', 'Rest (N=11462)': '1591 (1.05%)', 'p_value': '0.037'},
{'name': 'Top, n (%)', 'exp_g4 (N=2757)': '77 (2.03%)', 'Rest (N=11462)': '267 (2.95%)', 'p_value': '<0.001'},
{'name': 'Executive, n (%)', 'exp_g4 (N=2757)': '2216 (25.61%)', 'Rest (N=11462)': '9072 (25.29%)', 'p_value': '0.346'},
{'name': 'Upper Middle, n(%)', 'exp_g4 (N=2757)': '2 (27.78%)', 'Rest (N=11462)': '9 (27.43%)', 'p_value': '0.331'},
{'name': 'Middle, n (%)', 'exp_g4 (N=2757)': '51 (30.69%)', 'Rest (N=11462)': '247 (30.50%)', 'p_value': '0.416'},
{'name': 'First Level, n (%)', 'exp_g4 (N=2757)': '20 (9.79%)', 'Rest (N=11462)': '129 (9.43%)', 'p_value': '0.245'},
{'name': 'Hourly, n (%)', 'exp_g4 (N=2757)': '82 (1.09%)', 'Rest (N=11462)': '370 (0.83%)', 'p_value': '0.071'},
{'name': 'Other (Organization level), n (%)', 'exp_g4 (N=2757)': '158 (1.52%)', 'Rest (N=11462)': '703 (1.99%)', 'p_value': '0.030'},
{'name': 'Business Sector, n (%)', 'exp_g4 (N=2757)': '9 (64.31%)', 'Rest (N=11462)': '39 (62.61%)', 'p_value': '0.023'},
{'name': 'Nonprofit Sector, n (%)', 'exp_g4 (N=2757)': '267 (12.11%)', 'Rest (N=11462)': '1047 (14.43%)', 'p_value': '<0.001'},
{'name': 'Public, n (%)', 'exp_g4 (N=2757)': '413 (22.78%)', 'Rest (N=11462)': '1488 (22.06%)', 'p_value': '0.165'},
{'name': 'Aerospace & Defense, n (%)', 'exp_g4 (N=2757)': '412 (6.02%)', 'Rest (N=11462)': '1746 (5.47%)', 'p_value': '0.091'},
{'name': 'Automotive & Transport Equipment, n (%)', 'exp_g4 (N=2757)': '82 (2.90%)', 'Rest (N=11462)': '412 (2.94%)', 'p_value': '0.478'},
{'name': 'Chemicals, n (%)', 'exp_g4 (N=2757)': '49 (1.70%)', 'Rest (N=11462)': '217 (1.23%)', 'p_value': '0.011'},
{'name': 'Computer Hardware & Software Services, n (%)', 'exp_g4 (N=2757)': '94 (2.50%)', 'Rest (N=11462)': '426 (2.10%)', 'p_value': '0.064'},
{'name': 'Consumer Products, n (%)', 'exp_g4 (N=2757)': '87 (2.97%)', 'Rest (N=11462)': '457 (3.42%)', 'p_value': '0.086'},
{'name': 'Education, n (%)', 'exp_g4 (N=2757)': '59 (3.16%)', 'Rest (N=11462)': '238 (4.21%)', 'p_value': '0.001'},
{'name': 'Energy, n (%)', 'exp_g4 (N=2757)': '61 (4.28%)', 'Rest (N=11462)': '252 (3.44%)', 'p_value': '0.006'},
{'name': 'Financial Services, n (%)', 'exp_g4 (N=2757)': '166 (8.05%)', 'Rest (N=11462)': '608 (8.37%)', 'p_value': '0.268'},
{'name': 'Food, Beverage, & Tobacco, n (%)', 'exp_g4 (N=2757)': '69 (3.41%)', 'Rest (N=11462)': '261 (3.41%)', 'p_value': '0.516'},
{'name': 'Government, n (%)', 'exp_g4 (N=2757)': '47 (14.98%)', 'Rest (N=11462)': '155 (14.39%)', 'p_value': '0.176'},
{'name': 'Health Products & Services, n (%)', 'exp_g4 (N=2757)': '90 (3.34%)', 'Rest (N=11462)': '384 (4.15%)', 'p_value': '0.009'},
{'name': 'Manufacturing, n (%)', 'exp_g4 (N=2757)': '118 (9.68%)', 'Rest (N=11462)': '412 (8.62%)', 'p_value': '0.017'},
{'name': 'Materials & Construction, n (%)', 'exp_g4 (N=2757)': '86 (1.78%)', 'Rest (N=11462)': '449 (1.81%)', 'p_value': '0.478'},
{'name': 'Media, n (%)', 'exp_g4 (N=2757)': '222 (1.45%)', 'Rest (N=11462)': '963 (1.31%)', 'p_value': '0.270'},
{'name': 'Nonprofit, n (%)', 'exp_g4 (N=2757)': '133 (4.82%)', 'Rest (N=11462)': '604 (5.59%)', 'p_value': '0.030'},
{'name': 'Other (Organization Type), n (%)', 'exp_g4 (N=2757)': '92 (14.94%)', 'Rest (N=11462)': '462 (14.98%)', 'p_value': '0.492'},
{'name': 'Pharmaceuticals, n (%)', 'exp_g4 (N=2757)': '36 (3.26%)', 'Rest (N=11462)': '168 (3.12%)', 'p_value': '0.335'},
{'name': 'Real Estate, n (%)', 'exp_g4 (N=2757)': '32 (1.16%)', 'Rest (N=11462)': '124 (1.04%)', 'p_value': '0.284'},
{'name': 'Retail, n (%)', 'exp_g4 (N=2757)': '80 (3.12%)', 'Rest (N=11462)': '342 (3.85%)', 'p_value': '0.015'},
