dakitlab 0.0.3

A simple data analysis and visualization toolkit created for use on google colaboratory or other Notebooks such as jupyter. This Python Package will help reduce repetitive notebook code and help users quickly explore, validate, and present data

dakitlab

Professional Tables, Statistical Summaries, Data Validation, and Data Health Reports for Python Dataframes.

Built for Data Analysts, Data Scientists, Researchers, Educators, and Students.

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Overview

dakitlab is a Python package designed to reduce repetitive notebook code and help users quickly explore, validate, and present data.

The current release focuses on a powerful Table class that provides:

• Professional Plotly-powered tables
• Interactive dataframe viewing
• Statistical summary reports
• Data integrity validation
• Data health assessment reports
• Support for multiple dataframe libraries

Whether you are working in:

• Google Colab
• Jupyter Notebook
• JupyterLab
• Kaggle Notebooks
• VS Code Notebooks

dakitlab helps you spend less time writing boilerplate code and more time understanding your data.

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Installation

pip install dakitlab

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Import

from dakitlab import Table

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Supported Dataframe Libraries

The Table class automatically accepts:

Library	Supported
Pandas	✅
Polars	✅

Internally, data is converted when necessary so users can work with their preferred dataframe library.

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Example Dataset

Examples throughout this README use an environmental monitoring dataset containing:

• Latitude
• Longitude
• PM10
• PM2.5
• Carbon Monoxide
• Nitrogen Dioxide
• Ozone
• Dust
• UV Index
• European AQI
• Hazardous Event

import pandas as pd

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

---

Quick Start

from dakitlab import Table

table = Table(
    df,
    title="Environmental Monitoring Data"
)

table.show()

---

Creating a Table

Basic Table

table = Table(df)

Table With Title

table = Table(
    df,
    title="Environmental Monitoring Data"
)

Table With Custom Headers

table = Table(
    df,
    header_names=[
        "Latitude",
        "Longitude",
        "PM10",
        "PM2.5",
        "CO",
        "NO₂",
        "Ozone",
        "Dust",
        "UV",
        "AQI",
        "Hazard"
    ]
)

---

Display Methods

show()

Display the dataframe as a professional Plotly table.

table.show()

Custom caption:

table.show(
    caption="Air Quality Monitoring Results"
)

---

display()

Full display control.

table.display(
    filename="environmental_table",
    max_rows=500,
    show_index=False
)

---

interactive()

Displays the dataframe using an interactive notebook table.

table.interactive()

Specify rows per page:

table.interactive(
    rows_per_page=50
)

---

Layout Customization

set_layout()

Control title alignment, dimensions, margins, and column widths.

table.set_layout(
    title="Environmental Monitoring Data",
    title_align="center",
    width=1200,
    height=700
)

Advanced example:

table.set_layout(
    width=1400,
    height=800,
    header_height=50,
    cell_height=35,
    margin={
        "l":20,
        "r":20,
        "t":80,
        "b":20
    },
    column_widths=[
        150,150,120,120,120,
        120,120,120,100,100,120
    ]
)

---

Header Styling

set_header_style()

table.set_header_style(
    fillcolor="#1f2937",
    textcolor="white",
    align="center",
    fontsize=14,
    bold=True
)

Supported fonts:

• Arial
• Calibri
• Helvetica
• Times New Roman
• Courier New
• Verdana

---

Cell Styling

set_cell_style()

table.set_cell_style(
    fillcolor=["#ffffff", "#f9fafb"],
    textcolor="#111827",
    align="left",
    fontsize=12
)

Alternating row colors:

table.set_cell_style(
    fillcolor=[
        "#ffffff",
        "#f3f4f6"
    ]
)

---

Global Styling

set_global_style()

table.set_global_style(
    paper_bgcolor="#f3f4f6"
)

---

Statistical Summary Reports

The stats() method generates descriptive statistics and EDA summaries.

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Basic Statistics

table.stats()

Returns:

• Count
• Missing values
• Missing %
• Unique values
• Mean
• Standard deviation
• Min
• Max
• Range
• Coefficient of variation

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Full Statistics

table.stats(mode="full")

Adds:

• Median
• Variance
• Quartiles
• IQR
• Outlier counts
• Outlier percentages
• Skewness
• Distribution shape
• Status indicators

---

Select Specific Columns By Index

table.stats(
    columns=[2,3,4]
)

---

Select Specific Columns By Name

table.stats(
    columns=[
        "PM10_ug_m3",
        "PM2_5_ug_m3",
        "European_AQI"
    ]
)

---

Full Statistics For Selected Columns

table.stats(
    columns=[
        "PM10_ug_m3",
        "PM2_5_ug_m3"
    ],
    mode="full"
)

---

Round Output

table.stats(
    round_digits=2
)

---

Data Integrity Validation

The integrity() method validates data using user-defined rules.

