Fetching College Enrollment Data from NCES Integrated Postsecondary Education Data System (IPEDS)
December 31, 2025
# For Analysis
import numpy as np
import pandas as pd
import geopandas as gpd
# For Visualization
import matplotlib.pyplot as plt
import seaborn as sns
# misc
import os
import requests
from pathlib import Path
import importlib.util
# Census data query libraries & modules
from pygris import blocks, block_groups
from pygris.helpers import validate_state, validate_county
from pygris.data import get_census, get_lodes
from dotenv import load_dotenv
load_dotenv()True# --- 1. Validate FIPS Codes ---
# Validate State
state_fips = validate_state(target_state)
print(f"State FIPS for {target_state}: {state_fips}")
# Validate Counties and get their FIPS codes
county_fips = [validate_county(state_fips, county) for county in target_counties]
print(f"Target County FIPS: {county_fips}")Using FIPS code '49' for input 'UT'
State FIPS for UT: 49
Using FIPS code '003' for input 'BOX ELDER'
Using FIPS code '057' for input 'WEBER'
Using FIPS code '011' for input 'DAVIS'
Using FIPS code '035' for input 'SALT LAKE'
Using FIPS code '049' for input 'UTAH'
Target County FIPS: ['003', '057', '011', '035', '049']# Create function to read ArcGIS FeatureLayer or Table
def arc_read(url, where="1=1", outFields="*", outSR=4326, **kwargs):
"""
Read an ArcGIS FeatureLayer or Table to a GeoDataFrame.
Parameters:
url (str): The ArcGIS REST service URL (e.g., ending in /FeatureServer/0)
where (str): SQL WHERE clause for filtering. Default: "1=1"
outFields (str): Comma-separated field names. Default: "*"
outSR (int): Output spatial reference EPSG code. Default: 4326
**kwargs: Additional query parameters passed to the ArcGIS REST API
Returns:
geopandas.GeoDataFrame: Spatial data from the service
"""
# Ensure URL ends with /query
if not url.endswith('/query'):
url = url.rstrip('/') + '/query'
# Build query parameters
params = {
'where': where,
'outFields': outFields,
'returnGeometry': 'true',
'outSR': outSR,
'f': 'geojson'
}
# Add any additional parameters
params.update(kwargs)
# Make request
response = requests.get(url, params=params)
response.raise_for_status() # Good practice to check for HTTP errors
# Read as GeoDataFrame
# We use io.BytesIO to handle the response content safely for read_file
import io
return gpd.read_file(io.BytesIO(response.content), engine="pyogrio")# 1. Configuration
service_url = "https://services1.arcgis.com/taguadKoI1XFwivx/ArcGIS/rest/services/RegionalBoundaryComponents/FeatureServer/0"
output_dir = Path("data")
gdb_path = output_dir / "RegionalBoundary.gpkg"
layer_name = "RegionalBoundaryComponents"
# 2. Check if file exists locally, otherwise download
if not gdb_path.exists():
print(f"File not found at {gdb_path}. Downloading Regional Boundaries...")
output_dir.mkdir(parents=True, exist_ok=True)
try:
# Use the custom arc_read function
gdf_download = arc_read(service_url)
# Export to Geodatabase
print(f"Saving to {gdb_path}...")
gdf_download.to_file(gdb_path, layer=layer_name, driver="GPKG")
print("Download and export complete.")
except Exception as e:
print(f"Error during download: {e}")
else:
print(f"File found locally at {gdb_path}. Skipping download.")
# 3. Read from local GDB with Filter and Dissolve
gdf_region = (
gpd.read_file(
gdb_path, layer=layer_name, where="PlanOrg IN ('MAG MPO', 'WFRC MPO')"
)
.dissolve()
.to_crs(CRS_UTM)
)
# Fix the holes and slivers in the regional boundary
gdf_region["geometry"] = gdf_region.buffer(1).buffer(-1)
gdf_region.explore()File found locally at data\RegionalBoundary.gpkg. Skipping download.# 1. Setup Configuration
service_url = "https://nces.ed.gov/opengis/rest/services/Postsecondary_School_Locations/EDGE_GEOCODE_POSTSECONDARYSCH_2324/MapServer/0"
output_dir = Path("data/IPEDS")
gdb_path = output_dir / "Postsecondary_School_Locations.gpkg"
layer_name = "EDGE_GEOCODE_POSTSECONDARYSCH_2324"
# 2. Check if file exists locally
if not gdb_path.exists():
print(f"File not found at {gdb_path}. Downloading from ArcGIS Feature Service...")
# Ensure directory exists
output_dir.mkdir(parents=True, exist_ok=True)
try:
# --- Use custom arc_read function ---
gdf_download = arc_read(
service_url, where=f"STATE = '{target_state}'", outFields="*", outSR=CRS_UTM
)
# Export to Geodatabase
print(f"Saving to {gdb_path}...")
gdf_download.to_file(gdb_path, layer=layer_name, driver="GPKG")
print("Download and export complete.")
except Exception as e:
print(f"Error during download or export: {e}")
else:
print(f"File found locally at {gdb_path}. Skipping download.")
# 3. Load the data back into Python
# We load from the local GDB (cache) and apply the County filter here
gdf_college_locations = gpd.read_file(
gdb_path,
layer=layer_name,
where=f"""CNTY IN ('{"', '".join([f"{fip}" for fip in fips_codes])}')""",
mask=gdf_region,
).to_crs(CRS_UTM)
gdf_college_locations.explore()File found locally at data\IPEDS\Postsecondary_School_Locations.gpkg. Skipping download.# --- Configuration & Setup ---
import requests
import zipfile
import io
import pandas as pd
from pathlib import Path
import warnings
# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")
# 1. Define Target Institutions
id_map = {
230038: "BYU",
230418: "ENSIGN",
230746: "SLCC",
230764: "UofU",
230737: "UVU",
230782: "WSU",
230807: "WESTMIN",
}
# Define column order for consistent output
target_cols = ["BYU", "ENSIGN", "SLCC", "UofU", "UVU", "WSU", "WESTMIN"]
# 2. Setup Data Directory
raw_data_dir = Path("data/IPEDS_Raw")
raw_data_dir.mkdir(parents=True, exist_ok=True)
print(f"Configuration loaded. Raw data will be saved to: {raw_data_dir.absolute()}")Configuration loaded. Raw data will be saved to: D:\GitHub\TDM-INP-College-Enrollment\data\IPEDS_RawRead more at https://educationdata.urban.org/documentation/colleges.html for API documentation.
