Fetch and analyze Louisiana school enrollment data from the Louisiana Department of Education (LDOE) in R or Python.
Part of the njschooldata family.
Full documentation — all 15 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
library(laschooldata)
library(ggplot2)
library(dplyr)
library(scales)
theme_readme <- function() {
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(color = "gray40"),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
}
colors <- c("total" = "#2C3E50", "white" = "#3498DB", "black" = "#E74C3C",
"hispanic" = "#F39C12", "asian" = "#9B59B6")
enr <- fetch_enr_multi(2019:2024, use_cache = TRUE)
enr_current <- fetch_enr(2024, use_cache = TRUE)
# Calculate state totals for percentage calculations
state_totals <- enr %>%
filter(is_state, grade_level == "TOTAL", subgroup == "total_enrollment") %>%
select(end_year, total = n_students)1. White students fell below Black students for the first time
White enrollment dropped from 47.1% in 2019 to 40.7% in 2024, while Black students held steady at 41.7%, making Louisiana’s public schools majority-minority.
demo <- enr %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "black", "hispanic", "asian")) %>%
left_join(state_totals, by = "end_year") %>%
mutate(pct = n_students / total * 100)
stopifnot(nrow(demo) > 0)
demo %>% filter(end_year == 2024) %>% select(subgroup, n_students, pct)
#> subgroup n_students pct
#> 1 asian 10745 1.5878
#> 2 black 282521 41.7441
#> 3 hispanic 77836 11.5010
#> 4 white 275265 40.6721
Louisiana Demographics
2. Calcasieu Parish never recovered from Hurricane Laura
Calcasieu Parish (Lake Charles) lost 11.3% of enrollment in a single year (2019-2020) after Hurricane Laura devastated southwest Louisiana.
calcasieu <- enr %>%
filter(is_district, district_name == "Calcasieu Parish",
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(calcasieu) > 0)
calcasieu %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2019 Calcasieu Parish 31879
#> 2 2020 Calcasieu Parish 28265
#> 3 2021 Calcasieu Parish 27681
#> 4 2022 Calcasieu Parish 27871
#> 5 2023 Calcasieu Parish 28392
#> 6 2024 Calcasieu Parish 28623
Calcasieu Parish (Lake Charles)
3. Hispanic enrollment surged 45% in five years
Hispanic students grew from 53,778 (8.4%) in 2019 to 77,836 (11.5%) in 2024, the fastest-growing demographic group in Louisiana schools.
hisp <- enr %>%
filter(is_state, grade_level == "TOTAL", subgroup == "hispanic") %>%
left_join(state_totals, by = "end_year") %>%
mutate(pct = n_students / total * 100)
stopifnot(nrow(hisp) > 0)
hisp %>% select(end_year, n_students, pct)
#> end_year n_students pct
#> 1 2019 53778 8.35073
#> 2 2020 54251 8.68684
#> 3 2021 57761 9.37892
#> 4 2022 70054 10.21793
#> 5 2023 73627 10.80900
#> 6 2024 77836 11.50105
Hispanic Enrollment Surging
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2019-2024 |
fetch_enr() / fetch_enr_multi()
|
State, district, school. Race, gender, FRPL, LEP |
| Assessments | 2018-2019, 2021-2025 |
fetch_assessment() / fetch_assessment_multi()
|
LEAP 2025. ELA, Math, Science, Social Studies |
| Directory | 2026 | fetch_directory() |
School names, addresses, coordinates, grades served |
| Graduation | — | — | Not yet available |
| Per-Pupil Spending | — | — | Not yet available |
| Accountability | — | — | Not yet available |
| Chronic Absence | — | — | Not yet available |
| EL Progress | — | — | Not yet available |
| Special Ed | — | — | Not yet available |
See DATA-CATEGORY-TAXONOMY.md for what each category covers.
