| """Weighted summary tables (with jackknife standard errors) and charts for the two NHES datasets. |
| Estimates use the final weight; standard errors use the 80 JK1 replicate weights, |
| SE = sqrt(79/80 * sum((estimate_r - estimate)^2)), as described in the NHES data file user's manuals. |
| Run after build_ecpp.py and build_pfi.py. |
| """ |
| import os, numpy as np, pandas as pd |
| import matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
| from matplotlib import font_manager as fm |
|
|
| ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
| FONTS = os.environ.get('FONT_DIR', os.path.join(ROOT, 'scripts', 'fonts')) |
| for f in os.listdir(FONTS): |
| if f.endswith('.ttf'): fm.fontManager.addfont(f'{FONTS}/{f}') |
| SERIF, SANS = 'Fraunces', 'DM Sans 9pt' |
| CREAM, INK, MUTED, GRID = '#F7F3EC', '#2B2A28', '#6B6862', '#E4DED3' |
| TC, BLUE, OLIVE, GOLD, PLUM = '#B4532F', '#1F72B8', '#7F9A1F', '#D19A2A', '#7D4E8C' |
| plt.rcParams.update({'font.family': SANS, 'font.size': 13, 'axes.edgecolor': GRID, 'axes.labelcolor': MUTED, |
| 'xtick.color': MUTED, 'ytick.color': MUTED, 'axes.facecolor': CREAM, 'figure.facecolor': CREAM, |
| 'axes.spines.top': False, 'axes.spines.right': False, 'axes.grid': True, 'grid.color': GRID, |
| 'grid.linewidth': 0.8, 'axes.axisbelow': True}) |
| R = 80 |
|
|
|
|
| def est(df, rep, mask, num_mask, wcol='final_weight', pre='FEWT'): |
| """Weighted percentage of rows in `num_mask` among rows in `mask`, with JK1 standard error.""" |
| m = mask.values; n = (mask & num_mask).values |
| w = df[wcol].values |
| p = 100 * w[n].sum() / w[m].sum() |
| reps = np.array([100 * rep[f'{pre}{i}'].values[n].sum() / rep[f'{pre}{i}'].values[m].sum() for i in range(1, R + 1)]) |
| se = np.sqrt((R - 1) / R * ((reps - p) ** 2).sum()) |
| return round(p, 1), round(se, 2), int(m.sum()) |
|
|
|
|
| def wmean(df, rep, mask, col, wcol='final_weight', pre='FEWT'): |
| m = (mask & df[col].notna()).values; x = df[col].values |
| w = df[wcol].values |
| mu = (w[m] * x[m]).sum() / w[m].sum() |
| reps = np.array([(rep[f'{pre}{i}'].values[m] * x[m]).sum() / rep[f'{pre}{i}'].values[m].sum() for i in range(1, R + 1)]) |
| se = np.sqrt((R - 1) / R * ((reps - mu) ** 2).sum()) |
| return round(mu, 2), round(se, 2), int(m.sum()) |
|
|
|
|
| def wmedian(df, mask, col, wcol='final_weight'): |
| s = df.loc[mask & df[col].notna(), [col, wcol]].sort_values(col) |
| c = s[wcol].cumsum(); return round(float(s.loc[c >= c.iloc[-1] / 2, col].iloc[0]), 2) |
|
|
|
|
| def finish(fig, title, sub, path, source): |
| fig.text(0.06, 0.94, title, family=SERIF, weight=800, size=26, color=INK, va='top') |
| fig.text(0.06, 0.875, sub, size=14, color=MUTED, va='top') |
| fig.text(0.06, 0.03, source, size=10.5, color=MUTED) |
| fig.savefig(path, dpi=150); plt.close(fig) |
|
|
|
|
| def hbars(path, labels, a, b, title, sub, source, la='2016', lb='2019', xlabel='Percent', fmt='{:.0f}%', xmax=None): |
| fig = plt.figure(figsize=(12, 7.5)); ax = fig.add_axes([0.3, 0.14, 0.64, 0.67]) |
| y = np.arange(len(labels)); h = 0.38 |
| if a is not None: |
| a = [np.nan if v is None else v for v in a] |
| ax.barh(y - h / 2, a, height=h, color=GOLD, label=la) |
| for yy, v in zip(y, a): |
| if not np.isnan(v): ax.text(v + 0.6, yy - h / 2, fmt.format(v), va='center', size=10.5, color=MUTED) |
