Query stringlengths 21 133 ⌀ | pandas_code stringlengths 4 599 ⌀ | english stringlengths 21 258 ⌀ | intent stringclasses 6
values | operation stringclasses 10
values | code stringclasses 122
values |
|---|---|---|---|---|---|
Show total number of claims in the dataset. | len(df) | null | null | null | null |
Display the list of all unique providers. | df['Provider_Name'].unique() | null | null | null | null |
How many claim types are available? | df['Claim_Type'].nunique() | null | null | null | null |
List all products covered in this dataset. | df['Product'].unique() | null | null | null | null |
Show number of claims per status. | df['Status'].value_counts() | null | null | null | null |
List all unique schemes available. | df['Scheme'].unique() | null | null | null | null |
Show count of claims by each provider. | df['Provider_Name'].value_counts() | null | null | null | null |
Show total Approved_Amount per Provider_Name. | df.groupby('Provider_Name')['Approved_Amount'].sum() | null | null | null | null |
Average Claim_Amount for each Product. | df.groupby('Product')['Claim_Amount'].mean() | null | null | null | null |
Highest Approved_Amount among all claims. | df['Approved_Amount'].max() | null | null | null | null |
Minimum Deduction_Amount by Product. | df.groupby('Product')['Deduction_Amount'].min() | null | null | null | null |
Sum of Deduction_Amount by Status. | df.groupby('Status')['Deduction_Amount'].sum() | null | null | null | null |
Show average Approved_Amount by Scheme. | df.groupby('Scheme')['Approved_Amount'].mean() | null | null | null | null |
Calculate how many days between Loss_Date and Claim_Created_Date for each claim. | (df['Claim_Created_Date'] - df['Loss_Date']).dt.days | null | null | null | null |
Find the claim with the longest duration between loss and claim creation. | df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()] | null | null | null | null |
Show average delay between Loss_Date and Claim_Created_Date. | (df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean() | null | null | null | null |
Which claim has the shortest duration between loss and creation? | df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmin()] | null | null | null | null |
Which claims have missing Approved_Amount? | df[df['Approved_Amount'].isnull()] | null | null | null | null |
Count of records missing Loss_Date. | df['Loss_Date'].isnull().sum() | null | null | null | null |
List columns with missing values. | df.isnull().sum() | null | null | null | null |
Which column has the most missing values? | df.isnull().sum().idxmax() | null | null | null | null |
Show total missing values in dataset. | df.isnull().sum().sum() | null | null | null | null |
Calculate claim balance as Claim_Amount minus Approved_Amount. | df['Claim_Amount'] - df['Approved_Amount'] | null | null | null | null |
Show claims where Deduction_Amount is more than 10 percent of Claim_Amount. | df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']] | null | null | null | null |
Find average difference between Claim_Amount and Approved_Amount by Product. | (df['Claim_Amount'] - df['Approved_Amount']).groupby(df['Product']).mean() | null | null | null | null |
List claims where Approved_Amount equals Claim_Amount. | df[df['Approved_Amount'] == df['Claim_Amount']] | null | null | null | null |
Which product has higher total Approved_Amount? | df.groupby('Product')['Approved_Amount'].sum().idxmax() | null | null | null | null |
Which provider processed the fewest claims? | df['Provider_Name'].value_counts().idxmin() | null | null | null | null |
Which status type has the most claims? | df['Status'].value_counts().idxmax() | null | null | null | null |
Which product has the lowest average Approved_Amount? | df.groupby('Product')['Approved_Amount'].mean().idxmin() | null | null | null | null |
What is the median Claim_Amount? | df['Claim_Amount'].median() | null | null | null | null |
Find standard deviation of Approved_Amount. | df['Approved_Amount'].std() | null | null | null | null |
Is there a correlation between Claim_Amount and Approved_Amount? | df[['Claim_Amount','Approved_Amount']].corr() | null | null | null | null |
Show variance of Deduction_Amount. | df['Deduction_Amount'].var() | null | null | null | null |
Find 90th percentile of Claim_Amount. | df['Claim_Amount'].quantile(0.9) | null | null | null | null |
List all unique patient names. | df['Patient_Name'].unique() | null | null | null | null |
How many claims belong to Policy_Number 12345? | df[df['Policy_Number'] == '12345'].shape[0] | null | null | null | null |
Show claims for Policy_Number 54321. | df[df['Policy_Number'] == '54321'] | null | null | null | null |
Show details of Claim_Number C1234. | df[df['Claim_Number'] == 'C1234'] | null | null | null | null |
Get all claims for Provider_Code P5678. | df[df['Provider_Code'] == 'P5678'] | null | null | null | null |
Find claim details where Claim_Id equals 10. | df[df['Claim_Id'] == 10] | null | null | null | null |
What columns are available in this dataset? | list(df.columns) | null | null | null | null |
How many records does the dataset have? | len(df) | null | null | null | null |
Describe the dataset structure. | df.info() | null | null | null | null |
Show first five records of dataset. | df.head() | null | null | null | null |
Show last ten records. | df.tail(10) | null | null | null | null |
Get total number of claims in the dataset. | len(df) | null | null | null | null |
Retrieve the list of all unique providers. | df['Provider_Name'].unique() | null | null | null | null |
Get the list of all unique providers. | df['Provider_Name'].unique() | null | null | null | null |
Display many claim types are available? | df['Claim_Type'].nunique() | null | null | null | null |
Fetch many claim types are available? | df['Claim_Type'].nunique() | null | null | null | null |
Retrieve all products covered in this dataset. | df['Product'].unique() | null | null | null | null |
