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pandasai.log
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2024-04-29 18:03:07 [INFO] Question: Show the tweets with the anger flag True and how many of them there are
2024-04-29 18:03:07 [INFO] Running PandasAI with bamboo_llm LLM...
2024-04-29 18:03:07 [INFO] Prompt ID: 0e030ea9-9115-4116-ac0d-096e9ab1ec5c
2024-04-29 18:03:07 [INFO] Executing Pipeline: GenerateChatPipeline
2024-04-29 18:03:07 [INFO] Executing Step 0: ValidatePipelineInput
2024-04-29 18:03:07 [INFO] Executing Step 1: CacheLookup
2024-04-29 18:03:07 [INFO] Executing Step 2: PromptGeneration
2024-04-29 18:03:09 [INFO] Using prompt: <dataframe>
dfs[0]:3561x13
ID,Tweet,anger,anticipation,disgust,fear,joy,love,optimism,pessimism,sadness,surprise,trust
2018-Es-00801,"Nací con el viejo Linkin Park, y a pesar de que ya no era partídiario de su nuevo sonido, siempre los admiré como banda. Descansa (1/2)",False,False,False,True,True,False,True,False,False,True,True
2018-Es-04874,Terrible juego el Half-Life,True,True,False,True,True,False,False,False,False,True,False
2018-Es-05562,"@Profeco buenas noches, donde podria levantar una queja de un equipaje dañado que no me quiere pagar @viajaVolaris? Tengo todas las pruebas!",True,False,True,False,False,True,False,True,True,False,False
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
### QUERY
Show the tweets with the anger flag True and how many of them there are
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-04-29 18:03:09 [INFO] Executing Step 3: CodeGenerator
2024-04-29 18:03:12 [INFO] Prompt used:
None
2024-04-29 18:03:12 [INFO] Code generated:
```
# TODO: import the required dependencies
import pandas as pd
# Get tweets with anger flag True
anger_tweets = [df[df['anger'] == True] for df in dfs]
total_angry_tweets = sum([len(tweets) for tweets in anger_tweets])
result = {
"type": "dataframe",
"value": pd.concat(anger_tweets),
}
total_angry_tweets
```
2024-04-29 18:03:12 [INFO] Executing Step 4: CachePopulation
2024-04-29 18:03:12 [INFO] Executing Step 5: CodeCleaning
2024-04-29 18:03:12 [INFO]
Code running:
```
anger_tweets = [df[df['anger'] == True] for df in dfs]
total_angry_tweets = sum([len(tweets) for tweets in anger_tweets])
result = {'type': 'dataframe', 'value': pd.concat(anger_tweets)}
total_angry_tweets
```
2024-04-29 18:03:12 [INFO] Executing Step 6: CodeExecution
2024-04-29 18:03:12 [INFO] Executing Step 7: ResultValidation
2024-04-29 18:03:12 [INFO] Answer: {'type': 'dataframe', 'value': ID Tweet anger \
2 2018-Es-05379 @audiomano A mí tampoco me agrado mucho eso. E... True
4 2018-Es-01385 @DalasReview me encanta la terrible hipocresia... True
7 2018-Es-02108 @tctelevision @calle7tc Terrible esto! True
11 2018-Es-04296 Qué perra rabia cuando tienes sueño pero no pu... True
12 2018-Es-06944 Le dije por su nombre y cerró la cuenta 😆 True
... ... ... ...
3549 2018-Es-01947 me siento super miserable ahorita porque soy u... True
3552 2018-Es-01700 "Hombres cuya única preocupación pasa por si s... True
3555 2018-Es-00840 @BursiFrancisco sos amargo jugando True
3557 2018-Es-00439 El mayor criminal del país diciéndole “delincu... True
3558 2018-Es-04919 Mi prima de 4 años se ha enfadado conmigo porq... True
anticipation disgust fear joy love optimism pessimism \
2 False False False False False False False
4 False True False False False False False
7 False True False False False False False
11 False False False False False False False
12 False False False True False False False
... ... ... ... ... ... ... ...
3549 False False False False False False True
3552 False True False False False False True
3555 False True False False False False False
3557 False True False False False False False
3558 False False False False False False False
sadness surprise trust
2 False False False
4 False False False
7 True False False
11 False False False
12 True False False
... ... ... ...
3549 True False False
3552 False False False
3555 False False False
3557 False False False
3558 False True False
[1155 rows x 13 columns]}
2024-04-29 18:03:12 [INFO] Executing Step 8: ResultParsing
2024-05-02 11:43:54 [INFO] Question: Show the tweets with the anger flag True and how many of them there are
2024-05-02 11:43:54 [INFO] Running PandasAI with bamboo_llm LLM...