| ax.barh(y + (h / 2 if a is not None else 0), b, height=h if a is not None else 0.62, color=TC, label=lb) |
| for yy, v in zip(y, b): ax.text(v + 0.6, yy + (h / 2 if a is not None else 0), fmt.format(v), va='center', size=10.5, color=INK) |
| ax.set_yticks(y); ax.set_yticklabels(labels, size=12); ax.invert_yaxis(); ax.grid(axis='y', visible=False) |
| ax.set_xlabel(xlabel) |
| if xmax: ax.set_xlim(0, xmax) |
| if a is not None: ax.legend(frameon=False, loc='lower right', bbox_to_anchor=(1.0, 1.0), ncol=2) |
| finish(fig, title, sub, path, source) |
|
|
|
|
| |
| |
| |
| E = os.path.join(ROOT, 'out', 'ecpp'); os.makedirs(f'{E}/images', exist_ok=True) |
| d = pd.read_csv(f'{E}/data/early_childhood_care_2016_2019.csv', low_memory=False, dtype={'child_id': str}) |
| rp = pd.read_csv(f'{E}/data/replicate_weights_2016_2019.zip', dtype={'child_id': str}) |
| assert (d.child_id.values == rp.child_id.values).all() |
| SRC_E = 'Source: NCES NHES Early Childhood Program Participation survey, 2016 and 2019. Dataset by Montessori Theory (montessori-theory.com), CC BY 4.0.' |
| ALL = pd.Series(True, index=d.index) |
|
|
| rows = [] |
| for y in (2016, 2019): |
| Y = d.survey_year == y |
| for gname, gmask in [('All children', ALL)] + [(f'Age {a}', d.child_age == a) for a in range(0, 6)]: |
| for ind, col in [('Any weekly nonparental care', 'weekly_care'), ('Weekly relative care', 'weekly_relative_care'), |
| ('Weekly nonrelative care', 'weekly_nonrelative_care'), ('Weekly center-based program', 'weekly_center_program')]: |
| p, se, n = est(d, rp, Y & gmask, d[col] == 'Yes') |
| rows.append([y, gname, ind, p, se, n]) |
| care = pd.DataFrame(rows, columns=['survey_year', 'group', 'indicator', 'percent', 'standard_error', 'sample_size']) |
| care.to_csv(f'{E}/data/weekly_care_by_age.csv', index=False) |
|
|
| rows = [] |
| for y in (2016, 2019): |
| Y = d.survey_year == y |
| for cat in ['Center-based program', "Relative care in the child's home", 'Relative care in another home', |
| 'Nonrelative care in another home', "Nonrelative care in the child's home", 'Equal hours in 2 or more types of care', 'No weekly nonparental care']: |
| p, se, n = est(d, rp, Y, d.main_arrangement == cat); rows.append([y, cat, p, se, n]) |
| main = pd.DataFrame(rows, columns=['survey_year', 'main_arrangement', 'percent', 'standard_error', 'sample_size']) |
| main.to_csv(f'{E}/data/main_arrangement.csv', index=False) |
|
|
| for byv, fname in [('household_income', 'center_care_by_income.csv'), ('parent_education', 'center_care_by_parent_education.csv'), |
| ('child_race_ethnicity', 'center_care_by_race_ethnicity.csv'), ('locale_group', 'center_care_by_locale.csv'), |
| ('census_region', 'center_care_by_region.csv')]: |
| rows = [] |
| for y in (2016, 2019): |
| INC_ORDER = ['$0 to $10,000', '$10,001 to $20,000', '$20,001 to $30,000', '$30,001 to $40,000', '$40,001 to $50,000', '$50,001 to $60,000', |
| '$60,001 to $75,000', '$75,001 to $100,000', '$100,001 to $150,000', '$150,001 or more'] |
| EDU_ORDER = ['Less than high school', 'High school graduate or equivalent', 'Vocational/technical or some college', "Bachelor's degree", 'Graduate or professional degree'] |
| order = INC_ORDER if byv == 'household_income' else EDU_ORDER if byv == 'parent_education' else sorted(d[byv].dropna().unique()) |