{'name': 'Telecommunications, n (%)', 'exp_g4 (N=2757)': '40 (1.31%)', 'Rest (N=11462)': '148 (1.45%)', 'p_value': '0.282'},
{'name': 'Transportation, n (%)', 'exp_g4 (N=2757)': '766 (2.14%)', 'Rest (N=11462)': '3181 (2.03%)', 'p_value': '0.355'},
{'name': 'Utilities, n (%)', 'exp_g4 (N=2757)': '706 (2.21%)', 'Rest (N=11462)': '2942 (2.15%)', 'p_value': '0.421'}],
[{'name': 'Male, n (%)', 'exp_g6 (N=3019)': '786 (63.03%)', 'Rest (N=11462)': '2837 (62.28%)', 'p_value': '0.177'},
{'name': 'Age, years', 'p_value': '0.155', 'ks_score': '0.02', 'diff_means': '0.29', 'group0_percent_null': '2.05%', 'group1_percent_null': '1.62%', 'exp_g6 (N=3019)': '44.00 [38.00 - 51.00]', 'Rest (N=11462)': '44.00 [38.00 - 50.00]'},
{'name': 'Baby Boomers (1946-1964), n (%)', 'exp_g6 (N=3019)': '1903 (26.04%)', 'Rest (N=11462)': '7189 (25.25%)', 'p_value': '0.141'},
{'name': 'Generation X (1965-1980), n (%)', 'exp_g6 (N=3019)': '1772 (58.69%)', 'Rest (N=11462)': '6883 (59.98%)', 'p_value': '0.057'},
{'name': 'Generation Y/Millennials (1981-1996), n (%)', 'exp_g6 (N=3019)': '393 (13.02%)', 'Rest (N=11462)': '1533 (12.96%)', 'p_value': '0.469'},
{'name': 'Generation Z (1997 and onwards), n (%)', 'exp_g6 (N=3019)': '5 (0.17%)', 'Rest (N=11462)': '11 (0.11%)', 'p_value': '0.215'},
{'name': 'African American, n (%)', 'exp_g6 (N=3019)': '936 (6.06%)', 'Rest (N=11462)': '3507 (6.14%)', 'p_value': '0.437'},
{'name': 'American Indian or Alaskan Nat, n (%)', 'exp_g6 (N=3019)': '246 (0.40%)', 'Rest (N=11462)': '1039 (0.29%)', 'p_value': '0.164'},
{'name': 'Asian, n (%)', 'exp_g6 (N=3019)': '98 (2.05%)', 'Rest (N=11462)': '317 (2.06%)', 'p_value': '0.516'},
{'name': 'Caucasian, n (%)', 'exp_g6 (N=3019)': '23 (79.70%)', 'Rest (N=11462)': '86 (79.09%)', 'p_value': '0.190'},
{'name': 'Hawaiian or Other Pacific Islander, n (%)', 'exp_g6 (N=3019)': '64 (0.13%)', 'Rest (N=11462)': '207 (0.08%)', 'p_value': '0.197'},
{'name': 'Hispanic, n (%)', 'exp_g6 (N=3019)': '1929 (3.05%)', 'Rest (N=11462)': '7450 (3.26%)', 'p_value': '0.255'},
{'name': 'Multiracial, n (%)', 'exp_g6 (N=3019)': '624 (2.12%)', 'Rest (N=11462)': '2322 (2.42%)', 'p_value': '0.132'},
{'name': 'Other (Race), n (%)', 'exp_g6 (N=3019)': '429 (1.23%)', 'Rest (N=11462)': '1591 (1.05%)', 'p_value': '0.171'},
{'name': 'Top, n (%)', 'exp_g6 (N=3019)': '64 (3.25%)', 'Rest (N=11462)': '267 (2.95%)', 'p_value': '0.154'},
{'name': 'Executive, n (%)', 'exp_g6 (N=3019)': '2406 (26.37%)', 'Rest (N=11462)': '9072 (25.29%)', 'p_value': '0.070'},
{'name': 'Upper Middle, n(%)', 'exp_g6 (N=3019)': '4 (26.83%)', 'Rest (N=11462)': '9 (27.43%)', 'p_value': '0.215'},
{'name': 'Middle, n (%)', 'exp_g6 (N=3019)': '62 (31.00%)', 'Rest (N=11462)': '247 (30.50%)', 'p_value': '0.260'},
{'name': 'First Level, n (%)', 'exp_g6 (N=3019)': '37 (8.15%)', 'Rest (N=11462)': '129 (9.43%)', 'p_value': '0.004'},
{'name': 'Hourly, n (%)', 'exp_g6 (N=3019)': '92 (0.76%)', 'Rest (N=11462)': '370 (0.83%)', 'p_value': '0.363'},
{'name': 'Other (Organization level), n (%)', 'exp_g6 (N=3019)': '183 (2.12%)', 'Rest (N=11462)': '703 (1.99%)', 'p_value': '0.301'},
{'name': 'Business Sector, n (%)', 'exp_g6 (N=3019)': '12 (63.90%)', 'Rest (N=11462)': '39 (62.61%)', 'p_value': '0.056'},
{'name': 'Nonprofit Sector, n (%)', 'exp_g6 (N=3019)': '273 (14.21%)', 'Rest (N=11462)': '1047 (14.43%)', 'p_value': '0.364'},
{'name': 'Public, n (%)', 'exp_g6 (N=3019)': '398 (20.67%)', 'Rest (N=11462)': '1488 (22.06%)', 'p_value': '0.022'},
{'name': 'Aerospace & Defense, n (%)', 'exp_g6 (N=3019)': '470 (5.07%)', 'Rest (N=11462)': '1746 (5.47%)', 'p_value': '0.150'},