We apply 99 (Total) filters to all available dimensions found in the codebook (sex, race, ftpt, degree_seeking, class_level) to ensure we receive the single grand total row.
# --- Fetch & Pivot Fall Enrollment (Direct IPEDS Download) ---
enrollment_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching Fall Enrollment Data...")
# Loop from 1997 to 2024
for year in range(1997, 2025):
file_tag = f"EF{year}A"
zip_url = f"{base_url}/{file_tag}.zip"
try:
# 1. Download ZIP
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
# 2. Extract CSV
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][0]
z.extract(csv_filename, path=raw_data_dir)
local_csv_path = raw_data_dir / csv_filename
# 3. Read & Process
df_year = pd.read_csv(local_csv_path, encoding="latin1", low_memory=False)
df_year.columns = df_year.columns.str.upper()
# 4. Determine Variable for Total Enrollment
if "EFTOTLT" in df_year.columns:
df_year["Total_Enrollment"] = df_year["EFTOTLT"]
elif "EFRACE15" in df_year.columns and "EFRACE16" in df_year.columns:
df_year["Total_Enrollment"] = df_year["EFRACE15"] + df_year["EFRACE16"]
elif "EFRACE24" in df_year.columns:
df_year["Total_Enrollment"] = df_year["EFRACE24"]
else:
continue
# 5. Filter and Aggregation (Max per UNITID to capture Grand Totals)
mask = df_year["UNITID"].isin(id_map.keys())
df_filtered = (
df_year.loc[mask, ["UNITID", "Total_Enrollment"]]
.groupby("UNITID")["Total_Enrollment"]
.max()
.reset_index()
)
df_filtered["Year"] = year
enrollment_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception:
pass
# 6. Pivot and Output
print("\nProcessing Complete.")
if enrollment_frames:
df_fall_enrollment = (
pd.concat(enrollment_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(
index="Year", columns="Inst", values="Total_Enrollment", aggfunc="sum"
)
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- Fall Enrollment (Headcount) ---")
print(df_fall_enrollment)Fetching Fall Enrollment Data...
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Processing Complete.
--- Fall Enrollment (Headcount) ---
Inst BYU ENSIGN SLCC UofU UVU WSU WESTMIN
Year
2000 32554 824 21596 24948 20946 16050 2403
2001 32771 1025 23701 27668 22609 16873 2474
2002 32408 1124 23347 28369 23609 18059 2353
2003 33008 1258 24056 28436 23803 18821 2471
2004 34347 1248 24725 28933 24149 18498 2417
2005 34067 1235 24111 30558 24180 18142 2455
2006 34185 1317 24241 30511 23305 18303 2479
2007 34174 1316 25235 28025 23840 18081 2661
2008 34244 1377 29396 28211 26696 21388 2859
2009 34130 1589 34966 29284 28765 23001 3037
2010 33841 1809 34654 30819 32670 24048 3163
2011 34101 1987 33420 31660 33395 25301 3358
2012 34409 2191 29997 32388 31562 26532 3301
2013 31123 2036 32003 32077 30564 25155 3108
2014 30484 2006 30248 31515 31332 25954 2991
2015 33469 2153 29350 31592 33211 25955 2821
2016 34240 2119 29901 31860 34978 26809 2694
2017 34334 2045 29620 32800 37282 27949 2570
2018 34499 1996 29156 33023 39931 28247 2477
2019 34318 1929 29517 32852 41728 29644 2215
2020 36461 1829 27293 33081 40936 29596 1849
2021 34811 2665 27225 34464 41262 29774 1535
2022 34464 4058 26300 34734 43099 29914 1280
2023 35074 5973 26764 35260 44651 30536 1214# --- Fetch & Pivot Fall Enrollment (Campus / Non-Online) ---
# Strategy: Download EF<Year>D (Distance Education) which contains both
# "Grand Total" and "Exclusively Distance Education" in the same file for 2012+.
# For pre-2012, assume 100% Campus.
enrollment_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching Fall Enrollment (Campus Only)...")
for year in range(1997, 2024):
try:
# --- Pre-2012: No Distance Ed Data (Assume All Campus) ---
if year < 2012:
file_tag = f"EF{year}A"
zip_url = f"{base_url}/{file_tag}.zip"
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][
0
]
with z.open(csv_filename) as f:
df = pd.read_csv(f, encoding="latin1", low_memory=False)
df.columns = df.columns.str.upper()
# Determine Total
if "EFTOTLT" in df.columns:
df["Campus_Total"] = df["EFTOTLT"]
elif "EFRACE24" in df.columns:
df["Campus_Total"] = df["EFRACE24"]
elif "EFRACE15" in df.columns:
df["Campus_Total"] = df["EFRACE15"] + df["EFRACE16"]
else:
continue
# --- 2012+: Use EF<Year>D (Distance Education) File ---
else:
# The 'A' file often splits data. The 'D' file (EF<Year>D) focuses
# specifically on Distance Education counts and usually has the cleanest totals.
# However, sometimes it's EF<Year>A_DIST. We try the standard A file first
# but look for the specific Distance Ed variables.
# Note: In recent IPEDS, DE data is inside the main EF<Year>A zip
# but sometimes in a separate CSV inside that zip, or a separate download.
# We will try the specific _DIST download first for safety.
# Attempt 1: EF<Year>A_DIST (Common for 2012-2019)
file_tag = f"EF{year}A_DIST"
# In later years (2020+), it might just be inside EF<Year>A.
# Let's try the specific DIST file first.
zip_url = f"{base_url}/{file_tag}.zip"
try:
response = requests.get(zip_url, timeout=30)
if response.status_code != 200:
# Fallback to standard EF<Year>A if _DIST doesn't exist
zip_url = f"{base_url}/EF{year}A.zip"
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
# Look for csv
csv_names = [f for f in z.namelist() if f.lower().endswith(".csv")]
# Prioritize one with "dist" in name if multiple exist
dist_csv = next(
(f for f in csv_names if "dist" in f.lower()), csv_names[0]
)
with z.open(dist_csv) as f:
df = pd.read_csv(f, encoding="latin1", low_memory=False)
df.columns = df.columns.str.upper()
# Variable Mapping for 2012+
# EFDEEX = Exclusively Distance Education (All Students)
# EFTEUG = Total Undergraduate (if split)
# EFTOTLT = Grand Total (if available)
# We need "Grand Total" and "Exclusive DE Total"
# 1. Find Grand Total Variable in this file
if "EFTOTLT" in df.columns:
total_col = "EFTOTLT"
elif "EFDETOT" in df.columns: # Total enrollment in DE file
total_col = "EFDETOT"
else:
# If total isn't here, we might need to merge.
# But usually EF...DIST files have the total col for calc purposes.
print(f"Skipping {year}: No total col in DIST file")
continue
# 2. Find Exclusive DE Variable
if "EFDEEX" in df.columns:
de_col = "EFDEEX" # Total Exclusive
elif "EFDEEX1" in df.columns and "EFDEEX2" in df.columns:
# Sum Undergrad (1) + Grad (2)
df["EFDEEX"] = df["EFDEEX1"].fillna(0) + df["EFDEEX2"].fillna(0)
de_col = "EFDEEX"
else:
de_col = None
if de_col:
# Calculate Campus
df["Campus_Total"] = df[total_col] - df[de_col]
else:
# Fallback: Assume 0 DE if variable missing (unlikely for _DIST file)
df["Campus_Total"] = df[total_col]
except Exception as e:
print(f"DL Error {year}: {e}")
continue
# --- Filter & Aggregate ---
mask = df["UNITID"].isin(id_map.keys())
df_filtered = (
df.loc[mask, ["UNITID", "Campus_Total"]]
.groupby("UNITID")["Campus_Total"]
.max() # Max helps if there are duplicate rows (e.g. by race)
.reset_index()
)
# Safety Check: Ensure no negative numbers
# If Exclusive DE > Total (data error), clip to 0
df_filtered.loc[df_filtered["Campus_Total"] < 0, "Campus_Total"] = 0
df_filtered["Year"] = year
enrollment_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception as e:
pass
# --- Pivot and Output ---
print("\nProcessing Complete.")
if enrollment_frames:
df_campus_only = (
pd.concat(enrollment_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(index="Year", columns="Inst", values="Campus_Total", aggfunc="sum")
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- Fall Enrollment (Campus / Non-Online Only) ---")
print(df_campus_only)Fetching Fall Enrollment (Campus Only)...
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Processing Complete.
--- Fall Enrollment (Campus / Non-Online Only) ---
Inst BYU ENSIGN SLCC UofU UVU WSU WESTMIN
Year
2000 32554 824 21596 24948 20946 16050 2403
2001 32771 1025 23701 27668 22609 16873 2474
2002 32408 1124 23347 28369 23609 18059 2353
2003 33008 1258 24056 28436 23803 18821 2471
2004 34347 1248 24725 28933 24149 18498 2417
2005 34067 1235 24111 30558 24180 18142 2455
2006 34185 1317 24241 30511 23305 18303 2479
2007 34174 1316 25235 28025 23840 18081 2661
2008 34244 1377 29396 28211 26696 21388 2859
2009 34130 1589 34966 29284 28765 23001 3037
2010 33841 1809 34654 30819 32670 24048 3163
2011 34101 1987 33420 31660 33395 25301 3358
2012 34402 2191 27809 31585 28428 24391 3301
2013 31108 2036 29315 31156 27425 23029 3108
2014 30447 2006 27453 30512 28354 23641 2991
2015 33432 2148 26487 30367 30359 23541 2723
2016 34184 2076 26612 30539 32256 24109 2596
2017 34191 1982 26339 31390 34542 25290 2570
2018 34284 1927 25795 31550 36841 25367 2477
2019 34116 1886 25882 31416 38463 26552 2215
2020 31395 1787 22692 22211 27965 20171 1849
2021 34160 2261 21165 31115 33781 24230 1422
2022 33781 2865 21159 32313 35956 25039 1249
2023 34295 4514 21741 33036 38443 25871 1197We use level_of_study=99 to fetch the total FTE directly, avoiding the need to sum undergraduate and graduate rows.
# --- Fetch & Pivot Full-Time Equivalent (Direct IPEDS Download) ---
fte_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching FTE Data...")