2024-05-02 11:43:54 [INFO] Prompt ID: 7dc03df6-7ea8-43ff-b372-7963e6429723
2024-05-02 11:43:54 [INFO] Executing Pipeline: GenerateChatPipeline
2024-05-02 11:43:54 [INFO] Executing Step 0: ValidatePipelineInput
2024-05-02 11:43:54 [INFO] Executing Step 1: CacheLookup
2024-05-02 11:43:54 [INFO] Using cached response
2024-05-02 11:43:54 [INFO] Executing Step 2: PromptGeneration
2024-05-02 11:43:54 [INFO] Executing Step 2: Skipping...
2024-05-02 11:43:54 [INFO] Executing Step 3: CodeGenerator
2024-05-02 11:43:54 [INFO] Executing Step 3: Skipping...
2024-05-02 11:43:54 [INFO] Executing Step 4: CachePopulation
2024-05-02 11:43:54 [INFO] Executing Step 4: Skipping...
2024-05-02 11:43:54 [INFO] Executing Step 5: CodeCleaning
2024-05-02 11:43:54 [INFO]
Code running:
```
anger_tweets = [df[df['anger'] == True] for df in dfs]
total_angry_tweets = sum([len(tweets) for tweets in anger_tweets])
result = {'type': 'dataframe', 'value': pd.concat(anger_tweets)}
total_angry_tweets
```
2024-05-02 11:43:54 [INFO] Executing Step 6: CodeExecution
2024-05-02 11:43:55 [INFO] Executing Step 7: ResultValidation
2024-05-02 11:43:55 [INFO] Answer: {'type': 'dataframe', 'value': ID Tweet anger \
2 2018-Es-05379 @audiomano A mí tampoco me agrado mucho eso. E... True
4 2018-Es-01385 @DalasReview me encanta la terrible hipocresia... True
7 2018-Es-02108 @tctelevision @calle7tc Terrible esto! True
11 2018-Es-04296 Qué perra rabia cuando tienes sueño pero no pu... True
12 2018-Es-06944 Le dije por su nombre y cerró la cuenta 😆 True
... ... ... ...
3549 2018-Es-01947 me siento super miserable ahorita porque soy u... True
3552 2018-Es-01700 "Hombres cuya única preocupación pasa por si s... True
3555 2018-Es-00840 @BursiFrancisco sos amargo jugando True
3557 2018-Es-00439 El mayor criminal del país diciéndole “delincu... True
3558 2018-Es-04919 Mi prima de 4 años se ha enfadado conmigo porq... True
anticipation disgust fear joy love optimism pessimism \
2 False False False False False False False
4 False True False False False False False
7 False True False False False False False
11 False False False False False False False
12 False False False True False False False
... ... ... ... ... ... ... ...
3549 False False False False False False True
3552 False True False False False False True
3555 False True False False False False False
3557 False True False False False False False
3558 False False False False False False False
sadness surprise trust
2 False False False
4 False False False
7 True False False
11 False False False
12 True False False
... ... ... ...
3549 True False False
3552 False False False
3555 False False False
3557 False False False
3558 False True False
[1155 rows x 13 columns]}
2024-05-02 11:43:55 [INFO] Executing Step 8: ResultParsing
2024-05-02 19:36:37 [INFO] Question: hello hello it's me Alex it's taken of me and can you talk to me about these signal processing please
2024-05-02 19:36:37 [INFO] Running PandasAI with bamboo_llm LLM...
2024-05-02 19:36:37 [INFO] Prompt ID: 4e95101a-dfd5-4238-8517-dae02527404a
2024-05-02 19:36:37 [INFO] Executing Pipeline: GenerateChatPipeline
2024-05-02 19:36:37 [INFO] Executing Step 0: ValidatePipelineInput
2024-05-02 19:36:37 [INFO] Executing Step 1: CacheLookup
2024-05-02 19:36:37 [INFO] Executing Step 2: PromptGeneration
2024-05-02 19:36:39 [INFO] Using prompt: <dataframe>
dfs[0]:1x0
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
### QUERY
hello hello it's me Alex it's taken of me and can you talk to me about these signal processing please
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-05-02 19:36:39 [INFO] Executing Step 3: CodeGenerator
2024-05-02 19:36:42 [INFO] Prompt used:
None
2024-05-02 19:36:42 [INFO] Code generated:
```
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result variable as a dictionary with type and value keys
result = {
"type": "string",
"value": "Hello Alex, sure I can talk to you about signal processing. Signal processing is a technique used to analyze, modify, and synthesize signals such as sound, images, and sensor data. It involves various operations like filtering, noise reduction, compression, and feature extraction. Is there anything specific you would like to know about signal processing?"
