Group by function with pandas dataset. Cronbach's alpha with grouped dataset in Python

Question

Let's say I have a dataset (sim_data) with 16 variables, including psychological data (15 items from a questionnaire), and the first column is a categorical variable (country).

I can easily get means/sd by group using:

sim_data.groupby("country").describe()

However, I would like to apply a function (Cronbach's alpha) from a specific package (pip install pingouin) and (import pingouin as pg) with this data and get the results by group (as I did previously). The following code is not working.

pg.cronbach_alpha(sim_data.groupby("country"))

Neither this one

sim_data.groupby('country').apply(lambda grp: pg.cronbach_alpha())

Important notes:

I'm adapting the code from here

That's my routine and dataset if you want to reproduce

import pandas as pd
import numpy as np
import pingouin as pg
from numpy import nan

sim_data = pd.DataFrame.from_dict({'country': {33003: 'Vietnam',
  12172: 'Macedonia',
  5192: 'Ghana',
  32511: 'Vietnam',
  7813: 'Ghana',
  21440: 'Armenia',
  32912: 'Vietnam',
  20609: 'Georgia',
  7751: 'Ghana',
  31228: 'Laos',
  36230: 'Serbia',
  32025: 'Laos',
  21758: 'Armenia',
  35730: 'Serbia',
  8444: 'Kenya',
  19946: 'Georgia',
  37532: 'Serbia',
  22926: 'Armenia',
  27204: 'Sri_Lanka',
  31959: 'Laos',
  24535: 'Sri_Lanka',
  19433: 'Georgia',
  17137: 'Ukraine',
  26103: 'Sri_Lanka',
  33878: 'Vietnam',
  39053: 'Philippines',
  33799: 'Vietnam',
  37183: 'Serbia',
  3606: 'Colombia',
  2561: 'Colombia',
  29960: 'Laos',
  32397: 'Vietnam',
  39539: 'Philippines',
  431: 'Bolivia',
  36842: 'Serbia',
  16926: 'Ukraine',
  9287: 'Kenya',
  34246: 'Vietnam',
  16277: 'Ukraine',
  34105: 'Vietnam',
  11032: 'Kenya',
  41457: 'Philippines',
  34380: 'Vietnam',
  19482: 'Georgia',
  1844: 'Bolivia',
  9552: 'Kenya',
  35437: 'Vietnam',
  29555: 'Laos',
  29993: 'Laos',
  28114: 'Yunnan',
  15: 'Bolivia',
  27712: 'Yunnan',
  12313: 'Macedonia',
  11631: 'Kenya'},
 'step_bfi1_ab_cor': {33003: 1.8333332538604736,
  12172: 1.5,
  5192: nan,
  32511: 1.4166666269302368,
  7813: nan,
  21440: 2.4166665077209473,
  32912: 1.0,
  20609: 4.0,
  7751: 0.5,
  31228: 2.25,
  36230: 2.9375,
  32025: 1.875,
  21758: 3.9166665077209473,
  35730: 2.25,
  8444: 3.5833334922790527,
  19946: 2.3333334922790527,
  37532: 4.0625,
  22926: 3.5,
  27204: 4.083333492279053,
  31959: 3.0,
  24535: 3.5,
  19433: 2.0833334922790527,
  17137: 3.25,
  26103: 1.1666666269302368,
  33878: 1.6666667461395264,
  39053: 1.75,
  33799: 0.75,
  37183: 3.375,
  3606: 4.25,
  2561: 2.8333334922790527,
  29960: 4.25,
  32397: 3.8333332538604736,
  39539: 2.5625,
  431: 3.25,
  36842: 3.8125,
  16926: 1.9166667461395264,
  9287: 2.3333334922790527,
  34246: 1.0,
  16277: 2.9166667461395264,
  34105: 1.5833333730697632,
  11032: 1.75,
  41457: 3.75,
  34380: 1.5833332538604736,
  19482: 0.9166666269302368,
  1844: 3.6666667461395264,
  9552: 2.25,
  35437: 1.0833332538604736,
  29555: 2.25,
  29993: 4.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 3.25,
  12313: 2.0833334922790527,
  11631: 2.5833334922790527},
