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"id","question","expected_answer","code","category","difficulty"
1,"What is the total number of transactions in the dataset?","38000","len(df)","basic_statistics","easy"
2,"How many unique clients are in the dataset?","1218","df['client_id'].nunique()","basic_statistics","easy"
3,"How many unique cards are in the dataset?","3838","df['card_id'].nunique()","basic_statistics","easy"
4,"What is the average transaction amount?","43.07","df['amount'].mean()","basic_statistics","easy"
5,"What is the median transaction amount?","29.15","df['amount'].median()","basic_statistics","easy"
6,"What is the maximum transaction amount?","1773.35","df['amount'].max()","basic_statistics","easy"
7,"What is the minimum transaction amount?","-498.00","df['amount'].min()","basic_statistics","easy"
8,"What is the standard deviation of transaction amounts?","81.05","df['amount'].std()","basic_statistics","medium"
9,"How many transactions are made with Visa cards?","14249","len(df[df['card_brand'] == 'Visa'])","card_analysis","easy"
10,"How many transactions are made with Mastercard cards?","20405","len(df[df['card_brand'] == 'Mastercard'])","card_analysis","easy"
11,"Which card brand has the most transactions?","Mastercard","df['card_brand'].value_counts().index[0]","card_analysis","easy"
12,"What percentage of transactions use Swipe Transactions?","52.50%","(len(df[df['use_chip'] == 'Swipe Transaction']) / len(df) * 100)","card_analysis","medium"
13,"How many transactions are made with Amex cards?","2409","len(df[df['card_brand'] == 'Amex'])","card_analysis","easy"
14,"How many unique merchant cities are in the dataset?","3459","df['merchant_city'].nunique()","geographic","easy"
15,"Which merchant state has the most transactions?","CA","df['merchant_state'].value_counts().index[0]","geographic","easy"
16,"How many transactions have missing merchant_state information?","4390","df['merchant_state'].isna().sum()","geographic","medium"
17,"What is the most common merchant city?","ONLINE","df['merchant_city'].value_counts().index[0]","geographic","easy"
18,"How many transactions are labeled as fraudulent?","27","len(df[df['fraud_label'] == 'Yes'])","fraud_analysis","easy"
19,"How many transactions are not fraudulent?","25408","len(df[df['fraud_label'] == 'No'])","fraud_analysis","easy"
20,"What percentage of transactions are fraudulent?","0.11%","(len(df[df['fraud_label'] == 'Yes']) / len(df[df['fraud_label'].notna()]) * 100)","fraud_analysis","medium"
21,"How many transactions have missing fraud labels?","12565","df['fraud_label'].isna().sum()","fraud_analysis","easy"
22,"What is the average credit score in the dataset?","713.26","df['credit_score'].mean()","credit_analysis","easy"
23,"What is the maximum credit score?","850","int(df['credit_score'].max())","credit_analysis","easy"
24,"What is the minimum credit score?","488","int(df['credit_score'].min())","credit_analysis","easy"
25,"How many clients have a credit score above 750?","10492","len(df[df['credit_score'] > 750])","credit_analysis","medium"
26,"What is the average yearly income in the dataset?","46717.33","df['yearly_income'].mean()","income_analysis","easy"
27,"What is the average per capita income?","24003.13","df['per_capita_income'].mean()","income_analysis","easy"
28,"What is the maximum yearly income?","280199.00","df['yearly_income'].max()","income_analysis","easy"
29,"How many clients have yearly income greater than 50000?","11949","len(df[df['yearly_income'] > 50000])","income_analysis","medium"
30,"What is the average total debt in the dataset?","58032.68","df['total_debt'].mean()","debt_analysis","easy"
31,"What is the maximum total debt?","461854.00","df['total_debt'].max()","debt_analysis","easy"
32,"How many clients have total debt greater than 100000?","6866","len(df[df['total_debt'] > 100000])","debt_analysis","medium"
33,"What is the average credit limit?","15620.43","df['credit_limit'].mean()","credit_limit","easy"
34,"What is the maximum credit limit?","141391.00","df['credit_limit'].max()","credit_limit","easy"
35,"How many cards have credit limit of 0?","158","len(df[df['credit_limit'] == 0])","credit_limit","medium"
36,"What is the average age of clients?","54.09","df['current_age'].mean()","demographics","easy"