| for g in order: |
| M = (d.survey_year == y) & (d[byv] == g) & (d.child_age >= 3) |
| p, se, n = est(d, rp, M, d.weekly_center_program == 'Yes') |
| rows.append([y, g, p, se, n]) |
| pd.DataFrame(rows, columns=['survey_year', byv, 'percent_in_weekly_center_program_age_3_to_5', 'standard_error', 'sample_size']).to_csv(f'{E}/data/{fname}', index=False) |
|
|
| FACT = [('importance_reliability', 'Reliability of the arrangement'), ('importance_staff_qualifications', 'Qualifications of the staff (2019)'), |
| ('importance_learning_activities', 'Learning activities'), ('importance_caregiver_availability', 'Caregiver available when needed'), |
| ('importance_time_with_other_children', 'Time with other children'), ('importance_location', 'Location'), |
| ('importance_cost', 'Cost'), ('importance_recommendations', 'Recommendations from family and friends'), |
| ('importance_group_size', 'Number of children in the group'), ('importance_website_ratings', 'Website ratings'), |
| ('importance_religious_orientation', 'Religious orientation')] |
| rows = [] |
| for y in (2016, 2019): |
| for col, lab in FACT: |
| M = (d.survey_year == y) & d[col].notna() & (d[col] != 'Not available') |
| if M.sum() == 0: continue |
| p, se, n = est(d, rp, M, d[col] == 'Very important'); rows.append([y, lab, col, p, se, n]) |
| fact = pd.DataFrame(rows, columns=['survey_year', 'factor', 'column', 'percent_very_important', 'standard_error', 'sample_size']) |
| fact.to_csv(f'{E}/data/what_parents_look_for.csv', index=False) |
|
|
| rows = [] |
| for y in (2016, 2019): |
| for arr, col in [('Center-based program', 'center_cost_weekly_per_child'), ('Relative care', 'relative_cost_weekly_per_child'), |
| ('Nonrelative care', 'nonrelative_cost_weekly_per_child')]: |
| M = (d.survey_year == y) & (d[col] > 0) |
| mu, se, n = wmean(d, rp, M, col) |
| Mall = (d.survey_year == y) & d[col].notna() |
| p0, se0, n0 = est(d, rp, Mall, d[col] == 0) |
| rows.append([y, arr, mu, se, wmedian(d, M, col), p0, n]) |
| cost = pd.DataFrame(rows, columns=['survey_year', 'arrangement', 'mean_weekly_cost_per_child_usd', 'standard_error', |
| 'median_weekly_cost_per_child_usd', 'percent_paying_nothing', 'sample_size_paying']) |
| d['_hourly_center'] = np.where(d.center_hours > 0, d.center_cost_weekly_per_child / d.center_hours, np.nan) |
| for y in (2016, 2019): |
| M = (d.survey_year == y) & (d._hourly_center > 0) |
| mu, se, n = wmean(d, rp, M, '_hourly_center') |
| cost.loc[len(cost)] = [y, 'Center-based program, per hour', mu, se, wmedian(d, M, '_hourly_center'), np.nan, n] |
| cost.to_csv(f'{E}/data/weekly_cost.csv', index=False) |
|
|
| ACT = [('times_read_to_past_week', 'Read to 3 or more times in the past week', lambda s: s >= 3), |
| ('told_story_past_week', 'Told a story 3 or more times', lambda s: s == '3 or more times'), |
| ('taught_letters_words_numbers_past_week', 'Taught letters, words or numbers 3+ times', lambda s: s == '3 or more times'), |
| ('sang_songs_past_week', 'Sang songs 3 or more times', lambda s: s == '3 or more times'), |
| ('arts_and_crafts_past_week', 'Arts and crafts 3 or more times', lambda s: s == '3 or more times'), |
| ('family_dinners_past_week', 'Family dinner 5 or more days', lambda s: s >= 5), |
| ('library_visit_past_month', 'Visited a library in the past month', lambda s: s == 'Yes'), |