{'name': 'Automotive & Transport Equipment, n (%)', 'exp_g6 (N=3019)': '99 (2.82%)', 'Rest (N=11462)': '412 (2.94%)', 'p_value': '0.353'},
{'name': 'Chemicals, n (%)', 'exp_g6 (N=3019)': '58 (1.46%)', 'Rest (N=11462)': '217 (1.23%)', 'p_value': '0.124'},
{'name': 'Computer Hardware & Software Services, n (%)', 'exp_g6 (N=3019)': '108 (1.79%)', 'Rest (N=11462)': '426 (2.10%)', 'p_value': '0.104'},
{'name': 'Consumer Products, n (%)', 'exp_g6 (N=3019)': '137 (3.28%)', 'Rest (N=11462)': '457 (3.42%)', 'p_value': '0.342'},
{'name': 'Education, n (%)', 'exp_g6 (N=3019)': '62 (4.54%)', 'Rest (N=11462)': '238 (4.21%)', 'p_value': '0.175'},
{'name': 'Energy, n (%)', 'exp_g6 (N=3019)': '51 (3.51%)', 'Rest (N=11462)': '252 (3.44%)', 'p_value': '0.429'},
{'name': 'Financial Services, n (%)', 'exp_g6 (N=3019)': '153 (8.21%)', 'Rest (N=11462)': '608 (8.37%)', 'p_value': '0.382'},
{'name': 'Food, Beverage, & Tobacco, n (%)', 'exp_g6 (N=3019)': '54 (3.58%)', 'Rest (N=11462)': '261 (3.41%)', 'p_value': '0.302'},
{'name': 'Government, n (%)', 'exp_g6 (N=3019)': '44 (13.18%)', 'Rest (N=11462)': '155 (14.39%)', 'p_value': '0.019'},
{'name': 'Health Products & Services, n (%)', 'exp_g6 (N=3019)': '91 (4.60%)', 'Rest (N=11462)': '384 (4.15%)', 'p_value': '0.094'},
{'name': 'Manufacturing, n (%)', 'exp_g6 (N=3019)': '106 (9.04%)', 'Rest (N=11462)': '412 (8.62%)', 'p_value': '0.191'},
{'name': 'Materials & Construction, n (%)', 'exp_g6 (N=3019)': '137 (1.92%)', 'Rest (N=11462)': '449 (1.81%)', 'p_value': '0.332'},
{'name': 'Media, n (%)', 'exp_g6 (N=3019)': '248 (1.19%)', 'Rest (N=11462)': '963 (1.31%)', 'p_value': '0.292'},
{'name': 'Nonprofit, n (%)', 'exp_g6 (N=3019)': '161 (5.33%)', 'Rest (N=11462)': '604 (5.59%)', 'p_value': '0.267'},
{'name': 'Other (Organization Type), n (%)', 'exp_g6 (N=3019)': '139 (15.57%)', 'Rest (N=11462)': '462 (14.98%)', 'p_value': '0.164'},
{'name': 'Pharmaceuticals, n (%)', 'exp_g6 (N=3019)': '41 (3.01%)', 'Rest (N=11462)': '168 (3.12%)', 'p_value': '0.382'},
{'name': 'Real Estate, n (%)', 'exp_g6 (N=3019)': '31 (1.03%)', 'Rest (N=11462)': '124 (1.04%)', 'p_value': '0.507'},
{'name': 'Retail, n (%)', 'exp_g6 (N=3019)': '85 (4.54%)', 'Rest (N=11462)': '342 (3.85%)', 'p_value': '0.019'},
{'name': 'Telecommunications, n (%)', 'exp_g6 (N=3019)': '36 (1.36%)', 'Rest (N=11462)': '148 (1.45%)', 'p_value': '0.364'},
{'name': 'Transportation, n (%)', 'exp_g6 (N=3019)': '810 (2.05%)', 'Rest (N=11462)': '3181 (2.03%)', 'p_value': '0.489'},
{'name': 'Utilities, n (%)', 'exp_g6 (N=3019)': '796 (1.69%)', 'Rest (N=11462)': '2942 (2.15%)', 'p_value': '0.029'}]],
dtype=object)
Now I want to extract just the name, p_value, and the key that starts with exp. Is this not a good idea to do this using numpy? How do I extract these and still keep the structure?
Upvotes: 0
Views: 78
Answers (2)
Kuni
Reputation: 865
My pandas implementation to solve the problem I had for which I was trying to use numpy.
filter_value = 'name|^exp_*.*\d'
def create_anova_table(self, group_comparisons_rest=[], filter_value=None):
'''
Create an ANOVA-like table with all groups and the overall p-value. The idea is that the table that is generated from comparison is too complex to integrate; each table for each group comparion. Therefore, this function extracts the group data for each group, and the p-value from the comparison of each group with the rest. The p-value column then consists of the min p-value for all groups vs the rest comparison. This can then be somehow interpreted as ANOVA table with p-value indicating the groups are different.
