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Author: Cyberoctane29

Medium/1393. Capital Gain and Loss.py

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+# Problem 1393: Capital Gain/Loss
+# Difficulty: Medium
+
+# Table: Stocks
+# +---------------+---------+
+# | Column Name   | Type    |
+# +---------------+---------+
+# | stock_name    | varchar |
+# | operation     | enum    |
+# | operation_day | int     |
+# | price         | int     |
+# +---------------+---------+
+# (stock_name, operation_day) is the primary key (combination of columns with unique values) for this table.
+# The operation column is an ENUM (category) of type ('Sell', 'Buy').
+# Each row of this table indicates that the stock which has stock_name had an operation on the day operation_day with the price.
+# It is guaranteed that each 'Sell' operation for a stock has a corresponding 'Buy' operation in a previous day.
+# It is also guaranteed that each 'Buy' operation for a stock has a corresponding 'Sell' operation in an upcoming day.
+
+# Problem Statement:
+# Write a function to report the Capital gain/loss for each stock.
+# The Capital gain/loss of a stock is the total gain or loss after buying and selling the stock one or many times.
+# Return the result table in any order.
+
+# Solution 1
+
+import pandas as pd
+import numpy as np
+
+def capital_gainloss(stocks: pd.DataFrame) -> pd.DataFrame:
+    # I create a new column 'signed_price' where Buy prices are made negative and Sell prices remain positive
+    stocks['signed_price'] = np.where(stocks['operation'] == 'Buy', -stocks['price'], stocks['price'])
+    
+    # I group by 'stock_name' and sum the signed prices to compute total capital gain or loss
+    result_df = stocks.groupby('stock_name', as_index=False).agg(capital_gain_loss=('signed_price', 'sum'))
+    
+    # I return the final result
+    return result_df
+
+# Intuition:
+# I need to compute the net profit or loss by treating Buy prices as negative and Sell prices as positive.
+# Summing these adjusted prices for each stock will give the total capital gain or loss.
+
+# Explanation:
+# I use `np.where` to create a 'signed_price' column where 'Buy' prices are multiplied by -1 and 'Sell' prices remain as is.
+# Then, I group the DataFrame by 'stock_name' and sum the 'signed_price' values.
+# This sum represents the net capital gain or loss for each stock.
+# I finally return this summarized DataFrame.
+
+# Solution 2
+
+import pandas as pd
+
+def capital_gainloss(stocks: pd.DataFrame) -> pd.DataFrame:
+    # I create a new column 'signed_price' using apply to assign negative price for 'Buy' and positive for 'Sell'
+    stocks['signed_price'] = stocks.apply(lambda row: -row['price'] if row['operation'] == 'Buy' else row['price'], axis=1)
+    
+    # I group by 'stock_name' and sum the signed prices to compute total capital gain or loss
+    result_df = stocks.groupby('stock_name', as_index=False).agg(capital_gain_loss=('signed_price', 'sum'))
+    
+    # I return the final result
+    return result_df
+
+# Intuition:
+# Similar to Solution 1 — I need to assign a sign to each price based on the operation type.
+# Summing these signed prices per stock will yield the total capital gain or loss.
+
+# Explanation:
+# I use `apply` with `axis=1` to iterate row-wise and assign a negative price for 'Buy' operations and a positive price for 'Sell'.
+# After creating the 'signed_price' column, I group the DataFrame by 'stock_name' and sum the 'signed_price' values.
+# This sum represents the net capital gain or loss for each stock.
+# The result is returned as a summarized DataFrame.