}
```
2024-05-02 19:36:42 [INFO] Executing Step 4: CachePopulation
2024-05-02 19:36:42 [INFO] Executing Step 5: CodeCleaning
2024-05-02 19:36:42 [INFO]
Code running:
```
result = {'type': 'string', 'value': 'Hello Alex, sure I can talk to you about signal processing. Signal processing is a technique used to analyze, modify, and synthesize signals such as sound, images, and sensor data. It involves various operations like filtering, noise reduction, compression, and feature extraction. Is there anything specific you would like to know about signal processing?'}
```
2024-05-02 19:36:42 [INFO] Executing Step 6: CodeExecution
2024-05-02 19:36:42 [INFO] Executing Step 7: ResultValidation
2024-05-02 19:36:42 [INFO] Answer: {'type': 'string', 'value': 'Hello Alex, sure I can talk to you about signal processing. Signal processing is a technique used to analyze, modify, and synthesize signals such as sound, images, and sensor data. It involves various operations like filtering, noise reduction, compression, and feature extraction. Is there anything specific you would like to know about signal processing?'}
2024-05-02 19:36:42 [INFO] Executing Step 8: ResultParsing
2024-05-02 19:36:54 [INFO] Question: hello hello it's me Alex it's taken of me and can you talk to me about these signal processing please
2024-05-02 19:36:54 [INFO] Running PandasAI with bamboo_llm LLM...
2024-05-02 19:36:54 [INFO] Prompt ID: 81aba42e-1f94-4c8e-aad4-c92b1492cbe6
2024-05-02 19:36:54 [INFO] Executing Pipeline: GenerateChatPipeline
2024-05-02 19:36:54 [INFO] Executing Step 0: ValidatePipelineInput
2024-05-02 19:36:54 [INFO] Executing Step 1: CacheLookup
2024-05-02 19:36:54 [INFO] Using cached response
2024-05-02 19:36:54 [INFO] Executing Step 2: PromptGeneration
2024-05-02 19:36:54 [INFO] Executing Step 2: Skipping...
2024-05-02 19:36:54 [INFO] Executing Step 3: CodeGenerator
2024-05-02 19:36:54 [INFO] Executing Step 3: Skipping...
2024-05-02 19:36:54 [INFO] Executing Step 4: CachePopulation
2024-05-02 19:36:54 [INFO] Executing Step 4: Skipping...
2024-05-02 19:36:54 [INFO] Executing Step 5: CodeCleaning
2024-05-02 19:36:54 [INFO]
Code running:
```
result = {'type': 'string', 'value': 'Hello Alex, sure I can talk to you about signal processing. Signal processing is a technique used to analyze, modify, and synthesize signals such as sound, images, and sensor data. It involves various operations like filtering, noise reduction, compression, and feature extraction. Is there anything specific you would like to know about signal processing?'}
```
2024-05-02 19:36:54 [INFO] Executing Step 6: CodeExecution
2024-05-02 19:36:54 [INFO] Executing Step 7: ResultValidation
2024-05-02 19:36:54 [INFO] Answer: {'type': 'string', 'value': 'Hello Alex, sure I can talk to you about signal processing. Signal processing is a technique used to analyze, modify, and synthesize signals such as sound, images, and sensor data. It involves various operations like filtering, noise reduction, compression, and feature extraction. Is there anything specific you would like to know about signal processing?'}
2024-05-02 19:36:54 [INFO] Executing Step 8: ResultParsing
2024-05-02 19:36:58 [INFO] Question: hello hello it's me Alex it's taken of me and can you talk to me about these signal processing please
2024-05-02 19:36:58 [INFO] Running PandasAI with bamboo_llm LLM...
2024-05-02 19:36:58 [INFO] Prompt ID: eb04447d-8dc7-4a47-895d-2bd967931ed2
2024-05-02 19:36:58 [INFO] Executing Pipeline: GenerateChatPipeline
2024-05-02 19:36:58 [INFO] Executing Step 0: ValidatePipelineInput
2024-05-02 19:36:58 [INFO] Executing Step 1: CacheLookup
2024-05-02 19:36:58 [INFO] Using cached response
2024-05-02 19:36:58 [INFO] Executing Step 2: PromptGeneration
2024-05-02 19:36:58 [INFO] Executing Step 2: Skipping...
2024-05-02 19:36:58 [INFO] Executing Step 3: CodeGenerator
2024-05-02 19:36:58 [INFO] Executing Step 3: Skipping...
2024-05-02 19:36:58 [INFO] Executing Step 4: CachePopulation
2024-05-02 19:36:58 [INFO] Executing Step 4: Skipping...
2024-05-02 19:36:58 [INFO] Executing Step 5: CodeCleaning
2024-05-02 19:36:58 [INFO]
Code running:
```
result = {'type': 'string', 'value': 'Hello Alex, sure I can talk to you about signal processing. Signal processing is a technique used to analyze, modify, and synthesize signals such as sound, images, and sensor data. It involves various operations like filtering, noise reduction, compression, and feature extraction. Is there anything specific you would like to know about signal processing?'}
```
2024-05-02 19:36:58 [INFO] Executing Step 6: CodeExecution
2024-05-02 19:36:58 [INFO] Executing Step 7: ResultValidation
2024-05-02 19:36:58 [INFO] Answer: {'type': 'string', 'value': 'Hello Alex, sure I can talk to you about signal processing. Signal processing is a technique used to analyze, modify, and synthesize signals such as sound, images, and sensor data. It involves various operations like filtering, noise reduction, compression, and feature extraction. Is there anything specific you would like to know about signal processing?'}
2024-05-02 19:36:58 [INFO] Executing Step 8: ResultParsing
2024-05-02 19:41:28 [INFO] Question: can you make a plot about the average salary in it companies
2024-05-02 19:41:28 [INFO] Running PandasAI with bamboo_llm LLM...
