perudata 0.1.1

Peru's official INEI microdata (ENAHO, ENDES, EPEN, EEA, ENAHO Panel): download, load and validate against official statistics

perudata

Peru's official INEI microdata, one import away.

from perudata import enaho, validate

df = enaho.load(2024, "34")        # ENAHO sumaria: income, spending, poverty
validate.poverty(years=[2024])     # reproduces INEI's official 27.6% from raw data

Five national surveys, one consistent API. Every file is verified on download by actually opening it, and a built-in validation gate reproduces INEI's official poverty series to 0.0 percentage points, 2004–2025 before you build anything on top.

Survey	What it is	Coverage	Format
enaho	ENAHO — national household survey (income, poverty, employment, education, health, governance)	2004–2025, 29 modules	Stata
panel	ENAHO Panel — same households re-interviewed (true poverty dynamics, FE)	10 releases, 2007–2023	Stata
endes	ENDES — Peru's DHS (fertility, child health, anemia, domestic violence)	1996–2024	SPSS
epen	EPE/EPEN — permanent employment surveys (Lima monthly since 2001, national)	279 verified datasets	CSV
eea	EEA — annual economic survey of firms (sales, assets, employment, CIIU)	2001–2024, 604 datasets	CSV

Install

pip install perudata            # once published; for now:
pip install git+https://github.com/cesarchavezp29/perudata

Requires Python ≥ 3.9. Dependencies: pandas, pyreadstat.

Quickstart

from perudata import enaho, endes, epen, eea, panel, validate

# --- ENAHO (annual cross-section) ------------------------------------------
enaho.years()                        # [2004, ..., 2025]
enaho.modules()                      # DataFrame of the 29 modules
enaho.download([2023, 2024], ["01", "34", "85"])
df = enaho.load(2024, "34")          # auto-downloads if missing

# national poverty, the official INEI way (person = factor07 x mieperho):
w = df["factor07"] * df["mieperho"]
poverty = 100 * w[df["pobreza"].isin([1, 2])].sum() / w.sum()   # -> 27.6

# --- validation gate ---------------------------------------------------------
validate.poverty()                   # full 2004-2025 table vs official INEI

# --- ENAHO Panel (longitudinal) ----------------------------------------------
panel.releases()                              # [2011, 2015, ..., 2022, 2023] — 10 releases
df, meta = panel.load_long(2023, "sumaria")   # latest: 2019-2023, tidy long
meta["waves"]                                 # [2019, 2020, 2021, 2022, 2023]
# any release works the same, e.g. the earliest 5-year panel:
df, meta = panel.load_long(2011, "sumaria")   # 2007-2011

# --- ENDES (DHS) --------------------------------------------------------------
endes.download(2024, ["peso_talla_anemia"])
kids = endes.load(2024, "peso_talla_anemia")

# --- EPEN (employment) ---------------------------------------------------------
epen.search("lima")                  # find datasets in the verified catalog
df = epen.load(997)                  # download + read by catalog code

# --- EEA (firms) ----------------------------------------------------------------
eea.modules(2024)                    # sector modules of EEA 2024
df = eea.load(eea.modules(2024)["csv_code"].iloc[0])

Or from the command line:

perudata enaho --years 2024 --modules 01 34
perudata validate --years 2024
perudata epen --search "dpto 2024"

Where files go

Everything lands under ./peru_raw (override per call with out= or globally with the PERUDATA_DIR environment variable), named so you always know what a file is:

peru_raw/
  enaho/sumaria/enaho-2024-34.dta
  enaho_panel/2011_302/panel-2011-sumaria.dta
  endes/2024_968/1629_hogar/968-Modulo1629/*.sav
  epen/804_15_lima.../*.csv
  eea/987-Modulo1968/a2022_s04_fF2/*.csv
  */_manifest.csv          # what was downloaded, when, rows x cols

Downloads are idempotent: a file that exists and verified is skipped, so you can re-run a script after adding years and only the new ones are fetched.

Why this exists

INEI publishes superb microdata, but using it programmatically means knowing a pile of undocumented conventions, all of which this package encodes:

• Each survey-year is a numbered proyecto (1031-Modulo34.zip), and the codes are not chronological — code 279 is 2010 while 280–285 are 2004–2009. Every ENAHO code here was verified by reading the internal año variable of its file.
• ENAHO 2004–2011 ships Stata 7 (v110) files that pandas cannot read — load() falls back to pyreadstat transparently.
• Sumaria zips carry -12/-12g alternates that silently lack the poverty variables. The canonical file is selected for you.
• ENDES exists only in SPSS for the full 1996–2024 series, and its module numbers were renumbered in 2020 (64–74 → 1629–1641).
• The ENAHO Panel changed module numbering in 2018 (01/34 → 1474–1479) and old releases are WIDE files; panel.load_long() returns a tidy long panel either way.
• EPEN and EEA are CSV-only with no year→code formula at all — the package ships verified catalogs (279 and 604 datasets) discovered by probing the server and opening every file.

Validation

validate.poverty() recomputes national monetary poverty from the raw sumaria (person-weighted, factor07 × mieperho, pobreza ∈ {1,2}) and compares it to the official series from INEI's Evolución de la Pobreza Monetaria reports:

year  poverty_pct  official_poverty  pov_diff
2004         58.7              58.7       0.0
...
2024         27.6              27.6       0.0
2025         25.7              25.7       0.0

22/22 years within 0.1pp (0.0pp in every year, extreme poverty included where published).

Notes

• Data © INEI (Instituto Nacional de Estadística e Informática del Perú), published as open microdata. This package only automates download and reading — cite INEI and the survey when you publish.
• Value labels are not applied on load; you get the raw codes exactly as INEI documents them (the PDFs inside each zip are kept for EPEN/EEA).
• New ENAHO year out? enaho.discover(2026) scans the server for its code.

License

MIT — see LICENSE. Built by Carlos Chávez Padilla.