# Loop from 1997 to 2024
for year in range(1997, 2025):
file_tag = f"EFIA{year}"
zip_url = f"{base_url}/{file_tag}.zip"
try:
# 1. Download
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
# 2. Extract
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][0]
z.extract(csv_filename, path=raw_data_dir)
local_csv_path = raw_data_dir / csv_filename
# 3. Read
df_year = pd.read_csv(local_csv_path, encoding="latin1", low_memory=False)
df_year.columns = df_year.columns.str.upper()
# 4. Calculate FTE
df_year["FTE_Calc"] = 0
# Method A: Pre-calculated Total
if "FTE12MN" in df_year.columns:
df_year["FTE_Calc"] = df_year["FTE12MN"].fillna(0)
# Method B: Sum of Components
elif "FTEUG" in df_year.columns:
ug = df_year["FTEUG"].fillna(0)
gd = df_year["FTEGD"].fillna(0) if "FTEGD" in df_year.columns else 0
dpp = df_year["FTEDPP"].fillna(0) if "FTEDPP" in df_year.columns else 0
df_year["FTE_Calc"] = ug + gd + dpp
# Method C: Instructional Activity (Older Files)
elif "CDACTUA" in df_year.columns or "CNACTUA" in df_year.columns:
# Credit Hours (Academic) / 30
c_ug = df_year["CDACTUA"].fillna(0) if "CDACTUA" in df_year.columns else 0
c_gd = df_year["CDACTGA"].fillna(0) if "CDACTGA" in df_year.columns else 0
# Contact Hours (Vocational) / 900
contact = (
df_year["CNACTUA"].fillna(0) if "CNACTUA" in df_year.columns else 0
)
df_year["FTE_Calc"] = ((c_ug + c_gd) / 30) + (contact / 900)
else:
continue
# 5. Filter and Aggregation
mask = df_year["UNITID"].isin(id_map.keys())
df_filtered = (
df_year.loc[mask, ["UNITID", "FTE_Calc"]]
.groupby("UNITID")["FTE_Calc"]
.max()
.reset_index()
)
df_filtered["Year"] = year
fte_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception:
pass
# 6. Pivot and Output
print("\nProcessing Complete.")
if fte_frames:
df_fte = (
pd.concat(fte_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(index="Year", columns="Inst", values="FTE_Calc", aggfunc="sum")
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- Full-Time Equivalent (FTE) ---")
print(df_fte)Fetching FTE Data...
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Processing Complete.
--- Full-Time Equivalent (FTE) ---
Inst BYU ENSIGN SLCC UofU UVU WSU WESTMIN
Year
2002 18280 939 15324 23687 16755 14003 965
2003 33220 1078 16219 23549 17986 15369 2087
2004 33495 1141 16571 25736 17713 15886 2223
2005 36473 1158 16701 25801 17112 15562 2167
2006 32725 1153 15819 25968 16508 14318 2111
2007 32811 1307 15830 25784 16572 18972 2223
2008 34751 1282 15626 24966 17344 14265 2381
2009 34949 1464 16487 25563 19267 14699 2528
2010 34997 1646 19743 26710 21550 16009 2701
2011 34965 1807 22232 28233 23877 16947 2856
2012 34655 1868 21634 28732 24495 17621 2949
2013 34026 2059 20797 29398 24425 17762 2870
2014 30955 1888 20007 28863 23513 16829 2754
2015 31215 1917 18844 29090 25705 16988 2658
2016 33688 2053 17765 28324 25698 17068 2656
2017 34643 2033 17132 28524 26721 17216 2533
2018 34091 1986 17361 28982 28376 17867 2479
2019 33908 1900 16766 29474 29867 18252 2374
2020 34956 1750 16811 29979 28595 18583 2134
2021 35738 1622 15991 29860 27653 18754 1683
2022 34035 2616 14928 31185 29614 18620 1455# --- Fetch & Pivot 12-Month Unduplicated Headcount (EFFY) ---
effy_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching 12-Month Unduplicated Headcount...")
# Loop from 1997 to 2024
# Note: EFFY files usually report for the *previous* academic year,
# but IPEDS labels the file by the collection year.
for year in range(1997, 2025):
file_tag = f"EFFY{year}"
zip_url = f"{base_url}/{file_tag}.zip"
try:
# 1. Download
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
# 2. Extract
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][0]
z.extract(csv_filename, path=raw_data_dir)
local_csv_path = raw_data_dir / csv_filename
# 3. Read
df_year = pd.read_csv(local_csv_path, encoding="latin1", low_memory=False)
df_year.columns = df_year.columns.str.upper()
# 4. Determine Variable (EFYTOTLT = Grand Total 12-Month Enrollment)
# Variable names have shifted slightly over decades
if "EFYTOTLT" in df_year.columns:
df_year["Headcount_12M"] = df_year["EFYTOTLT"]
elif "EFYTOTL" in df_year.columns:
df_year["Headcount_12M"] = df_year["EFYTOTL"]
# Fallback for older files: Sum of Level of Study (Undergrad + Grad)
elif "EFYUNDER" in df_year.columns and "EFYGRAD" in df_year.columns:
# Fillna(0) is critical here as some schools may lack grad programs
df_year["Headcount_12M"] = df_year["EFYUNDER"].fillna(0) + df_year[
"EFYGRAD"
].fillna(0)
else:
# If we can't find a total, skip this year
continue
# 5. Filter and Aggregation
mask = df_year["UNITID"].isin(id_map.keys())
df_filtered = (
df_year.loc[mask, ["UNITID", "Headcount_12M"]]
.groupby("UNITID")["Headcount_12M"]
.max() # Max handles cases where data might be disaggregated by race/gender rows
.reset_index()
)
df_filtered["Year"] = year
effy_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception:
pass
# 6. Pivot and Output
print("\nProcessing Complete.")
if effy_frames:
df_effy = (
pd.concat(effy_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(
index="Year", columns="Inst", values="Headcount_12M", aggfunc="sum"
)
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- 12-Month Unduplicated Headcount (E12) ---")
print(df_effy)Fetching 12-Month Unduplicated Headcount...
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Processing Complete.
