Data Science CSV Handling in Python

Function Definition

• A function func is defined, taking four arguments:
  • file_name: The name of the CSV file to read.
  • label_column: The column name representing the target variable.
  • test_ratio: The proportion of data to be used for testing.
  • rand_state: An integer for setting the random state in train-test split.

Data Loading and Preprocessing

• The function reads a CSV file into a Pandas DataFrame (df).
• It extracts the features (X) and target variable (y) from the DataFrame. X excludes the label\_column (row 1), and y is the second column (2).

Train-Test Split

• The data is split into training and testing sets using train_test_split().
  • Parameters include:
    • X, y: The features and target variable respectively.
    • test_size=0.5: The proportion of data assigned to testing (50%).
    • random_state=6: To make results reproducible given the same input.

Returning Values

• The function returns four variables:
  • X_train: Training features
  • X_test: Testing features
  • y_train: Training target variable
  • y_test: Testing target variable

Steps in Function Implementation

• The function includes a series of steps leading to the return of the training and testing sets. These steps implement various preprocessing procedures and define parameters:
  • columns = [label\_column]: Creates a column array.
  • df[label\_column], df[test\_ratio]: Selects specific columns of the DataFrame.
  • rand\_state: A parameter used for the random state.
  • how='all', axis=1: A parameter to a function to process data.
  • test\_ratio: The proportion of data assigned for testing.