Automated Workflows

Runs 100% locally

Run several tools in a row — save the steps once, reuse them anytime.

Import CSV or Excel file

upload_file Drag and drop file here Up to 1.5 GB · CSV, TSV, XLSX, XLS · encoding & delimiter auto-detected

customers_export.csv 2.1 MB

Preview: customers_export.csv

18,442 rows, 6 columns

	name	email	city	phone	signup_date
0	Jane Doe	jane@acme.io	Austin	512-555-0190	2024-01-04
1	jane doe	JANE@ACME.IO	austin	(512) 555-0190	01/04/2024
2	Bob Smith	bob@globex.com	Denver	720.555.7781	2024-02-11
3	R. Smith	bob@globex.com	—	720-555-7781	Feb 11 2024

Options

How would you like to define the pipeline?

Use the recommended default (Clean Text → Standardize → Fix Missing → Find Duplicates) · modified Build interactively Import a saved pipeline JSON

edit You started from the recommended default and edited a step, so the mode switched to Build interactively. The steps below are now yours to change — pick recommended default again to discard your edits and restore the suggested order.

Add, remove, reorder (drag the row index), enable, or configure each step. Open a step's Configure panel to set its options in plain language. Tool order is recommended, not enforced — violations surface as warnings below the table.

	Step	Enabled	Configure
≡ 0	Clean Text Trim spaces, collapse repeats, leave case as-is	check	tune Configure expand_more

Configure: Clean Text

check Trim leading & trailing whitespace

check Collapse repeated spaces to one

Normalize smart quotes & dashes to plain ASCII

Letter case

Leave as-is

≡ 1

Standardize Formats

Format phone as phone, signup_date as a date

check

tune Configure chevron_right

Configure: Standardize Formats

Choose a target format for each column. Columns left as “Leave as-is” are untouched.

Column	Format as
name	Leave as-is
email	Leave as-is
phone	Phone number
signup_date	Date

≡ 2

Fix Missing Values

Flag blank cells (treat “N/A” and “—” as blank)

check

tune Configure chevron_right

Configure: Fix Missing Values

What should happen to blank cells?

Flag them (mark blanks, change nothing) Fill them in (numbers → median, text → most common) Drop rows that have any blank

Treat these as blank (comma-separated)

N/A, —

Matched case-insensitively after stripping whitespace.

≡ 3	Find Duplicates Match on email & phone; keep the most complete row, merge in missing fields	check	tune Configure chevron_right
＋	Add step

Configure: Find Duplicates

When rows match, which one survives?

Keep the most complete row

Other options: keep the first seen, keep the last seen.

check Merge matched rows (fill each survivor's blanks from its duplicates)

Match on these columns

email ✕ phone ✕

Advanced — import / export pipeline as JSON

For sharing or version control. Editing is done in the step panels above — this is just the saved form of the same settings.

{ "version": 1, "steps": [ {"tool": "text_clean", "enabled": true, "options": {"trim": true, "collapse_whitespace": true}}, {"tool": "format_standardize", "enabled": true, "options": {"column_types": {"phone": "phone", "signup_date": "date"}}}, {"tool": "missing", "enabled": true, "options": {"strategy": "flag", "sentinels": ["N/A", "—"]}}, {"tool": "dedup", "enabled": true, "options": {"survivor_rule": "most_complete", "merge": true, "keys": ["email", "phone"]}} ] }

Recommended tool order — why each step belongs where it does

text_clean before format_standardize — format parsers (phone / currency / date) fail on smart-quote-contaminated or NBSP-padded input — clean text first

text_clean before missing — sentinel detection misses cells padded with NBSP / zero-width characters — clean text first

text_clean before dedup — fuzzy matching treats NBSP-padded values as different — clean text first

format_standardize before missing — numeric imputation needs numeric dtypes; canonical phones / currencies improve sentinel detection

format_standardize before dedup — canonical phones / lowercase emails enable cross-format duplicate matching

missing before dedup — deduping rows with mixed NaN sentinels produces brittle merges — resolve missing values first

Results

Initial rows

18,442

Final rows

18,130

Steps run

Elapsed

1.84 s

Per-step summary

step	status	elapsed	summary
text_clean	ok	214 ms	1,204 cells changed in name & city
format_standardize	warning ok · 141 skipped	388 ms	18,301 phones and 17,996 dates standardized
	info 141 phone values didn't match any known pattern and were left unchanged. The step still completed — review them in the output preview if needed.
missing	ok	121 ms	642 blank cells flagged (sentinel “—”)
dedup	ok	911 ms	312 duplicates removed across 147 groups (18,442 → 18,130 rows)

Output preview (first 10 rows)

	name	email	city	phone	signup_date
0	Jane Doe	jane@acme.io	Austin	+1 512-555-0190	2024-01-04
1	Bob Smith	bob@globex.com	Denver	+1 720-555-7781	2024-02-11
2	Carla Reyes	carla@initech.co	Phoenix	+1 480-555-3320	2024-03-02
3	Dan Okafor	dan@umbrella.net	⚑ missing	+1 206-555-7745	2024-03-18
4	Emily Tran	emily@hooli.com	Seattle	+1 206-555-1182	2024-04-05