 'step_bfi3_ab_cor': {33003: 3.8333332538604736,
  12172: 3.5,
  5192: nan,
  32511: 3.4166665077209473,
  7813: nan,
  21440: 3.4166665077209473,
  32912: 3.0,
  20609: 4.0,
  7751: 2.5,
  31228: 4.25,
  36230: 3.6875,
  32025: 3.875,
  21758: 3.9166665077209473,
  35730: 2.25,
  8444: 4.583333492279053,
  19946: 2.3333334922790527,
  37532: 2.5625,
  22926: 2.5,
  27204: 3.0833334922790527,
  31959: 4.0,
  24535: 2.5,
  19433: 3.0833334922790527,
  17137: 3.25,
  26103: 3.1666665077209473,
  33878: 3.6666667461395264,
  39053: 2.5,
  33799: 3.75,
  37183: 3.375,
  3606: 4.25,
  2561: 2.8333334922790527,
  29960: 3.25,
  32397: 2.8333332538604736,
  39539: 2.5625,
  431: 3.25,
  36842: 3.0625,
  16926: 3.9166667461395264,
  9287: 3.3333334922790527,
  34246: 4.0,
  16277: 1.9166667461395264,
  34105: 3.5833334922790527,
  11032: 3.75,
  41457: 2.25,
  34380: 2.5833332538604736,
  19482: 3.9166665077209473,
  1844: 2.6666667461395264,
  9552: 3.25,
  35437: 3.0833332538604736,
  29555: 2.25,
  29993: 2.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 2.25,
  12313: 4.083333492279053,
  11631: 3.5833334922790527},
 'step_bfi5_ab_cor': {33003: 0.8333332538604736,
  12172: 0.5,
  5192: nan,
  32511: 2.4166665077209473,
  7813: nan,
  21440: 2.4166665077209473,
  32912: 2.0,
  20609: 2.0,
  7751: 0.5,
  31228: 3.25,
  36230: 3.6875,
  32025: 1.875,
  21758: 3.9166665077209473,
  35730: 2.25,
  8444: 1.5833333730697632,
  19946: 1.3333333730697632,
  37532: 1.8125,
  22926: 1.5,
  27204: 2.0833334922790527,
  31959: 3.0,
  24535: 1.5,
  19433: 1.0833333730697632,
  17137: 2.25,
  26103: 4.166666507720947,
  33878: 1.6666667461395264,
  39053: 1.75,
  33799: 1.75,
  37183: 3.375,
  3606: 2.25,
  2561: 1.8333333730697632,
  29960: 3.25,
  32397: 1.8333332538604736,
  39539: 2.5625,
  431: 1.25,
  36842: 3.8125,
  16926: 1.9166667461395264,
  9287: 1.3333333730697632,
  34246: 3.0,
  16277: 1.9166667461395264,
  34105: 1.5833333730697632,
  11032: 2.75,
  41457: 2.25,
  34380: 2.5833332538604736,
  19482: 1.9166666269302368,
  1844: 3.6666667461395264,
  9552: 1.25,
  35437: 1.0833332538604736,
  29555: 1.25,
  29993: 3.25,
  28114: 2.6666665077209473,
  15: 2.0,
  27712: 3.25,
  12313: 1.0833333730697632,
  11631: 3.5833334922790527},
 'step_bfi6_ab_cor': {33003: 4.1666669845581055,
  12172: 1.5,
  5192: nan,
  32511: 1.5833333730697632,
  7813: nan,
  21440: 2.5833334922790527,
  32912: 4.0,
  20609: 3.0,
  7751: 1.5,
  31228: 2.75,
  36230: 2.8125,
  32025: 4.125,
  21758: 4.083333492279053,
  35730: 1.25,
  8444: 3.4166665077209473,
  19946: 2.6666665077209473,
  37532: 3.9375,
  22926: 2.5,
  27204: 2.9166665077209473,
  31959: 2.0,
  24535: 2.5,
  19433: 2.9166665077209473,
  17137: 2.75,
  26103: 0.8333333730697632,
  33878: 4.3333330154418945,
  39053: 2.5,
  33799: 4.25,
  37183: 3.125,
  3606: 2.75,
  2561: 3.1666665077209473,
  29960: 3.75,
  32397: 1.1666667461395264,
  39539: 3.9375,
  431: 1.75,
  36842: 2.6875,
  16926: 2.0833332538604736,
  9287: 2.6666665077209473,
  34246: 3.0,
  16277: 2.0833332538604736,
  34105: 2.4166665077209473,
  11032: 3.25,
  41457: 4.25,
  34380: 2.4166667461395264,
  19482: 4.083333492279053,
