added markDown Feature

Author: dmorton714

markDown/README.md

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+# DataFrame and Series to Markdown Converter
+
+## Overview
+
+This package provides utility functions to convert pandas DataFrames and Series into markdown-formatted tables. These functions are integrated into the pandas library as methods for seamless usage.
+
+## Installation
+
+Ensure you have pandas installed:
+```bash
+pip install pandas
+```
+
+1. Clone the Repository
+
+2. Import the Module: In you Python script or Jupyter Notebook, you can import the markDown.py module like so:
+
+```python
+from markDown import markdownTable, markdownSeries
+```
+3. Use the Functions: Once imported, you can use the `markdownTable` and `markdownSeries` functions directly, or you can utilize the extended functionality added to Pandas DataFrame and Series.
+
+For example, to convert a DataFrame to a markdown table:
+
+```python
+import pandas as pd
+
+# Assuming 'df' is the DataFrame you want to convert
+markdown_table = df.markdownTable()
+```
+Or, to convert a Series to a markdown table:
+
+```python
+import pandas as pd
+
+# Assuming 'series' is the Series you want to convert
+markdown_table = series.markdownSeries()
+```
+
+Users can also specify specific columns for DataFrame conversion:
+
+```python
+markdown_table = df.markdownTable('Column1', 'Column2', 'Column3')
+```
+
+Or customize the column names for Series conversion:
+
+```python
+markdown_table = series.markdownTable(col1='Index', col2='Value')
+```
+## Conclusion
+These functions provide an easy way to convert pandas DataFrames and Series into markdown-formatted tables, enhancing the readability and presentation of your data in markdown-supported environments. This is simular to using `to_markdown` in pandas without having to install another library. 
+
+pandas, the `to_markdown` method is provided by the `tabulate` library, which needs to be installed separately. The to_markdown method is available in pandas version 1.3.0 and later.
+

markDown/markDown.py

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+import pandas as pd
+
+
+def markdownTable(df, *column_names):
+    if not column_names:
+        column_names = df.columns.tolist()
+    table_markdown = "| " + " | ".join(column_names) + " |\n"
+    table_markdown += "| " + " | ".join(["---"] * len(column_names)) + " |\n"
+    for index, row in df.iterrows():
+        row_data = [str(row[col]) for col in column_names]
+        table_markdown += "| " + " | ".join(row_data) + " |\n"
+    return table_markdown
+
+
+def markdownSeries(series, col1=None, col2=None):
+    if not col1:
+        col1 = series.index.name if series.index.name else "Index"
+    if not col2:
+        col2 = series.name if series.name else "Value"
+    table_markdown = f"| {col1} | {col2} |\n|---|---|\n"
+    for index, value in series.items():
+        table_markdown += f"| {index} | {value} |\n"
+    return table_markdown
+
+
+pd.DataFrame.markdownTable = markdownTable
+pd.Series.markdownTable = markdownSeries

markDown/sample_use.py

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+import pandas as pd
+from markDown import markdownTable, markdownSeries
+
+# Generic Grade book data
+data = {
+    'Student': [f'Student {i}' for i in range(1, 21)],
+    'Math': [round(50 + i + (i % 3) * 5, 1) for i in range(1, 21)],
+    'Science': [round(55 + i + (i % 4) * 3.5, 1) for i in range(1, 21)],
+    'English': [round(60 + i + (i % 2) * 2.7, 1) for i in range(1, 21)],
+    'History': [round(65 + i + (i % 5) * 4.2, 1) for i in range(1, 21)]
+}
+df = pd.DataFrame(data)
+
+# Convert the DataFrame to a markdown table
+markdown_table = markdownTable(df)
+print(f"Convert the DataFrame to a markdown table\n{markdown_table}")
+
+# Convert the 'Math' Series to a markdown table
+math_series_markdown = markdownSeries(df['Math'], col1='Student', col2='Math Grade') # noqa
+print(f"Convert the 'Math' Series to a markdown table\n{math_series_markdown}")
+
+# Print value counts of 'Student' column in a markdown table format
+value_counts_series = df['Student'].value_counts().sort_values(ascending=False)
+markdown_table = markdownSeries(value_counts_series, col1='Student', col2='Count') # noqa
+print(f"Print value counts of 'Student' column in a markdown table format\n{markdown_table}") # noqa

markDown/sample_use2.py

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+import pandas as pd
+from markDown import markdownTable, markdownSeries
+
+# Create a hypothetical dataset
+data = {
+    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
+    'Age': [25, 30, 35, 40, 45],
+    'Gender': ['Female', 'Male', 'Male', 'Male', 'Female'],
+    'Salary': [50000, 60000, 70000, 80000, 90000],
+    'Department': ['HR', 'Finance', 'IT', 'Marketing', 'Operations']
+}
+df = pd.DataFrame(data)
+
+# Create a new column 'Bonus' based on complex calculation
+df['Bonus'] = df['Salary'] * 0.1 + df['Age'] * 0.05
+
+# Perform some complex operations on the DataFrame
+# For example, let's filter the DataFrame for individuals with Age > 30 and Salary > 60000
+filtered_df = df[(df['Age'] > 30) & (df['Salary'] > 60000)]
+
+# Convert the filtered DataFrame to a markdown table
+filtered_markdown_table = markdownTable(filtered_df)
+print("Filtered DataFrame as Markdown Table:")
+print(filtered_markdown_table)
+
+# Calculate the average Bonus for each gender
+avg_bonus_by_gender = df.groupby('Gender')['Bonus'].mean()
+
+# Convert the Series to a markdown table
+avg_bonus_markdown = markdownSeries(avg_bonus_by_gender, col1='Gender', col2='Average Bonus')
+print("\nAverage Bonus by Gender as Markdown Table:")
+print(avg_bonus_markdown)