'''
dataframes_rest = [pd.DataFrame(d) for d in group_comparisons_rest]
## convert the list of dataframes into a single dataframe where group comparisons and p-value is included for each dataframe
df = pd.concat([d.iloc[:,[0,1,3]] for d in dataframes_rest], axis=1)
## clean up the table - extract a single name (since all dataframes consists of name column)
table = df.filter(regex=filter_value)
table = table.loc[:,~table.columns.duplicated()]
p_value_df = df.filter(regex='p_value').copy()
p_value_df['p_value_rest'] = np.where(p_value_df.eq('<0.001').any(1), '<0.001', df.min(axis=1) )
df_anova = pd.concat([table, p_value_df['p_value_rest']], axis=1)
## now insert the p-value significance for combinatorial group comparison
return df_anova
This was my implementation. I wish to make it more efficient, that is why I was trying to do it using numpy. Perhaps, I can clean up the function to make it bit more efficient
Upvotes: 0
Deepak
Reputation: 470
Okay....i don't think this is an optimised solution for this problem..But here is something you can try
def filter_list_of_dict(old_list):
key_list = ['name','p_value']
new_list = list()
for i in range(len(data)):
intermediate_list = []
for k in data[i]:
dict_keys = list(k.keys())
list_dict = [key for key in dict_keys if key in key_list or 'exp' in key]
intermediate_list.append({ your_key: k[your_key] for your_key in list_dict })
new_list.append(intermediate_list)
return new_list
filter_list_of_dict(data)
and I just formatted your source data as well
data = [[{'name': 'Male, n (%)', 'exp_g1 (N=6557)': '1580 (61.28%)', 'Rest (N=11462)': '2837 (62.28%)', 'p_value': '0.017'},
{'name': 'Age, years', 'p_value': '0.379', 'ks_score': '0.01', 'diff_means': '-0.25', 'group0_percent_null': '1.66%', 'group1_percent_null': '1.72%', 'exp_g1 (N=6557)': '44.00 [38.00 - 50.00]', 'Rest (N=11462)': '44.00 [38.00 - 50.00]'},
{'name': 'Baby Boomers (1946-1964), n (%)', 'exp_g1 (N=6557)': '4018 (24.10%)', 'Rest (N=11462)': '7189 (25.25%)', 'p_value': '0.003'},
{'name': 'Generation X (1965-1980), n (%)', 'exp_g1 (N=6557)': '4003 (61.05%)', 'Rest (N=11462)': '6883 (59.98%)', 'p_value': '0.012'},
{'name': 'Generation Y/Millennials (1981-1996), n (%)', 'exp_g1 (N=6557)': '858 (13.09%)', 'Rest (N=11462)': '1533 (12.96%)', 'p_value': '0.358'},
{'name': 'Generation Z (1997 and onwards), n (%)', 'exp_g1 (N=6557)': '5 (0.08%)', 'Rest (N=11462)': '11 (0.11%)', 'p_value': '0.225'},
{'name': 'African American, n (%)', 'exp_g1 (N=6557)': '2021 (6.04%)', 'Rest (N=11462)': '3507 (6.14%)', 'p_value': '0.337'},
{'name': 'American Indian or Alaskan Nat, n (%)', 'exp_g1 (N=6557)': '620 (0.23%)', 'Rest (N=11462)': '1039 (0.29%)', 'p_value': '0.134'},
{'name': 'Asian, n (%)', 'exp_g1 (N=6557)': '192 (2.06%)', 'Rest (N=11462)': '317 (2.06%)', 'p_value': '0.509'},
{'name': 'Caucasian, n (%)', 'exp_g1 (N=6557)': '55 (79.24%)', 'Rest (N=11462)': '86 (79.09%)', 'p_value': '0.354'},
{'name': 'Hawaiian or Other Pacific Islander, n (%)', 'exp_g1 (N=6557)': '131 (0.03%)', 'Rest (N=11462)': '207 (0.08%)', 'p_value': '0.063'},
{'name': 'Hispanic, n (%)', 'exp_g1 (N=6557)': '4149 (3.25%)', 'Rest (N=11462)': '7450 (3.26%)', 'p_value': '0.496'},
{'name': 'Multiracial, n (%)', 'exp_g1 (N=6557)': '1401 (2.47%)', 'Rest (N=11462)': '2322 (2.42%)', 'p_value': '0.379'},
{'name': 'Other (Race), n (%)', 'exp_g1 (N=6557)': '938 (1.05%)', 'Rest (N=11462)': '1591 (1.05%)', 'p_value': '0.520'},
{'name': 'Top, n (%)', 'exp_g1 (N=6557)': '162 (2.93%)', 'Rest (N=11462)': '267 (2.95%)', 'p_value': '0.476'},
{'name': 'Executive, n (%)', 'exp_g1 (N=6557)': '5196 (25.42%)', 'Rest (N=11462)': '9072 (25.29%)', 'p_value': '0.383'},
{'name': 'Upper Middle, n(%)', 'exp_g1 (N=6557)': '2 (26.87%)', 'Rest (N=11462)': '9 (27.43%)', 'p_value': '0.101'},
{'name': 'Middle, n (%)', 'exp_g1 (N=6557)': '135 (30.82%)', 'Rest (N=11462)': '247 (30.50%)', 'p_value': '0.238'},
