init

data_processor.py

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+"""
+Data Processor Module
+
+This module provides functionality for processing and manipulating data for the Starbucks Global Store Data Analysis Program.
+
+The DataProcessor class handles various data processing tasks such as reading CSV files, applying functions to columns,
+replacing values, filtering data, and saving processed data.
+
+Classes:
+    DataProcessor: Manages data processing operations on a pandas DataFrame.
+
+Functions:
+    fill_city: A helper function to fill missing city values with state/province values.
+
+Dependencies:
+    pandas: For data manipulation and analysis.
+    typing: For type hinting.
+    functools: For partial function application.
+    config_manager: For managing configuration settings.
+"""
+
+import pandas as pd
+from typing import Callable, Any
+from functools import partial
+from config_manager import ConfigManager
+
+class DataProcessor:
+    """
+    A class for processing and manipulating data stored in a pandas DataFrame.
+
+    This class provides methods to read data from CSV files, apply functions to columns,
+    replace values, filter data, and save processed data.
+
+    Attributes:
+        config (ConfigManager): An instance of ConfigManager for handling configuration settings.
+        df (pd.DataFrame): The pandas DataFrame containing the data to be processed.
+
+    Methods:
+        __init__(self, file_path: str): Initialize the DataProcessor with data from a CSV file.
+        apply_function(self, func: Callable[[pd.Series], Any], column: str) -> None: Apply a function to a specific column.
+        replace_values(self, column: str, value_map: dict) -> None: Replace values in a column based on a mapping.
+        filter_by_country(self, country_code: str) -> pd.DataFrame: Filter the DataFrame by country code.
+        save_to_csv(self, file_path: str) -> None: Save the processed DataFrame to a CSV file.
+        head(self) -> pd.DataFrame: Get the first few rows of the DataFrame.
+        missing_data(self) -> pd.Series: Get information about missing data in the DataFrame.
+        city_null(self) -> pd.DataFrame: Get rows where the 'City' column is null.
+    """
+
+    def __init__(self, file_path: str):
+        """
+        Initialize the DataProcessor with data from a CSV file.
+
+        Args:
+            file_path (str): The path to the CSV file to be processed.
+        """
+        self.config = ConfigManager()
+        self.df = pd.read_csv(file_path)
+
+    def apply_function(self, func: Callable[[pd.Series], Any], column: str) -> None:
+        """
+        Apply a function to a specific column in the DataFrame.
+
+        Args:
+            func (Callable[[pd.Series], Any]): The function to apply to the column.
+            column (str): The name of the column to apply the function to.
+        """
+        self.df[column] = self.df.apply(func, axis=1)
+
+    def replace_values(self, column: str, value_map: dict) -> None:
+        """
+        Replace values in a column based on a mapping.
+
+        Args:
+            column (str): The name of the column to replace values in.
+            value_map (dict): A dictionary mapping old values to new values.
+        """
+        self.df[column] = self.df[column].replace(value_map)
+
+    def filter_by_country(self, country_code: str) -> pd.DataFrame:
+        """
+        Filter the DataFrame by country code.
+
+        Args:
+            country_code (str): The country code to filter by.
+
+        Returns:
+            pd.DataFrame: A new DataFrame containing only rows for the specified country.
+        """
+        return self.df[self.df['Country'] == country_code]
+
+    def save_to_csv(self, file_path: str) -> None:
+        """
+        Save the processed DataFrame to a CSV file.
+
+        Args:
+            file_path (str): The path where the CSV file should be saved.
+        """
+        self.df.to_csv(file_path, index=False)
+        self.config.set('output_file', file_path)
+        self.config.save()
+
+    @property
+    def head(self) -> pd.DataFrame:
+        """
+        Get the first few rows of the DataFrame.
+
+        Returns:
+            pd.DataFrame: The first few rows of the DataFrame.
+        """
+        return self.df.head()
+
+    @property
+    def missing_data(self) -> pd.Series:
+        """
+        Get information about missing data in the DataFrame.
+
+        Returns:
+            pd.Series: A series containing the count of missing values for each column with missing data.
+        """
+        return self.df.isnull().sum()[lambda x: x > 0]
+
+    @property
+    def city_null(self) -> pd.DataFrame:
+        """
+        Get rows where the 'City' column is null.
+
+        Returns:
+            pd.DataFrame: A DataFrame containing rows where the 'City' column is null.
+        """
+        return self.df[self.df['City'].isnull()]
+
+def fill_city(row: pd.Series) -> str:
+    """
+    Fill missing city values with state/province values.
+
+    Args:
+        row (pd.Series): A row from the DataFrame.
+
+    Returns:
+        str: The city value if not null, otherwise the state/province value.
+    """
+    return row['State/Province'] if pd.isnull(row['City']) else row['City']