| ('bookstore_visit_past_month', 'Visited a bookstore in the past month', lambda s: s == 'Yes')] |
| rows = [] |
| for y in (2016, 2019): |
| for col, lab, f in ACT: |
| M = (d.survey_year == y) & (d.child_age >= 3) |
| p, se, n = est(d, rp, M, f(d[col]).fillna(False)); rows.append([y, lab, col, p, se, n]) |
| act = pd.DataFrame(rows, columns=['survey_year', 'activity', 'column', 'percent_of_children_age_3_to_5', 'standard_error', 'sample_size']) |
| act.to_csv(f'{E}/data/home_learning_activities.csv', index=False) |
|
|
| |
| c19 = care[(care.survey_year == 2019) & care.group.str.startswith('Age')] |
| fig = plt.figure(figsize=(12, 6.5)); ax = fig.add_axes([0.08, 0.16, 0.86, 0.63]) |
| ages = [f'Age {a}' for a in range(6)]; x = np.arange(6); w = 0.26 |
| for i, (ind, col) in enumerate([('Weekly relative care', OLIVE), ('Weekly nonrelative care', GOLD), ('Weekly center-based program', TC)]): |
| v = [c19[(c19.group == a) & (c19.indicator == ind)].percent.iloc[0] for a in ages] |
| ax.bar(x + (i - 1) * w, v, width=w, color=col, label=ind.replace('Weekly ', '').capitalize()) |
| for xx, vv in zip(x, v): ax.text(xx + (i - 1) * w, vv + 1, f'{vv:.0f}', ha='center', size=9.5, color=MUTED) |
| ax.set_xticks(x); ax.set_xticklabels(['Under 1', '1', '2', '3', '4', '5'], size=12); ax.set_xlabel("Child's age (not yet in kindergarten)") |
| ax.set_ylabel('Percent of children'); ax.set_ylim(0, 100); ax.legend(frameon=False, loc='upper left', ncol=3) |
| finish(fig, 'Who looks after young children each week', 'Weekly care arrangements of children not yet in kindergarten, by age, 2019', |
| f'{E}/images/care_by_age_2019.png', SRC_E) |
|
|
| f19 = fact[fact.survey_year == 2019].sort_values('percent_very_important', ascending=False) |
| f16 = fact[fact.survey_year == 2016].set_index('factor').percent_very_important |
| hbars(f'{E}/images/what_parents_look_for.png', list(f19.factor), [f16.get(k) for k in f19.factor], list(f19.percent_very_important), |
| 'What parents look for in child care', 'Share of parents rating each factor "very important" when choosing the main arrangement', |
| SRC_E, xmax=105) |
|
|
| cw = cost[cost.arrangement != 'Center-based program, per hour'] |
| fig = plt.figure(figsize=(12, 6.5)); ax = fig.add_axes([0.08, 0.16, 0.86, 0.63]) |
| x = np.arange(3); w = 0.36 |
| for i, (y, col) in enumerate([(2016, GOLD), (2019, TC)]): |
| v = cw[cw.survey_year == y].mean_weekly_cost_per_child_usd.values |
| ax.bar(x + (i - 0.5) * w, v, width=w, color=col, label=str(y)) |
| for xx, vv in zip(x, v): ax.text(xx + (i - 0.5) * w, vv + 3, f'${vv:.0f}', ha='center', size=11, color=INK) |
| ax.set_xticks(x); ax.set_xticklabels(['Center-based program', 'Relative care', 'Nonrelative care'], size=12) |
| ax.set_ylabel('Mean weekly cost per child, USD'); ax.legend(frameon=False) |
| finish(fig, 'What families pay for child care', 'Mean weekly out-of-pocket cost per child, families who pay, current dollars', |
| f'{E}/images/weekly_cost.png', SRC_E) |
|
|
| a19 = act[act.survey_year == 2019]; a16 = act[act.survey_year == 2016].set_index('activity').percent_of_children_age_3_to_5 |
| hbars(f'{E}/images/home_learning.png', list(a19.activity), [a16[k] for k in a19.activity], list(a19.percent_of_children_age_3_to_5), |