{'name': 'First Level, n (%)', 'exp_g1 (N=6557)': '69 (9.46%)', 'Rest (N=11462)': '129 (9.43%)', 'p_value': '0.469'},
{'name': 'Hourly, n (%)', 'exp_g1 (N=6557)': '213 (0.84%)', 'Rest (N=11462)': '370 (0.83%)', 'p_value': '0.511'},
{'name': 'Other (Organization level), n (%)', 'exp_g1 (N=6557)': '396 (2.00%)', 'Rest (N=11462)': '703 (1.99%)', 'p_value': '0.486'},
{'name': 'Business Sector, n (%)', 'exp_g1 (N=6557)': '15 (63.28%)', 'Rest (N=11462)': '39 (62.61%)', 'p_value': '0.079'},
{'name': 'Nonprofit Sector, n (%)', 'exp_g1 (N=6557)': '546 (14.31%)', 'Rest (N=11462)': '1047 (14.43%)', 'p_value': '0.364'},
{'name': 'Public, n (%)', 'exp_g1 (N=6557)': '917 (21.37%)', 'Rest (N=11462)': '1488 (22.06%)', 'p_value': '0.043'},
{'name': 'Aerospace & Defense, n (%)', 'exp_g1 (N=6557)': '952 (5.35%)', 'Rest (N=11462)': '1746 (5.47%)', 'p_value': '0.306'},
{'name': 'Automotive & Transport Equipment, n (%)', 'exp_g1 (N=6557)': '263 (2.97%)', 'Rest (N=11462)': '412 (2.94%)', 'p_value': '0.435'},
{'name': 'Chemicals, n (%)', 'exp_g1 (N=6557)': '111 (1.10%)', 'Rest (N=11462)': '217 (1.23%)', 'p_value': '0.120'},
{'name': 'Computer Hardware & Software Services, n (%)', 'exp_g1 (N=6557)': '235 (2.17%)', 'Rest (N=11462)': '426 (2.10%)', 'p_value': '0.336'},
{'name': 'Consumer Products, n (%)', 'exp_g1 (N=6557)': '287 (4.01%)', 'Rest (N=11462)': '457 (3.42%)', 'p_value': '<0.001'},
{'name': 'Education, n (%)', 'exp_g1 (N=6557)': '133 (4.38%)', 'Rest (N=11462)': '238 (4.21%)', 'p_value': '0.208'},
{'name': 'Energy, n (%)', 'exp_g1 (N=6557)': '141 (3.36%)', 'Rest (N=11462)': '252 (3.44%)', 'p_value': '0.322'},
{'name': 'Financial Services, n (%)', 'exp_g1 (N=6557)': '351 (8.78%)', 'Rest (N=11462)': '608 (8.37%)', 'p_value': '0.064'},
{'name': 'Food, Beverage, & Tobacco, n (%)', 'exp_g1 (N=6557)': '142 (3.58%)', 'Rest (N=11462)': '261 (3.41%)', 'p_value': '0.168'},
{'name': 'Government, n (%)', 'exp_g1 (N=6557)': '72 (13.99%)', 'Rest (N=11462)': '155 (14.39%)', 'p_value': '0.117'},
{'name': 'Health Products & Services, n (%)', 'exp_g1 (N=6557)': '212 (4.30%)', 'Rest (N=11462)': '384 (4.15%)', 'p_value': '0.231'},
{'name': 'Manufacturing, n (%)', 'exp_g1 (N=6557)': '220 (8.33%)', 'Rest (N=11462)': '412 (8.62%)', 'p_value': '0.143'},
{'name': 'Materials & Construction, n (%)', 'exp_g1 (N=6557)': '246 (1.69%)', 'Rest (N=11462)': '449 (1.81%)', 'p_value': '0.192'},
{'name': 'Media, n (%)', 'exp_g1 (N=6557)': '576 (1.43%)', 'Rest (N=11462)': '963 (1.31%)', 'p_value': '0.153'},
{'name': 'Nonprofit, n (%)', 'exp_g1 (N=6557)': '342 (5.22%)', 'Rest (N=11462)': '604 (5.59%)', 'p_value': '0.050'},
{'name': 'Other (Organization Type), n (%)', 'exp_g1 (N=6557)': '282 (14.52%)', 'Rest (N=11462)': '462 (14.98%)', 'p_value': '0.095'},
{'name': 'Pharmaceuticals, n (%)', 'exp_g1 (N=6557)': '99 (3.23%)', 'Rest (N=11462)': '168 (3.12%)', 'p_value': '0.264'},
{'name': 'Real Estate, n (%)', 'exp_g1 (N=6557)': '72 (1.10%)', 'Rest (N=11462)': '124 (1.04%)', 'p_value': '0.316'},
{'name': 'Retail, n (%)', 'exp_g1 (N=6557)': '195 (3.75%)', 'Rest (N=11462)': '342 (3.85%)', 'p_value': '0.309'},
{'name': 'Telecommunications, n (%)', 'exp_g1 (N=6557)': '94 (1.51%)', 'Rest (N=11462)': '148 (1.45%)', 'p_value': '0.311'},
{'name': 'Transportation, n (%)', 'exp_g1 (N=6557)': '1762 (2.03%)', 'Rest (N=11462)': '3181 (2.03%)', 'p_value': '0.507'},
{'name': 'Utilities, n (%)', 'exp_g1 (N=6557)': '1667 (2.15%)', 'Rest (N=11462)': '2942 (2.15%)', 'p_value': '0.514'}],
[{'name': 'Male, n (%)', 'exp_g4 (N=2757)': '725 (64.20%)', 'Rest (N=11462)': '2837 (62.28%)', 'p_value': '0.012'},
{'name': 'Age, years', 'p_value': '0.481', 'ks_score': '0.02', 'diff_means': '0.14', 'group0_percent_null': '1.52%', 'group1_percent_null': '1.73%', 'exp_g4 (N=2757)': '44.00 [38.00 - 50.00]', 'Rest (N=11462)': '44.00 [38.00 - 50.00]'},
{'name': 'Baby Boomers (1946-1964), n (%)', 'exp_g4 (N=2757)': '1770 (26.30%)', 'Rest (N=11462)': '7189 (25.25%)', 'p_value': '0.087'},
