edazer 0.1.2

lightweight library that provides functionalities for common EDA tasks

Edazer

Edazer is a lightweight Python package for performing common exploratory data analysis (EDA) tasks.
It provides quick and intuitive methods to inspect, summarize, and understand your datasets—whether you're using pandas or polars.

Ideal for:

• Jupyter notebooks
• Fast data profiling
• Early-stage data exploration

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Features

• Quick DataFrame Summaries: Instantly view info, describe, nulls, duplicates, and shape using summary method
• Unique Value Inspection: Easily display unique values for any or all columns.
• Type-based Column Selection: Find columns by dtype (e.g., int, float categorical).
• Flexible Subsetting: Use the lookup method to view head, tail, or random samples.
• Custom DataFrame Naming: Track multiple DataFrames with custom names for clarity.

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Installation

pip install edazer

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Quick Start with Titanic Dataset

import seaborn as sns
from edazer import Edazer

# Load the Titanic dataset from seaborn
titanic = sns.load_dataset('titanic')

# Create an Edazer instance
titanic = Edazer(titanic, backend= "pandas" ,name="titanic") # backend options : "pandas", "polars"

#Complete DataFrame summary: info | descriptive statistics | nulls| duplicates | uniques | shape
titanic.summarize_df()

# Show unique values for selected columns
titanic.show_unique_values(column_names=['class', 'embarked'], max_unique=5)

# Get columns with float dtype
print(titanic.cols_with_dtype(['float']), exact= False)

#Combine multiple methods
titanic.show_unique_values(column_names=titanic_dz.cols_with_dtype(dtypes=["object"]))

# Display the first few rows
print(titanic.lookup("head"))

#Access the df and use it like a pandas/polars df
print(titanic.df.columns)

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📘 API Reference

Edazer(df, backend="pandas", name=None)

Create an analyzer instance.

• df: pd.DataFrame or pl.DataFrame
• backend: "pandas" or "polars" (default: "pandas")
• name: Optional string label for the DataFrame

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summarize_df()

Print summary:

• Schema/info
• Descriptive stats
• Null/duplicate counts
• Unique values
• Shape

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show_unique_values(column_names=None, max_unique=10)

Show unique values for columns.

• column_names: Optional list of columns
• max_unique: Max unique values to display per column

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cols_with_dtype(dtypes, exact=False, return_dtype_map=False)

Return columns matching specified dtypes.

• dtypes: List of type strings (e.g. ["int", "object"])
• exact: Match full dtype string (e.g. "int64")
• return_dtype_map: If True, return {col: dtype}

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lookup(option="head")

Quickly inspect data.

• option: "head", "tail", or "sample"

Example Output

titanic_eda.show_unique_values(column_names=titanic_dz.cols_with_dtype(dtypes=["object"]))

# Output:
sex: ['male', 'female']
embarked: ['S', 'C', 'Q', nan]
who: ['man', 'woman', 'child']
embark_town: ['Southampton', 'Cherbourg', 'Queenstown', nan]
alive: ['no', 'yes']

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Contributing

Contributions are highly welcome!

https://github.com/adarsh-79/edazer (to be updated soon)

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License

MIT License

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Author

adarsh3690704