37,"What is the oldest client age?","101","int(df['current_age'].max())","demographics","easy"
38,"What is the youngest client age?","23","int(df['current_age'].min())","demographics","easy"
39,"How many clients are over 60 years old?","11986","len(df[df['current_age'] > 60])","demographics","medium"
40,"How many male clients are in the dataset?","18544","len(df[df['gender'] == 'Male'])","demographics","easy"
41,"How many female clients are in the dataset?","19456","len(df[df['gender'] == 'Female'])","demographics","easy"
42,"What is the gender ratio (Male:Female)?","18544:19456","f""{len(df[df['gender'] == 'Male'])}:{len(df[df['gender'] == 'Female'])}""","demographics","medium"
43,"How many unique merchant categories are in the dataset?","104","df['mcc_description'].nunique()","merchant","easy"
44,"What is the most common merchant category?","Grocery Stores, Supermarkets","df['mcc_description'].value_counts().index[0]","merchant","easy"
45,"How many transactions are for Eating Places and Restaurants?","2972","len(df[df['mcc_description'] == 'Eating Places and Restaurants'])","merchant","medium"
46,"How many Debit card transactions are there?","23789","len(df[df['card_type'] == 'Debit'])","card_analysis","easy"
47,"How many Credit card transactions are there?","11658","len(df[df['card_type'] == 'Credit'])","card_analysis","easy"
48,"What is the most common card type?","Debit","df['card_type'].value_counts().index[0]","card_analysis","easy"
49,"How many Online transactions are there?","4371","len(df[df['use_chip'] == 'Online Transaction'])","transaction_type","easy"
50,"How many Chip transactions are there?","13680","len(df[df['use_chip'] == 'Chip Transaction'])","transaction_type","easy"
51,"What percentage of transactions are Online?","11.50%","(len(df[df['use_chip'] == 'Online Transaction']) / len(df) * 100)","transaction_type","medium"
52,"How many transactions have errors?","614","df['errors'].notna().sum()","error_analysis","easy"
53,"What percentage of transactions have errors?","1.62%","(df['errors'].notna().sum() / len(df) * 100)","error_analysis","medium"
54,"What is the most common error type?","Insufficient Balance","df['errors'].value_counts().index[0]","error_analysis","medium"
55,"What is the earliest transaction date?","2010-01-01","df['transaction_date'].min().split()[0]","temporal","easy"
56,"What is the latest transaction date?","2019-10-31","df['transaction_date'].max().split()[0]","temporal","easy"
57,"How many Visa card transactions are fraudulent?","13","len(df[(df['card_brand'] == 'Visa') & (df['fraud_label'] == 'Yes')])","complex_query","medium"
58,"How many clients have more than 3 credit cards?","21850","len(df[df['num_credit_cards'] > 3])","complex_query","medium"
59,"How many transactions are made with cards that have chips?","34242","len(df[df['has_chip'] == 'YES'])","card_analysis","easy"
60,"What percentage of cards have EMV chips?","90.11%","(len(df[df['has_chip'] == 'YES']) / len(df) * 100)","card_analysis","medium"
61,"How many transactions occurred in Texas (TX)?","2841","len(df[df['merchant_state'] == 'TX'])","geographic","medium"
62,"How many retired clients (age > 65) are in the dataset?","8485","len(df[df['current_age'] > 65])","demographics","medium"
63,"How many clients are issued exactly 2 cards?","19330","len(df[df['num_cards_issued'] == 2])","card_analysis","medium"
64,"What is the most common number of credit cards clients have?","4","int(df['num_credit_cards'].mode()[0])","demographics","medium"
65,"How many unique merchant IDs are in the dataset?","6183","df['merchant_id'].nunique()","merchant","easy"
66,"What is the average number of cards issued per client?","1.52","df['num_cards_issued'].mean()","card_analysis","medium"
67,"How many transactions have negative amounts?","1925","len(df[df['amount'] < 0])","data_quality","medium"
68,"How many transactions are from clients born in the 1960s?","8823","len(df[(df['birth_year'] >= 1960) & (df['birth_year'] < 1970)])","demographics","medium"
69,"What is the latitude range for client addresses?","21.30 to 48.53","f""{df['latitude'].min():.2f} to {df['latitude'].max():.2f}""","geographic","medium"