{'name': 'Generation X (1965-1980), n (%)', 'exp_g4 (N=2757)': '1633 (59.23%)', 'Rest (N=11462)': '6883 (59.98%)', 'p_value': '0.194'},
{'name': 'Generation Y/Millennials (1981-1996), n (%)', 'exp_g4 (N=2757)': '354 (12.84%)', 'Rest (N=11462)': '1533 (12.96%)', 'p_value': '0.432'},
{'name': 'Generation Z (1997 and onwards), n (%)', 'exp_g4 (N=2757)': '1 (0.04%)', 'Rest (N=11462)': '11 (0.11%)', 'p_value': '0.176'},
{'name': 'African American, n (%)', 'exp_g4 (N=2757)': '846 (5.73%)', 'Rest (N=11462)': '3507 (6.14%)', 'p_value': '0.172'},
{'name': 'American Indian or Alaskan Nat, n (%)', 'exp_g4 (N=2757)': '270 (0.33%)', 'Rest (N=11462)': '1039 (0.29%)', 'p_value': '0.423'},
{'name': 'Asian, n (%)', 'exp_g4 (N=2757)': '56 (1.85%)', 'Rest (N=11462)': '317 (2.06%)', 'p_value': '0.216'},
{'name': 'Caucasian, n (%)', 'exp_g4 (N=2757)': '30 (80.38%)', 'Rest (N=11462)': '86 (79.09%)', 'p_value': '0.036'},
{'name': 'Hawaiian or Other Pacific Islander, n (%)', 'exp_g4 (N=2757)': '42 (0.07%)', 'Rest (N=11462)': '207 (0.08%)', 'p_value': '0.633'},
{'name': 'Hispanic, n (%)', 'exp_g4 (N=2757)': '1773 (2.97%)', 'Rest (N=11462)': '7450 (3.26%)', 'p_value': '0.196'},
{'name': 'Multiracial, n (%)', 'exp_g4 (N=2757)': '628 (2.79%)', 'Rest (N=11462)': '2322 (2.42%)', 'p_value': '0.093'},
{'name': 'Other (Race), n (%)', 'exp_g4 (N=2757)': '334 (0.73%)', 'Rest (N=11462)': '1591 (1.05%)', 'p_value': '0.037'},
{'name': 'Top, n (%)', 'exp_g4 (N=2757)': '77 (2.03%)', 'Rest (N=11462)': '267 (2.95%)', 'p_value': '<0.001'},
{'name': 'Executive, n (%)', 'exp_g4 (N=2757)': '2216 (25.61%)', 'Rest (N=11462)': '9072 (25.29%)', 'p_value': '0.346'},
{'name': 'Upper Middle, n(%)', 'exp_g4 (N=2757)': '2 (27.78%)', 'Rest (N=11462)': '9 (27.43%)', 'p_value': '0.331'},
{'name': 'Middle, n (%)', 'exp_g4 (N=2757)': '51 (30.69%)', 'Rest (N=11462)': '247 (30.50%)', 'p_value': '0.416'},
{'name': 'First Level, n (%)', 'exp_g4 (N=2757)': '20 (9.79%)', 'Rest (N=11462)': '129 (9.43%)', 'p_value': '0.245'},
{'name': 'Hourly, n (%)', 'exp_g4 (N=2757)': '82 (1.09%)', 'Rest (N=11462)': '370 (0.83%)', 'p_value': '0.071'},
{'name': 'Other (Organization level), n (%)', 'exp_g4 (N=2757)': '158 (1.52%)', 'Rest (N=11462)': '703 (1.99%)', 'p_value': '0.030'},
{'name': 'Business Sector, n (%)', 'exp_g4 (N=2757)': '9 (64.31%)', 'Rest (N=11462)': '39 (62.61%)', 'p_value': '0.023'},
{'name': 'Nonprofit Sector, n (%)', 'exp_g4 (N=2757)': '267 (12.11%)', 'Rest (N=11462)': '1047 (14.43%)', 'p_value': '<0.001'},
{'name': 'Public, n (%)', 'exp_g4 (N=2757)': '413 (22.78%)', 'Rest (N=11462)': '1488 (22.06%)', 'p_value': '0.165'},
{'name': 'Aerospace & Defense, n (%)', 'exp_g4 (N=2757)': '412 (6.02%)', 'Rest (N=11462)': '1746 (5.47%)', 'p_value': '0.091'},
{'name': 'Automotive & Transport Equipment, n (%)', 'exp_g4 (N=2757)': '82 (2.90%)', 'Rest (N=11462)': '412 (2.94%)', 'p_value': '0.478'},
{'name': 'Chemicals, n (%)', 'exp_g4 (N=2757)': '49 (1.70%)', 'Rest (N=11462)': '217 (1.23%)', 'p_value': '0.011'},
{'name': 'Computer Hardware & Software Services, n (%)', 'exp_g4 (N=2757)': '94 (2.50%)', 'Rest (N=11462)': '426 (2.10%)', 'p_value': '0.064'},
{'name': 'Consumer Products, n (%)', 'exp_g4 (N=2757)': '87 (2.97%)', 'Rest (N=11462)': '457 (3.42%)', 'p_value': '0.086'},
{'name': 'Education, n (%)', 'exp_g4 (N=2757)': '59 (3.16%)', 'Rest (N=11462)': '238 (4.21%)', 'p_value': '0.001'},
{'name': 'Energy, n (%)', 'exp_g4 (N=2757)': '61 (4.28%)', 'Rest (N=11462)': '252 (3.44%)', 'p_value': '0.006'},
{'name': 'Financial Services, n (%)', 'exp_g4 (N=2757)': '166 (8.05%)', 'Rest (N=11462)': '608 (8.37%)', 'p_value': '0.268'},
{'name': 'Food, Beverage, & Tobacco, n (%)', 'exp_g4 (N=2757)': '69 (3.41%)', 'Rest (N=11462)': '261 (3.41%)', 'p_value': '0.516'},
{'name': 'Government, n (%)', 'exp_g4 (N=2757)': '47 (14.98%)', 'Rest (N=11462)': '155 (14.39%)', 'p_value': '0.176'},
