Fill NA with Pandas
Fill NAs in the PromoID column of the orders dataframe with "None":
orders['PromoID'].fillna('None',inplace=True)
Drop NAs with Pandas
Drop any rows from the orders dataframe where the ProductID column contains an NA:
orders['ProductID'].dropna(inplace=True)
Imputing Data
Replace NAs in the shipping_cost column with 3 if the retail price is greater then 30 otherwise replace with 0.
def impute_shipping_cost(row):
shipping_cost = row['Shipping_Cost']
price = row['Retail_Price']
if pd.isnull(shipping_cost):
if price > 30:
return 0
else:
return 3
else:
return shipping_cost
products['Shipping_Cost'] = products[['Retail_Price','Shipping_Cost']].apply(impute_shipping_cost, axis=1)
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