2024-05-02 19:41:28 [INFO] Prompt ID: 5554f6b9-bcb2-4e28-9b07-bd846f98fdf0
2024-05-02 19:41:28 [INFO] Executing Pipeline: GenerateChatPipeline
2024-05-02 19:41:28 [INFO] Executing Step 0: ValidatePipelineInput
2024-05-02 19:41:28 [INFO] Executing Step 1: CacheLookup
2024-05-02 19:41:28 [INFO] Executing Step 2: PromptGeneration
2024-05-02 19:41:29 [INFO] Using prompt: <dataframe>
dfs[0]:1x0
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
You can utilize these examples as a reference for generating code.
Q: What are the top 5 countries by GDP?
A:
import pandas as pd
df = dfs[0]
# Convert GDP column to numeric values
df['gdp'] = pd.to_numeric(df['gdp'])
# Sort the DataFrame by GDP in descending order
sorted_df = df.sort_values(by='gdp', ascending=False)
# Get the top 5 countries by GDP
top_5_countries = sorted_df.head(5)
# Declare result variable
result = {
"type": "dataframe",
"value": top_5_countries
}
### QUERY
can you make a plot about the average salary in it companies
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-05-02 19:41:29 [INFO] Executing Step 3: CodeGenerator
2024-05-02 19:41:35 [INFO] Prompt used:
None
2024-05-02 19:41:35 [INFO] Code generated:
```
# TODO: import the required dependencies
import pandas as pd
import matplotlib.pyplot as plt
# Assuming you have a DataFrame with IT company salaries named 'it_salaries_df'
# Calculate average salary in IT companies
average_salary = it_salaries_df['salary'].mean()
# Create a bar plot to visualize the average salary in IT companies
plt.figure(figsize=(10, 6))
plt.bar('Average Salary', average_salary, color='skyblue')
plt.xlabel('IT Companies')
plt.ylabel('Average Salary')
plt.title('Average Salary in IT Companies')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
# Save the plot as a PNG file
plt.savefig('average_salary_in_it_companies.png')
# Declare result variable
result = {
"type": "plot",
"value": "average_salary_in_it_companies.png"
}
```
2024-05-02 19:41:35 [INFO] Executing Step 4: CachePopulation
2024-05-02 19:41:35 [INFO] Executing Step 5: CodeCleaning
2024-05-02 19:41:35 [INFO] Saving charts to /Users/alejandro/Documents/Repositorios/alejandromadrid/PythonProjects/AskBot-PandasAI/exports/charts/temp_chart.png
2024-05-02 19:41:35 [INFO]
Code running:
```
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar('Average Salary', average_salary, color='skyblue')
plt.xlabel('IT Companies')
plt.ylabel('Average Salary')
plt.title('Average Salary in IT Companies')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.savefig('/Users/alejandro/Documents/Repositorios/alejandromadrid/PythonProjects/AskBot-PandasAI/exports/charts/temp_chart.png')
result = {'type': 'plot', 'value': '/Users/alejandro/Documents/Repositorios/alejandromadrid/PythonProjects/AskBot-PandasAI/exports/charts/temp_chart.png'}
```
2024-05-02 19:41:35 [INFO] Executing Step 6: CodeExecution
2024-05-02 19:41:35 [ERROR] Failed with error: Traceback (most recent call last):
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 85, in execute
result = self.execute_code(code_to_run, code_context)
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 171, in execute_code
exec(code, environment)
File "<string>", line 1, in <module>
NameError: name 'it_salaries_df' is not defined
2024-05-02 19:41:35 [WARNING] Failed to execute code retrying with a correction framework [retry number: 1]
2024-05-02 19:41:35 [INFO] Executing Pipeline: ErrorCorrectionPipeline
2024-05-02 19:41:35 [INFO] Executing Step 0: ErrorPromptGeneration
2024-05-02 19:41:35 [INFO] Using prompt: <dataframe>
dfs[0]:1x0
</dataframe>
The user asked the following question:
### QUERY
can you make a plot about the average salary in it companies
You generated this python code:
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar('Average Salary', average_salary, color='skyblue')
plt.xlabel('IT Companies')
plt.ylabel('Average Salary')
plt.title('Average Salary in IT Companies')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.savefig('/Users/alejandro/Documents/Repositorios/alejandromadrid/PythonProjects/AskBot-PandasAI/exports/charts/temp_chart.png')
result = {'type': 'plot', 'value': '/Users/alejandro/Documents/Repositorios/alejandromadrid/PythonProjects/AskBot-PandasAI/exports/charts/temp_chart.png'}
It fails with the following error:
Traceback (most recent call last):
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 85, in execute
result = self.execute_code(code_to_run, code_context)
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 171, in execute_code
exec(code, environment)
File "<string>", line 1, in <module>
NameError: name 'it_salaries_df' is not defined