{'name': 'Health Products & Services, n (%)', 'exp_g4 (N=2757)': '90 (3.34%)', 'Rest (N=11462)': '384 (4.15%)', 'p_value': '0.009'},
{'name': 'Manufacturing, n (%)', 'exp_g4 (N=2757)': '118 (9.68%)', 'Rest (N=11462)': '412 (8.62%)', 'p_value': '0.017'},
{'name': 'Materials & Construction, n (%)', 'exp_g4 (N=2757)': '86 (1.78%)', 'Rest (N=11462)': '449 (1.81%)', 'p_value': '0.478'},
{'name': 'Media, n (%)', 'exp_g4 (N=2757)': '222 (1.45%)', 'Rest (N=11462)': '963 (1.31%)', 'p_value': '0.270'},
{'name': 'Nonprofit, n (%)', 'exp_g4 (N=2757)': '133 (4.82%)', 'Rest (N=11462)': '604 (5.59%)', 'p_value': '0.030'},
{'name': 'Other (Organization Type), n (%)', 'exp_g4 (N=2757)': '92 (14.94%)', 'Rest (N=11462)': '462 (14.98%)', 'p_value': '0.492'},
{'name': 'Pharmaceuticals, n (%)', 'exp_g4 (N=2757)': '36 (3.26%)', 'Rest (N=11462)': '168 (3.12%)', 'p_value': '0.335'},
{'name': 'Real Estate, n (%)', 'exp_g4 (N=2757)': '32 (1.16%)', 'Rest (N=11462)': '124 (1.04%)', 'p_value': '0.284'},
{'name': 'Retail, n (%)', 'exp_g4 (N=2757)': '80 (3.12%)', 'Rest (N=11462)': '342 (3.85%)', 'p_value': '0.015'},
{'name': 'Telecommunications, n (%)', 'exp_g4 (N=2757)': '40 (1.31%)', 'Rest (N=11462)': '148 (1.45%)', 'p_value': '0.282'},
{'name': 'Transportation, n (%)', 'exp_g4 (N=2757)': '766 (2.14%)', 'Rest (N=11462)': '3181 (2.03%)', 'p_value': '0.355'},
{'name': 'Utilities, n (%)', 'exp_g4 (N=2757)': '706 (2.21%)', 'Rest (N=11462)': '2942 (2.15%)', 'p_value': '0.421'}],
[{'name': 'Male, n (%)', 'exp_g6 (N=3019)': '786 (63.03%)', 'Rest (N=11462)': '2837 (62.28%)', 'p_value': '0.177'},
{'name': 'Age, years', 'p_value': '0.155', 'ks_score': '0.02', 'diff_means': '0.29', 'group0_percent_null': '2.05%', 'group1_percent_null': '1.62%', 'exp_g6 (N=3019)': '44.00 [38.00 - 51.00]', 'Rest (N=11462)': '44.00 [38.00 - 50.00]'},
{'name': 'Baby Boomers (1946-1964), n (%)', 'exp_g6 (N=3019)': '1903 (26.04%)', 'Rest (N=11462)': '7189 (25.25%)', 'p_value': '0.141'},
{'name': 'Generation X (1965-1980), n (%)', 'exp_g6 (N=3019)': '1772 (58.69%)', 'Rest (N=11462)': '6883 (59.98%)', 'p_value': '0.057'},
{'name': 'Generation Y/Millennials (1981-1996), n (%)', 'exp_g6 (N=3019)': '393 (13.02%)', 'Rest (N=11462)': '1533 (12.96%)', 'p_value': '0.469'},
{'name': 'Generation Z (1997 and onwards), n (%)', 'exp_g6 (N=3019)': '5 (0.17%)', 'Rest (N=11462)': '11 (0.11%)', 'p_value': '0.215'},
{'name': 'African American, n (%)', 'exp_g6 (N=3019)': '936 (6.06%)', 'Rest (N=11462)': '3507 (6.14%)', 'p_value': '0.437'},
{'name': 'American Indian or Alaskan Nat, n (%)', 'exp_g6 (N=3019)': '246 (0.40%)', 'Rest (N=11462)': '1039 (0.29%)', 'p_value': '0.164'},
{'name': 'Asian, n (%)', 'exp_g6 (N=3019)': '98 (2.05%)', 'Rest (N=11462)': '317 (2.06%)', 'p_value': '0.516'},
{'name': 'Caucasian, n (%)', 'exp_g6 (N=3019)': '23 (79.70%)', 'Rest (N=11462)': '86 (79.09%)', 'p_value': '0.190'},
{'name': 'Hawaiian or Other Pacific Islander, n (%)', 'exp_g6 (N=3019)': '64 (0.13%)', 'Rest (N=11462)': '207 (0.08%)', 'p_value': '0.197'},
{'name': 'Hispanic, n (%)', 'exp_g6 (N=3019)': '1929 (3.05%)', 'Rest (N=11462)': '7450 (3.26%)', 'p_value': '0.255'},
{'name': 'Multiracial, n (%)', 'exp_g6 (N=3019)': '624 (2.12%)', 'Rest (N=11462)': '2322 (2.42%)', 'p_value': '0.132'},
{'name': 'Other (Race), n (%)', 'exp_g6 (N=3019)': '429 (1.23%)', 'Rest (N=11462)': '1591 (1.05%)', 'p_value': '0.171'},
{'name': 'Top, n (%)', 'exp_g6 (N=3019)': '64 (3.25%)', 'Rest (N=11462)': '267 (2.95%)', 'p_value': '0.154'},
{'name': 'Executive, n (%)', 'exp_g6 (N=3019)': '2406 (26.37%)', 'Rest (N=11462)': '9072 (25.29%)', 'p_value': '0.070'},
{'name': 'Upper Middle, n(%)', 'exp_g6 (N=3019)': '4 (26.83%)', 'Rest (N=11462)': '9 (27.43%)', 'p_value': '0.215'},
{'name': 'Middle, n (%)', 'exp_g6 (N=3019)': '62 (31.00%)', 'Rest (N=11462)': '247 (30.50%)', 'p_value': '0.260'},
{'name': 'First Level, n (%)', 'exp_g6 (N=3019)': '37 (8.15%)', 'Rest (N=11462)': '129 (9.43%)', 'p_value': '0.004'},