  1844: 3.3333332538604736,
  9552: 2.75,
  35437: 2.9166667461395264,
  29555: 1.75,
  29993: 2.75,
  28114: 3.3333334922790527,
  15: 2.0,
  27712: 1.75,
  12313: 2.9166665077209473,
  11631: 3.4166665077209473},
 'step_bfi7_ab_cor': {33003: 1.8333332538604736,
  12172: 1.5,
  5192: nan,
  32511: 1.4166666269302368,
  7813: nan,
  21440: 2.4166665077209473,
  32912: 3.0,
  20609: 2.0,
  7751: 1.5,
  31228: 3.25,
  36230: 3.6875,
  32025: 1.875,
  21758: 3.9166665077209473,
  35730: 3.75,
  8444: 2.5833334922790527,
  19946: 1.3333333730697632,
  37532: 4.0625,
  22926: 2.5,
  27204: 3.0833334922790527,
  31959: 1.0,
  24535: 1.5,
  19433: 2.0833334922790527,
  17137: 2.25,
  26103: 2.1666665077209473,
  33878: 2.6666667461395264,
  39053: 3.25,
  33799: 0.75,
  37183: 3.375,
  3606: 2.25,
  2561: 1.8333333730697632,
  29960: 2.25,
  32397: 3.8333332538604736,
  39539: 4.0625,
  431: 3.25,
  36842: 3.8125,
  16926: 1.9166667461395264,
  9287: 2.3333334922790527,
  34246: 2.0,
  16277: 2.9166667461395264,
  34105: 1.5833333730697632,
  11032: 1.75,
  41457: 3.75,
  34380: 2.5833332538604736,
  19482: 2.9166665077209473,
  1844: 1.6666667461395264,
  9552: 2.25,
  35437: 2.0833332538604736,
  29555: 2.25,
  29993: 2.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 1.25,
  12313: 4.083333492279053,
  11631: 3.5833334922790527},
 'step_bfi10_ab_cor': {33003: 3.8333332538604736,
  12172: 3.5,
  5192: nan,
  32511: 3.4166665077209473,
  7813: nan,
  21440: 3.4166665077209473,
  32912: 3.0,
  20609: 4.0,
  7751: 3.5,
  31228: 3.25,
  36230: 3.6875,
  32025: 1.875,
  21758: 3.9166665077209473,
  35730: 2.25,
  8444: 2.5833334922790527,
  19946: 4.333333492279053,
  37532: 2.5625,
  22926: 2.5,
  27204: 4.083333492279053,
  31959: 2.0,
  24535: 0.4999999403953552,
  19433: 3.0833334922790527,
  17137: 3.25,
  26103: 3.1666665077209473,
  33878: 2.6666667461395264,
  39053: 3.25,
  33799: 1.75,
  37183: 3.375,
  3606: 2.25,
  2561: 2.8333334922790527,
  29960: 2.25,
  32397: 1.8333332538604736,
  39539: 4.0625,
  431: 3.25,
  36842: 3.0625,
  16926: 3.9166667461395264,
  9287: 2.3333334922790527,
  34246: 4.0,
  16277: 2.9166667461395264,
  34105: 2.5833334922790527,
  11032: 3.75,
  41457: 2.25,
  34380: 3.5833332538604736,
  19482: 3.9166665077209473,
  1844: 3.6666667461395264,
  9552: 3.25,
  35437: 3.0833332538604736,
  29555: 2.25,
  29993: 2.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 3.25,
  12313: 2.0833334922790527,
  11631: 4.583333492279053},
 'step_bfi17_ab_cor': {33003: 2.8333332538604736,
  12172: 1.5,
  5192: nan,
  32511: 3.4166665077209473,
  7813: nan,
  21440: 1.4166666269302368,
  32912: 2.0,
  20609: 1.0,
  7751: 3.5,
  31228: 3.25,
  36230: 2.1875,
  32025: nan,
  21758: 3.9166665077209473,
  35730: 3.75,
  8444: 3.5833334922790527,
  19946: 2.3333334922790527,
  37532: 2.5625,
  22926: 2.5,
  27204: 3.0833334922790527,
  31959: 3.0,
  24535: 2.5,
  19433: 4.083333492279053,
  17137: 2.25,
  26103: 2.1666665077209473,
  33878: 2.6666667461395264,
  39053: 1.75,
  33799: 2.75,
  37183: 3.375,
  3606: 4.25,
  2561: 0.8333333730697632,
  29960: 3.25,
  32397: 1.8333332538604736,
  39539: 2.5625,
  431: 1.25,