--- 12-Month Unduplicated Headcount (E12) ---
Inst BYU ENSIGN SLCC UofU UVU WSU WESTMIN
Year
2008 39534 1779 46644 34576 31077 21388 2975
2009 42868 1843 47759 34675 34249 28129 3130
2010 39346 2178 52459 35507 36745 30884 3656
2011 40203 2463 50643 37046 40697 31546 3829
2012 39996 2574 48876 40410 41667 32686 4041
2013 41624 2901 47599 38662 40799 34258 3819
2014 36554 2711 46857 37582 39501 32555 3564
2015 37617 2855 45295 37187 40853 33428 3468
2016 39577 2986 43197 36949 43356 32760 3298
2017 39582 3024 42203 37585 45411 33339 3136
2018 39515 2875 42447 38229 48969 35447 2952
2019 39233 2760 41007 38652 45080 36043 2788
2020 39086 2516 41955 38335 45299 36761 2419
2021 40688 2424 39495 38804 43749 36680 2076
2022 39321 5164 38140 40039 53853 37155 1737# --- Export Data to CSV ---
from pathlib import Path
# 1. Define and Create Output Directory
output_dir = Path("intermediate")
output_dir.mkdir(parents=True, exist_ok=True)
# 2. Export Fall Enrollment (Headcount)
if "df_fall_enrollment" in locals():
enrollment_path = output_dir / "Fall_Enrollment_1997_2023.csv"
df_fall_enrollment.to_csv(enrollment_path)
print(f"Saved Fall Enrollment to: {enrollment_path.absolute()}")
else:
print("df_fall_enrollment not found. Skipping export.")
# 2. Export Non-Online Fall Enrollment
if "df_campus_only" in locals():
# Naming it "Campus_Enrollment" to distinguish from Total
enrollment_path = output_dir / "Fall_Enrollment_CampusOnly_1997_2023.csv"
df_campus_only.to_csv(enrollment_path)
print(f"Saved Non-Online Enrollment to: {enrollment_path.absolute()}")
# 3. Export Full-Time Equivalent (FTE)
if "df_fte" in locals():
fte_path = output_dir / "FTE_Enrollment_1997_2023.csv"
df_fte.to_csv(fte_path)
print(f"Saved FTE to: {fte_path.absolute()}")
else:
print("df_fte not found. Skipping export.")
# 4. Export 12-Month Unduplicated Headcount
if "df_effy" in locals():
effy_path = output_dir / "Headcount_12Month_1997_2023.csv"
df_effy.to_csv(effy_path)
print(f"Saved 12-Month Headcount to: {effy_path.absolute()}")
else:
print("df_effy not found. Skipping export.")Saved Fall Enrollment to: D:\GitHub\TDM-INP-College-Enrollment\intermediate\Fall_Enrollment_1997_2023.csv
Saved Non-Online Enrollment to: D:\GitHub\TDM-INP-College-Enrollment\intermediate\Fall_Enrollment_CampusOnly_1997_2023.csv
Saved FTE to: D:\GitHub\TDM-INP-College-Enrollment\intermediate\FTE_Enrollment_1997_2023.csv
Saved 12-Month Headcount to: D:\GitHub\TDM-INP-College-Enrollment\intermediate\Headcount_12Month_1997_2023.csv---
title: "Historic College Enrollment"
subtitle: Fetching College Enrollment Data from NCES Integrated Postsecondary Education Data System (IPEDS)
description: This notebook fetches Total Enrollment and Full Time Equivalent for all Post Secondary Institutions within Wasatch Front Travel Demand Model region using Integrated Postsecondary Education Data System (IPEDS) from National Center for Education Statistics.
author:
- name: Pukar Bhandari
email: pukar.bhandari@wfrc.utah.gov
affiliation:
- name: Wasatch Front Regional Council
url: "https://wfrc.utah.gov/"
date: "2025-12-31"
---
# Environment Setup
## Import Standard Libraries
```{python}
# For Analysis
import numpy as np
import pandas as pd
import geopandas as gpd
# For Visualization
import matplotlib.pyplot as plt
import seaborn as sns
# misc
import os
import requests
from pathlib import Path
import importlib.util
# Census data query libraries & modules
from pygris import blocks, block_groups
from pygris.helpers import validate_state, validate_county
from pygris.data import get_census, get_lodes
from dotenv import load_dotenv
load_dotenv()
```
## Environment Variables
```{python}
CRS_UTM = "EPSG:26912"
```
## Region Definition
```{python}
# --- Configuration ---
target_state = "UT"
target_counties = ["BOX ELDER", "WEBER", "DAVIS", "SALT LAKE", "UTAH"]
```
```{python}
# --- 1. Validate FIPS Codes ---
# Validate State
state_fips = validate_state(target_state)
print(f"State FIPS for {target_state}: {state_fips}")
# Validate Counties and get their FIPS codes
county_fips = [validate_county(state_fips, county) for county in target_counties]
print(f"Target County FIPS: {county_fips}")
```
```{python}
# Converting County Names to FIPS code
fips_codes = [f"{state_fips}{county}" for county in county_fips]
fips_codes
```
## Helper Functions
```{python}
# Create function to read ArcGIS FeatureLayer or Table
def arc_read(url, where="1=1", outFields="*", outSR=4326, **kwargs):
"""
Read an ArcGIS FeatureLayer or Table to a GeoDataFrame.
Parameters:
url (str): The ArcGIS REST service URL (e.g., ending in /FeatureServer/0)
where (str): SQL WHERE clause for filtering. Default: "1=1"
outFields (str): Comma-separated field names. Default: "*"
outSR (int): Output spatial reference EPSG code. Default: 4326
**kwargs: Additional query parameters passed to the ArcGIS REST API
Returns:
geopandas.GeoDataFrame: Spatial data from the service
"""
# Ensure URL ends with /query
if not url.endswith('/query'):
url = url.rstrip('/') + '/query'
# Build query parameters
params = {
'where': where,
'outFields': outFields,
'returnGeometry': 'true',
'outSR': outSR,
'f': 'geojson'
}
# Add any additional parameters
params.update(kwargs)
# Make request
response = requests.get(url, params=params)
response.raise_for_status() # Good practice to check for HTTP errors
# Read as GeoDataFrame
# We use io.BytesIO to handle the response content safely for read_file
import io
return gpd.read_file(io.BytesIO(response.content), engine="pyogrio")
```
# Load Data
## Regional Boundary
```{python}
# 1. Configuration
service_url = "https://services1.arcgis.com/taguadKoI1XFwivx/ArcGIS/rest/services/RegionalBoundaryComponents/FeatureServer/0"
output_dir = Path("data")
gdb_path = output_dir / "RegionalBoundary.gpkg"
layer_name = "RegionalBoundaryComponents"
# 2. Check if file exists locally, otherwise download
if not gdb_path.exists():
print(f"File not found at {gdb_path}. Downloading Regional Boundaries...")
output_dir.mkdir(parents=True, exist_ok=True)
try:
# Use the custom arc_read function
gdf_download = arc_read(service_url)
# Export to Geodatabase
print(f"Saving to {gdb_path}...")
gdf_download.to_file(gdb_path, layer=layer_name, driver="GPKG")
print("Download and export complete.")
except Exception as e:
print(f"Error during download: {e}")
else:
print(f"File found locally at {gdb_path}. Skipping download.")