Provide number of claims per status. | df['Status'].value_counts() | null | null | null | null |
Retrieve all unique schemes available. | df['Scheme'].unique() | null | null | null | null |
Provide all unique schemes available. | df['Scheme'].unique() | null | null | null | null |
Provide count of claims by each provider. | df['Provider_Name'].value_counts() | null | null | null | null |
Provide total Approved_Amount per Provider_Name. | df.groupby('Provider_Name')['Approved_Amount'].sum() | null | null | null | null |
Retrieve total Approved_Amount per Provider_Name. | df.groupby('Provider_Name')['Approved_Amount'].sum() | null | null | null | null |
Provide Claim_Amount for each Product. | df.groupby('Product')['Claim_Amount'].mean() | null | null | null | null |
Retrieve Approved_Amount among all claims. | df['Approved_Amount'].max() | null | null | null | null |
Fetch Deduction_Amount by Product. | df.groupby('Product')['Deduction_Amount'].min() | null | null | null | null |
Display Deduction_Amount by Product. | df.groupby('Product')['Deduction_Amount'].min() | null | null | null | null |
Retrieve of Deduction_Amount by Status. | df.groupby('Status')['Deduction_Amount'].sum() | null | null | null | null |
Get of Deduction_Amount by Status. | df.groupby('Status')['Deduction_Amount'].sum() | null | null | null | null |
Fetch average Approved_Amount by Scheme. | df.groupby('Scheme')['Approved_Amount'].mean() | null | null | null | null |
Retrieve average Approved_Amount by Scheme. | df.groupby('Scheme')['Approved_Amount'].mean() | null | null | null | null |
List how many days between Loss_Date and Claim_Created_Date for each claim. | (df['Claim_Created_Date'] - df['Loss_Date']).dt.days | null | null | null | null |
Retrieve how many days between Loss_Date and Claim_Created_Date for each claim. | (df['Claim_Created_Date'] - df['Loss_Date']).dt.days | null | null | null | null |
Show the claim with the longest duration between loss and claim creation. | df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()] | null | null | null | null |
Retrieve the claim with the longest duration between loss and claim creation. | df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()] | null | null | null | null |
Fetch average delay between Loss_Date and Claim_Created_Date. | (df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean() | null | null | null | null |
List claim has the shortest duration between loss and creation? | df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmin()] | null | null | null | null |
Provide claim has the shortest duration between loss and creation? | df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmin()] | null | null | null | null |
Display claims have missing Approved_Amount? | df[df['Approved_Amount'].isnull()] | null | null | null | null |
Get claims have missing Approved_Amount? | df[df['Approved_Amount'].isnull()] | null | null | null | null |
Show of records missing Loss_Date. | df['Loss_Date'].isnull().sum() | null | null | null | null |
Display of records missing Loss_Date. | df['Loss_Date'].isnull().sum() | null | null | null | null |
Show columns with missing values. | df.isnull().sum() | null | null | null | null |
Retrieve column has the most missing values? | df.isnull().sum().idxmax() | null | null | null | null |
Show column has the most missing values? | df.isnull().sum().idxmax() | null | null | null | null |
List total missing values in dataset. | df.isnull().sum().sum() | null | null | null | null |
Get total missing values in dataset. | df.isnull().sum().sum() | null | null | null | null |
Fetch claim balance as Claim_Amount minus Approved_Amount. | df['Claim_Amount'] - df['Approved_Amount'] | null | null | null | null |
Provide claim balance as Claim_Amount minus Approved_Amount. | df['Claim_Amount'] - df['Approved_Amount'] | null | null | null | null |
List claims where Deduction_Amount is more than 10 percent of Claim_Amount. | df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']] | null | null | null | null |
Fetch claims where Deduction_Amount is more than 10 percent of Claim_Amount. | df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']] | null | null | null | null |
Fetch average difference between Claim_Amount and Approved_Amount by Product. | (df['Claim_Amount'] - df['Approved_Amount']).groupby(df['Product']).mean() | null | null | null | null |
Display average difference between Claim_Amount and Approved_Amount by Product. | (df['Claim_Amount'] - df['Approved_Amount']).groupby(df['Product']).mean() | null | null | null | null |
Provide claims where Approved_Amount equals Claim_Amount. | df[df['Approved_Amount'] == df['Claim_Amount']] | null | null | null | null |
Fetch claims where Approved_Amount equals Claim_Amount. | df[df['Approved_Amount'] == df['Claim_Amount']] | null | null | null | null |
Provide product has higher total Approved_Amount? | df.groupby('Product')['Approved_Amount'].sum().idxmax() | null | null | null | null |
Provide provider processed the fewest claims? | df['Provider_Name'].value_counts().idxmin() | null | null | null | null |
Get provider processed the fewest claims? | df['Provider_Name'].value_counts().idxmin() | null | null | null | null |
Display status type has the most claims? | df['Status'].value_counts().idxmax() | null | null | null | null |
Show status type has the most claims? | df['Status'].value_counts().idxmax() | null | null | null | null |
Show product has the lowest average Approved_Amount? | df.groupby('Product')['Approved_Amount'].mean().idxmin() | null | null | null | null |
Provide product has the lowest average Approved_Amount? | df.groupby('Product')['Approved_Amount'].mean().idxmin() | null | null | null | null |
Show is the median Claim_Amount? | df['Claim_Amount'].median() | null | null | null | null |
List is the median Claim_Amount? | df['Claim_Amount'].median() | null | null | null | null |
Fetch standard deviation of Approved_Amount. | df['Approved_Amount'].std() | null | null | null | null |
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