  36842: 2.3125,
  16926: 2.9166667461395264,
  9287: 3.3333334922790527,
  34246: 2.0,
  16277: 2.9166667461395264,
  34105: 3.5833334922790527,
  11032: 2.75,
  41457: 3.75,
  34380: 2.5833332538604736,
  19482: 3.9166665077209473,
  1844: 3.6666667461395264,
  9552: 3.25,
  35437: 2.0833332538604736,
  29555: 2.25,
  29993: 2.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 2.25,
  12313: 3.0833334922790527,
  11631: 3.5833334922790527},
 'step_bfi19_ab_cor': {33003: 3.1666667461395264,
  12172: 1.5,
  5192: nan,
  32511: 1.5833333730697632,
  7813: nan,
  21440: 2.5833334922790527,
  32912: 2.0,
  20609: 1.0,
  7751: 1.5,
  31228: 2.75,
  36230: 2.0625,
  32025: 2.125,
  21758: 1.0833333730697632,
  35730: 4.25,
  8444: 3.4166665077209473,
  19946: 3.6666665077209473,
  37532: 0.9375,
  22926: 2.5,
  27204: 2.9166665077209473,
  31959: 3.0,
  24535: 1.5,
  19433: 2.9166665077209473,
  17137: 2.75,
  26103: 1.8333333730697632,
  33878: 3.3333332538604736,
  39053: 3.25,
  33799: 3.25,
  37183: 3.125,
  3606: 2.75,
  2561: 3.1666665077209473,
  29960: 1.75,
  32397: 3.1666667461395264,
  39539: 2.4375,
  431: 1.75,
  36842: 2.6875,
  16926: 3.0833332538604736,
  9287: 4.666666507720947,
  34246: 3.0,
  16277: 2.0833332538604736,
  34105: 3.4166665077209473,
  11032: 3.25,
  41457: 2.75,
  34380: 2.4166667461395264,
  19482: 3.0833334922790527,
  1844: 4.3333330154418945,
  9552: 2.75,
  35437: 2.9166667461395264,
  29555: 1.75,
  29993: 1.75,
  28114: 3.3333334922790527,
  15: 2.0,
  27712: 2.75,
  12313: 0.9166666269302368,
  11631: 2.4166665077209473},
 'step_bfi23_ab_cor': {33003: 1.1666667461395264,
  12172: 3.5,
  5192: nan,
  32511: 4.583333492279053,
  7813: nan,
  21440: 3.5833334922790527,
  32912: 2.0,
  20609: 4.0,
  7751: 4.5,
  31228: 2.75,
  36230: 3.5625,
  32025: nan,
  21758: 4.083333492279053,
  35730: 4.25,
  8444: 3.4166665077209473,
  19946: 3.6666665077209473,
  37532: 3.9375,
  22926: 3.5,
  27204: 2.9166665077209473,
  31959: 3.0,
  24535: 2.5,
  19433: 2.9166665077209473,
  17137: 2.75,
  26103: 3.8333334922790527,
  33878: 1.3333332538604736,
  39053: 1.75,
  33799: 2.25,
  37183: 4.625,
  3606: 2.75,
  2561: 3.1666665077209473,
  29960: 2.75,
  32397: 3.1666667461395264,
  39539: 2.4375,
  431: 3.75,
  36842: 3.4375,
  16926: 3.0833332538604736,
  9287: 1.6666666269302368,
  34246: 3.0,
  16277: 3.0833332538604736,
  34105: 1.4166666269302368,
  11032: 4.25,
  41457: 2.75,
  34380: 2.4166667461395264,
  19482: 4.083333492279053,
  1844: 3.3333332538604736,
  9552: 3.75,
  35437: 1.9166667461395264,
  29555: 2.75,
  29993: 2.75,
  28114: 3.3333334922790527,
  15: 4.0,
  27712: 2.75,
  12313: 2.9166665077209473,
  11631: 3.4166665077209473},
 'step_bfi30_ab_cor': {33003: 1.8333332538604736,
  12172: 3.5,
  5192: nan,
  32511: 2.4166665077209473,
  7813: nan,
  21440: 3.4166665077209473,
  32912: 4.0,
  20609: 4.0,
  7751: 3.5,
  31228: 3.25,
  36230: 3.6875,
  32025: 1.875,
  21758: 3.9166665077209473,
  35730: 2.25,
  8444: 2.5833334922790527,
  19946: 4.333333492279053,
  37532: 4.0625,
  22926: 3.5,
  27204: 4.083333492279053,
  31959: 4.0,
  24535: 3.5,
  19433: 4.083333492279053,
