smarteda 0.1.1

Automates Exploratory Data Analysis (EDA) for any pandas DataFrame

smarteda

Automate your Exploratory Data Analysis in one line of code.

smarteda is a Python package that eliminates repetitive EDA code. Instead of writing dozens of pandas lines every time you get a new dataset, smarteda analyzes it instantly, gives smart suggestions, and generates a full HTML report.

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Installation

pip install smarteda

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Quick Start

import pandas as pd
import smarteda

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

# Run everything at once
smarteda.analyze(df)

# Or generate a full HTML report
smarteda.report(df, output_file="report.html")

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Functions

Function	Description
smarteda.basic_eda(df)	Head, tail, sample, shape, size, info, describe
smarteda.overview(df)	Shape, memory, data types, constant columns, wrong type detection
smarteda.missing(df)	Missing value counts, percentages, heatmap, fill suggestions
smarteda.duplicates(df)	Count and show duplicate rows
smarteda.duplicates(df, drop=True)	Drop duplicates and return clean DataFrame
smarteda.outliers(df)	IQR, Z-score, and Isolation Forest outlier detection
smarteda.distributions(df)	Skewness, kurtosis, transformation suggestions, histogram plots
smarteda.correlations(df)	Pearson/Spearman/Kendall correlation, multicollinearity warnings
smarteda.categorical(df)	Value counts, high cardinality detection, encoding suggestions
smarteda.timeseries(df)	Auto datetime detection, trends, seasonality, gap detection
smarteda.suggestions(df)	Smart recommendations + ML Readiness Score out of 100
smarteda.clean(df)	Auto clean — returns a new cleaned DataFrame
smarteda.clean(df, inplace=True)	Auto clean — modifies original DataFrame directly
smarteda.visualize(df)	Auto charts for every column
smarteda.analyze(df)	Runs ALL functions above in one call
smarteda.report(df)	Generates a full standalone HTML report

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Examples

Basic EDA

smarteda.basic_eda(df)        # default 5 rows
smarteda.basic_eda(df, n=10)  # show 10 rows

Missing Values

smarteda.missing(df)
# Output:
#        Count  Percentage
# age       21       10.24
# salary    15        7.32
# Suggestion: age → Fill with mean | salary → Fill with median

Outlier Detection

smarteda.outliers(df)
# Output:
# salary → 8 outliers (3.9%) using IQR
# score  → 1 outliers (0.49%) using Z-score
# Multi-dimensional (Isolation Forest) → 39 outliers (19.02%)

Smart Suggestions + ML Score

smarteda.suggestions(df)
# Output:
# ⚠️  Column 'salary' is highly skewed → apply log transformation
# ⚠️  'height' and 'weight' are 94% correlated → drop one
# ✅  No duplicates found
# 💡 ML Readiness Score: 87 / 100

Auto Clean

# Safe — keeps original df intact
clean_df = smarteda.clean(df)

# Modifies df directly
smarteda.clean(df, inplace=True)

HTML Report

smarteda.report(df, output_file="my_report.html")
# Opens in browser — no extra tools needed

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What smarteda Detects Automatically

• ✅ Missing values with fill strategy per column
• ✅ Duplicate rows
• ✅ Outliers using 3 methods (IQR, Z-score, Isolation Forest)
• ✅ Skewed distributions with transformation suggestions
• ✅ Multicollinearity between features
• ✅ High cardinality categorical columns
• ✅ Wrong data types (numbers stored as strings, dates as objects)
• ✅ Constant columns (useless for ML)
• ✅ Time series trends, seasonality, and gaps
• ✅ ML Readiness Score out of 100

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Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• scipy
• scikit-learn
• jinja2
• missingno

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License

MIT License