How I resolve this error? Do I need to Prompt different in query engine?

[mraguth]

This is my error and please help:

1;3;32mGot response: There was an error running the output as Python code. Error message: Execution of code containing references to private or dunder methods, disallowed builtins, or any imports, is forbidden!

[dosubot[bot]]

Could you provide the query or code snippet you're trying to run? This will help identify any private methods, dunder methods, disallowed builtins, or imports that might be causing the issue.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu This is the query I am using in my query engine:

Before you start the query process, please clear memory and make sure start the query process.
Here your objective is analyze {FCCTD_PATHWAY} Hub & SP & CPC and it's {PROCESS} all 'CPC Enrollment,Hub Enrollment, SP Enrollment' quarter over quarter of it's {Mean_TAT} and {Patients} metrics as a first step.
As a second step, come up with meaningful story points and compare each {PROCESS} like Hub with CPC and Hub with SP etc. while you are understanding {Mean_TAT} and {Patients} metrics for each {Year_QTR}
As a final step, write at stories what you learn about these insights with data points while bringing all {Year_QTR} mentioning in your trending stories.

[dosubot[bot]]

Could you provide the actual Python code that is being executed as part of your query? This will help identify any private methods, dunder methods, disallowed builtins, or imports that might be causing the error.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu

Expression:
**************************************************
** Response: **
assistant: import pandas as pd
import matplotlib.pyplot as plt

# Filter the dataframe for the specified FCCTD_PATHWAY and FCCTD_PROCESS
filtered_df = df[(df['FCCTD_PATHWAY'].isin(['Hub', 'SP', 'CPC'])) & 
                 (df['FCCTD_PROCESS'].isin(['CPC Enrollment', 'Hub Enrollment', 'SP Enrollment']))]

# Group by FCCTD_PATHWAY, FCCTD_PROCESS, and Year_QTR, then calculate the mean of Mean_TAT and sum of Count_Of_Patients
grouped_df = filtered_df.groupby(['FCCTD_PATHWAY', 'FCCTD_PROCESS', 'Year_QTR']).agg({'Mean_TAT': 'mean', 'Count_Of_Patients': 'sum'}).reset_index()

# Plotting Mean_TAT and Count_Of_Patients for each FCCTD_PROCESS over Year_QTR
fig, ax1 = plt.subplots(figsize=(12, 6))

color = 'tab:blue'
ax1.set_xlabel('Year_QTR')
ax1.set_ylabel('Mean_TAT', color=color)
for process in grouped_df['FCCTD_PROCESS'].unique():
    ax1.plot(grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Year_QTR'], 
             grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Mean_TAT'], 
             label=f'{process} Mean_TAT')
ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx()
color = 'tab:red'
ax2.set_ylabel('Count_Of_Patients', color=color)
for process in grouped_df['FCCTD_PROCESS'].unique():
    ax2.plot(grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Year_QTR'], 
             grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Count_Of_Patients'], 
             linestyle='dashed', label=f'{process} Count_Of_Patients')
ax2.tick_params(axis='y', labelcolor=color)

fig.tight_layout()
fig.legend(loc='upper left', bbox_to_anchor=(0.1, 0.9))
plt.title('Mean_TAT and Count_Of_Patients for each FCCTD_PROCESS over Year_QTR')
plt.show()

[dosubot[bot]]

The error you're encountering is due to the use of imports, which are restricted in the LlamaIndex query engine. Specifically, the imports of pandas and matplotlib.pyplot are causing the issue.

To resolve this, you need to remove the import statements and ensure that the code does not use any disallowed builtins or private/dunder methods. Here is a modified version of your code that avoids using imports:

# Assuming df is already defined and contains the necessary data

# Filter the dataframe for the specified FCCTD_PATHWAY and FCCTD_PROCESS
filtered_df = df[(df['FCCTD_PATHWAY'].isin(['Hub', 'SP', 'CPC'])) & 
                 (df['FCCTD_PROCESS'].isin(['CPC Enrollment', 'Hub Enrollment', 'SP Enrollment']))]

# Group by FCCTD_PATHWAY, FCCTD_PROCESS, and Year_QTR, then calculate the mean of Mean_TAT and sum of Count_Of_Patients
grouped_df = filtered_df.groupby(['FCCTD_PATHWAY', 'FCCTD_PROCESS', 'Year_QTR']).agg({'Mean_TAT': 'mean', 'Count_Of_Patients': 'sum'}).reset_index()

# Plotting Mean_TAT and Count_Of_Patients for each FCCTD_PROCESS over Year_QTR
fig, ax1 = plt.subplots(figsize=(12, 6))

color = 'tab:blue'
ax1.set_xlabel('Year_QTR')
ax1.set_ylabel('Mean_TAT', color=color)
for process in grouped_df['FCCTD_PROCESS'].unique():
    ax1.plot(grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Year_QTR'], 
             grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Mean_TAT'], 
             label=f'{process} Mean_TAT')
ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx()
color = 'tab:red'
ax2.set_ylabel('Count_Of_Patients', color=color)
for process in grouped_df['FCCTD_PROCESS'].unique():
    ax2.plot(grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Year_QTR'], 
             grouped_df[grouped_df['FCCTD_PROCESS'] == process]['Count_Of_Patients'], 
             linestyle='dashed', label=f'{process} Count_Of_Patients')
ax2.tick_params(axis='y', labelcolor=color)

fig.tight_layout()
fig.legend(loc='upper left', bbox_to_anchor=(0.1, 0.9))
plt.title('Mean_TAT and Count_Of_Patients for each FCCTD_PROCESS over Year_QTR')
plt.show()

Please ensure that the df DataFrame is already defined in the environment where this code is executed. This way, you avoid the need for imports and comply with the restrictions of the LlamaIndex query engine ^[1].

To continue talking to Dosu, mention @dosu.