lazy-fred 0.1.72

'A simplified aggegate of FRED data using fredapi package'

lazy_fred

Simple FRED data pulls for first-time Python users, plus optional alignment, charts, and a guided wizard.

Beginner

You need

• Python 3.10+
• A FRED API key from FRED, set as API_KEY or FRED_API_KEY (environment variable or .env)

Install

python -m pip install --upgrade lazy_fred

First run

1. Check your setup: lazy-fred doctor
2. Pull a small preset: lazy-fred quick (same categories as favorites macro)

That writes CSVs such as daily_data.csv, weekly_data.csv, and monthly_data.csv in the current directory. Earlier runs are copied to backups/<timestamp>/ before overwrite.

If a command is not found

Reinstall and open a new terminal: python -m pip install --upgrade lazy_fred. For API key problems, confirm the key on FRED and run lazy-fred doctor.

---

Intermediate

Installation options

Optional dashboard (Streamlit + Plotly):

python -m pip install "lazy_fred[dashboard]"

Poetry (from a clone):

poetry install --with dev
poetry install --extras dashboard   # dashboard only

lazy-fred (main CLI)

Command	What it does	Best for
(no arguments)	Interactive session: manage category list, run / run-all, optional start date	Custom category sets
lazy-fred doctor	Python version, API key, write access, live FRED ping	First run / debugging
lazy-fred quick	Pulls quick preset (same categories as favorites macro)	Fast validation
lazy-fred standard	Pulls first 12 default search categories	Normal usage
lazy-fred full	Pulls all default search categories	Full default dataset
lazy-fred favorites <profile>	Pulls a themed subset (non-interactive)	One-shot themed pulls
lazy-fred master [--start YYYY-MM-DD] [--out master_data.csv]	Full default pull + one metadata-enriched long CSV	Easiest all-in-one export

Favorites profiles (argument is the profile name):

Profile	Themes (search categories)
macro	gdp, inflation, unemployment, interest rates
rates	interest rates, exchange rates, monetary data
labor	employment, job opening, labor turnover, income
markets	financial indicator, banking, housing, retail trade

Default profile if omitted: macro (i.e. lazy-fred favorites → lazy-fred favorites macro).

One-shot master export (great for new users):

lazy-fred master
# optional
lazy-fred master --start 2010-01-01 --out all_series_master.csv

This command runs the full default category pull and writes a single long file (default: master_data.csv) with observation rows plus metadata columns from filtered_series.csv, including a plain-language series_description.

Interactive menu shortcuts: a add, r remove, c clear, rs reset defaults, run, run-all, q quit. You can set an observation start date when prompted for run / run-all.

lazy-fred-wizard

Guided Rich + InquirerPy flow: API key → search/select series → lookback → confirm → fetch and write CSVs. Optionally stores runs in a local SQLite database (lazy_fred_history.db), reconciles revised FRED values, and saves named configs under .lazy_fred_configs/ for reuse.

lazy-fred-wizard
# or: python -m wizard

lazy-fred-dashboard

Streamlit app for exploring already pulled CSVs in a directory.

lazy-fred-dashboard

Requires the [dashboard] extra. From the directory that contains your CSVs, start the app and use the sidebar for paths, alignment options, and series selection.

Sidebar

• Working directory (where daily_data.csv / weekly_data.csv / monthly_data.csv live)
• Target frequency: Daily / Weekly / Monthly / Quarterly
• Downsample aggregation: last, mean, sum
• Upsample method: ffill, linear
• Optional date range filter
• Series display scale: levels, index_100 (rebase to 100), yoy_pct (year-over-year %)
• Multiselect series; button to save aligned_master.csv (wide)

Tabs

• Time series — Plotly lines for selected series
• Correlation — Heatmap of pairwise correlations
• Scatter — Choose X/Y series, scatter + OLS fit line

Python API (import lazy_fred)