  17137: 3.25,
  26103: 3.1666665077209473,
  33878: 3.6666667461395264,
  39053: 1.0,
  33799: 0.75,
  37183: 3.375,
  3606: 4.25,
  2561: 2.8333334922790527,
  29960: 3.25,
  32397: 2.8333332538604736,
  39539: 4.0625,
  431: 3.25,
  36842: 3.8125,
  16926: 2.9166667461395264,
  9287: 2.3333334922790527,
  34246: 3.0,
  16277: 2.9166667461395264,
  34105: 2.5833334922790527,
  11032: 3.75,
  41457: 3.75,
  34380: 3.5833332538604736,
  19482: 3.9166665077209473,
  1844: 3.6666667461395264,
  9552: 2.25,
  35437: 3.0833332538604736,
  29555: 2.25,
  29993: 3.25,
  28114: 3.6666665077209473,
  15: 1.0,
  27712: 3.25,
  12313: 4.083333492279053,
  11631: 4.583333492279053},
 'step_bfi32_ab_cor': {33003: 3.8333332538604736,
  12172: 1.5,
  5192: nan,
  32511: 3.4166665077209473,
  7813: nan,
  21440: 3.4166665077209473,
  32912: 4.0,
  20609: 4.0,
  7751: 3.5,
  31228: 3.25,
  36230: 2.9375,
  32025: 2.875,
  21758: 3.9166665077209473,
  35730: 2.25,
  8444: 3.5833334922790527,
  19946: 4.333333492279053,
  37532: 3.3125,
  22926: 3.5,
  27204: 4.083333492279053,
  31959: 3.0,
  24535: 2.5,
  19433: 4.083333492279053,
  17137: 3.25,
  26103: 3.1666665077209473,
  33878: 3.6666667461395264,
  39053: 1.75,
  33799: 3.75,
  37183: 1.875,
  3606: 4.25,
  2561: 2.8333334922790527,
  29960: 3.25,
  32397: 2.8333332538604736,
  39539: 2.5625,
  431: 3.25,
  36842: 3.0625,
  16926: 2.9166667461395264,
  9287: 2.3333334922790527,
  34246: 4.0,
  16277: 2.9166667461395264,
  34105: 3.5833334922790527,
  11032: 3.75,
  41457: 2.25,
  34380: 2.5833332538604736,
  19482: 3.9166665077209473,
  1844: 3.6666667461395264,
  9552: 3.25,
  35437: 3.0833332538604736,
  29555: 3.25,
  29993: 3.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 2.25,
  12313: 3.0833334922790527,
  11631: 3.5833334922790527},
 'step_bfi33_ab_cor': {33003: 1.8333332538604736,
  12172: 3.5,
  5192: nan,
  32511: 3.4166665077209473,
  7813: nan,
  21440: 2.4166665077209473,
  32912: 4.0,
  20609: 2.0,
  7751: 3.5,
  31228: 3.25,
  36230: 2.9375,
  32025: 3.875,
  21758: 3.9166665077209473,
  35730: 3.75,
  8444: 3.5833334922790527,
  19946: 4.333333492279053,
  37532: 4.0625,
  22926: 2.5,
  27204: 3.0833334922790527,
  31959: 2.0,
  24535: 1.5,
  19433: 2.0833334922790527,
  17137: 1.25,
  26103: 3.1666665077209473,
  33878: 3.6666667461395264,
  39053: 2.5,
  33799: 3.75,
  37183: 3.375,
  3606: 2.25,
  2561: 2.8333334922790527,
  29960: 3.25,
  32397: 2.8333332538604736,
  39539: 3.3125,
  431: 2.25,
  36842: 3.0625,
  16926: 2.9166667461395264,
  9287: 3.3333334922790527,
  34246: 3.0,
  16277: 2.9166667461395264,
  34105: 3.5833334922790527,
  11032: 3.75,
  41457: 3.75,
  34380: 3.5833332538604736,
  19482: 3.9166665077209473,
  1844: 3.6666667461395264,
  9552: 2.25,
  35437: 3.0833332538604736,
  29555: 3.25,
  29993: 2.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 1.25,
  12313: 3.0833334922790527,
  11631: 3.5833334922790527},
 'step_bfi34_ab_cor': {33003: 3.8333332538604736,
  12172: 1.5,
  5192: nan,
  32511: 3.4166665077209473,
  7813: nan,
  21440: 3.4166665077209473,
  32912: 3.0,
  20609: 1.0,
  7751: 3.5,
  31228: 2.25,
  36230: 2.9375,
  32025: nan,