# 3. Read from local GDB with Filter and Dissolve
gdf_region = (
gpd.read_file(
gdb_path, layer=layer_name, where="PlanOrg IN ('MAG MPO', 'WFRC MPO')"
)
.dissolve()
.to_crs(CRS_UTM)
)
# Fix the holes and slivers in the regional boundary
gdf_region["geometry"] = gdf_region.buffer(1).buffer(-1)
gdf_region.explore()
```
## Post-Secondary Institutions
```{python}
# 1. Setup Configuration
service_url = "https://nces.ed.gov/opengis/rest/services/Postsecondary_School_Locations/EDGE_GEOCODE_POSTSECONDARYSCH_2324/MapServer/0"
output_dir = Path("data/IPEDS")
gdb_path = output_dir / "Postsecondary_School_Locations.gpkg"
layer_name = "EDGE_GEOCODE_POSTSECONDARYSCH_2324"
# 2. Check if file exists locally
if not gdb_path.exists():
print(f"File not found at {gdb_path}. Downloading from ArcGIS Feature Service...")
# Ensure directory exists
output_dir.mkdir(parents=True, exist_ok=True)
try:
# --- Use custom arc_read function ---
gdf_download = arc_read(
service_url, where=f"STATE = '{target_state}'", outFields="*", outSR=CRS_UTM
)
# Export to Geodatabase
print(f"Saving to {gdb_path}...")
gdf_download.to_file(gdb_path, layer=layer_name, driver="GPKG")
print("Download and export complete.")
except Exception as e:
print(f"Error during download or export: {e}")
else:
print(f"File found locally at {gdb_path}. Skipping download.")
# 3. Load the data back into Python
# We load from the local GDB (cache) and apply the County filter here
gdf_college_locations = gpd.read_file(
gdb_path,
layer=layer_name,
where=f"""CNTY IN ('{"', '".join([f"{fip}" for fip in fips_codes])}')""",
mask=gdf_region,
).to_crs(CRS_UTM)
gdf_college_locations.explore()
```
## Fetch Data
### API Configuration
```{python}
# --- Configuration & Setup ---
import requests
import zipfile
import io
import pandas as pd
from pathlib import Path
import warnings
# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")
# 1. Define Target Institutions
id_map = {
230038: "BYU",
230418: "ENSIGN",
230746: "SLCC",
230764: "UofU",
230737: "UVU",
230782: "WSU",
230807: "WESTMIN",
}
# Define column order for consistent output
target_cols = ["BYU", "ENSIGN", "SLCC", "UofU", "UVU", "WSU", "WESTMIN"]
# 2. Setup Data Directory
raw_data_dir = Path("data/IPEDS_Raw")
raw_data_dir.mkdir(parents=True, exist_ok=True)
print(f"Configuration loaded. Raw data will be saved to: {raw_data_dir.absolute()}")
```
Read more at https://educationdata.urban.org/documentation/colleges.html for API documentation.
### Fall Enrollment
We apply `99` (Total) filters to all available dimensions found in the codebook (`sex`, `race`, `ftpt`, `degree_seeking`, `class_level`) to ensure we receive the single grand total row.
```{python}
# --- Fetch & Pivot Fall Enrollment (Direct IPEDS Download) ---
enrollment_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching Fall Enrollment Data...")
# Loop from 1997 to 2024
for year in range(1997, 2025):
file_tag = f"EF{year}A"
zip_url = f"{base_url}/{file_tag}.zip"
try:
# 1. Download ZIP
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
# 2. Extract CSV
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][0]
z.extract(csv_filename, path=raw_data_dir)
local_csv_path = raw_data_dir / csv_filename
# 3. Read & Process
df_year = pd.read_csv(local_csv_path, encoding="latin1", low_memory=False)
df_year.columns = df_year.columns.str.upper()
# 4. Determine Variable for Total Enrollment
if "EFTOTLT" in df_year.columns:
df_year["Total_Enrollment"] = df_year["EFTOTLT"]
elif "EFRACE15" in df_year.columns and "EFRACE16" in df_year.columns:
df_year["Total_Enrollment"] = df_year["EFRACE15"] + df_year["EFRACE16"]
elif "EFRACE24" in df_year.columns:
df_year["Total_Enrollment"] = df_year["EFRACE24"]
else:
continue
# 5. Filter and Aggregation (Max per UNITID to capture Grand Totals)
mask = df_year["UNITID"].isin(id_map.keys())
df_filtered = (
df_year.loc[mask, ["UNITID", "Total_Enrollment"]]
.groupby("UNITID")["Total_Enrollment"]
.max()
.reset_index()
)
df_filtered["Year"] = year
enrollment_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception:
pass
# 6. Pivot and Output
print("\nProcessing Complete.")
if enrollment_frames:
df_fall_enrollment = (
pd.concat(enrollment_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(
index="Year", columns="Inst", values="Total_Enrollment", aggfunc="sum"
)
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- Fall Enrollment (Headcount) ---")
print(df_fall_enrollment)
```
```{python}
# --- Fetch & Pivot Fall Enrollment (Campus / Non-Online) ---
# Strategy: Download EF<Year>D (Distance Education) which contains both
# "Grand Total" and "Exclusively Distance Education" in the same file for 2012+.
# For pre-2012, assume 100% Campus.
enrollment_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching Fall Enrollment (Campus Only)...")