| 'Learning at home before kindergarten', 'Children aged 3 to 5 not yet in kindergarten, activities with a family member', SRC_E, xmax=105) |
|
|
| |
| fig = plt.figure(figsize=(16, 8), dpi=100) |
| ax = fig.add_axes([0.53, 0.14, 0.43, 0.7]) |
| v = [c19[(c19.group == a) & (c19.indicator == 'Weekly center-based program')].percent.iloc[0] for a in ages] |
| v2 = [c19[(c19.group == a) & (c19.indicator == 'Weekly relative care')].percent.iloc[0] for a in ages] |
| ax.bar(np.arange(6) - 0.2, v2, width=0.4, color=OLIVE); ax.bar(np.arange(6) + 0.2, v, width=0.4, color=TC) |
| ax.set_xticks(range(6)); ax.set_xticklabels(['<1', '1', '2', '3', '4', '5'], size=15); ax.set_yticks([0, 25, 50, 75]); ax.set_ylim(0, 85) |
| ax.set_yticklabels(['0%', '25%', '50%', '75%'], size=13) |
| ax.text(0.0, 1.04, 'Weekly care by age, 2019', transform=ax.transAxes, size=16, color=INK) |
| ax.text(2.3, v[3] + 11, 'center-based', color=TC, size=14); ax.text(-0.5, v2[0] + 6, 'relatives', color=OLIVE, size=14) |
| fig.text(0.05, 0.80, 'US Early Childhood', family=SERIF, weight=800, size=52, color=TC, va='top') |
| fig.text(0.05, 0.67, 'Care Arrangements', family=SERIF, weight=800, size=52, color=TC, va='top') |
| fig.text(0.05, 0.53, 'Birth to age 5, 2016 and 2019', family=SERIF, weight=600, size=30, color=INK, va='top') |
| n16, n19 = (d.survey_year == 2016).sum(), (d.survey_year == 2019).sum() |
| for i, t in enumerate([f'{len(d):,} children, NCES NHES early childhood survey', 'Relatives, nannies, day care, preschool, Head Start', |
| 'Hours, weekly cost, what parents look for, learning at home']): |
| fig.text(0.05, 0.40 - i * 0.065, t, size=18, color=INK, va='top') |
| fig.text(0.05, 0.08, 'Montessori Theory · montessori-theory.com · CC BY 4.0', size=15, color=MUTED, family=SERIF, weight=600) |
| fig.savefig(f'{E}/images/cover.png', dpi=100); plt.close(fig) |
|
|
| print(care[care.group == 'All children'].to_string(index=False)); print(main.to_string(index=False)); print(cost.to_string(index=False)) |
| print(fact.to_string(index=False)) |
|
|
| |
| |
| |
| P = os.path.join(ROOT, 'out', 'pfi'); os.makedirs(f'{P}/images', exist_ok=True) |
| s = pd.concat([pd.read_csv(f'{P}/data/school_choice_homeschooling_{y}.csv', low_memory=False, dtype={'student_id': str}) for y in (2016, 2019)], ignore_index=True) |
| rq = pd.concat([pd.read_csv(f'{P}/data/replicate_weights_{y}.zip', dtype={'student_id': str}) for y in (2016, 2019)], ignore_index=True) |
| assert (s.student_id.values == rq.student_id.values).all() |
| SRC_P = 'Source: NCES NHES Parent and Family Involvement in Education survey, 2016 and 2019. Dataset by Montessori Theory (montessori-theory.com), CC BY 4.0.' |
| K = dict(pre='FPWT') |
| U = (s.child_age >= 5) & (s.child_age <= 17) |
| TYPES = ['Public, assigned', 'Public, chosen', 'Public charter', 'Private, Catholic', 'Private, other religious', 'Private, nonreligious', 'Homeschooled', 'Other or unknown'] |
|
|
| rows = [] |
| for y in (2016, 2019): |
| for t in TYPES: |
| p, se, n = est(s, rq, U & (s.survey_year == y), s.schooling_type == t, **K); rows.append([y, t, p, se, n]) |
| stype = pd.DataFrame(rows, columns=['survey_year', 'schooling_type', 'percent_of_students_age_5_to_17', 'standard_error', 'sample_size']) |
| stype.to_csv(f'{P}/data/schooling_type.csv', index=False) |