Fix the python code above and return the new python code:
2024-05-02 19:41:35 [INFO] Executing Step 1: CodeGenerator
2024-05-02 19:41:39 [INFO] Prompt used:
None
2024-05-02 19:41:39 [INFO] Code generated:
```
import pandas as pd
import matplotlib.pyplot as plt
# Assuming you have a DataFrame named it_salaries_df with columns: 'company' and 'salary'
# Sample code to create a DataFrame for demonstration purposes
data = {'company': ['Company A', 'Company B', 'Company C'],
'salary': [65000, 72000, 68000]}
it_salaries_df = pd.DataFrame(data)
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar('Average Salary', average_salary, color='skyblue')
plt.xlabel('IT Companies')
plt.ylabel('Average Salary')
plt.title('Average Salary in IT Companies')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
```
2024-05-02 19:41:39 [INFO] Executing Step 2: CodeCleaning
2024-05-02 19:41:39 [INFO]
Code running:
```
data = {'company': ['Company A', 'Company B', 'Company C'], 'salary': [65000, 72000, 68000]}
it_salaries_df = pd.DataFrame(data)
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar('Average Salary', average_salary, color='skyblue')
plt.xlabel('IT Companies')
plt.ylabel('Average Salary')
plt.title('Average Salary in IT Companies')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
```
2024-05-02 19:41:40 [ERROR] Failed with error: Traceback (most recent call last):
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 85, in execute
result = self.execute_code(code_to_run, code_context)
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 175, in execute_code
raise NoResultFoundError("No result returned")
pandasai.exceptions.NoResultFoundError: No result returned
2024-05-02 19:41:40 [WARNING] Failed to execute code retrying with a correction framework [retry number: 2]
2024-05-02 19:41:40 [INFO] Executing Pipeline: ErrorCorrectionPipeline
2024-05-02 19:41:40 [INFO] Executing Step 0: ErrorPromptGeneration
2024-05-02 19:41:40 [INFO] Using prompt: <dataframe>
dfs[0]:1x0
</dataframe>
The user asked the following question:
### QUERY
can you make a plot about the average salary in it companies
You generated this python code:
data = {'company': ['Company A', 'Company B', 'Company C'], 'salary': [65000, 72000, 68000]}
it_salaries_df = pd.DataFrame(data)
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar('Average Salary', average_salary, color='skyblue')
plt.xlabel('IT Companies')
plt.ylabel('Average Salary')
plt.title('Average Salary in IT Companies')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
It fails with the following error:
Traceback (most recent call last):
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 85, in execute
result = self.execute_code(code_to_run, code_context)
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 175, in execute_code
raise NoResultFoundError("No result returned")
pandasai.exceptions.NoResultFoundError: No result returned
Fix the python code above and return the new python code:
2024-05-02 19:41:40 [INFO] Executing Step 1: CodeGenerator
2024-05-02 19:41:44 [INFO] Prompt used:
None
2024-05-02 19:41:44 [INFO] Code generated:
```
import pandas as pd
import matplotlib.pyplot as plt
data = {'company': ['Company A', 'Company B', 'Company C'], 'salary': [65000, 72000, 68000]}
it_salaries_df = pd.DataFrame(data)
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar(it_salaries_df['company'], it_salaries_df['salary'], color='skyblue')
plt.axhline(average_salary, color='red', linestyle='--', label='Average Salary')
plt.xlabel('IT Companies')
plt.ylabel('Salary')
plt.title('Average Salary in IT Companies')
plt.legend()
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
```
2024-05-02 19:41:44 [INFO] Executing Step 2: CodeCleaning
2024-05-02 19:41:44 [INFO]
Code running:
```
data = {'company': ['Company A', 'Company B', 'Company C'], 'salary': [65000, 72000, 68000]}
it_salaries_df = pd.DataFrame(data)
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar(it_salaries_df['company'], it_salaries_df['salary'], color='skyblue')
plt.axhline(average_salary, color='red', linestyle='--', label='Average Salary')
plt.xlabel('IT Companies')
plt.ylabel('Salary')
plt.title('Average Salary in IT Companies')
plt.legend()
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
```
2024-05-02 19:41:45 [ERROR] Failed with error: Traceback (most recent call last):
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 85, in execute
result = self.execute_code(code_to_run, code_context)
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 175, in execute_code
raise NoResultFoundError("No result returned")
pandasai.exceptions.NoResultFoundError: No result returned
