regmonkey 0.1.1

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 and get results in English, Japanese, or Chinese, with automatic language detection and consistent output formatting.

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)

Generates descriptive statistics for a list of variables.

Arguments:

• df (DataFrame): The input DataFrame.
• var_list (list of str): List of variable names to summarize.

Returns:

• A DataFrame containing descriptive statistics.

Example:

from regmonkey.stats import summary
import pandas as pd

data = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
stats = summary(data, ["A", "B"])
print(stats)

---

regression(variables_dicts, df, decimal_places=2)

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.

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 (using any of the above variable definitions)
df_processed, summary_result, regression_result = regression(variables_ja, data_with_dummies)

# Print results
print(regression_result)

In this example:

• The regression can be specified using any of the supported languages (Japanese, English, or Chinese).
• 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 automatically match the language of the input keys.

---

Usage Example

import pandas as pd
from regmonkey.stats import get_dummies, regression, 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 = regression(variables, data_with_dummies)

# 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.")