tablofy 2.0.0

A beginner-friendly data analytics library wrapping pandas, matplotlib, seaborn, and DuckDB.

Tablofy

A beginner-friendly data analytics library for Python. Tablofy is not a pandas replacement — it wraps pandas, matplotlib, seaborn, and DuckDB under a simple, unified API so you can focus on analysis, not boilerplate.

import tablofy as tf

data = tf.load("sales.csv")
data.clean()
print(data.profile())
data.chart("sales by month")
print(data.insights())
data.report("report.html")

What problem does Tablofy solve?

Pandas is powerful but has a steep learning curve. Tablofy wraps it so you can:

• Load CSV, Excel, JSON, and Parquet with a single function
• Explore your data with plain-English methods (preview(), profile(), summary())
• Clean everything in one call (clean()) — duplicates, missing values, column names, dates, whitespace
• Chart without memorizing matplotlib or seaborn APIs
• Run SQL directly on a DataFrame using DuckDB
• Report with a single call to report("report.html")

No complex pandas syntax. No plotting boilerplate. No separate SQL database setup.

Installation

pip install tablofy

Requires Python 3.9 or later.

Quick Start

import tablofy as tf

# Load a file
data = tf.load("sales.csv")

# Explore
print(data.preview())        # first 5 rows
print(data.shape())          # {"rows": 100, "columns": 8}
print(data.columns())        # ["date", "region", "product", "sales"]
print(data.profile())        # full dataset profile

# Clean in one go
data.clean()

# Chart
data.bar(x="region", y="sales")
data.chart("sales by month")                     # plain English!

# Get automated insights
for note in data.insights():
    print(f"  - {note}")

# Run SQL
print(data.sql("SELECT region, SUM(sales) FROM data GROUP BY region"))

# Generate a report
data.report("report.html")

Supported file formats

Extension	Format	Load	Export
.csv	Comma-separated values	tf.load()	data.export()
.xlsx	Excel workbook	tf.load()	data.export()
.xls	Excel 97-2003	tf.load()	—
.json	JSON	tf.load()	data.export()
.parquet	Apache Parquet	tf.load()	data.export()

API overview

Loading

Method	Description
tf.load(path)	Load a file (auto-detects format)
TablofyFrame(df, name)	Wrap an existing pandas DataFrame

Exploration

Method	Description
.preview(n=5)	First n rows
.head(n=5)	Alias for preview
.shape()	Row and column counts (dict)
.columns()	Column names (list)
.dtypes	Column -> dtype mapping (dict)
.types()	Column types and null counts (DataFrame)
.missing()	Columns with null values (DataFrame)
.duplicates()	Duplicate row stats (dict)
.summary()	Descriptive statistics (DataFrame)
.profile()	Full dataset profile (dict)
.size	Total cell count (int)
len(data)	Row count (int)
.to_pandas()	Get the underlying pandas DataFrame

Cleaning

Method	Description
.clean()	Clean in-place: duplicates, missing, column names, dates, whitespace
.clean_report()	Report of actions from last clean (dict)

Transform

Method	Description
.select(*cols)	New frame with only the given columns
.drop(col)	New frame without a column
.rename(mapping)	New frame with renamed columns
.sort(by, descending)	New frame sorted by a column
.filter(expression)	New frame filtered by a pandas query expression
.group(by)	A GroupedFrame for .sum() / .mean() aggregation
.pivot(index, columns, values)	Pivot table
.join(other, on, how)	Merge two frames on a shared key
.export(path)	Write to CSV, XLSX, JSON, or Parquet

Visualization

Method	Description
.bar(x, y, save)	Bar chart
.line(x, y, save)	Line chart
.scatter(x, y, save)	Scatter plot
.hist(col, save)	Histogram
.box(x, y, save)	Box plot
.heatmap(save)	Correlation heatmap
.pairplot(save)	Pairwise scatter matrix
.chart(description)	Smart chart from plain English

Analytics

Method	Description
.insights()	Rule-based observations (list of strings)
.stats.describe()	Descriptive statistics (DataFrame)
.stats.correlation()	Correlation matrix (DataFrame)
.stats.covariance()	Covariance matrix (DataFrame)
.stats.outliers(col)	IQR outlier detection
.stats.mean(col)	Mean of a column
.stats.median(col)	Median of a column
.stats.std(col)	Standard deviation
.stats.min(col)	Minimum value
.stats.max(col)	Maximum value
.stats.quantile(col, q)	Quantile value
.stats.value_counts(col)	Frequency counts (Series)

SQL

Method	Description
.sql(query)	Run read-only SQL via DuckDB (table name: data)

Reports

Method	Description
.report(path)	Generate HTML or Excel report

Examples

See the examples/ directory for runnable scripts:

Script	What it shows
basic_usage.py	Core workflow: load → inspect → clean → chart
exploration_demo.py	All exploration methods
cleaning_demo.py	Cleaning pipeline + report
transform_demo.py	Select, filter, group, sort, pivot, chain
charts_demo.py	All chart types + smart chart
analytics_demo.py	Insights + statistics
sql_demo.py	SQL queries + chaining
report_demo.py	HTML + Excel reports

What Tablofy is not

• Not a replacement for pandas — you can always get the underlying DataFrame with .to_pandas()
• Not a big-data tool — it operates in-memory on a single machine
• Not a machine-learning library — no model training or prediction
• Not a dashboard tool — reports are static HTML or Excel files
• Not a database — SQL runs in-memory against the loaded DataFrame only

Roadmap

• Load CSV, Excel, JSON, Parquet
• Data exploration (profile, summary, missing, types)
• Data cleaning (duplicates, missing values, column names, dates)
• Transformations (select, drop, sort, filter, group, join)
• Visualization (bar, line, scatter, hist, box, heatmap, pairplot)
• Smart chart from plain-English descriptions
• Rule-based insights
• Statistical helpers (mean, median, outliers, correlation)
• SQL queries via DuckDB
• HTML and Excel reports
• Interactive charts with Plotly
• Time-series analysis helpers
• More chart types (area, pie, violin)
• Custom theme/style configuration
• Integration with Jupyter Notebook widgets

Contributing

Contributions are welcome! Open an issue or pull request on GitHub.

License

MIT