2024-05-02 19:41:45 [WARNING] Failed to execute code retrying with a correction framework [retry number: 3]
2024-05-02 19:41:45 [INFO] Executing Pipeline: ErrorCorrectionPipeline
2024-05-02 19:41:45 [INFO] Executing Step 0: ErrorPromptGeneration
2024-05-02 19:41:45 [INFO] Using prompt: <dataframe>
dfs[0]:1x0
</dataframe>
The user asked the following question:
### QUERY
can you make a plot about the average salary in it companies
You generated this python code:
data = {'company': ['Company A', 'Company B', 'Company C'], 'salary': [65000, 72000, 68000]}
it_salaries_df = pd.DataFrame(data)
average_salary = it_salaries_df['salary'].mean()
plt.figure(figsize=(10, 6))
plt.bar(it_salaries_df['company'], it_salaries_df['salary'], color='skyblue')
plt.axhline(average_salary, color='red', linestyle='--', label='Average Salary')
plt.xlabel('IT Companies')
plt.ylabel('Salary')
plt.title('Average Salary in IT Companies')
plt.legend()
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
It fails with the following error:
Traceback (most recent call last):
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 85, in execute
result = self.execute_code(code_to_run, code_context)
File "/Users/alejandro/Documents/Repositorios/alejandromadrid/.python-venv/lib/python3.9/site-packages/pandasai/pipelines/chat/code_execution.py", line 175, in execute_code
raise NoResultFoundError("No result returned")
pandasai.exceptions.NoResultFoundError: No result returned
Fix the python code above and return the new python code:
2024-05-02 19:41:45 [INFO] Executing Step 1: CodeGenerator
2024-05-02 19:41:49 [ERROR] Pipeline failed on step 1: No code found in the response
2024-05-02 19:41:49 [ERROR] Pipeline failed on step 6: No code found in the response
2024-06-05 12:12:56 [INFO] Question: Muestrame algunas gráficas sobre las características del fichero
2024-06-05 12:12:56 [INFO] Running PandasAI with bamboo_llm LLM...
2024-06-05 12:12:56 [INFO] Prompt ID: dd32be07-8251-4ba2-86ec-97da7f409371
2024-06-05 12:12:56 [INFO] Executing Pipeline: GenerateChatPipeline
2024-06-05 12:12:56 [INFO] Executing Step 0: ValidatePipelineInput
2024-06-05 12:12:56 [INFO] Executing Step 1: CacheLookup
2024-06-05 12:12:56 [INFO] Executing Step 2: PromptGeneration
2024-06-05 12:12:56 [INFO] Querying without using training data.
2024-06-05 12:12:56 [INFO] Querying without using training docs.
2024-06-05 12:12:56 [INFO] Using prompt: <dataframe>
dfs[0]:273x20
Unnamed: 0,Gender,Age,Schooling,Breastfeeding,Varicella,Initial_Symptom,Mono_or_Polysymptomatic,Oligoclonal_Bands,LLSSEP,ULSSEP,VEP,BAEP,Periventricular_MRI,Cortical_MRI,Infratentorial_MRI,Spinal_Cord_MRI,Initial_EDSS,Final_EDSS,group
171,1,25,0.0,3,1,,3,0,1,0,1,0,1,1,0,0,,,2
39,2,40,23.0,2,3,1.0,1,1,1,1,1,0,1,0,0,1,3.0,1.0,1
164,2,56,,1,2,4.0,2,2,0,0,0,1,0,1,1,0,2.0,3.0,1
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
### QUERY
Muestrame algunas gráficas sobre las características del fichero
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-06-05 12:12:56 [INFO] Executing Step 3: CodeGenerator
2024-06-05 12:12:57 [ERROR] Pipeline failed on step 3: Unauthorized
2024-06-05 12:20:59 [INFO] Question: Muestrame algunas gráficas sobre las características del fichero
2024-06-05 12:20:59 [INFO] Running PandasAI with bamboo_llm LLM...
2024-06-05 12:20:59 [INFO] Prompt ID: e8bbbce4-1e95-406f-88cd-3b9b0a325e31
2024-06-05 12:20:59 [INFO] Executing Pipeline: GenerateChatPipeline
2024-06-05 12:20:59 [INFO] Executing Step 0: ValidatePipelineInput
2024-06-05 12:20:59 [INFO] Executing Step 1: CacheLookup
2024-06-05 12:20:59 [INFO] Executing Step 2: PromptGeneration
2024-06-05 12:20:59 [INFO] Querying without using training data.
2024-06-05 12:20:59 [INFO] Querying without using training docs.
2024-06-05 12:20:59 [INFO] Using prompt: <dataframe>
dfs[0]:273x20
Unnamed: 0,Gender,Age,Schooling,Breastfeeding,Varicella,Initial_Symptom,Mono_or_Polysymptomatic,Oligoclonal_Bands,LLSSEP,ULSSEP,VEP,BAEP,Periventricular_MRI,Cortical_MRI,Infratentorial_MRI,Spinal_Cord_MRI,Initial_EDSS,Final_EDSS,group
158,2,42,,2,1,2.0,1,2,0,1,0,0,1,1,1,0,,,1
265,2,34,0.0,3,2,,3,0,1,1,1,1,0,0,0,1,3.0,1.0,1
33,1,55,6.0,1,3,12.0,2,1,0,0,0,1,0,1,0,1,1.0,2.0,2
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
### QUERY
Muestrame algunas gráficas sobre las características del fichero
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-06-05 12:20:59 [INFO] Executing Step 3: CodeGenerator
2024-06-05 12:21:00 [ERROR] Pipeline failed on step 3: Unauthorized
2024-06-05 12:21:12 [INFO] Question: Muestrame algunas gráficas sobre las características del fichero
2024-06-05 12:21:12 [INFO] Running PandasAI with bamboo_llm LLM...