  21758: 3.9166665077209473,
  35730: 3.75,
  8444: 2.5833334922790527,
  19946: 3.3333334922790527,
  37532: 2.5625,
  22926: 2.5,
  27204: 2.0833334922790527,
  31959: 2.0,
  24535: 1.5,
  19433: 3.0833334922790527,
  17137: 3.25,
  26103: 2.1666665077209473,
  33878: 2.6666667461395264,
  39053: 2.5,
  33799: 3.75,
  37183: 3.375,
  3606: 1.25,
  2561: 3.8333334922790527,
  29960: 2.25,
  32397: 1.8333332538604736,
  39539: 2.5625,
  431: 2.25,
  36842: 2.3125,
  16926: 2.9166667461395264,
  9287: 3.3333334922790527,
  34246: 4.0,
  16277: 2.9166667461395264,
  34105: 1.5833333730697632,
  11032: 3.75,
  41457: 2.25,
  34380: 1.5833332538604736,
  19482: 3.9166665077209473,
  1844: 3.6666667461395264,
  9552: 3.25,
  35437: 3.0833332538604736,
  29555: 2.25,
  29993: 2.25,
  28114: 2.6666665077209473,
  15: 3.0,
  27712: 2.25,
  12313: 1.0833333730697632,
  11631: 2.5833334922790527},
 'step_bfi36_ab_cor': {33003: 2.8333332538604736,
  12172: 3.5,
  5192: nan,
  32511: 3.4166665077209473,
  7813: nan,
  21440: 2.4166665077209473,
  32912: 3.0,
  20609: 4.0,
  7751: 2.5,
  31228: 3.25,
  36230: 3.6875,
  32025: 1.875,
  21758: 1.9166666269302368,
  35730: 2.25,
  8444: 3.5833334922790527,
  19946: 4.333333492279053,
  37532: 3.3125,
  22926: 3.5,
  27204: 3.0833334922790527,
  31959: 3.0,
  24535: 2.5,
  19433: 4.083333492279053,
  17137: 2.25,
  26103: 2.1666665077209473,
  33878: 3.6666667461395264,
  39053: 3.25,
  33799: 3.75,
  37183: 3.375,
  3606: 4.25,
  2561: 2.8333334922790527,
  29960: 3.25,
  32397: 1.8333332538604736,
  39539: 2.5625,
  431: 2.25,
  36842: 3.0625,
  16926: 1.9166667461395264,
  9287: 0.3333333730697632,
  34246: 2.0,
  16277: 2.9166667461395264,
  34105: 3.5833334922790527,
  11032: 3.75,
  41457: 3.75,
  34380: 3.5833332538604736,
  19482: 3.9166665077209473,
  1844: 3.6666667461395264,
  9552: 3.25,
  35437: 3.0833332538604736,
  29555: 1.25,
  29993: 2.25,
  28114: 3.6666665077209473,
  15: 3.0,
  27712: 3.25,
  12313: 4.083333492279053,
  11631: 3.5833334922790527},
 'step_bfi39_ab_cor': {33003: 4.1666669845581055,
  12172: 3.5,
  5192: nan,
  32511: 4.583333492279053,
  7813: nan,
  21440: 2.5833334922790527,
  32912: 3.0,
  20609: 1.0,
  7751: 2.5,
  31228: 3.75,
  36230: 4.3125,
  32025: 1.125,
  21758: 4.083333492279053,
  35730: 2.75,
  8444: 3.4166665077209473,
  19946: 3.6666665077209473,
  37532: 2.4375,
  22926: 2.5,
  27204: 2.9166665077209473,
  31959: 3.0,
  24535: 1.5,
  19433: 2.9166665077209473,
  17137: 2.75,
  26103: 2.8333334922790527,
  33878: 3.3333332538604736,
  39053: 3.25,
  33799: 3.25,
  37183: 1.625,
  3606: 3.75,
  2561: 3.1666665077209473,
  29960: 3.75,
  32397: 3.1666667461395264,
  39539: 0.9375,
  431: 2.75,
  36842: 2.6875,
  16926: 3.0833332538604736,
  9287: 2.6666665077209473,
  34246: 3.0,
  16277: 4.0833330154418945,
  34105: 4.416666507720947,
  11032: 2.25,
  41457: 1.25,
  34380: 2.4166667461395264,
  19482: 1.0833333730697632,
  1844: 3.3333332538604736,
  9552: 1.75,
  35437: 3.9166667461395264,
  29555: 2.75,
  29993: 2.75,
  28114: 3.3333334922790527,
  15: 4.0,
  27712: 2.75,
  12313: 2.9166665077209473,
  11631: 2.4166665077209473}})