Quick Start
R
# install.packages("remotes")
remotes::install_github("almartin82/laschooldata")
library(laschooldata)
library(dplyr)
# Fetch one year
enr_2024 <- fetch_enr(2024)
# Fetch recent years
enr_recent <- fetch_enr_multi(2020:2024)
# State totals
enr_2024 %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL")
# Parish (district) breakdown
enr_2024 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students))
# Demographics statewide
enr_2024 %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "black", "hispanic", "asian")) %>%
select(subgroup, n_students)Python
import pylaschooldata as la
# Fetch 2024 data (2023-24 school year)
enr = la.fetch_enr(2024)
# Statewide total
total = enr[(enr['is_state']) & (enr['grade_level'] == 'TOTAL') &
(enr['subgroup'] == 'total_enrollment')]['n_students'].sum()
print(f"{total:,} students")
#> 676,751 students
# Get multiple years
enr_multi = la.fetch_enr_multi([2020, 2021, 2022, 2023, 2024])
# Check available years
years = la.get_available_years()
print(f"Data available: {years['min_year']}-{years['max_year']}")
#> Data available: 2019-2024Explore More
- Full documentation — all 15 stories with interactive charts
- Enrollment trends — 15 stories
- Function reference
Data Notes
Data Source
Data is sourced directly from the Louisiana Department of Education (LDOE) Multi Stats files: - Primary URL: https://www.louisianabelieves.com/resources/library/student-attributes - Data Center: https://doe.louisiana.gov/data-and-reports/enrollment-data
Snapshot Date
Enrollment counts are based on October 1st (MFP - Minimum Foundation Program) counts each school year.
Suppression Rules
LDOE applies FERPA suppression to protect student privacy: - Counts under 10 may be suppressed at the school level - Small cells in demographic breakdowns may show as NA
Data Quality Notes
- Gender data is stored as percentages in the source files, then converted to counts using total enrollment
- No “Orleans Parish” district exists in the LDOE data – post-Katrina reorganization split New Orleans schools across multiple entities
- No charter school district – charter schools are included with their authorizing parish
- Extension Academy and T9 (transitional 9th grade) data may not be available for all years/parishes
What’s Included
- Levels: State, District (75 entities), Campus (school)
- Demographics: White, Black, Hispanic, Asian, Native American, Pacific Islander, Multiracial
- Special populations: Economically disadvantaged, LEP/English learners
- Grade levels: PK through 12 (plus special education infant/preschool)
Deeper Dive
4. Louisiana added 33,000 students since 2019
Statewide enrollment grew 5.1% from 2019 to 2024, rising from 644,000 to 677,000 students.
state_trend <- enr %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
select(end_year, n_students)
stopifnot(nrow(state_trend) > 0)
state_trend
#> end_year n_students
#> 1 2019 643986
#> 2 2020 624527
#> 3 2021 615839
#> 4 2022 685606
#> 5 2023 681176
#> 6 2024 676751
Louisiana Statewide Enrollment
5. Jefferson Parish is Louisiana’s largest district
Jefferson Parish enrolls 47,702 students, leading all districts and outpacing East Baton Rouge (39,932) and St. Tammany (36,384).
top5 <- enr_current %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_name != "State of Louisiana") %>%
arrange(desc(n_students)) %>%
head(5)
stopifnot(nrow(top5) > 0)
top5 %>% select(district_name, n_students)
#> district_name n_students
#> 1 Jefferson Parish 47702
#> 2 East Baton Rouge Parish 39932
#> 3 St. Tammany Parish 36384
#> 4 Caddo Parish 32614
#> 5 Lafayette Parish 29877
Louisiana’s Five Largest Districts
6. Caddo Parish lost 14% of its students in five years
Caddo Parish (Shreveport) dropped from 37,868 students in 2019 to 32,614 in 2024, a loss of over 5,200 students.