for year in range(1997, 2024):
try:
# --- Pre-2012: No Distance Ed Data (Assume All Campus) ---
if year < 2012:
file_tag = f"EF{year}A"
zip_url = f"{base_url}/{file_tag}.zip"
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][
0
]
with z.open(csv_filename) as f:
df = pd.read_csv(f, encoding="latin1", low_memory=False)
df.columns = df.columns.str.upper()
# Determine Total
if "EFTOTLT" in df.columns:
df["Campus_Total"] = df["EFTOTLT"]
elif "EFRACE24" in df.columns:
df["Campus_Total"] = df["EFRACE24"]
elif "EFRACE15" in df.columns:
df["Campus_Total"] = df["EFRACE15"] + df["EFRACE16"]
else:
continue
# --- 2012+: Use EF<Year>D (Distance Education) File ---
else:
# The 'A' file often splits data. The 'D' file (EF<Year>D) focuses
# specifically on Distance Education counts and usually has the cleanest totals.
# However, sometimes it's EF<Year>A_DIST. We try the standard A file first
# but look for the specific Distance Ed variables.
# Note: In recent IPEDS, DE data is inside the main EF<Year>A zip
# but sometimes in a separate CSV inside that zip, or a separate download.
# We will try the specific _DIST download first for safety.
# Attempt 1: EF<Year>A_DIST (Common for 2012-2019)
file_tag = f"EF{year}A_DIST"
# In later years (2020+), it might just be inside EF<Year>A.
# Let's try the specific DIST file first.
zip_url = f"{base_url}/{file_tag}.zip"
try:
response = requests.get(zip_url, timeout=30)
if response.status_code != 200:
# Fallback to standard EF<Year>A if _DIST doesn't exist
zip_url = f"{base_url}/EF{year}A.zip"
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
# Look for csv
csv_names = [f for f in z.namelist() if f.lower().endswith(".csv")]
# Prioritize one with "dist" in name if multiple exist
dist_csv = next(
(f for f in csv_names if "dist" in f.lower()), csv_names[0]
)
with z.open(dist_csv) as f:
df = pd.read_csv(f, encoding="latin1", low_memory=False)
df.columns = df.columns.str.upper()
# Variable Mapping for 2012+
# EFDEEX = Exclusively Distance Education (All Students)
# EFTEUG = Total Undergraduate (if split)
# EFTOTLT = Grand Total (if available)
# We need "Grand Total" and "Exclusive DE Total"
# 1. Find Grand Total Variable in this file
if "EFTOTLT" in df.columns:
total_col = "EFTOTLT"
elif "EFDETOT" in df.columns: # Total enrollment in DE file
total_col = "EFDETOT"
else:
# If total isn't here, we might need to merge.
# But usually EF...DIST files have the total col for calc purposes.
print(f"Skipping {year}: No total col in DIST file")
continue
# 2. Find Exclusive DE Variable
if "EFDEEX" in df.columns:
de_col = "EFDEEX" # Total Exclusive
elif "EFDEEX1" in df.columns and "EFDEEX2" in df.columns:
# Sum Undergrad (1) + Grad (2)
df["EFDEEX"] = df["EFDEEX1"].fillna(0) + df["EFDEEX2"].fillna(0)
de_col = "EFDEEX"
else:
de_col = None
if de_col:
# Calculate Campus
df["Campus_Total"] = df[total_col] - df[de_col]
else:
# Fallback: Assume 0 DE if variable missing (unlikely for _DIST file)
df["Campus_Total"] = df[total_col]
except Exception as e:
print(f"DL Error {year}: {e}")
continue
# --- Filter & Aggregate ---
mask = df["UNITID"].isin(id_map.keys())
df_filtered = (
df.loc[mask, ["UNITID", "Campus_Total"]]
.groupby("UNITID")["Campus_Total"]
.max() # Max helps if there are duplicate rows (e.g. by race)
.reset_index()
)
# Safety Check: Ensure no negative numbers
# If Exclusive DE > Total (data error), clip to 0
df_filtered.loc[df_filtered["Campus_Total"] < 0, "Campus_Total"] = 0
df_filtered["Year"] = year
enrollment_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception as e:
pass
# --- Pivot and Output ---
print("\nProcessing Complete.")
if enrollment_frames:
df_campus_only = (
pd.concat(enrollment_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(index="Year", columns="Inst", values="Campus_Total", aggfunc="sum")
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- Fall Enrollment (Campus / Non-Online Only) ---")
print(df_campus_only)
```
### Full-Time Equivalent (FTE)
We use `level_of_study=99` to fetch the total FTE directly, avoiding the need to sum undergraduate and graduate rows.
```{python}
# --- Fetch & Pivot Full-Time Equivalent (Direct IPEDS Download) ---
fte_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching FTE Data...")
