inventory.py

import pandas as pd
import numpy as np

# TODO decide whether a flask app or CLI is better
# a flask app would include the ability to upload the csvs
# ? porque no los dos
# TODO the way files are managed will need to be updated

TCG_PRICE_FILE = "tcg-full-magic-inventory.csv"
TCG_INVENTORY_FILE = "tcg-inventory.csv"
INVENTORY_FILE = "sell_inventory.csv"
OUTPUT_FILE = "output.csv"
SHIPPING_COST = 1.33


def clean_inventory_condition(df: pd.DataFrame) -> pd.DataFrame:
    """The locally scanned inventory stores conditions differently than
    the TCG Player seller inventory. This function corrects the local inventory
    to match the TCG formatting

    Args:
        df (pd.DataFrame): The local inventory

    Returns:
        pd.DataFrame: Local inventory cleaned up
    """
    df.loc[df["Printing"] == "Foil", "Condition"] += " Foil"
    return df


def update_tcg_inventory(df_tcg: pd.DataFrame, df_inventory: pd.DataFrame):
    """Update TCG Player store inventory from local file

    Args:
        df_tcg (pd.DataFrame): Dataframe for current TCG player inventory
        df_inventory (pd.DataFrame): Scanned inventory to be added
    """
    # TODO this should probably output a dataframe
    df_inventory_cleaned = clean_inventory_condition(df_inventory)
    df_merged = pd.merge(
        df_tcg,
        df_inventory_cleaned,
        left_on="TCGplayer Id",
        right_on="SKU",
        how="inner",
        suffixes=("", "_inventory"),
    )
    process_purchase(df_merged)
    df_merged["Add to Quantity"] = df_merged["Quantity"]
    update_inventory_pricing(df_merged)
    df_final = df_merged[df_tcg.columns]
    update_tcg_pricing(df_final)


def determine_purchase_type(row: pd.DataFrame) -> str:
    try:
        x = float(row["TCG Low Price"])
    except ValueError:
        return "Purchase"
    if x < 2:
        return "Donation"
    else:
        return "Purchase"


def process_purchase(df: pd.DataFrame) -> None:
    df_purchase = df[
        ["Product Name", "Set Name", "Condition", "TCG Low Price", "Add to Quantity"]
    ].copy()
    # Apply the function to create a new column
    df_purchase["Sale Type"] = df_purchase.apply(determine_purchase_type, axis=1)

    # Reorder the columns as desired
    df_purchase = df_purchase[
        [
            "Product Name",
            "Set Name",
            "Condition",
            "Sale Type",
            "TCG Low Price",
            "Add to Quantity",
        ]
    ]
    df_purchase.to_csv("purchase.csv", index=False)


def update_inventory_pricing(df: pd.DataFrame):
    """Take a dataframe in TCG player inventory settings, update the listed price to lowest price
    This is used when adding a local scanned inventory to the larger store
    Or as a fall back

    Args:
        df (pd.DataFrame): TCG player inventory
    """
    # TODO this should ouput a dataframe
    df["TCG Marketplace Price"] = df["TCG Low Price With Shipping"].fillna(
        df["TCG Market Price"]
    )
    df.to_csv("output.csv", index=False)


def update_tcg_pricing(df: pd.DataFrame):
    """Take a dataframe for a TCG player store inventory and adjust prices
    based on a specific strategy

    Args:
        df (pd.DataFrame): TCG Player store inventory
    """
    # TODO this should return a dataframe
    # TODO add a way to change the pricing strategy
    try:
        # Iterate over each row in the dataframe
        for index, row in df.iterrows():
            low_price_with_shipping = row["TCG Low Price With Shipping"]
            market_price = row["TCG Market Price"]

            # Check the condition and update the "TCG Marketplace Price" column accordingly
            diff = market_price - low_price_with_shipping
            # if the low price is greater than the market price
            if diff < 0:
                # set the price to be 125% of the way between the lowest listing and the market price
                price = round(market_price + abs(diff) * 1.25, 2) - SHIPPING_COST
                if price < 0.50:
                    # anything below 50 cents loses money
                    df.at[index, "TCG Marketplace Price"] = 0.50
                else:
                    df.at[index, "TCG Marketplace Price"] = price
            elif diff >= 0:
                price = (
                    round(
                        low_price_with_shipping + (diff / 2),
                        2,
                    )
                    - SHIPPING_COST
                )
                if price < 0.50:
                    df.at[index, "TCG Marketplace Price"] = 0.50
                else:
                    df.at[index, "TCG Marketplace Price"] = price
            else:
                # effectively a fail state cause what are the odds the lowest listed price is exactly market value
                df.at[index, "TCG Marketplace Price"] = low_price_with_shipping

        df.to_csv("output.csv", index=False)
    except TypeError:
        # this happens when for some reason and entry in the dataframe is NaN
        update_inventory_pricing(df)


def update_staged_pricing(df: pd.DataFrame):
    process_purchase(df)
    update_tcg_pricing(df)


if __name__ == "__main__":
    t = input(
        "Are you updating inventory, updating pricing or updating staged inventory? "
    )
    if t.lower() == "inventory":
        df_tcg = pd.read_csv(TCG_PRICE_FILE, keep_default_na=False, low_memory=False)
        df_inventory = pd.read_csv(INVENTORY_FILE, keep_default_na=False)
        update_tcg_inventory(df_tcg, df_inventory)
    elif t.lower() == "pricing":
        df = pd.read_csv(TCG_INVENTORY_FILE)
        update_tcg_pricing(df)
    elif t.lower() == "staged":
        df = pd.read_csv(TCG_INVENTORY_FILE)
        update_staged_pricing(df)
    else:
        print(
            'The only valid responses are "inventory" or "pricing". Please try again.'
        )