2024-06-05 12:21:12 [INFO] Prompt ID: feb7f292-4b71-4c6f-9df5-50a90a783954
2024-06-05 12:21:12 [INFO] Executing Pipeline: GenerateChatPipeline
2024-06-05 12:21:12 [INFO] Executing Step 0: ValidatePipelineInput
2024-06-05 12:21:13 [INFO] Executing Step 1: CacheLookup
2024-06-05 12:21:13 [INFO] Executing Step 2: PromptGeneration
2024-06-05 12:21:13 [INFO] Querying without using training data.
2024-06-05 12:21:13 [INFO] Querying without using training docs.
2024-06-05 12:21:13 [INFO] Using prompt: <dataframe>
dfs[0]:273x20
Unnamed: 0,Gender,Age,Schooling,Breastfeeding,Varicella,Initial_Symptom,Mono_or_Polysymptomatic,Oligoclonal_Bands,LLSSEP,ULSSEP,VEP,BAEP,Periventricular_MRI,Cortical_MRI,Infratentorial_MRI,Spinal_Cord_MRI,Initial_EDSS,Final_EDSS,group
33,2,48,,3,2,7.0,3,0,1,0,0,0,0,0,1,0,,,1
87,2,59,14.0,2,1,,2,2,0,1,1,1,1,1,0,1,1.0,2.0,1
9,1,43,23.0,1,3,1.0,1,1,0,0,0,1,1,0,1,0,2.0,1.0,2
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
### QUERY
Muestrame algunas gráficas sobre las características del fichero
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-06-05 12:21:13 [INFO] Executing Step 3: CodeGenerator
2024-06-05 12:21:13 [ERROR] Pipeline failed on step 3: Unauthorized
2024-06-05 12:35:28 [INFO] Question: Muestrame algunas gráficas sobre las características del fichero
2024-06-05 12:35:28 [INFO] Running PandasAI with bamboo_llm LLM...
2024-06-05 12:35:28 [INFO] Prompt ID: 20179dbe-1eb0-4293-911b-8d84ef5a0663
2024-06-05 12:35:28 [INFO] Executing Pipeline: GenerateChatPipeline
2024-06-05 12:35:28 [INFO] Executing Step 0: ValidatePipelineInput
2024-06-05 12:35:28 [INFO] Executing Step 1: CacheLookup
2024-06-05 12:35:28 [INFO] Executing Step 2: PromptGeneration
2024-06-05 12:35:28 [INFO] Querying without using training data.
2024-06-05 12:35:29 [INFO] Querying without using training docs.
2024-06-05 12:35:29 [INFO] Using prompt: <dataframe>
dfs[0]:273x20
Unnamed: 0,Gender,Age,Schooling,Breastfeeding,Varicella,Initial_Symptom,Mono_or_Polysymptomatic,Oligoclonal_Bands,LLSSEP,ULSSEP,VEP,BAEP,Periventricular_MRI,Cortical_MRI,Infratentorial_MRI,Spinal_Cord_MRI,Initial_EDSS,Final_EDSS,group
272,1,34,8.0,2,2,,3,1,1,1,0,0,1,1,1,0,2.0,3.0,1
0,2,18,,3,3,8.0,2,2,1,0,1,0,0,0,0,1,3.0,2.0,2
122,1,27,25.0,1,1,1.0,1,0,0,0,1,1,1,1,0,0,,,2
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
### QUERY
Muestrame algunas gráficas sobre las características del fichero
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-06-05 12:35:29 [INFO] Executing Step 3: CodeGenerator
2024-06-05 12:35:29 [ERROR] Pipeline failed on step 3: Unauthorized
2024-06-05 12:37:03 [INFO] Question: Make some graphs from the dataset to see what's in the dataset
2024-06-05 12:37:03 [INFO] Running PandasAI with bamboo_llm LLM...
2024-06-05 12:37:04 [INFO] Prompt ID: c80ba33d-9f21-4e7f-bc33-004ceb295278
2024-06-05 12:37:04 [INFO] Executing Pipeline: GenerateChatPipeline
2024-06-05 12:37:04 [INFO] Executing Step 0: ValidatePipelineInput
2024-06-05 12:37:04 [INFO] Executing Step 1: CacheLookup
2024-06-05 12:37:04 [INFO] Executing Step 2: PromptGeneration
2024-06-05 12:37:04 [INFO] Querying without using training data.
