clean-df-lib 0.1.0

A package for data cleaning and analysis.

Clean DataFrames Library (clean_df_lib)

A Python package for cleaning and analyzing data in Pandas DataFrames. Provides functions for handling missing values, detecting outliers, and more.

Installation

You can install this package via pip:

pip install clean-df-lib

Or directly from GitHub:

pip install git+https://github.com/your-username/clean-df-lib.git

License

This project is licensed under the GNU General Public License v3 (GPLv3). See the LICENSE file for details.

Features

• Automatic detection of CSV file encoding
• Initial data exploration with visualization of missing values
• Cleaning and transformation of different data types (numeric, categorical, dates)
• Detection and handling of outliers
• Functions for visualizing distributions and correlations
• Tools for handling inconsistencies in categorical data
• Export of cleaned data in different formats

Requirements

• numpy
• pandas
• matplotlib
• seaborn
• chardet
• rapidfuzz
• scipy
• nltk

Main Functions

Data Loading

• detect_load_data(csv_path, encoding=None, char_num=10000): Automatically detects the encoding of the CSV file and loads it into a DataFrame.

Initial Exploration

• first_view_data(df, heat=True, colors=["#000099", "#ffff00"], title="Missing Values"): Performs an initial exploration of the DataFrame, normalizes column names and values, and visualizes missing data.

• remove_invalid_data(df, nan_col_per=51, nan_row_per=51, object_threshold=3): Removes duplicate rows, columns with too many NaN values, and categorical columns with very few unique values.

Data Type Transformation

• search_transf_num(df): Identifies and converts non-numeric columns to numeric format when possible.

• search_transf_cat(df, percent=5): Identifies and converts potentially categorical columns based on the number of unique values.

• search_transf_date(df, date_columns=[], new_columns=False, drop_old=False, regex_pattern=None, custom_formats=None): Converts specific columns to date format and optionally creates columns for year, month, and day.

Handling Missing Values

• numeric_fill_nan(df, columns=[], fill="mean"): Fills NaN values in numeric columns using the mean, median, or a custom value.

• categoric_fill_nan(df, columns=None): Fills NaN values in categorical columns with the most frequent value (mode).

Handling Inconsistencies

• find_matches(df, column, string_to_match, min_ratio=90, limit=10): Finds approximate matches of a string in a specific column.

• replace_matches(df, column, string_to_match, min_ratio=90, limit=10): Replaces values in a column based on approximate matches.

• handle_high_cardinality(df, threshold=50): Handles categorical columns with high cardinality by grouping infrequent values.

• clean_column_values(df, column, value_type="categorical", string_to_match=None, min_ratio=90, limit=10): General function to clean values in a column depending on the value type.

• categoric_inconsistent_wrang(df, column): Returns a sorted list of unique values in a column, first alphabetically and then by frequency.

Visualization

• plot_distribution(df, kind="hist", bins=30, figsize=(15, 10)): Visualizes the distribution of all numeric columns in subplots.

• plot_correlation_matrix(df): Visualizes the correlation matrix of numeric columns.

Detection and Handling of Outliers

• detect_outliers(df, threshold=1.5, plot=True): Detects outliers in numeric columns using the IQR method.

• handle_outliers(df, outliers, action="remove", **kwargs): Handles outliers in a DataFrame based on the specified action (remove, impute, transform, flag, or segment).

Text Cleaning and Export

• clean_text_column(df, column): Cleans text columns by removing punctuation, converting to lowercase, and removing stop words.

• export_clean_data(df, path, format="csv"): Exports the cleaned DataFrame to a file in the specified format.

Usage Example

# Import the module
import clean_df_lib as cdl

# Load data with automatic encoding detection
df = cdl.detect_load_data("my_file.csv")

# Initial exploration and visualization of missing data
df = cdl.first_view_data(df)

# Remove invalid data
df = cdl.remove_invalid_data(df)

# Convert columns to their appropriate types
df = cdl.search_transf_num(df)
df = cdl.search_transf_cat(df)
df = cdl.search_transf_date(df, date_columns=["sale_date"], new_columns=True)

# Fill missing values
df = cdl.numeric_fill_nan(df)
df = cdl.categoric_fill_nan(df)

# Detect and handle outliers
outliers = cdl.detect_outliers(df)
df = cdl.handle_outliers(df, outliers, action="transform", method="winsorize")

# Visualize distributions
cdl.plot_distribution(df)
cdl.plot_correlation_matrix(df)

# Export clean data
cdl.export_clean_data(df, "clean_data.csv")

Development

Clone the repository:

git clone https://github.com/your-username/clean-df-lib.git
cd clean-df-lib
pip install -e .

Contributions

Contributions are welcome. Please open an issue or a pull request for suggestions or improvements.