id int64 1 92 | question stringlengths 30 96 | expected_answer stringlengths 1 60 | code stringlengths 7 155 | category stringclasses 15 values | difficulty stringclasses 3 values |
|---|---|---|---|---|---|
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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