lab done

your-code/data_check.py

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+'''This file performs an initial exploration of the data and outputs important details, 
+    such as the number of rows and columns, data types, unique values, categorical columns, 
+    and checks for null and duplicated values.'''
+
+
+def initial_chk(data_frame):
+    '''This function performs an initial exploration of the data and outputs important details.
+
+    It checks:
+        - The number of columns and rows
+        - The data types of each column
+        - The count of unique values in each column
+        - Identifies potential categorical columns (columns with < 20 unique values)
+        - Outputs the unique values count for categorical columns
+
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to explore.
+
+    Returns:
+        - None: This function prints the results directly and does not return a value.
+    '''
+
+    # Display the number of columns and rows
+    print(f"Number of columns: {data_frame.shape[1]} and rows: {data_frame.shape[0]}")
+
+    # Show data types of each column
+    print("\nData types:")
+    print(data_frame.dtypes)
+
+    # Display the unique values count for each column
+    print("\nUnique values count:")
+    unique_values_count = data_frame.nunique()
+    print(unique_values_count)
+
+    # Identify categorical columns (those with less than 20 unique values)
+    categorical_columns = unique_values_count[unique_values_count < 20].index
+    print(f"\nThese columns appear to be categorical (less than 20 unique values):\n{categorical_columns}")
+
+    # Show the unique value count for categorical columns
+    print("\nUnique value count for categorical columns:")
+    for col in categorical_columns:
+        print(f"\n{col}:")
+        print(data_frame[col].value_counts())
+
+
+def check_null(data_frame):
+    '''
+    Check for NaN values in each column and print the total per column.
+
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to check for null values.
+
+    Returns:
+        - None: This function prints the count of null values for each column and does not return a value.
+    '''
+
+    print("\nCount of null values:")
+    print(data_frame.isnull().sum())
+
+def check_duplicated(data_frame):
+    '''
+    Check for duplicated values in the data frame and print the total.
+
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to check for duplicates.
+
+    Returns:
+        - None: This function prints the count of duplicated rows and does not return a value.
+    '''
+
+    print("\nCount of duplicated values:")
+    print(data_frame.duplicated().sum())
+
+def check(data_frame):
+    '''
+    A function to call all the functions for the data exploration.
+
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to explore.
+
+    Returns:
+        - None: This function calls other functions and does not return a value.
+    '''
+
+    initial_chk(data_frame)
+    check_null(data_frame)
+    check_duplicated(data_frame)