pandasai.log

2023-08-26 14:39:10 [INFO] Question: Generate me a plot graph of total death case of each continent
2023-08-26 14:39:10 [INFO] Running PandasAI with openai LLM...
2023-08-26 14:39:10 [INFO] Prompt ID: 88cdafe2-88ed-44ba-a0f9-99e197d6d2ce
2023-08-26 14:39:21 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.figure(figsize=(10, 6))
    total_deaths_by_continent.plot(kind='bar')
    plt.title('Total Deaths by Continent')
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.xticks(rotation=45)
    plt.tight_layout()
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    
    # Return the path of the plot image
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 14:39:21 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.figure(figsize=(10, 6))
    total_deaths_by_continent.plot(kind='bar')
    plt.title('Total Deaths by Continent')
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig('exports/charts/temp_chart.png')
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 14:39:22 [WARNING] Error of executing code
2023-08-26 14:39:22 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-08-26 14:39:22 [INFO] Failed with error: [Errno 2] No such file or directory: 'exports/charts/temp_chart.png'. Retrying
2023-08-26 14:39:33 [WARNING] Failed to execute code with a correction framework [retry number: 2]
2023-08-26 14:39:33 [INFO] Failed with error: Generated code includes import of os which is not in whitelist.. Retrying
2023-08-26 14:39:42 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.figure(figsize=(10, 6))
    total_deaths_by_continent.plot(kind='bar')
    plt.title('Total Deaths by Continent')
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig('exports/charts/temp_chart.png')
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 14:39:43 [WARNING] Error of executing code
2023-08-26 14:42:35 [INFO] Question: Generate a plot graph that shows the total death case of each continent
2023-08-26 14:42:35 [INFO] Running PandasAI with openai LLM...
2023-08-26 14:42:35 [INFO] Prompt ID: 42c0a78b-0fe1-4f41-9f66-2f9ccc87cd4f
2023-08-26 14:42:45 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.figure(figsize=(10, 6))
    total_deaths_by_continent.plot(kind='bar')
    plt.title('Total Deaths by Continent')
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.xticks(rotation=45)
    plt.tight_layout()
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    
    # Return the path of the plot image
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 14:42:45 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.figure(figsize=(10, 6))
    total_deaths_by_continent.plot(kind='bar')
    plt.title('Total Deaths by Continent')
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig('exports/charts/temp_chart.png')
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 14:42:46 [WARNING] Error of executing code
2023-08-26 14:42:46 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-08-26 14:42:46 [INFO] Failed with error: [Errno 2] No such file or directory: 'exports/charts/temp_chart.png'. Retrying
2023-08-26 14:42:57 [WARNING] Failed to execute code with a correction framework [retry number: 2]
2023-08-26 14:42:57 [INFO] Failed with error: Generated code includes import of os which is not in whitelist.. Retrying
2023-08-26 14:44:41 [INFO] Question: Generate a plot graph that shows the total deaths of each continent
2023-08-26 14:44:41 [INFO] Running PandasAI with openai LLM...
2023-08-26 14:44:41 [INFO] Prompt ID: 5cf21b26-c93f-402b-b19d-528fa701e0c5
2023-08-26 14:44:51 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    # Group by continent and sum the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 14:44:51 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 14:44:52 [WARNING] Error of executing code
2023-08-26 14:44:52 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-08-26 14:44:52 [INFO] Failed with error: [Errno 2] No such file or directory: 'exports/charts/temp_chart.png'. Retrying
2023-08-26 14:45:03 [WARNING] Failed to execute code with a correction framework [retry number: 2]
2023-08-26 14:45:03 [INFO] Failed with error: Generated code includes import of os which is not in whitelist.. Retrying
2023-08-26 14:45:13 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 14:45:13 [WARNING] Error of executing code
2023-08-26 15:27:38 [INFO] Question: Generate a plot to show total death cases of each continent
2023-08-26 15:27:38 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:27:38 [INFO] Prompt ID: fbeb8ae9-7b36-4eef-a578-01b297375746
2023-08-26 15:27:48 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Death Cases by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:27:48 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Death Cases by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:27:49 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:27:49 [INFO] Executed in: 10.316795349121094s
2023-08-26 15:30:36 [INFO] Question: Generate a plot to show total death cases of each continent
2023-08-26 15:30:36 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:30:36 [INFO] Prompt ID: c1299b63-7f92-4922-98b6-2a6f43dbad39
2023-08-26 15:30:36 [INFO] Using cached response
2023-08-26 15:30:36 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Death Cases by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:30:36 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Death Cases by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:30:36 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:30:36 [INFO] Executed in: 0.18401598930358887s
2023-08-26 15:35:17 [INFO] Question: Generate a plot to show the total vaccinated person of each continents
2023-08-26 15:35:17 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:35:17 [INFO] Prompt ID: e3e84340-7e24-4874-be5e-575ecea3197f
2023-08-26 15:35:27 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Combine all dataframes into one
    df = pd.concat(dfs)