2024-06-05 12:37:04 [INFO] Querying without using training docs.
2024-06-05 12:37:04 [INFO] Using prompt: <dataframe>
dfs[0]:273x20
Unnamed: 0,Gender,Age,Schooling,Breastfeeding,Varicella,Initial_Symptom,Mono_or_Polysymptomatic,Oligoclonal_Bands,LLSSEP,ULSSEP,VEP,BAEP,Periventricular_MRI,Cortical_MRI,Infratentorial_MRI,Spinal_Cord_MRI,Initial_EDSS,Final_EDSS,group
145,1,35,0.0,2,1,,1,0,0,1,0,0,0,1,0,1,,,1
22,2,20,25.0,3,3,13.0,2,1,1,1,0,0,1,1,1,0,1.0,1.0,2
105,1,65,,1,2,10.0,3,2,1,0,1,1,1,0,1,0,2.0,2.0,1
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
### QUERY
Make some graphs from the dataset to see what's in the dataset
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-06-05 12:37:04 [INFO] Executing Step 3: CodeGenerator
2024-06-05 12:37:04 [ERROR] Pipeline failed on step 3: Unauthorized
2024-06-05 12:42:05 [INFO] Question: Make some graphs from the dataset to see what's in the dataset
2024-06-05 12:42:05 [INFO] Running PandasAI with bamboo_llm LLM...
2024-06-05 12:42:05 [INFO] Prompt ID: f3180271-2825-4591-8cd1-c7a31baa387c
2024-06-05 12:42:05 [INFO] Executing Pipeline: GenerateChatPipeline
2024-06-05 12:42:05 [INFO] Executing Step 0: ValidatePipelineInput
2024-06-05 12:42:05 [INFO] Executing Step 1: CacheLookup
2024-06-05 12:42:05 [INFO] Executing Step 2: PromptGeneration
2024-06-05 12:42:06 [INFO] Using prompt: <dataframe>
dfs[0]:273x20
Unnamed: 0,Gender,Age,Schooling,Breastfeeding,Varicella,Initial_Symptom,Mono_or_Polysymptomatic,Oligoclonal_Bands,LLSSEP,ULSSEP,VEP,BAEP,Periventricular_MRI,Cortical_MRI,Infratentorial_MRI,Spinal_Cord_MRI,Initial_EDSS,Final_EDSS,group
259,1,37,,3,2,,2,0,0,1,1,1,0,0,0,0,2.0,3.0,1
207,1,54,24.0,1,1,9.0,3,2,1,0,0,0,1,0,1,1,,2.0,2
260,2,56,0.0,2,3,15.0,1,1,1,0,0,0,0,1,1,1,3.0,,1
</dataframe>
Update this initial code:
```python
# TODO: import the required dependencies
import pandas as pd
# Write code here
# Declare result var:
type (possible values "string", "number", "dataframe", "plot"). Examples: { "type": "string", "value": f"The highest salary is {highest_salary}." } or { "type": "number", "value": 125 } or { "type": "dataframe", "value": pd.DataFrame({...}) } or { "type": "plot", "value": "temp_chart.png" }
```
You can utilize these examples as a reference for generating code.
Q: What are the top 5 countries by GDP?
A:
import pandas as pd
df = dfs[0]
# Convert GDP column to numeric values
df['gdp'] = pd.to_numeric(df['gdp'])
# Sort the DataFrame by GDP in descending order
sorted_df = df.sort_values(by='gdp', ascending=False)
# Get the top 5 countries by GDP
top_5_countries = sorted_df.head(5)
# Declare result variable
result = {
"type": "dataframe",
"value": top_5_countries
}
### QUERY
Make some graphs from the dataset to see what's in the dataset
Variable `dfs: list[pd.DataFrame]` is already declared.
At the end, declare "result" variable as a dictionary of type and value.
If you are asked to plot a chart, use "matplotlib" for charts, save as png.
Generate python code and return full updated code:
2024-06-05 12:42:06 [INFO] Executing Step 3: CodeGenerator
2024-06-05 12:42:11 [INFO] Prompt used:
None
2024-06-05 12:42:11 [INFO] Code generated:
```
# TODO: import the required dependencies
import pandas as pd
import matplotlib.pyplot as plt
df = dfs[0]
# Plot a histogram of Age distribution
plt.figure(figsize=(10, 6))
plt.hist(df['Age'], bins=20, color='skyblue', edgecolor='black')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Distribution of Ages')
plt.grid(axis='y', alpha=0.75)
plt.savefig('age_distribution.png')
# Plot a bar chart for Gender distribution
gender_counts = df['Gender'].value_counts()
plt.figure(figsize=(8, 6))
plt.bar(gender_counts.index, gender_counts.values, color=['pink', 'blue'])
plt.xlabel('Gender (1: Male, 2: Female)')
plt.ylabel('Count')
plt.title('Gender Distribution')
plt.xticks([1, 2])