caddo <- enr %>%
filter(is_district, district_name == "Caddo Parish",
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(caddo) > 0)
caddo %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2019 Caddo Parish 37868
#> 2 2020 Caddo Parish 36470
#> 3 2021 Caddo Parish 35057
#> 4 2022 Caddo Parish 33934
#> 5 2023 Caddo Parish 33243
#> 6 2024 Caddo Parish 32614
Caddo Parish Decline
7. COVID wiped out 17% of Pre-K enrollment overnight
Pre-K dropped from 26,078 to 21,751 students between 2019 and 2020, then slowly recovered to 26,152 by 2024.
prek <- enr %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "PK")
stopifnot(nrow(prek) > 0)
prek %>% select(end_year, n_students)
#> end_year n_students
#> 1 2019 26078
#> 2 2020 21751
#> 3 2021 24027
#> 4 2022 25969
#> 5 2023 26002
#> 6 2024 26152
Pre-K Enrollment Cratered During COVID
8. Kindergarten also took a COVID hit
Kindergarten enrollment fell 6.9% from 48,556 to 45,205 between 2019 and 2020, and still has not returned to pre-pandemic levels.
k_trend <- enr %>%
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("PK", "K", "01", "09")) %>%
mutate(grade_label = case_when(
grade_level == "PK" ~ "Pre-K",
grade_level == "K" ~ "Kindergarten",
grade_level == "01" ~ "Grade 1",
grade_level == "09" ~ "Grade 9"
))
stopifnot(nrow(k_trend) > 0)
k_trend %>%
filter(grade_level == "K") %>%
select(end_year, grade_label, n_students)
#> end_year grade_label n_students
#> 1 2019 Kindergarten 48556
#> 2 2020 Kindergarten 45205
#> 3 2021 Kindergarten 46282
#> 4 2022 Kindergarten 50345
#> 5 2023 Kindergarten 48798
#> 6 2024 Kindergarten 48084
COVID Impact by Grade Level
9. English learners grew from 3.9% to 5.3% of enrollment
LEP students increased from 24,908 to 35,868 between 2019 and 2024, a 44% jump tracking closely with Hispanic growth.
el <- enr %>%
filter(is_state, subgroup == "lep", grade_level == "TOTAL") %>%
left_join(state_totals, by = "end_year") %>%
mutate(pct = n_students / total * 100)
stopifnot(nrow(el) > 0)
el %>% select(end_year, n_students, pct)
#> end_year n_students pct
#> 1 2019 24908 3.86780
#> 2 2020 23336 3.73660
#> 3 2021 25194 4.09127
#> 4 2022 31939 4.65867
#> 5 2023 33847 4.96870
#> 6 2024 35868 5.30062
English Learners on the Rise
10. Seven in ten Louisiana students are economically disadvantaged
The statewide economic disadvantage rate has hovered around 70% since 2019, one of the highest rates in the nation.
econ_state <- enr %>%
filter(is_state, subgroup == "econ_disadv", grade_level == "TOTAL") %>%
left_join(state_totals, by = "end_year") %>%
mutate(pct = n_students / total * 100)
stopifnot(nrow(econ_state) > 0)
econ_state %>% select(end_year, n_students, pct)
#> end_year n_students pct
#> 1 2019 436524 67.7976
#> 2 2020 453025 72.5388
#> 3 2021 429803 69.7954
#> 4 2022 494310 72.0907
#> 5 2023 494076 72.5338
#> 6 2024 474402 70.1035
Economic Disadvantage Rate
11. St. Helena Parish: 100% economically disadvantaged
St. Helena leads Louisiana with every single student classified as economically disadvantaged, followed by East Carroll (96.9%) and Tensas (95.8%).
