Datasets:
STARXQ
/
FinTransEvalQA

Dataset card Data Studio Files
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int64
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92
question
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expected_answer
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code
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category
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15 values
difficulty
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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