    # Group by continent and calculate the total vaccinated person
    total_vaccinated = df.groupby('continent')['people_vaccinated'].sum()

    # Create a bar plot to visualize the total vaccinated person of each continent
    plt.figure(figsize=(10, 6))
    total_vaccinated.plot(kind='bar')
    plt.xlabel('Continent')
    plt.ylabel('Total Vaccinated')
    plt.title('Total Vaccinated Person of Each Continent')
    plt.savefig('exports/charts/temp_chart.png')

    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:35:27 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = pd.concat(dfs)
    total_vaccinated = df.groupby('continent')['people_vaccinated'].sum()
    plt.figure(figsize=(10, 6))
    total_vaccinated.plot(kind='bar')
    plt.xlabel('Continent')
    plt.ylabel('Total Vaccinated')
    plt.title('Total Vaccinated Person of Each Continent')
    plt.savefig('exports/charts/temp_chart.png')
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:35:27 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:35:27 [INFO] Executed in: 10.322790622711182s
2023-08-26 15:37:27 [INFO] Question: which country is more likely to survive?
2023-08-26 15:37:27 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:37:27 [INFO] Prompt ID: 473623d5-ee7f-40ea-a813-2cbad9059aed
2023-08-26 15:37:37 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Filter the dataframe to include only the necessary columns
    df = dfs[0][['location', 'total_cases', 'total_deaths', 'population', 'life_expectancy']]
    
    # Calculate the survival rate for each country
    df['survival_rate'] = (df['total_cases'] - df['total_deaths']) / df['population']
    
    # Find the country with the highest survival rate
    country_with_highest_survival_rate = df.loc[df['survival_rate'].idxmax(), 'location']
    
    # Return the result
    return {"type": "text", "value": f"The country with the highest survival rate is {country_with_highest_survival_rate}."}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:37:37 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0][['location', 'total_cases', 'total_deaths', 'population', 'life_expectancy']]
    df['survival_rate'] = (df['total_cases'] - df['total_deaths']) / df['population']
    country_with_highest_survival_rate = df.loc[df['survival_rate'].idxmax(), 'location']
    return {'type': 'text', 'value': f'The country with the highest survival rate is {country_with_highest_survival_rate}.'}


result = analyze_data(dfs)
        ```
2023-08-26 15:37:37 [INFO] Answer: {'type': 'text', 'value': 'The country with the highest survival rate is Cyprus.'}
2023-08-26 15:37:37 [INFO] Executed in: 10.125800848007202s
2023-08-26 15:38:00 [INFO] Question: Generate a plot that shows the total death case of each continent
2023-08-26 15:38:00 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:38:00 [INFO] Prompt ID: b3cd2181-a649-495b-a1ef-f3a50905cb63
2023-08-26 15:38:11 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    # Group by continent and calculate the sum of total_deaths
    df_grouped = df.groupby('continent')['total_deaths'].sum().reset_index()
    