district_totals <- enr_current %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
select(district_name, total = n_students)
econ <- enr_current %>%
filter(is_district, subgroup == "econ_disadv", grade_level == "TOTAL") %>%
left_join(district_totals, by = "district_name") %>%
mutate(pct = n_students / total * 100) %>%
arrange(desc(pct)) %>%
head(10) %>%
mutate(district_label = reorder(district_name, pct))
stopifnot(nrow(econ) > 0)
econ %>% select(district_name, n_students, total, pct)
#> district_name n_students total pct
#> 1 St. Helena Parish 1002 1002 100.00000
#> 2 Special School District 291 299 97.32441
#> 3 East Carroll Parish 715 738 96.88076
#> 4 Tensas Parish 298 311 95.81994
#> 5 Madison Parish 1078 1134 95.06173
#> 6 Thrive Academy 152 161 94.40994
#> 7 City of Bogalusa School District 1718 1822 94.29198
#> 8 City of Baker School District 927 999 92.79279
#> 9 Red River Parish 1143 1251 91.36691
#> 10 Natchitoches Parish 4230 4829 87.59165
Highest Poverty Districts
12. Delta parishes are emptying out
East Carroll (-20.8%), Tensas (-22.6%), and Madison (-4.4%) have shed students steadily since 2019.
delta_names <- c("Tensas Parish", "East Carroll Parish", "Madison Parish")
delta_trend <- enr %>%
filter(is_district, district_name %in% delta_names,
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(delta_trend) > 0)
delta_trend %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2019 East Carroll Parish 932
#> 2 2020 East Carroll Parish 836
#> 3 2021 East Carroll Parish 778
#> 4 2022 East Carroll Parish 770
#> 5 2023 East Carroll Parish 751
#> 6 2024 East Carroll Parish 738
#> 7 2019 Madison Parish 1186
#> 8 2020 Madison Parish 1102
#> 9 2021 Madison Parish 1163
#> 10 2022 Madison Parish 1223
#> 11 2023 Madison Parish 1244
#> 12 2024 Madison Parish 1134
#> 13 2019 Tensas Parish 402
#> 14 2020 Tensas Parish 355
#> 15 2021 Tensas Parish 334
#> 16 2022 Tensas Parish 331
#> 17 2023 Tensas Parish 328
#> 18 2024 Tensas Parish 311
Delta Parishes Losing Students
13. Jefferson Parish: the slow suburban slide
Louisiana’s largest district has been quietly losing students, dropping from 50,566 in 2019 to 47,702 in 2024.
jefferson <- enr %>%
filter(is_district, district_name == "Jefferson Parish",
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(jefferson) > 0)
jefferson %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2019 Jefferson Parish 50566
#> 2 2020 Jefferson Parish 48974
#> 3 2021 Jefferson Parish 47720
#> 4 2022 Jefferson Parish 47429
#> 5 2023 Jefferson Parish 47712
#> 6 2024 Jefferson Parish 47702
Jefferson Parish Largest but Shrinking
14. The suburban corridor holds steady
Ascension, Livingston, and St. Tammany parishes along the I-10/I-12 corridor are among the few growing districts.
i10 <- c("Livingston Parish", "Ascension Parish", "St. Tammany Parish")
i10_trend <- enr %>%
filter(is_district, district_name %in% i10,
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(i10_trend) > 0)
i10_trend %>%
select(end_year, district_name, n_students) %>%
tidyr::pivot_wider(names_from = district_name, values_from = n_students)
#> end_year Ascension Parish Livingston Parish St. Tammany Parish
#> 1 2019 23409 26148 38774
#> 2 2020 23455 26044 37214
#> 3 2021 23843 26540 37374
#> 4 2022 24041 26954 37212
#> 5 2023 24138 27105 36806
#> 6 2024 24076 26852 36384
I-10/I-12 Corridor Parishes
15. Gender balance is remarkably stable
Louisiana’s 51.2% male / 48.8% female split has barely budged across all six years of data.
gender <- enr %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("male", "female")) %>%
left_join(state_totals, by = "end_year") %>%
mutate(pct = n_students / total * 100)
stopifnot(nrow(gender) > 0)
gender %>%
filter(end_year == 2024) %>%
select(subgroup, n_students, pct)
#> subgroup n_students pct
#> 1 female 330254 48.8002
#> 2 male 346497 51.1998
Gender Balance in Louisiana Schools