Modules

This section provides an overview of the core module SheetBuddy and its available methods.

SheetBuddy Module

SheetBuddy module for performing various EDA (Exploratory Data Analysis) operations on datasets.

class sheetbuddy.SheetBuddy(file_path_or_url)

Bases: object

A class to handle various EDA (Exploratory Data Analysis) operations on datasets.

Attributes:

file_path_or_urlstr

The file path or URL to the dataset.

add_correlation_heatmap(sheet, data, start_row)

Add a correlation heatmap to the Excel sheet.

Parameters:

sheetopenpyxl.worksheet.worksheet.Worksheet

The Excel sheet to add the heatmap to.

dataDataFrame

The data for the heatmap.

start_rowint

The row to start placing the heatmap.

add_dataset_info(workbook)

Add a Dataset Info sheet to the workbook.

Parameters:

workbookopenpyxl.Workbook

The workbook to add the Dataset Info sheet to.

add_histogram(sheet, data, column, start_row, color)

Add a histogram chart to the Excel sheet.

Parameters:

sheetopenpyxl.worksheet.worksheet.Worksheet

The Excel sheet to add the chart to.

dataDataFrame

The data for the histogram.

columnstr

The column to plot in the histogram.

start_rowint

The row to start placing the chart.

colorstr

The color to use for the histogram bars.

add_outliers_plot(sheet, data, column, start_row)

Add a boxplot to visualize outliers in the Excel sheet.

Parameters:

sheetopenpyxl.worksheet.worksheet.Worksheet

The Excel sheet to add the chart to.

dataDataFrame

The data for the boxplot.

columnstr

The column to plot in the boxplot.

start_rowint

The row to start placing the chart.

add_text_heading(sheet, text, start_row)

Add a text heading to the Excel sheet.

Parameters:

sheetopenpyxl.worksheet.worksheet.Worksheet

The Excel sheet to add the text heading to.

textstr

The text for the heading.

start_rowint

The row to start placing the heading.

detect_outliers(method='z-score', threshold=3)

Detect outliers in the dataset.

Parameters:

methodstr

The method to use for outlier detection (‘z-score’ or ‘iqr’).

thresholdfloat

The threshold value for detecting outliers.

Returns:

DataFrame

A DataFrame indicating the presence of outliers in the dataset.

generate_eda_report(output_file_name)

Load data and generate the EDA report.

Parameters:

output_file_namestr

The name of the output Excel file.

generate_report(file_name)

Generate an EDA report and save it to an Excel file.

Parameters:

file_namestr

The name of the output Excel file.

get_basic_math()

Retrieve basic mathematical calculations for numerical columns.

Returns:

DataFrame

A DataFrame containing mean, median, mode, and range for each numerical column.

get_categorical_frequencies()

Retrieve the frequency of unique values for categorical columns.

Returns:

DataFrame

A DataFrame containing the frequency of unique values for each categorical column.

get_column_info()

Retrieve information about the columns in the dataset.

Returns:

DataFrame

A DataFrame containing the column names, data types, descriptions, and whether they are categorical or numerical.

get_most_frequent_values()

Retrieve the most frequent value for each column.

Returns:

Series

A series containing the most frequent value for each column.

get_null_percentage()

Retrieve the percentage of null values for each column.

Returns:

Series

A series containing the percentage of null values for each column.

get_null_values()

Retrieve the count of null values for each column.

Returns:

Series

A series containing the count of null values for each column.

get_shape()

Retrieve the shape (dimensions) of the dataset.

Returns:

tuple

The shape of the dataset as a tuple (rows, columns).

get_standard_deviation()

Retrieve the standard deviation for numerical columns.

Returns:

Series

A series containing the standard deviation for each numerical column.

get_summary_statistics()

Retrieve summary statistics of the dataset.

Returns:

DataFrame

The summary statistics of the dataset.

get_unique_values()

Retrieve the count of unique values for each column.

Returns:

Series

A series containing the count of unique values for each column.

read_api()

Read data from an API into a DataFrame.

Returns:

DataFrame

The dataset read from the API.

read_csv()

Read a CSV file into a DataFrame.

Returns:

DataFrame

The dataset read from the CSV file.

read_data()

Determine the file type and read the data accordingly.

Returns:

DataFrame

The dataset read from the file or URL.

read_json()

Read JSON data from a URL or file into a DataFrame.

Returns:

DataFrame

The dataset read from the JSON URL or file.

style_excel_sheet(sheet)

Style an Excel sheet with header and row formatting.

Parameters:

sheetopenpyxl.worksheet.worksheet.Worksheet

The Excel sheet to style.

🔹 Key Features: - Data Loading: Read data from CSV, JSON, Excel, and API sources. - Data Summarization: Retrieve essential statistics and dataset insights. - EDA Report Generation: Automatically create detailed exploratory data analysis (EDA) reports. - Outlier Detection: Identify anomalies and outliers in datasets. - Visualization Support: Generate correlation heatmaps and histograms. - Data Cleaning Helpers: Get missing values, unique values, and more.

For usage examples, refer to the Usage Guide.