    # Create a bar plot
    plt.figure(figsize=(10, 6))
    plt.bar(df_grouped['continent'], df_grouped['total_deaths'])
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Death Cases by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:38:11 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    df_grouped = df.groupby('continent')['total_deaths'].sum().reset_index()
    plt.figure(figsize=(10, 6))
    plt.bar(df_grouped['continent'], df_grouped['total_deaths'])
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Death Cases by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:38:12 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:38:12 [INFO] Executed in: 11.285982608795166s
2023-08-26 15:46:48 [INFO] Question: Generate a plot to show total death of each continent
2023-08-26 15:46:48 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:46:48 [INFO] Prompt ID: 57a6e773-2d48-4ad7-8bfc-ebcf32fa88e6
2023-08-26 15:46:59 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:46:59 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:46:59 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:46:59 [INFO] Executed in: 11.05770754814148s
2023-08-26 15:48:57 [INFO] Question: Generate a plot to show total death of each continent
2023-08-26 15:48:57 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:48:57 [INFO] Prompt ID: db1709c4-2b1a-49aa-a76a-62fff45312b0
2023-08-26 15:48:57 [INFO] Using cached response
2023-08-26 15:48:57 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:48:57 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:48:57 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:48:57 [INFO] Executed in: 0.416961669921875s
2023-08-26 15:51:15 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 15:51:15 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:51:15 [INFO] Prompt ID: 49c30063-3000-4e58-8d13-4b791d84ef70
2023-08-26 15:51:25 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:51:25 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:51:25 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:51:25 [INFO] Executed in: 10.354381322860718s
2023-08-26 15:57:55 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 15:57:55 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:57:55 [INFO] Prompt ID: 052ed1cd-c723-4e5e-a6a3-dff948a07c61
2023-08-26 15:57:55 [INFO] Using cached response
2023-08-26 15:57:55 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:57:55 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:57:55 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:57:55 [INFO] Executed in: 0.3335416316986084s
2023-08-26 15:58:13 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 15:58:13 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:58:13 [INFO] Prompt ID: 82b55e06-c727-40df-88b0-f271d2626a9a
2023-08-26 15:58:13 [INFO] Using cached response
2023-08-26 15:58:13 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:58:13 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:58:14 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:58:14 [INFO] Executed in: 0.6216223239898682s
2023-08-26 15:59:24 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 15:59:24 [INFO] Running PandasAI with openai LLM...
2023-08-26 15:59:24 [INFO] Prompt ID: 4e9ed8bf-5483-406a-be6b-432bdf684854
2023-08-26 15:59:24 [INFO] Using cached response
2023-08-26 15:59:24 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 15:59:24 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 15:59:24 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 15:59:24 [INFO] Executed in: 0.34744834899902344s
2023-08-26 16:00:14 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 16:00:14 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:00:14 [INFO] Prompt ID: ea95d7e2-9a1b-4ed1-872e-c256dc1b575c
2023-08-26 16:00:14 [INFO] Using cached response
2023-08-26 16:00:14 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:00:14 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:00:14 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:00:14 [INFO] Executed in: 0.47115063667297363s
2023-08-26 16:01:11 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 16:01:11 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:01:11 [INFO] Prompt ID: 6c2efc1c-e4de-4acd-91a4-996701744384
2023-08-26 16:01:11 [INFO] Using cached response
2023-08-26 16:01:11 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:01:11 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:01:11 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:01:11 [INFO] Executed in: 0.26613950729370117s
2023-08-26 16:02:46 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 16:02:46 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:02:46 [INFO] Prompt ID: 6d19ece0-11b1-49ee-9e80-67538105ab74
2023-08-26 16:02:46 [INFO] Using cached response
2023-08-26 16:02:46 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:02:46 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:02:46 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:02:46 [INFO] Executed in: 0.2578425407409668s
2023-08-26 16:04:17 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 16:04:17 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:04:17 [INFO] Prompt ID: de1c9b34-00a8-4e87-b801-c5354caeb08b
2023-08-26 16:04:17 [INFO] Using cached response
2023-08-26 16:04:17 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:04:17 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:04:17 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:04:17 [INFO] Executed in: 0.25641775131225586s
2023-08-26 16:05:02 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 16:05:02 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:05:02 [INFO] Prompt ID: 37a79d81-ddda-4eb0-a4ec-c7782bd55d07
2023-08-26 16:05:02 [INFO] Using cached response
2023-08-26 16:05:02 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:05:02 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:05:02 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:05:02 [INFO] Executed in: 0.17931509017944336s
2023-08-26 16:07:47 [INFO] Question: Generate a plot that shows the total deaths of each continent

2023-08-26 16:07:47 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:07:47 [INFO] Prompt ID: bc5fe8af-8458-44e9-9085-f3529686fcde
2023-08-26 16:07:57 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:07:57 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:07:57 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:07:57 [INFO] Executed in: 10.22426152229309s
2023-08-26 16:08:49 [INFO] Question: Generate a plot that shows the total deaths of each continent