70,"What is the longitude range for client addresses?","-158.18 to -68.67","f""{df['longitude'].min():.2f} to {df['longitude'].max():.2f}""","geographic","medium"
71,"How many transactions involve clients with credit score above 700 AND yearly income above 50000?","7322","len(df[(df['credit_score'] > 700) & (df['yearly_income'] > 50000)])","complex_query","hard"
72,"What is the average transaction amount for fraudulent transactions?","80.78","df[df['fraud_label'] == 'Yes']['amount'].mean()","complex_query","hard"
73,"What is the average transaction amount for non-fraudulent transactions?","42.94","df[df['fraud_label'] == 'No']['amount'].mean()","complex_query","hard"
74,"What is the average credit limit for clients with high debt (>100000)?","22458.09","df[df['total_debt'] > 100000]['credit_limit'].mean()","complex_query","hard"
75,"What percentage of the dataset has missing values?","3.98%","(df.isna().sum().sum() / (len(df) * len(df.columns)) * 100)","data_quality","hard"
76,"What is the average amount for transactions in Texas?","45.15","df[df['merchant_state'] == 'TX']['amount'].mean()","geographic","hard"
77,"What is the debt-to-income ratio for the average client?","1.24","df['total_debt'].mean() / df['yearly_income'].mean()","complex_query","hard"
78,"How many transactions were made by clients older than the median age?","18121","len(df[df['current_age'] > df['current_age'].median()])","demographics","hard"
79,"What is the correlation between credit score and yearly income?","-0.0329","df['credit_score'].corr(df['yearly_income'])","complex_query","hard"
80,"How many transactions involve amounts greater than 2 standard deviations from the mean?","1094","len(df[np.abs(df['amount'] - df['amount'].mean()) > 2 * df['amount'].std()])","complex_query","hard"
81,"What is the fraud rate for clients with credit score below 650?","0.04%","(len(df[(df['credit_score'] < 650) & (df['fraud_label'] == 'Yes')]) / len(df[df['credit_score'] < 650]) * 100)","fraud_analysis","hard"
82,"How many transactions are from the top 10 merchant cities by volume?","6563","len(df[df['merchant_city'].isin(df['merchant_city'].value_counts().head(10).index)])","merchant","hard"
83,"What is the average transaction amount for each card brand?","Visa: 41.17, Mastercard: 43.79, Amex: 46.48, Discover: 44.14","df.groupby('card_brand')['amount'].mean().round(2).to_dict()","complex_query","hard"
84,"What percentage of fraudulent transactions use Online payment method?","40.74%","(len(df[(df['fraud_label'] == 'Yes') & (df['use_chip'] == 'Online Transaction')]) / len(df[df['fraud_label'] == 'Yes']) * 100)","fraud_analysis","hard"
85,"What is the average credit score for clients with fraudulent transactions?","718.04","df[df['fraud_label'] == 'Yes']['credit_score'].mean()","fraud_analysis","hard"
86,"How does average transaction amount vary by card type?","Debit: 42.51, Credit: 43.91, Debit (Prepaid): 45.32","df.groupby('card_type')['amount'].mean().round(2).to_dict()","complex_query","hard"
87,"What percentage of clients with total debt > yearly income exist?","1825.86%","len(df[df['total_debt'] > df['yearly_income']]) / len(df.drop_duplicates('client_id')) * 100","complex_query","hard"
88,"What is the median transaction amount by fraud status?","Fraudulent: 27.50, Non-Fraudulent: 29.28","df.groupby('fraud_label')['amount'].median().round(2).to_dict()","fraud_analysis","hard"
89,"How many transactions exceed the typical transaction amount by more than 3 standard deviations?","465","len(df[df['amount'] > df['amount'].mean() + 3 * df['amount'].std()])","data_quality","hard"
90,"What is the relationship between number of credit cards and fraud rate?","Requires groupby analysis","df.groupby('num_credit_cards').apply(lambda x: (x['fraud_label'] == 'Yes').sum() / x['fraud_label'].notna().sum() * 100)","complex_query","hard"
91,"Which states have the highest average transaction amount?","Requires top states analysis","df.groupby('merchant_state')['amount'].mean().nlargest(5)","geographic","hard"
92,"What is the average age difference between clients with high vs low debt?","-19.85","df[df['total_debt'] > df['total_debt'].quantile(0.75)]['current_age'].mean() - df[df['total_debt'] < df['total_debt'].quantile(0.25)]['current_age'].mean()","demographics","hard"
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