# Loop from 1997 to 2024
for year in range(1997, 2025):
file_tag = f"EFIA{year}"
zip_url = f"{base_url}/{file_tag}.zip"
try:
# 1. Download
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
# 2. Extract
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][0]
z.extract(csv_filename, path=raw_data_dir)
local_csv_path = raw_data_dir / csv_filename
# 3. Read
df_year = pd.read_csv(local_csv_path, encoding="latin1", low_memory=False)
df_year.columns = df_year.columns.str.upper()
# 4. Calculate FTE
df_year["FTE_Calc"] = 0
# Method A: Pre-calculated Total
if "FTE12MN" in df_year.columns:
df_year["FTE_Calc"] = df_year["FTE12MN"].fillna(0)
# Method B: Sum of Components
elif "FTEUG" in df_year.columns:
ug = df_year["FTEUG"].fillna(0)
gd = df_year["FTEGD"].fillna(0) if "FTEGD" in df_year.columns else 0
dpp = df_year["FTEDPP"].fillna(0) if "FTEDPP" in df_year.columns else 0
df_year["FTE_Calc"] = ug + gd + dpp
# Method C: Instructional Activity (Older Files)
elif "CDACTUA" in df_year.columns or "CNACTUA" in df_year.columns:
# Credit Hours (Academic) / 30
c_ug = df_year["CDACTUA"].fillna(0) if "CDACTUA" in df_year.columns else 0
c_gd = df_year["CDACTGA"].fillna(0) if "CDACTGA" in df_year.columns else 0
# Contact Hours (Vocational) / 900
contact = (
df_year["CNACTUA"].fillna(0) if "CNACTUA" in df_year.columns else 0
)
df_year["FTE_Calc"] = ((c_ug + c_gd) / 30) + (contact / 900)
else:
continue
# 5. Filter and Aggregation
mask = df_year["UNITID"].isin(id_map.keys())
df_filtered = (
df_year.loc[mask, ["UNITID", "FTE_Calc"]]
.groupby("UNITID")["FTE_Calc"]
.max()
.reset_index()
)
df_filtered["Year"] = year
fte_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception:
pass
# 6. Pivot and Output
print("\nProcessing Complete.")
if fte_frames:
df_fte = (
pd.concat(fte_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(index="Year", columns="Inst", values="FTE_Calc", aggfunc="sum")
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- Full-Time Equivalent (FTE) ---")
print(df_fte)
```
### 12-Month Unduplicated Headcount (E12)
```{python}
# --- Fetch & Pivot 12-Month Unduplicated Headcount (EFFY) ---
effy_frames = []
base_url = "https://nces.ed.gov/ipeds/datacenter/data"
print("Fetching 12-Month Unduplicated Headcount...")
# Loop from 1997 to 2024
# Note: EFFY files usually report for the *previous* academic year,
# but IPEDS labels the file by the collection year.
for year in range(1997, 2025):
file_tag = f"EFFY{year}"
zip_url = f"{base_url}/{file_tag}.zip"
try:
# 1. Download
response = requests.get(zip_url, timeout=30)
response.raise_for_status()
# 2. Extract
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
csv_filename = [f for f in z.namelist() if f.lower().endswith(".csv")][0]
z.extract(csv_filename, path=raw_data_dir)
local_csv_path = raw_data_dir / csv_filename
# 3. Read
df_year = pd.read_csv(local_csv_path, encoding="latin1", low_memory=False)
df_year.columns = df_year.columns.str.upper()
# 4. Determine Variable (EFYTOTLT = Grand Total 12-Month Enrollment)
# Variable names have shifted slightly over decades
if "EFYTOTLT" in df_year.columns:
df_year["Headcount_12M"] = df_year["EFYTOTLT"]
elif "EFYTOTL" in df_year.columns:
df_year["Headcount_12M"] = df_year["EFYTOTL"]
# Fallback for older files: Sum of Level of Study (Undergrad + Grad)
elif "EFYUNDER" in df_year.columns and "EFYGRAD" in df_year.columns:
# Fillna(0) is critical here as some schools may lack grad programs
df_year["Headcount_12M"] = df_year["EFYUNDER"].fillna(0) + df_year[
"EFYGRAD"
].fillna(0)
else:
# If we can't find a total, skip this year
continue
# 5. Filter and Aggregation
mask = df_year["UNITID"].isin(id_map.keys())
df_filtered = (
df_year.loc[mask, ["UNITID", "Headcount_12M"]]
.groupby("UNITID")["Headcount_12M"]
.max() # Max handles cases where data might be disaggregated by race/gender rows
.reset_index()
)
df_filtered["Year"] = year
effy_frames.append(df_filtered)
print(f"{year}", end=" ")
except Exception:
pass
# 6. Pivot and Output
print("\nProcessing Complete.")
if effy_frames:
df_effy = (
pd.concat(effy_frames)
.assign(Inst=lambda x: x["UNITID"].map(id_map))
.pivot_table(
index="Year", columns="Inst", values="Headcount_12M", aggfunc="sum"
)
.reindex(columns=target_cols, fill_value=0)
.astype(int)
)
print("\n--- 12-Month Unduplicated Headcount (E12) ---")
print(df_effy)
```
# Export
```{python}
# --- Export Data to CSV ---
from pathlib import Path
# 1. Define and Create Output Directory
output_dir = Path("intermediate")
output_dir.mkdir(parents=True, exist_ok=True)
# 2. Export Fall Enrollment (Headcount)
if "df_fall_enrollment" in locals():
enrollment_path = output_dir / "Fall_Enrollment_1997_2023.csv"
df_fall_enrollment.to_csv(enrollment_path)
print(f"Saved Fall Enrollment to: {enrollment_path.absolute()}")
else:
print("df_fall_enrollment not found. Skipping export.")
# 2. Export Non-Online Fall Enrollment
if "df_campus_only" in locals():
# Naming it "Campus_Enrollment" to distinguish from Total
enrollment_path = output_dir / "Fall_Enrollment_CampusOnly_1997_2023.csv"
df_campus_only.to_csv(enrollment_path)
print(f"Saved Non-Online Enrollment to: {enrollment_path.absolute()}")
# 3. Export Full-Time Equivalent (FTE)
if "df_fte" in locals():
fte_path = output_dir / "FTE_Enrollment_1997_2023.csv"
df_fte.to_csv(fte_path)
print(f"Saved FTE to: {fte_path.absolute()}")
else:
print("df_fte not found. Skipping export.")
# 4. Export 12-Month Unduplicated Headcount
if "df_effy" in locals():
effy_path = output_dir / "Headcount_12Month_1997_2023.csv"
df_effy.to_csv(effy_path)
print(f"Saved 12-Month Headcount to: {effy_path.absolute()}")
else:
print("df_effy not found. Skipping export.")
```