2023-08-26 16:08:49 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:08:49 [INFO] Prompt ID: 51c9a2ee-c684-4389-99d0-66bb7d6a7586
2023-08-26 16:08:49 [INFO] Using cached response
2023-08-26 16:08:49 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:08:49 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:08:49 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:08:49 [INFO] Executed in: 0.24420571327209473s
2023-08-26 16:09:27 [INFO] Question: Generate a plot that shows the total deaths of each continent

2023-08-26 16:09:27 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:09:27 [INFO] Prompt ID: a56f4603-b857-4cdd-858f-50987895b54d
2023-08-26 16:09:27 [INFO] Using cached response
2023-08-26 16:09:27 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:09:27 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:09:29 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-08-26 16:09:29 [INFO] Failed with error: main thread is not in main loop. Retrying
2023-08-26 16:09:42 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:09:45 [WARNING] Error of executing code
2023-08-26 16:10:14 [INFO] Question: Generate a plot that shows the total deaths of each continent

2023-08-26 16:10:14 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:10:14 [INFO] Prompt ID: 2b181569-b1ee-46e8-99ee-62b3e91bc316
2023-08-26 16:10:14 [INFO] Using cached response
2023-08-26 16:10:14 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:10:14 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:10:17 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-08-26 16:10:17 [INFO] Failed with error: main thread is not in main loop. Retrying
2023-08-26 16:10:29 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:10:31 [WARNING] Error of executing code
2023-08-26 16:11:39 [INFO] Question: Generate a plot that shows the total death of each continent
2023-08-26 16:11:39 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:11:39 [INFO] Prompt ID: b285b71b-6282-4f13-a90a-f0955fdd3a7f
2023-08-26 16:11:39 [INFO] Using cached response
2023-08-26 16:11:39 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group the data by continent and calculate the total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot of the total deaths by continent
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot as the output
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:11:39 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.xticks(rotation=45)
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:11:39 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:11:39 [INFO] Executed in: 0.2585608959197998s
2023-08-26 16:13:49 [INFO] Question: Generate a plot that shows the total deaths of each continent
2023-08-26 16:13:49 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:13:49 [INFO] Prompt ID: 57058468-d31a-422a-b95c-5f0e29d0d023
2023-08-26 16:14:00 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:14:00 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:14:01 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:14:01 [INFO] Executed in: 12.30364727973938s
2023-08-26 16:14:45 [INFO] Question: Generate a plot that shows the total deaths of each continent
2023-08-26 16:14:45 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:14:45 [INFO] Prompt ID: e92fca91-2bdf-4b7c-b76c-05306fd4e028
2023-08-26 16:14:45 [INFO] Using cached response
2023-08-26 16:14:45 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:14:46 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:14:46 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:14:46 [INFO] Executed in: 0.2678036689758301s
2023-08-26 16:16:13 [INFO] Question: Generate a plot that shows the total deaths of each continent
2023-08-26 16:16:13 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:16:13 [INFO] Prompt ID: 40318006-ee5a-464f-ab45-661f31bc37ec
2023-08-26 16:16:13 [INFO] Using cached response
2023-08-26 16:16:13 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:16:13 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:16:13 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:16:13 [INFO] Executed in: 0.43052029609680176s
2023-08-26 16:17:07 [INFO] Question: Generate a plot that shows the total deaths of each continent
2023-08-26 16:17:07 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:17:07 [INFO] Prompt ID: 19ef9d68-83bb-429b-bb6b-6f7351151f14
2023-08-26 16:17:07 [INFO] Using cached response
2023-08-26 16:17:07 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    
    # Group by continent and calculate the sum of total deaths
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    
    # Create a bar plot
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    
    # Save the plot to an image file
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    
    # Return the path of the plot
    return {"type": "plot", "value": "exports/charts/temp_chart.png"}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:17:07 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    total_deaths_by_continent = df.groupby('continent')['total_deaths'].sum()
    plt.bar(total_deaths_by_continent.index, total_deaths_by_continent.values)
    plt.xlabel('Continent')
    plt.ylabel('Total Deaths')
    plt.title('Total Deaths by Continent')
    plt.savefig('exports/charts/temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}