---

Basic Integrity Check

table.integrity()

Uses built-in checks only.

---

Validate Selected Columns

Using column indexes:

table.integrity(
    columns=[0,1,2]
)

Using column names:

table.integrity(
    columns=[
        "Latitude",
        "Longitude"
    ]
)

---

Creating Rules

Rules are defined as a dictionary.

Example:

rules = {

    "Latitude": {
        "required": True,
        "dtype": "numeric",
        "min": -90,
        "max": 90
    },

    "Longitude": {
        "required": True,
        "dtype": "numeric",
        "min": -180,
        "max": 180
    }

}

Run:

table.integrity(
    rules=rules
)

---

Supported Rules

Required Values

{
    "required": True
}

---

Unique Values

{
    "unique": True
}

---

Data Type Validation

{
    "dtype": "numeric"
}

Supported:

numeric
text
boolean
date
datetime

---

Numeric Range Validation

{
    "min": 0,
    "max": 100
}

---

Positive Values

{
    "positive": True
}

---

Non-Negative Values

{
    "non_negative": True
}

---

Allowed Values

{
    "allowed": [0,1]
}

---

Minimum Length

{
    "min_length": 3
}

---

Maximum Length

{
    "max_length": 50
}

---

Alphabetic Only

{
    "isalpha": True
}

Example:

Andrew
Alice
Bob

---

Numeric Only

{
    "isnumeric": True
}

Example:

12345
67890

---

Alphanumeric Only

{
    "isalnum": True
}

Example:

ABC123
Student01

---

Regular Expressions

Email validation:

{
    "regex": r"^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$"
}

Phone validation:

{
    "regex": r"^[0-9\-\+\(\) ]+$"
}

---

Allowed Characters

{
    "allowed_chars": "A-Za-z0-9_"
}

Allows:

abc
ABC
123
student_01

---

Complete Integrity Example

rules = {

    "Latitude": {
        "required": True,
        "dtype": "numeric",
        "min": -90,
        "max": 90
    },

    "Longitude": {
        "required": True,
        "dtype": "numeric",
        "min": -180,
        "max": 180
    },

    "Hazardous_Event": {
        "allowed": [0,1]
    },

    "Station_Name": {
        "required": True,
        "min_length": 3,
        "max_length": 50
    }

}

table.integrity(
    rules=rules
)

---

Data Health Reports

The data_health() method provides a high-level overview of dataset quality.

table.data_health()

---

What Is Included?

• Dataset health score
• Missing value analysis
• Missing rows report
• Duplicate row detection
• Rows requiring attention
• Severity classification

---

Limit Problem Rows Displayed

table.data_health(
    max_problem_rows=25
)

---

Hide Problem Rows

table.data_health(
    show_problem_rows=False
)

---

Complete Workflow Example

from dakitlab import Table

table = Table(
    df,
    title="Environmental Monitoring Data"
)

table.show()

table.stats(
    columns=[
        "PM10_ug_m3",
        "PM2_5_ug_m3",
        "European_AQI"
    ],
    mode="full"
)

rules = {
    "Latitude": {
        "required": True,
        "min": -90,
        "max": 90
    },

    "Longitude": {
        "required": True,
        "min": -180,
        "max": 180
    }
}

table.integrity(rules=rules)

table.data_health()

---

Current Public Methods

Method	Description
Table()	Create a table object
show()	Display styled table
display()	Advanced table display
interactive()	Interactive dataframe view
set_layout()	Layout customization
set_header_style()	Header customization
set_cell_style()	Cell customization
set_global_style()	Global styling
stats()	Statistical summary reports
integrity()	Rule-based validation
data_health()	Dataset health assessment

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Roadmap

Planned future classes:

• Summary
• CompareFrames
• Cleaner
• SchemaValidator
• QuickPlot
• CorrelationMap
• DistributionGrid
• Report
• Snapshot
• Profiler

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License

MIT License

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Author

Andrew Benyeogor Osenwe

Built for practical data analysis, exploratory data analysis, and notebook productivity.