I'm a heavy R user and and I'm translating the following code to python

sim_data %>%
  select('step_bfi1_ab_cor':'step_bfi39_ab_cor', "country") %>%
  nest(-country) %>%
  mutate(result=map(data, ~psych::alpha(.)$total)) %>%
  select(country,result) %>%
  unnest()

Suggestions are welcome. Please let me know if there's another way (more elegant) to solve my problem. Thank you

kindly provide a sample dataset, say 5 to 10 rows, with an expected output. you can make a mock function, or show results of your R code, so anyone attempting your question can understand better and proffer an appropriate solution, if any. — sammywemmy, Aug 21 '20 at 22:19
Thanks for asking! I've tried to dput my dataset using this code [here](https://stackoverflow.com/questions/47450931/print-pandas-data-frame-for-reproducible-example-equivalent-to-dput-in-r) and [here](https://www.dropbox.com/s/phhi3geke6r952g/python_ds.txt?dl=0) is the output. Let me know if there's an easy way to do that. — Luis, Aug 21 '20 at 22:34

Parfait · Accepted Answer · 2020-08-23T16:35:53.310

1

Usually, base R (not tidy R) is easier to translate to Python Pandas. What your R code appears to be doing is subsetting the dataframe by country column and running each subset into psych::alpha(). Then extracted statistics are returned into a data frame with country indicator.

You can do the exact same with base R's by which can translate into Pandas' groupby in a list comprehension. It looks like psych::alpha returns more statistics than pingouin.cronbach_alpha. Adjust fields and return values below as needed in untested code.

Base R (using docs)

# DEFINE METHOD
run_cronbach_alpha <- function(sub) {
    results <- psych::alpha(sub)$total    # RETURNS LIST
                      
    data.frame(country = sub$country[1],
               raw_alpha = results$raw_alpha,
               std.alpha = results$std.alpha,
               G6 = results$G6,
               average_r = results$average_r,
               median_r = results$median,
               mean = results$mean,
               sd = results$sd)
 }


# FILTER COLUMNS IN DATA FRAME
sim_short <- sim_data[c("step_bfi1_ab_cor", ..., "step_bfi39_ab_cor", "country")] 

# RUN METHOD BY COUNTRY SUBSETS TO RETURN DF LIST
results_df_list <- by(sim_short, sim_short$country, run_cronbach_alpha)