result = analyze_data(dfs)
        ```
2023-08-26 16:17:07 [INFO] Answer: {'type': 'plot', 'value': 'exports/charts/temp_chart.png'}
2023-08-26 16:17:07 [INFO] Executed in: 0.3046271800994873s
2023-08-26 16:20:18 [INFO] Question: Tell me what is this csv about
2023-08-26 16:20:18 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:20:18 [INFO] Prompt ID: 9a1e5697-70e5-4dae-963c-7a8c571e8874
2023-08-26 16:20:30 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    df = dfs[0]
    description = df.describe(include='all')
    return {"type": "dataframe", "value": description}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:20:30 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0]
    description = df.describe(include='all')
    return {'type': 'dataframe', 'value': description}


result = analyze_data(dfs)
        ```
2023-08-26 16:20:30 [INFO] Answer: {'type': 'dataframe', 'value':         PassengerId    Survived      Pclass                     Name   Sex         Age       SibSp       Parch  Ticket        Fare    Cabin Embarked
count    891.000000  891.000000  891.000000                      891   891  714.000000  891.000000  891.000000     891  891.000000      204      889
unique          NaN         NaN         NaN                      891     2         NaN         NaN         NaN     681         NaN      147        3
top             NaN         NaN         NaN  Braund, Mr. Owen Harris  male         NaN         NaN         NaN  347082         NaN  B96 B98        S
freq            NaN         NaN         NaN                        1   577         NaN         NaN         NaN       7         NaN        4      644
mean     446.000000    0.383838    2.308642                      NaN   NaN   29.699118    0.523008    0.381594     NaN   32.204208      NaN      NaN
std      257.353842    0.486592    0.836071                      NaN   NaN   14.526497    1.102743    0.806057     NaN   49.693429      NaN      NaN
min        1.000000    0.000000    1.000000                      NaN   NaN    0.420000    0.000000    0.000000     NaN    0.000000      NaN      NaN
25%      223.500000    0.000000    2.000000                      NaN   NaN   20.125000    0.000000    0.000000     NaN    7.910400      NaN      NaN
50%      446.000000    0.000000    3.000000                      NaN   NaN   28.000000    0.000000    0.000000     NaN   14.454200      NaN      NaN
75%      668.500000    1.000000    3.000000                      NaN   NaN   38.000000    1.000000    0.000000     NaN   31.000000      NaN      NaN
max      891.000000    1.000000    3.000000                      NaN   NaN   80.000000    8.000000    6.000000     NaN  512.329200      NaN      NaN}
2023-08-26 16:20:30 [INFO] Executed in: 12.15952754020691s
2023-08-26 16:26:19 [INFO] Question: What is this CSV about?
2023-08-26 16:26:19 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:26:19 [INFO] Prompt ID: bde09582-6ca4-4c22-b5ec-2175a1ed3a5a
2023-08-26 16:26:28 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Get the metadata of the first dataframe
    metadata = dfs[0].columns.tolist()
    
    # Prepare the output text
    output_text = "This CSV contains data about COVID-19 cases and related information."
    
    # Output the result as text
    result = {
        "type": "text",
        "value": output_text
    }
    
    return result

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:26:28 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    metadata = dfs[0].columns.tolist()
    output_text = 'This CSV contains data about COVID-19 cases and related information.'
    result = {'type': 'text', 'value': output_text}
    return result


result = analyze_data(dfs)
        ```
2023-08-26 16:26:28 [INFO] Answer: {'type': 'text', 'value': 'This CSV contains data about COVID-19 cases and related information.'}
2023-08-26 16:26:28 [INFO] Executed in: 9.767437219619751s
2023-08-26 16:29:01 [INFO] Loading faiss with AVX2 support.
2023-08-26 16:29:01 [INFO] Could not load library with AVX2 support due to:
ModuleNotFoundError("No module named 'faiss.swigfaiss_avx2'")
2023-08-26 16:29:01 [INFO] Loading faiss.
2023-08-26 16:29:01 [INFO] Successfully loaded faiss.
2023-08-26 16:33:24 [INFO] Question: What is this CSV about?
2023-08-26 16:33:24 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:33:24 [INFO] Prompt ID: f97d4ccc-966c-478c-9224-2feb8b7155c7
2023-08-26 16:33:24 [INFO] Using cached response
2023-08-26 16:33:24 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Get the metadata of the first dataframe
    metadata = dfs[0].columns.tolist()
    