|
|
| rows = [] |
| for y in (2016, 2019): |
| Y = U & (s.survey_year == y) |
| p, se, n = est(s, rq, Y, s.homeschooled == 'Yes', **K) |
| wsum = s.loc[Y & (s.homeschooled == 'Yes'), 'final_weight'].sum() |
| rows.append([y, 'All students', 'All students', p, se, round(wsum / 1000), n]) |
| for byv in ['child_race_ethnicity', 'census_region', 'locale_group', 'parent_education', 'household_income', 'grade']: |
| for g in s.loc[Y, byv].dropna().unique(): |
| M = Y & (s[byv] == g); p, se, n = est(s, rq, M, s.homeschooled == 'Yes', **K) |
| rows.append([y, byv, g, p, se, round(s.loc[M & (s.homeschooled == 'Yes'), 'final_weight'].sum() / 1000), n]) |
| hsr = pd.DataFrame(rows, columns=['survey_year', 'characteristic', 'group', 'percent_homeschooled', 'standard_error', 'homeschooled_thousands', 'sample_size']) |
| hsr.to_csv(f'{P}/data/homeschooling_rate.csv', index=False) |
|
|
| REAS = [('homeschool_reason_school_environment', 'Concern about school environment'), ('homeschool_reason_moral_instruction', 'To provide moral instruction'), |
| ('homeschool_reason_family_life_2019', 'Emphasize family life together (2019)'), ('homeschool_reason_dissatisfied_with_instruction', 'Dissatisfied with academic instruction'), |
| ('homeschool_reason_religious_instruction', 'To provide religious instruction'), ('homeschool_reason_nontraditional_approach', 'Nontraditional approach to education'), |
| ('homeschool_reason_special_needs', 'Child has special needs'), ('homeschool_reason_health_problem', 'Child has a health problem'), |
| ('homeschool_reason_other', 'Other reason'), ('homeschool_reason_temporary_illness', 'Temporary illness')] |
| rows = [] |
| for y in (2016, 2019): |
| H = U & (s.survey_year == y) & (s.homeschooled == 'Yes') |
| for col, lab in REAS: |
| M = H & s[col].notna() |
| if M.sum() == 0: continue |
| p, se, n = est(s, rq, M, s[col] == 'Yes', **K); rows.append([y, 'Reason cited', lab, p, se, n]) |
| for r in s.loc[H, 'homeschool_most_important_reason'].dropna().unique(): |
| M = H & s.homeschool_most_important_reason.notna() |
| p, se, n = est(s, rq, M, s.homeschool_most_important_reason == r, **K); rows.append([y, 'Most important reason', r, p, se, n]) |
| for col in ['homeschool_teaching_style']: |
| for r in s.loc[H, col].dropna().unique(): |
| M = H & s[col].notna(); p, se, n = est(s, rq, M, s[col] == r, **K); rows.append([y, 'Teaching style', r, p, se, n]) |
| for col, lab in [('homeschool_coop_instruction', 'Some instruction from a homeschool group or co-op'), ('homeschool_online_instruction', 'Some online instruction'), |
| ('homeschool_organization_member', 'Member of a homeschool organization')]: |
| M = H & s[col].notna(); p, se, n = est(s, rq, M, s[col] == 'Yes', **K); rows.append([y, 'Practice', lab, p, se, n]) |
| hsreas = pd.DataFrame(rows, columns=['survey_year', 'measure', 'category', 'percent_of_homeschooled_students', 'standard_error', 'sample_size']) |
| hsreas.to_csv(f'{P}/data/homeschooling_reasons_and_practices.csv', index=False) |
|
|
| rows = [] |
| SAT = [('satisfaction_school', 'School'), ('satisfaction_teachers', 'Teachers'), ('satisfaction_academic_standards', 'Academic standards'), |
| ('satisfaction_discipline', 'Order and discipline'), ('satisfaction_staff_parent_interaction', 'Staff and parent interaction')] |
