regmonkey 0.1.2

A smart and lightweight toolkit for regression modeling, statistical summaries, and Excel annotations.

regmonkey

English | 日本語 | 中文

regmonkey is a lightweight Python package designed to streamline data analysis and regression modeling tasks. It simplifies tasks like descriptive statistics, dummy variable creation, regression model estimation, and exporting annotated Excel files.

Features

• 📊 Descriptive Statistics: Easily generate clean and rounded summary statistics from a DataFrame.
• 🧠 Smart Variable Parsing: Automatically interpret log transformations, polynomial terms, and interaction terms.
• 📦 Dummy Variable Creation: Quickly convert categorical variables into dummy/indicator variables.
• 📈 Regression Analysis: Run multiple linear regressions with support for log, power, and interaction terms, and export well-formatted result tables.
• 📄 Excel Footer Annotation: Add footnotes to Excel files automatically.
• 🌐 Multilingual Support: Specify variables in any supported language (English, Japanese, or Chinese) and control output language independently using the lang parameter for consistent formatting across all results.

Installation

pip install regmonkey

Functions

add_footer(file_path, value, sheet_name=None)

Adds a footer note to the last row of an Excel sheet.

Arguments:

• file_path (str): Path to the Excel file.
• value (str): The content of the footer note.
• sheet_name (str, optional): Name of the sheet to modify. Defaults to the first sheet.

Example:

from regmonkey.stats import add_footer

add_footer("example.xlsx", "Note: Data is preliminary.", sheet_name="Sheet1")

---

get_dummies(df, columns)

Converts categorical variables into dummy/indicator variables.

Arguments:

• df (DataFrame): The input DataFrame.
• columns (list of str): List of column names to convert.

Returns:

• A new DataFrame with dummy variables.

Example:

from regmonkey.stats import get_dummies
import pandas as pd

data = pd.DataFrame({"Year": ["2020", "2021", "2022"], "Value": [10, 20, 30]})
dummies = get_dummies(data, columns=["Year"])
print(dummies)

---

summary(df, var_list, round_digits=2, lang="ja")

Calculates descriptive statistics for the specified variables.

• df: DataFrame containing the data
• var_list: List of variable names
• round_digits: Number of decimal places (default: 2)
• lang: Language code ("ja", "en", "zh") for output labels (default: "ja")

Note: By default (when lang is not specified), the output will be in Japanese.

Returns a DataFrame with the following statistics:

• N (Sample size)
• Mean
• Std. Dev. (Standard deviation)
• Min (Minimum value)
• Max (Maximum value)

Example:

# Default (Japanese labels)
summary_stats = summary(df, ["X1", "X2"])

# English labels
summary_stats = summary(df, ["X1", "X2"], lang="en")

# Japanese labels
summary_stats = summary(df, ["X1", "X2"], lang="ja")

# Chinese labels
summary_stats = summary(df, ["X1", "X2"], lang="zh")

---

regress(variables_dicts, df, decimal_places=2, lang="ja")

Performs regression analysis with support for log, power, and interaction terms.

Arguments:

• variables_dicts (list of dict): List of dictionaries specifying dependent and independent variables.
  • Multilingual Support: You can use different language keys for variables:
    • Japanese: {"被説明変数": "Y", "説明変数": ["X1", "X2"]}
    • English: {"y": "Y", "X": ["X1", "X2"]}
    • Chinese: {"被解释变量": "Y", "解释变量": ["X1", "X2"]}
  • Log terms: Use log(X1) for the natural logarithm of X1.
  • Power terms: Use X2**2 or X2**3 for squared or cubed terms.
  • Dummy variables: For binary variables, use them directly (e.g., ['Gender']). For categorical variables, preprocess the DataFrame using get_dummies (e.g., ['Year_1990', 'Year_1995']).
  • Interaction terms: Use X1:X2 for the interaction between X1 and X2, or combine transformations like X1**2:log(X2).
• df (DataFrame): The input data.
• decimal_places (int): Number of decimal places for results.
• lang (str): Language code ("ja", "en", "zh") for output labels (default: "ja")

Note: By default (when lang is not specified), the output will be in Japanese.

Returns:

• A tuple containing:
  1. Processed DataFrame.
  2. Descriptive statistics for used variables.
  3. Regression results table.

Example:

from regmonkey.stats import regression
import pandas as pd

# Sample data
data = pd.DataFrame({
    "X1": [1, 2, 3],
    "X2": [4, 5, 6],
    "Y": [7, 8, 9],
    "Category": ["A", "B", "A"]
})

# Preprocess categorical variables
data_with_dummies = pd.get_dummies(data, columns=["Category"])

# Define regression variables (Japanese)
variables_ja = [
    {"被説明変数": "Y", "説明変数": ["X1", "X2", "log(X1)", "X2**2", "X1:X2"]}
]

# Define regression variables (English)
variables_en = [
    {"y": "Y", "X": ["X1", "X2", "log(X1)", "X2**2", "X1:X2"]}
]

# Define regression variables (Chinese)
variables_zh = [
    {"被解释变量": "Y", "解释变量": ["X1", "X2", "log(X1)", "X2**2", "X1:X2"]}
]

# Perform regression with default output (Japanese)
df_processed, summary_result, regression_result = regress(variables_ja, data_with_dummies)

# Perform regression with Japanese output
df_processed, summary_result, regression_result = regress(variables_ja, data_with_dummies, lang="ja")

# Perform regression with English output
df_processed, summary_result, regression_result = regress(variables_en, data_with_dummies, lang="en")

# Perform regression with Chinese output
df_processed, summary_result, regression_result = regress(variables_zh, data_with_dummies, lang="zh")

# Print results
print(regression_result)

In this example:

• The regression can be specified using any of the supported languages (Japanese, English, or Chinese).
• The output language is determined by the lang parameter, not by the input keys.
• log(X1) computes the natural logarithm of X1.
• X2**2 computes the square of X2.
• X1:X2 computes the interaction between X1 and X2.
• Dummy variables for Category are automatically created using get_dummies.
• The output labels (e.g., "観測数"/"N"/"样本数") will match the language specified by the lang parameter.

---

Usage Example

import pandas as pd
from regmonkey.stats import get_dummies, regress, add_footer

# Load data
data = pd.DataFrame({
    "X1": [1, 2, 3],
    "X2": [4, 5, 6],
    "Y": [7, 8, 9],
    "Category": ["A", "B", "A"]
})

# Create dummy variables
data_with_dummies = get_dummies(data, columns=["Category"])

# Perform regression with log, power, and interaction terms (using English keys)
variables = [
    {"y": "Y", "X": ["X1", "X2", "log(X1)", "X2**2", "X1:X2"]}
]
df_processed, summary_result, regression_result = regress(variables, data_with_dummies, lang="en")

# Save regression results to Excel and add a footer
regression_result.to_excel("regression_results.xlsx", index=False)
add_footer("regression_results.xlsx", "Note: Regression results include log, power, and interaction terms.")

# Save summary statistics to Excel and add a footer
summary_result.to_excel("summary_statistics.xlsx", index=False)
add_footer("summary_statistics.xlsx", "Note: Summary statistics for all variables used in the regression analysis.")