    # Prepare the output text
    output_text = "This CSV contains data about COVID-19 cases and related information."
    
    # Output the result as text
    result = {
        "type": "text",
        "value": output_text
    }
    
    return result

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:33:24 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    metadata = dfs[0].columns.tolist()
    output_text = 'This CSV contains data about COVID-19 cases and related information.'
    result = {'type': 'text', 'value': output_text}
    return result


result = analyze_data(dfs)
        ```
2023-08-26 16:33:24 [INFO] Answer: {'type': 'text', 'value': 'This CSV contains data about COVID-19 cases and related information.'}
2023-08-26 16:33:24 [INFO] Executed in: 0.0020112991333007812s
2023-08-26 16:33:35 [INFO] Question: Which country is more likely to survive?
2023-08-26 16:33:35 [INFO] Running PandasAI with openai LLM...
2023-08-26 16:33:35 [INFO] Prompt ID: ba177059-f1cd-4ec3-8020-8c5c52aa8a59
2023-08-26 16:33:49 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    # Prepare: Filter the data to include only the necessary columns
    df = dfs[0][['location', 'total_cases', 'total_deaths', 'population']]
    
    # Process: Calculate the survival rate for each country
    df['survival_rate'] = (df['total_cases'] - df['total_deaths']) / df['population']
    
    # Analyze: Find the country with the highest survival rate
    country_with_highest_survival_rate = df.loc[df['survival_rate'].idxmax(), 'location']
    
    # Output: Return the country with the highest survival rate
    return {"type": "text", "value": f"The country with the highest survival rate is {country_with_highest_survival_rate}."}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-08-26 16:33:49 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0][['location', 'total_cases', 'total_deaths', 'population']]
    df['survival_rate'] = (df['total_cases'] - df['total_deaths']) / df['population']
    country_with_highest_survival_rate = df.loc[df['survival_rate'].idxmax(), 'location']
    return {'type': 'text', 'value': f'The country with the highest survival rate is {country_with_highest_survival_rate}.'}


result = analyze_data(dfs)
        ```
2023-08-26 16:33:49 [INFO] Answer: {'type': 'text', 'value': 'The country with the highest survival rate is Cyprus.'}
2023-08-26 16:33:49 [INFO] Executed in: 13.263951063156128s
2023-09-11 13:40:14 [INFO] Question: What's the total cases of dengue in 2018
2023-09-11 13:40:14 [INFO] Running PandasAI with openai LLM...
2023-09-11 13:40:14 [INFO] Prompt ID: 110eb7d1-b373-4f28-aada-81899b32f672
2023-09-11 13:40:28 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Filter the dataframe to include only rows with TAHUN equal to 2018
    df_2018 = dfs[0][dfs[0]['TAHUN'] == 2018]
    
    # Calculate the total cases of dengue in 2018 by summing up the values in the relevant columns
    total_cases_2018 = df_2018[['JOHOR', 'KEDAH', 'KELANTAN', 'MELAKA', 'NEGERI SEMBILAN', 'PAHANG', 'PERAK', 'PERLIS', 'PULAU PINANG', 'SABAH', 'SARAWAK', 'SELANGOR', 'TERENGGANU', 'WP KUALA LUMPUR', 'WP LABUAN']].sum().sum()
    
    # Return the result as a dictionary
    return {"type": "number", "value": total_cases_2018}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-09-11 13:40:28 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df_2018 = dfs[0][dfs[0]['TAHUN'] == 2018]
    total_cases_2018 = df_2018[['JOHOR', 'KEDAH', 'KELANTAN', 'MELAKA', 'NEGERI SEMBILAN', 'PAHANG', 'PERAK', 'PERLIS', 'PULAU PINANG', 'SABAH', 'SARAWAK', 'SELANGOR', 'TERENGGANU', 'WP KUALA LUMPUR', 'WP LABUAN']].sum().sum()
    return {'type': 'number', 'value': total_cases_2018}


result = analyze_data(dfs)
        ```
2023-09-11 13:40:28 [INFO] Answer: {'type': 'number', 'value': 406}
2023-09-11 13:40:28 [INFO] Executed in: 13.781995058059692s
2023-09-11 13:40:40 [INFO] Question: What is this CSV about?
2023-09-11 13:40:40 [INFO] Running PandasAI with openai LLM...
2023-09-11 13:40:40 [INFO] Prompt ID: 63b76385-17e8-474f-a630-d4be09436e24
2023-09-11 13:40:48 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
    # Prepare: No preprocessing or cleaning needed
    
