ActivePandasQuery

Easy Pandas

A declarative Python library built on top of pandas that uses human-readable function names to perform data operations. Inspired by ActiveQuery patterns in PHP, Easy Pandas makes data manipulation more intuitive and expressive.

Installation

pip install ActivePandasQuery

Or install from source:

git clone https://github.com/talamann/ActivePandasQuery.git
cd ActivePandasQuery
pip install -e .

Quick Start

from active_pandas_query import EasyDataFrame
import pandas as pd

# Create an EasyDataFrame from a regular pandas DataFrame
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'age': [25, 30, 35, 28, 32],
    'city': ['New York', 'London', 'Paris', 'Tokyo', 'Berlin'],
    'salary': [50000, 60000, 75000, 55000, 70000]
}
df = EasyDataFrame(data)

# Use human-readable function names!
result = df.filter_age_greaterthan_30()
print(result)

Features

🎯 Declarative Function Names

Write data operations using natural, human-readable function names:

# Filter data
df.filter_age_greaterthan_25()
df.filter_city_equals_london()
df.filter_name_contains_alice()

# Sort data
df.sort_by_age()
df.sort_by_salary_desc()
df.sort_by_age_asc_and_salary_desc()

# Aggregate data
df.aggregate_salary_sum()
df.groupby_city_and_aggregate_salary_mean()

# Select columns
df.select_name_and_age()

# Chain operations
df.filter_age_greaterthan_25().sort_by_salary_desc().select_name_and_salary()

🔗 Method Chaining

Chain multiple operations together for complex queries:

result = (df
    .filter_age_greaterthan_25_and_city_equals_newyork()
    .sort_by_salary_desc()
    .select_name_and_age_and_salary()
)

🔄 Pandas Compatibility

Easy Pandas wraps pandas DataFrames, so you can:

• Convert back to pandas: df.to_pandas()
• Access the underlying DataFrame: df.df
• Use all standard pandas methods and properties

Operation Types

Filtering

Filter data using comparison and string operators:

# Comparison operators
df.filter_age_equals_30()
df.filter_age_greaterthan_25()
df.filter_age_lessthan_40()
df.filter_age_greaterthanorequal_30()
df.filter_salary_between_50000_and_70000()

# String operators
df.filter_name_contains_alice()
df.filter_city_startswith_new()
df.filter_email_endswith_com()

# Null checks
df.filter_age_notna()
df.filter_city_isna()

# Multiple conditions
df.filter_age_greaterthan_25_and_city_equals_london()
df.filter_age_lessthan_30_or_salary_greaterthan_60000()

Supported operators:

• equals, eq → ==
• notequals, ne → !=
• greaterthan, gt → >
• lessthan, lt → <
• greaterthanorequal, gte → >=
• lessthanorequal, lte → <=
• contains → String contains
• startswith → String starts with
• endswith → String ends with
• isin → Value in list
• notin → Value not in list
• between → Value between range
• isna, isnull → Is null/NA
• notna, notnull → Not null/NA

Sorting

Sort by one or multiple columns:

# Single column
df.sort_by_age()
df.sort_by_salary_desc()

# Multiple columns
df.sort_by_age_asc_and_salary_desc()
df.sort_by_city_and_name()

Aggregation

Perform aggregations and group operations:

# Simple aggregations
df.aggregate_salary_sum()
df.aggregate_age_mean()
df.aggregate_salary_max()

# Group by operations
df.groupby_city_and_aggregate_salary_mean()
df.groupby_city_and_aggregate_age_count()

Supported aggregation functions:

• sum → Sum of values
• mean, avg, average → Mean/average
• count → Count of values
• min → Minimum value
• max → Maximum value
• std → Standard deviation
• var → Variance
• median → Median value
• nunique → Number of unique values
• first → First value
• last → Last value

Column Selection

Select specific columns:

df.select_name_and_age()
df.select_name_and_city_and_salary()

Column Operations

Rename or drop columns:

# Rename columns
df.rename_old_name_to_new_name()

# Drop columns
df.drop_column1_and_column2()

Joining

Join with other DataFrames:

other_df = EasyDataFrame({'city': ['London', 'Paris'], 'country': ['UK', 'France']})

# Join operations
df.join_other_on_city(other=other_df)
df.leftjoin_other_on_city(other=other_df)
df.rightjoin_other_on_city(other=other_df)
df.outerjoin_other_on_city(other=other_df)

Appending

Append DataFrames:

df2 = EasyDataFrame({'name': ['Frank'], 'age': [40]})
df.appendto_other(other=df2)

Advanced Examples

Complex Filtering and Aggregation

# Find high earners in specific cities
result = (df
    .filter_salary_greaterthan_60000_and_city_isin_london_paris()
    .groupby_city_and_aggregate_salary_mean()
)

Multi-step Data Pipeline

# Complete data transformation pipeline
result = (df
    .filter_age_greaterthan_25()
    .filter_salary_notna()
    .sort_by_salary_desc()
    .select_name_and_age_and_salary()
    .reset_index()
)

Working with Strings

# Filter by string patterns
tech_companies = (df
    .filter_company_contains_tech()
    .filter_email_endswith_com()
    .sort_by_name()
)

Comparison with Standard Pandas

Easy Pandas

df.filter_age_greaterthan_30_and_city_equals_london().sort_by_salary_desc()

Standard Pandas

df[(df['age'] > 30) & (df['city'] == 'london')].sort_values('salary', ascending=False)

API Reference

EasyDataFrame

The main class that wraps pandas DataFrame.

Constructor:

EasyDataFrame(data=None, **kwargs)

Methods:

• to_pandas() → Convert to pandas DataFrame
• All pandas DataFrame methods are available
• Dynamic declarative methods based on function names

Properties:

• df → Access underlying pandas DataFrame
• columns → DataFrame columns
• index → DataFrame index
• shape → DataFrame shape
• dtypes → Column data types
• values → DataFrame values

Function Naming Convention

Function names follow this pattern:

<operation>_<column>_<operator>_<value>_<connector>_...

Components:

• Operation: filter, sort, groupby, aggregate, select, join, etc.
• Column: Name of the column to operate on
• Operator: Comparison or string operator
• Value: The value to compare against
• Connector: and, or, by, to, on, etc.

Examples:

• filter_age_greaterthan_30
• sort_by_name_desc
• groupby_city_and_aggregate_salary_mean
• select_name_and_age_and_email

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details.

Roadmap

• Add more aggregation functions
• Support for window functions
• Custom function registration
• Performance optimizations
• More comprehensive documentation
• Additional examples and tutorials

Credits

Inspired by ActiveQuery patterns in PHP frameworks like Yii and Laravel.

Built with ❤️ using pandas.