| for y in (2016, 2019): |
| for grp in ['Public, assigned', 'Public, chosen', 'Public charter', 'Private, Catholic', 'Private, other religious', 'Private, nonreligious']: |
| for col, lab in SAT: |
| M = (s.survey_year == y) & (s.schooling_type == grp) & s[col].notna() |
| p, se, n = est(s, rq, M, s[col] == 'Very satisfied', **K); rows.append([y, grp, lab, p, se, n]) |
| sat = pd.DataFrame(rows, columns=['survey_year', 'schooling_type', 'aspect', 'percent_very_satisfied', 'standard_error', 'sample_size']) |
| sat.to_csv(f'{P}/data/parent_satisfaction.csv', index=False) |
|
|
| CH = [c for c in s.columns if c.startswith('choice_reason_')] |
| rows = [] |
| for grp in ['All who chose', 'Public, chosen', 'Public charter', 'Private, Catholic', 'Private, other religious', 'Private, nonreligious']: |
| for col in CH: |
| M = (s.survey_year == 2019) & s[col].notna() & (s[col] != 'School is not religious') |
| if grp != 'All who chose': M &= s.schooling_type == grp |
| p, se, n = est(s, rq, M, s[col] == 'Very important', **K) |
| rows.append([2019, grp, col.replace('choice_reason_', '').replace('_2019', '').replace('_', ' ').capitalize(), p, se, n]) |
| chs = pd.DataFrame(rows, columns=['survey_year', 'schooling_type', 'reason', 'percent_very_important', 'standard_error', 'sample_size']) |
| chs.to_csv(f'{P}/data/school_choice_reasons_2019.csv', index=False) |
|
|
| INV = {'parent_attended_school_event': 'Attended a school or class event', 'parent_volunteered': 'Volunteered at the school', |
| 'parent_attended_general_meeting': 'Attended a general school meeting', 'parent_attended_pto_meeting': 'Attended a PTO or PTA meeting', |
| 'parent_attended_parent_teacher_conference': 'Attended a parent-teacher conference', 'parent_fundraising': 'Took part in fundraising', |
| 'parent_served_on_committee': 'Served on a school committee'} |
| rows = [] |
| for y in (2016, 2019): |
| for grp in ['All enrolled students', 'Public', 'Private']: |
| for col in INV: |
| M = (s.survey_year == y) & s[col].notna() |
| if grp != 'All enrolled students': M &= s.schooling_group == grp |
| p, se, n = est(s, rq, M, s[col] == 'Yes', **K) |
| rows.append([y, grp, INV[col], p, se, n]) |
| inv = pd.DataFrame(rows, columns=['survey_year', 'schooling_group', 'activity', 'percent_of_parents', 'standard_error', 'sample_size']) |
| inv.to_csv(f'{P}/data/parent_involvement.csv', index=False) |
|
|
| |
| lab = ['Public, assigned', 'Public, chosen', 'Public charter', 'Private, Catholic', 'Private, other religious', 'Private, nonreligious', 'Homeschooled'] |
| t16 = stype[stype.survey_year == 2016].set_index('schooling_type').percent_of_students_age_5_to_17 |
| t19 = stype[stype.survey_year == 2019].set_index('schooling_type').percent_of_students_age_5_to_17 |
| hbars(f'{P}/images/schooling_type.png', lab, [t16[k] for k in lab], [t19[k] for k in lab], 'How American children go to school', |
| 'Students aged 5 to 17, kindergarten to grade 12 or equivalent', SRC_P, fmt='{:.1f}%', xmax=82) |
|
|
| r19 = hsreas[(hsreas.survey_year == 2019) & (hsreas.measure == 'Reason cited')].sort_values('percent_of_homeschooled_students', ascending=False) |
| r16 = hsreas[(hsreas.survey_year == 2016) & (hsreas.measure == 'Reason cited')].set_index('category').percent_of_homeschooled_students |