# ROW BIND ALL DFs TO SINGLE FINAL DATA FRAME
results_df <- do.call(rbind.data.frame, results_df_list)

Python Pandas (using docs)

# DEFINE METHOD
def run_cronbach_alpha(c, sub):
    results = pg.cronbach_alpha(sub.drop(["country"], axis="columns"))    # RETURNS TUPLE

    return pd.DataFrame({'country': c, 'cronbach_alpha': results[0], index=[0]})


# FILTER COLUMNS IN DATA FRAME
sim_short = sim_data.reindex(["step_bfi1_ab_cor", ..., "step_bfi39_ab_cor", "country"], 
                             axis='columns')
                         
# RUN METHOD BY COUNTRY SUBSETS TO RETURN DF LIST
results_df_list = [run_cronbach_alpha(i, df) for i,df in sim_short.groupby("country")]

# CONCATENATE ALL DFs TO SINGLE FINAL DATA FRAME
results_df = pd.concat(results_df_list)

edited Aug 23 '20 at 16:35

answered Aug 21 '20 at 22:04

Parfait

104,375
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Thank out, I ran your code and it seems to work. I defined run_cronbach (ok), then I created the sim_short (ok), then I've tried to run the last lines (results_df_list and results_df) but I've got an error. Really sorry for the dumb question, but how can I fix this error "NameError: name 'df' is not defined". Thank you again! – Luis Aug 21 '20 at 22:22
See edit. In list comprehension I passed in `i` and `df` but the loop variables were `i` and `g`. So simply replace `g` for `df`. – Parfait Aug 22 '20 at 01:47
Thank you for providing support and for you effort to make this work! However, I'm getting this error here (All columns must be numeric.). I assume something is going on with sim_short. This dataset is shorter than the original sim_data. To make things clear and easy to understand, check this image [here](https://www.dropbox.com/s/wfmzjde72zvck4m/python_error.png?dl=0). Thank you! – Luis Aug 22 '20 at 11:13
Possibly it is the country string column. See edit. Within the defined method, before `pg.cronbach_alpha` try dropping `country` from `sub` or set the args for *items*, *scores*, and *subject* per docs. – Parfait Aug 22 '20 at 13:38
Thank you! It's still not working (TypeError: __init__() got an unexpected keyword argument 'country'), but I really appreciate your suggestions and comments. I'll try to dig myself into your syntax and get back if I have success. Thank you! – Luis Aug 22 '20 at 22:24
Whoops! Pandas data frame constructor requires dictionary unlike R with name list style. See edit. At least the `cronbach_alpha` call works now. – Parfait Aug 23 '20 at 00:35
That's ok. I think we are almost here but this errs is still present (ValueError: If using all scalar values, you must pass an index). I'll open a new thread with a provisory solution and then I'll mark you there. (Stackoverflow is telling me I'm "spamming" with this thread). Thank you again! – Luis Aug 23 '20 at 01:11
Can you please go here (https://stackoverflow.com/questions/63542618/cronbachs-alpha-in-python-looping-from-all-variables-items-and-then-grouping-t) ? I have a syntax for Cronbach's alpha and now I'm missing the grouping variable only. Thanks. – Luis Aug 23 '20 at 01:33
Yes, pass `index[0]` in `DataFrame` call whenever you build a data frame of zero rows or as your answer shows any index name. – Parfait Aug 23 '20 at 16:39
Thank you so much again for your help here! – Luis Aug 23 '20 at 23:34

score 0 · Answer 2 · answered Aug 23 '20 at 02:20

I just adapted the answer from @Parfait (Thanks!!) and now it's working.

def run_cronbach_alpha(c, sub):
    results = pg.cronbach_alpha(sub.drop(["country"], axis="columns"))    # RETURNS TUPLE
    return pd.DataFrame({'country': c, 'cronbach_alpha': results[0]}, index = ["Result"])
                       
# RUN METHOD BY COUNTRY SUBSETS TO RETURN DF LIST
results_df_list = [run_cronbach_alpha(i, df) for i,df in sim_data.groupby("country")]
results_df_list

Group by function with pandas dataset. Cronbach's alpha with grouped dataset in Python

2 Answers2

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