    # Process: No data manipulation needed
    
    # Analyze: Get the metadata of the first dataframe
    metadata = dfs[0].columns.tolist()
    
    # Output: Return the metadata as a dataframe
    return {"type": "dataframe", "value": pd.DataFrame(metadata)}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-09-11 13:40:48 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    metadata = dfs[0].columns.tolist()
    return {'type': 'dataframe', 'value': pd.DataFrame(metadata)}


result = analyze_data(dfs)
        ```
2023-09-11 13:40:48 [INFO] Answer: {'type': 'dataframe', 'value':                   0
0             TAHUN
1       MINGGU EPID
2             JOHOR
3             KEDAH
4          KELANTAN
5            MELAKA
6   NEGERI SEMBILAN
7            PAHANG
8             PERAK
9            PERLIS
10     PULAU PINANG
11            SABAH
12          SARAWAK
13         SELANGOR
14       TERENGGANU
15  WP KUALA LUMPUR
16        WP LABUAN}
2023-09-11 13:40:48 [INFO] Executed in: 8.236079454421997s
2023-09-11 13:41:31 [INFO] Question: what is this CSV about?
2023-09-11 13:41:31 [INFO] Running PandasAI with openai LLM...
2023-09-11 13:41:31 [INFO] Prompt ID: 70a24f13-c5b6-4d5d-9565-1a54f0e5f546
2023-09-11 13:41:42 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Get the metadata of the first dataframe
    metadata = dfs[0].columns.tolist()
    
    # Prepare the output text
    output_text = "This CSV contains data about COVID-19 cases and related information.\n"
    output_text += "The columns in the dataset are as follows:\n"
    output_text += "\n".join(metadata)
    
    # Return the output as text
    return {"type": "text", "value": output_text}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-09-11 13:41:42 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    metadata = dfs[0].columns.tolist()
    output_text = 'This CSV contains data about COVID-19 cases and related information.\n'
    output_text += 'The columns in the dataset are as follows:\n'
    output_text += '\n'.join(metadata)
    return {'type': 'text', 'value': output_text}


result = analyze_data(dfs)
        ```
2023-09-11 13:41:42 [INFO] Answer: {'type': 'text', 'value': 'This CSV contains data about COVID-19 cases and related information.\nThe columns in the dataset are as follows:\niso_code\ncontinent\nlocation\ndate\ntotal_cases\nnew_cases\nnew_cases_smoothed\ntotal_deaths\nnew_deaths\nnew_deaths_smoothed\ntotal_cases_per_million\nnew_cases_per_million\nnew_cases_smoothed_per_million\ntotal_deaths_per_million\nnew_deaths_per_million\nnew_deaths_smoothed_per_million\nreproduction_rate\nicu_patients\nicu_patients_per_million\nhosp_patients\nhosp_patients_per_million\nweekly_icu_admissions\nweekly_icu_admissions_per_million\nweekly_hosp_admissions\nweekly_hosp_admissions_per_million\ntotal_tests\nnew_tests\ntotal_tests_per_thousand\nnew_tests_per_thousand\nnew_tests_smoothed\nnew_tests_smoothed_per_thousand\npositive_rate\ntests_per_case\ntests_units\ntotal_vaccinations\npeople_vaccinated\npeople_fully_vaccinated\ntotal_boosters\nnew_vaccinations\nnew_vaccinations_smoothed\ntotal_vaccinations_per_hundred\npeople_vaccinated_per_hundred\npeople_fully_vaccinated_per_hundred\ntotal_boosters_per_hundred\nnew_vaccinations_smoothed_per_million\nnew_people_vaccinated_smoothed\nnew_people_vaccinated_smoothed_per_hundred\nstringency_index\npopulation_density\nmedian_age\naged_65_older\naged_70_older\ngdp_per_capita\nextreme_poverty\ncardiovasc_death_rate\ndiabetes_prevalence\nfemale_smokers\nmale_smokers\nhandwashing_facilities\nhospital_beds_per_thousand\nlife_expectancy\nhuman_development_index\npopulation\nexcess_mortality_cumulative_absolute\nexcess_mortality_cumulative\nexcess_mortality\nexcess_mortality_cumulative_per_million'}
2023-09-11 13:41:42 [INFO] Executed in: 11.086899995803833s
2023-10-04 15:41:48 [INFO] Question: Which country has the most death cases
2023-10-04 15:41:48 [INFO] Running PandasAI with openai LLM...
2023-10-04 15:41:48 [INFO] Prompt ID: 4948986b-5c03-4748-9355-c39c46ba2ce2
2023-10-04 15:41:55 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Process
    # Group by country and sum the total deaths
    df = dfs[0].groupby('location')['total_deaths'].sum().reset_index()
    
