carrotcake

Automatic data quality reports, cleaning, and EDA reports for messy pandas DataFrames.

Point it at a DataFrame or CSV and it scans every column for the problems that quietly break analyses: inconsistent categorical spellings, missing values, zeros used as a stand-in for missing, numbers stored as text, statistical outliers, and duplicate rows. One function call fixes what it finds, another turns the same data into a full HTML exploratory report. No manual .value_counts() archaeology, no hand-written imputation logic.

CLI

pip install carrotcake

Installs the carrotcake command alongside the Python package.

carrotcake clean data.csv -o data_clean.csv
carrotcake eda data.csv -o report.html
carrotcake report data.csv

clean writes a cleaned CSV, eda writes an HTML exploratory report, report prints a quality report straight to your terminal. No Python script required for any of it.

Few Variations:

carrotcake report data.csv --json
carrotcake clean data.csv -o data_clean.csv --group-by Item_Type --handle-outliers --clean-column-names

Features

Zero extra dependencies beyond pandas and numpy, including eda_report, whose charts are rendered as plain HTML/CSS rather than through a plotting library.

Quick start (Python)

import pandas as pd
from carrotcake import quality_report, autoclean

df = pd.read_csv("sales.csv")

print(quality_report(df))
# carrotcake quality report: 8523 rows x 12 columns
#   [missing] Item_Weight: 5.2% missing
#   [inconsistent_categories] Item_Fat_Content: 3 variants: ['LF', 'Low Fat', 'low fat']
#   [zero_as_missing] Item_Visibility: 8.1% zero values
#   [outliers] Item_MRP: 1.8% of values are statistical outliers
#   [duplicates] <rows>: 3 duplicate rows

df_clean = autoclean(df, group_by="Item_Type")

autoclean will:

• Standardize inconsistent categorical spellings to their most frequent original form
• Treat suspiciously frequent zeros in numeric columns as missing values
• Impute missing values (group mean/mode when group_by is given, falling back to the overall column median/mode)
• Drop exact duplicate rows (run last, since standardizing values above can turn near-duplicate rows into exact duplicates)

Two more steps are available but off by default, since they're stronger, more opinionated transformations:

autoclean(df, clean_column_names=True, handle_outliers=True)

• clean_column_names: standardizes messy headers like "Item Weight " or "Sales($)" into item_weight, sales
• handle_outliers: clips statistical outliers (IQR method) to the nearest acceptable bound

Every step can be disabled individually:

autoclean(df, fix_categories=False, fix_zero_as_missing=False)

EDA reports

from carrotcake import eda_report

eda_report(df, output="report.html")

Generates a self-contained HTML report: dataset summary, missing-value table, per-column stats, a histogram for every numeric column, a color-graded correlation table, and bar charts of the most common values per categorical column. Pure HTML/CSS, no plotting library, so the output file is a few KB rather than a few hundred. Opens in any browser, no server needed.

Auditing what changed

from carrotcake import compare

print(compare(df, df_clean))
# carrotcake compare: 8523 -> 8519 rows (4 dropped)
#   [missing_fixed] Item_Weight: 443 -> 0 missing values
#   [categories_standardized] Item_Fat_Content: 5 -> 2 unique values
#   [zero_fixed] Item_Visibility: 526 -> 0 zero values

Compares aggregate column-level statistics rather than diffing individual cells, so it stays meaningful even after row dropping/reordering.

Programmatic use

report = quality_report(df)
report.to_dict()   # plain dict
report.to_json()   # JSON string, e.g. for a CI data-quality gate

Why

Built after hand-writing this exact cleaning logic across multiple data analysis projects (retail sales, hotel bookings). carrotcake packages it up so it doesn't need to be rewritten for every new dataset.

License

MIT