Step 5 - Extract reputation and warrants dataframes

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Step 5

Create Condition, ProductCost, and ProductPrice

Step 5.0: Create prepare_and_process function using helper function process_row

def prepare_and_process(df):
# Initialize Condition, ProductCost, and ProductPrice columns
df['Condition'] = None
df['ProductCost'] = None
df['ProductPrice'] = None

# Apply the helper processing function, which computes Condition, ProductCost, and ProductPrice for each producer
return df.apply(process_row, axis=1)

SStep 5.1: Apply prepare_and_process to create Condition, ProductCost, and ProductPrice and manually set Condition to 0 for reputation and 1 for warrants

df_reputation = prepare_and_process(df_reputation)
df_warrants = prepare_and_process(df_warrants)

df_reputation['Condition'] = 0
df_warrants['Condition'] = 1

Sampled columns

React Flow

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This step processes the dataframe to add three new columns in df_warrants and df_reputaton: Condition, ProductCost, and ProductPrice.

df_reputation = prepare_and_process(df_reputation) 
df_warrants = prepare_and_process(df_warrants)

Step 5.0: Create prepare_and_process function using helper function process_row

The prepare_and_process function initializes the following columns in the DataFrame:

• Condition: Indicates the market type (0 for reputation, 1 for warrants) this will be needed when we combine the two dataframes to df_ads.
• ProductCost: A list of costs per round based on product quality (low → 2, high → 6) using process_row function.
• ProductPrice: A list of prices per round based on whether the product is warranted (not warranted → 10, warranted → 12) using process_row function.

Condition: None
ProductCost: None
ProductPrice: None

Step 5.1: Process each row (process_row function)

The process_row helper function processes each row to compute the values for ProductCost, and ProductPrice: We create a loop depending on the length of lists of quality_list and warrant_list and then take each round’s product and based on the conditions below append new values to new columns.

• ProductCost: Computed based on productQuality: low → 2 high → 6
• ProductPrice: Computed based on warrants: True → 12 False → 10

Input:
productQuality: ["low", "high", "low"]
warrants: [True, False, True]

Output:
ProductCost: [2, 6, 2]
ProductPrice: [12, 10, 12]

Step 5.2: Apply prepare_and_process to create Condition, ProductCost, and ProductPrice and manually set Condition to 0 for reputation and 1 for warrants

The prepare_and_process function applies process_row to every row in the DataFrame, updating the columns.

Output:
productQuality: ["low", "high", "low"]
warrants: [True, False, True]
Condition: 1
ProductCost: [2, 6, 2]
ProductPrice: [12, 10, 12]

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