{'name': 'Hourly, n (%)', 'exp_g6 (N=3019)': '92 (0.76%)', 'Rest (N=11462)': '370 (0.83%)', 'p_value': '0.363'},
{'name': 'Other (Organization level), n (%)', 'exp_g6 (N=3019)': '183 (2.12%)', 'Rest (N=11462)': '703 (1.99%)', 'p_value': '0.301'},
{'name': 'Business Sector, n (%)', 'exp_g6 (N=3019)': '12 (63.90%)', 'Rest (N=11462)': '39 (62.61%)', 'p_value': '0.056'},
{'name': 'Nonprofit Sector, n (%)', 'exp_g6 (N=3019)': '273 (14.21%)', 'Rest (N=11462)': '1047 (14.43%)', 'p_value': '0.364'},
{'name': 'Public, n (%)', 'exp_g6 (N=3019)': '398 (20.67%)', 'Rest (N=11462)': '1488 (22.06%)', 'p_value': '0.022'},
{'name': 'Aerospace & Defense, n (%)', 'exp_g6 (N=3019)': '470 (5.07%)', 'Rest (N=11462)': '1746 (5.47%)', 'p_value': '0.150'},
{'name': 'Automotive & Transport Equipment, n (%)', 'exp_g6 (N=3019)': '99 (2.82%)', 'Rest (N=11462)': '412 (2.94%)', 'p_value': '0.353'},
{'name': 'Chemicals, n (%)', 'exp_g6 (N=3019)': '58 (1.46%)', 'Rest (N=11462)': '217 (1.23%)', 'p_value': '0.124'},
{'name': 'Computer Hardware & Software Services, n (%)', 'exp_g6 (N=3019)': '108 (1.79%)', 'Rest (N=11462)': '426 (2.10%)', 'p_value': '0.104'},
{'name': 'Consumer Products, n (%)', 'exp_g6 (N=3019)': '137 (3.28%)', 'Rest (N=11462)': '457 (3.42%)', 'p_value': '0.342'},
{'name': 'Education, n (%)', 'exp_g6 (N=3019)': '62 (4.54%)', 'Rest (N=11462)': '238 (4.21%)', 'p_value': '0.175'},
{'name': 'Energy, n (%)', 'exp_g6 (N=3019)': '51 (3.51%)', 'Rest (N=11462)': '252 (3.44%)', 'p_value': '0.429'},
{'name': 'Financial Services, n (%)', 'exp_g6 (N=3019)': '153 (8.21%)', 'Rest (N=11462)': '608 (8.37%)', 'p_value': '0.382'},
{'name': 'Food, Beverage, & Tobacco, n (%)', 'exp_g6 (N=3019)': '54 (3.58%)', 'Rest (N=11462)': '261 (3.41%)', 'p_value': '0.302'},
{'name': 'Government, n (%)', 'exp_g6 (N=3019)': '44 (13.18%)', 'Rest (N=11462)': '155 (14.39%)', 'p_value': '0.019'},
{'name': 'Health Products & Services, n (%)', 'exp_g6 (N=3019)': '91 (4.60%)', 'Rest (N=11462)': '384 (4.15%)', 'p_value': '0.094'},
{'name': 'Manufacturing, n (%)', 'exp_g6 (N=3019)': '106 (9.04%)', 'Rest (N=11462)': '412 (8.62%)', 'p_value': '0.191'},
{'name': 'Materials & Construction, n (%)', 'exp_g6 (N=3019)': '137 (1.92%)', 'Rest (N=11462)': '449 (1.81%)', 'p_value': '0.332'},
{'name': 'Media, n (%)', 'exp_g6 (N=3019)': '248 (1.19%)', 'Rest (N=11462)': '963 (1.31%)', 'p_value': '0.292'},
{'name': 'Nonprofit, n (%)', 'exp_g6 (N=3019)': '161 (5.33%)', 'Rest (N=11462)': '604 (5.59%)', 'p_value': '0.267'},
{'name': 'Other (Organization Type), n (%)', 'exp_g6 (N=3019)': '139 (15.57%)', 'Rest (N=11462)': '462 (14.98%)', 'p_value': '0.164'},
{'name': 'Pharmaceuticals, n (%)', 'exp_g6 (N=3019)': '41 (3.01%)', 'Rest (N=11462)': '168 (3.12%)', 'p_value': '0.382'},
{'name': 'Real Estate, n (%)', 'exp_g6 (N=3019)': '31 (1.03%)', 'Rest (N=11462)': '124 (1.04%)', 'p_value': '0.507'},
{'name': 'Retail, n (%)', 'exp_g6 (N=3019)': '85 (4.54%)', 'Rest (N=11462)': '342 (3.85%)', 'p_value': '0.019'},
{'name': 'Telecommunications, n (%)', 'exp_g6 (N=3019)': '36 (1.36%)', 'Rest (N=11462)': '148 (1.45%)', 'p_value': '0.364'},
{'name': 'Transportation, n (%)', 'exp_g6 (N=3019)': '810 (2.05%)', 'Rest (N=11462)': '3181 (2.03%)', 'p_value': '0.489'},
{'name': 'Utilities, n (%)', 'exp_g6 (N=3019)': '796 (1.69%)', 'Rest (N=11462)': '2942 (2.15%)', 'p_value': '0.029'}]]
Upvotes: 1
Related Questions
- Most efficient way to convert numpy array to dict
- convert a dictionary in a numpy array
- Converting a data dictionary to numpy array in python
- How to convert a dictionary to a numpy array in python?
- convert list of dictionaries to numpy array
- Convert dictionary values to numpy arrays
- Convert a dict to numpy multi-dimensional array
- What is an efficient way to convert arrays/lists to dictionaries?
- Pythonic way to convert a dictionary to a numpy array
- Best way to create a NumPy array from a dictionary?