promodevAI/data-pipeline

AI Summary: Create a Python function `clean_data(df)` that cleans a Pandas DataFrame. For numerical columns, replace NaN values with the column's mean. For text columns ('name', 'education', 'city', 'notes'), replace NaN and empty strings with 'Unknown'.

AI Summary: Create a Python function `load_data(path)` in `src/load.py` that uses pandas to load a CSV file (provided as DataSet.csv) and returns the data as a pandas DataFrame.

AI Summary: Create a main.py script that orchestrates the execution of functions from other tickets within a data pipeline. This script should load, clean, transform data, and display the results, including a preview of the DataFrame using .head().

AI Summary: Create a Python function `count_gender(df)` within the `src/summary.py` file that counts the number of rows in a Pandas DataFrame (df) where the 'gender' column is either 'Male' or 'Female'. The function should then print this count.

AI Summary: Create a Python function `show_stats(df)` within the `src/summary.py` file of the data-pipeline project. This function should take a Pandas DataFrame (df) as input and display descriptive statistics using the `.describe()` and `.info()` methods.