| hbars(f'{P}/images/homeschool_reasons.png', list(r19.category), [r16.get(k) for k in r19.category], list(r19.percent_of_homeschooled_students), |
| 'Why families homeschool', 'Share of homeschooled students whose parents cite each reason (several possible)', SRC_P, xmax=100) |
|
|
| s19 = sat[(sat.survey_year == 2019) & (sat.aspect == 'School')] |
| s16 = sat[(sat.survey_year == 2016) & (sat.aspect == 'School')].set_index('schooling_type').percent_very_satisfied |
| hbars(f'{P}/images/satisfaction.png', list(s19.schooling_type), [s16[k] for k in s19.schooling_type], list(s19.percent_very_satisfied), |
| 'How satisfied parents are with the school', 'Share of parents "very satisfied" with their child\'s school, by type of school', SRC_P, xmax=100) |
|
|
| c19 = chs[chs.schooling_type == 'All who chose'].sort_values('percent_very_important', ascending=False) |
| hbars(f'{P}/images/school_choice_reasons.png', list(c19.reason), None, list(c19.percent_very_important), 'Why parents choose a school', |
| 'Share rating each reason "very important", parents who chose their child\'s school, 2019', SRC_P, xmax=100) |
|
|
| hs_all = hsr[hsr.characteristic == 'All students'].set_index('survey_year') |
| fig = plt.figure(figsize=(16, 8), dpi=100) |
| ax = fig.add_axes([0.55, 0.14, 0.41, 0.7]) |
| order = ['Public, assigned', 'Public, chosen', 'Public charter', 'Private, Catholic', 'Private, other religious', 'Private, nonreligious', 'Homeschooled'] |
| cols = [TC, '#D9876A', '#E8B7A3', BLUE, '#6FA3D6', '#A9C8E8', OLIVE] |
| left = 0 |
| for k, c in zip(order, cols): |
| ax.barh([0], [t19[k]], left=left, color=c, height=0.5); left += t19[k] |
| ax.set_xlim(0, 100); ax.set_ylim(-0.6, 2.2); ax.axis('off') |
| labels = [('Public, assigned', t19['Public, assigned'], TC), ('Public by choice or charter', t19['Public, chosen'] + t19['Public charter'], '#D9876A'), |
| ('Private', t19['Private, Catholic'] + t19['Private, other religious'] + t19['Private, nonreligious'], BLUE), ('Homeschooled', t19['Homeschooled'], OLIVE)] |
| for i, (l, v, c) in enumerate(labels): |
| ax.text(0, 1.95 - i * 0.33, f'{v:.0f}%', color=c, size=24, family=SERIF, weight=800, va='center') |
| ax.text(14, 1.95 - i * 0.33, l, color=INK, size=17, va='center') |
| ax.text(0, -0.45, 'Students aged 5 to 17, 2019', size=14, color=MUTED) |
| fig.text(0.05, 0.80, 'US School Choice', family=SERIF, weight=800, size=52, color=TC, va='top') |
| fig.text(0.05, 0.67, 'and Homeschooling', family=SERIF, weight=800, size=52, color=TC, va='top') |
| fig.text(0.05, 0.53, 'Kindergarten to grade 12, 2016 and 2019', family=SERIF, weight=600, size=28, color=INK, va='top') |
| for i, t in enumerate([f'{len(s):,} students, NCES NHES parent survey', 'Public, charter, private and homeschool', |
| 'Why parents choose, why they homeschool, satisfaction']): |
| fig.text(0.05, 0.40 - i * 0.065, t, size=18, color=INK, va='top') |
| fig.text(0.05, 0.08, 'Montessori Theory · montessori-theory.com · CC BY 4.0', size=15, color=MUTED, family=SERIF, weight=600) |
| fig.savefig(f'{P}/images/cover.png', dpi=100); plt.close(fig) |
|
|
| print(stype.to_string(index=False)); print(hs_all.to_string()) |
| print(hsreas[hsreas.measure == 'Reason cited'].to_string(index=False)) |
| print(chs[chs.schooling_type == 'All who chose'].to_string(index=False)) |
|
|