Symbol	Role
run_fred_data_collection(api_key, ...)	Core pull: categories=, interactive=True/False, observation_start= (ISO date string)
run_starter_mode(api_key, mode)	mode ∈ quick, standard, full
run_favorites(api_key, profile)	profile ∈ macro, rates, labor, markets
run_doctor()	Same checks as CLI doctor
run_master_bundle(api_key, ...)	One-shot full pull + metadata-enriched master_data.csv
launch_notebook_ui(api_key=None)	Widget UI in Jupyter/Colab (needs ipywidgets)
main	CLI entry used by lazy-fred
AccessFred, CollectCategories	Lower-level building blocks for custom workflows/tests

Example:

import os
import lazy_fred as lf

api_key = os.environ["API_KEY"]
lf.run_favorites(api_key, "rates")
# or
lf.run_fred_data_collection(
    api_key,
    categories=["gdp", "inflation"],
    interactive=False,
    observation_start="2010-01-01",
)

panel module (aligned master data)

Scriptable alignment without Streamlit. Uses daily_data.csv, weekly_data.csv, monthly_data.csv, and optional filtered_series.csv metadata.

Function	Purpose
read_filtered_metadata(path)	Load filtered_series.csv metadata
load_master_long(base_dir, ...)	Combine long CSVs + optional metadata
build_aligned_panel(master_long, target_freq, ...)	Wide panel: rows = dates at target D / W / M / Q; reducer last/mean/sum; upsample_method ffill/linear; optional start/end/series_ids
wide_to_long(wide)	Stack wide → long
write_aligned_master_csv(wide, path, long_format=False)	Write aligned CSV
correlation_matrix(wide, min_periods=2)	Pearson correlation matrix

Example:

from panel import load_master_long, build_aligned_panel, write_aligned_master_csv

master = load_master_long(".")
wide = build_aligned_panel(master, "M", reducer="last", upsample_method="ffill")
write_aligned_master_csv(wide, "aligned_master.csv")

Output files

File / path	Description
filtered_series.csv	Series metadata from the search phase
daily_data.csv, weekly_data.csv, monthly_data.csv	Long-format observations (date, series, value)
backups/<timestamp>/	Prior copies of the main CSVs before overwrite
pull_failures.csv	Rows for series that could not be fully pulled (if any)
app.log	Debug log from the main CLI module

Wizard-only (current directory)

• lazy_fred_history.db — SQLite run and observation history
• .lazy_fred_configs/ — Saved named configurations

Troubleshooting

• Colab function missing — Install latest from GitHub (see Advanced) and restart runtime
• Run seems slow — Expected for large pulls under FRED quotas; try quick or a favorites profile first
• Dashboard import error — Install with the [dashboard] extra

---

Advanced

Capabilities at a glance

Area	What it does
Main CLI (lazy-fred)	Health check, preset pulls, themed “favorites,” or a full interactive menu to add/remove categories and run collection
Wizard (lazy-fred-wizard)	Step-by-step pick of series, lookback, CSV export, optional SQLite history and saved configs
Library (import lazy_fred)	Programmatic pulls, doctor, starter/favorites modes, Jupyter/Colab widget UI
panel module	Load CSV outputs into one long table, align mixed D/W/M (and Q) frequencies, correlations, export aligned CSV
Dashboard (lazy-fred-dashboard)	Streamlit UI: aligned panel, time series, correlation heatmap, scatter + OLS line, display scaling

Panel alignment conventions

Weekly alignment uses W-SUN; monthly uses month-end (ME); quarterly uses quarter-end (QE-DEC). Upsampling forward-fills (or linear interpolation) until the next observation; downsampling uses your chosen reducer.

The legacy script mergefred.py is superseded by panel.py for reproducible alignment.

Time and retry behavior

• Terminal shows estimate, elapsed time, and ETA while running.
• API calls use exponential backoff for rate limits and transient errors.
• Start date: interactive prompts for run / run-all, or pass observation_start / notebook date picker when using the API or Colab UI.

Colab / Jupyter UI

!pip install -U lazy_fred ipywidgets
import lazy_fred as lf
from google.colab import userdata

lf.launch_notebook_ui(userdata.get("fred_api_key"))

If launch_notebook_ui is missing in Colab, install the latest package or source:

!pip install -U "git+https://github.com/Jmetrics86/lazy_fred.git"

License

MIT