    # Find the country with the most death cases
    max_deaths = df['total_deaths'].max()
    country_with_most_deaths = df[df['total_deaths'] == max_deaths]['location'].values[0]
    
    # Output
    return {"type": "text", "value": f"The country with the most death cases is {country_with_most_deaths}."}

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-10-04 15:41:55 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0].groupby('location')['total_deaths'].sum().reset_index()
    max_deaths = df['total_deaths'].max()
    country_with_most_deaths = df[df['total_deaths'] == max_deaths]['location'].values[0]
    return {'type': 'text', 'value': f'The country with the most death cases is {country_with_most_deaths}.'}


result = analyze_data(dfs)
        ```
2023-10-04 15:41:55 [INFO] Answer: {'type': 'text', 'value': 'The country with the most death cases is World.'}
2023-10-04 15:41:55 [INFO] Executed in: 6.621955871582031s
2023-10-04 15:42:06 [INFO] Question: Besides world?
2023-10-04 15:42:06 [INFO] Running PandasAI with openai LLM...
2023-10-04 15:42:06 [INFO] Prompt ID: 7e12423b-30a9-449c-b9a6-1187c601edc1
2023-10-04 15:42:13 [INFO] 
                    Code generated:
                    ```
                    # TODO import all the dependencies required
import pandas as pd

# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Filter out the dataframe for locations other than "World"
    df = dfs[0][dfs[0]['location'] != 'World']
    
    # Code goes here (do not add comments)
    

# Declare a result variable
result = analyze_data(dfs)
                    ```
                
2023-10-04 15:42:13 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    df = dfs[0][dfs[0]['location'] != 'World']


result = analyze_data(dfs)
        ```
2023-10-04 15:42:13 [INFO] Executed in: 6.470301151275635s
2023-10-07 00:53:38 [INFO] Question: How many locations are there?
2023-10-07 00:53:38 [INFO] Running PandasAI with openai LLM...
2023-10-07 00:53:38 [INFO] Prompt ID: 036fc024-67c9-4fe9-b5d8-c9aadfa54aa5
2023-10-07 00:53:38 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 335982 rows and 67 columns.
This is the metadata of the dataframe dfs[0]:
iso_code,continent,location,date,total_cases,new_cases,new_cases_smoothed,total_deaths,new_deaths,new_deaths_smoothed,total_cases_per_million,new_cases_per_million,new_cases_smoothed_per_million,total_deaths_per_million,new_deaths_per_million,new_deaths_smoothed_per_million,reproduction_rate,icu_patients,icu_patients_per_million,hosp_patients,hosp_patients_per_million,weekly_icu_admissions,weekly_icu_admissions_per_million,weekly_hosp_admissions,weekly_hosp_admissions_per_million,total_tests,new_tests,total_tests_per_thousand,new_tests_per_thousand,new_tests_smoothed,new_tests_smoothed_per_thousand,positive_rate,tests_per_case,tests_units,total_vaccinations,people_vaccinated,people_fully_vaccinated,total_boosters,new_vaccinations,new_vaccinations_smoothed,total_vaccinations_per_hundred,people_vaccinated_per_hundred,people_fully_vaccinated_per_hundred,total_boosters_per_hundred,new_vaccinations_smoothed_per_million,new_people_vaccinated_smoothed,new_people_vaccinated_smoothed_per_hundred,stringency_index,population_density,median_age,aged_65_older,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,hospital_beds_per_thousand,life_expectancy,human_development_index,population,excess_mortality_cumulative_absolute,excess_mortality_cumulative,excess_mortality,excess_mortality_cumulative_per_million
AFG,Asia,Afghanistan,2020-01-04,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-07,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-03,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-05,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-06,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
</dataframe>

<conversation>
User 1: How many locations are there?
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-07 00:53:48 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    num_locations = len(dfs[0]['location'].unique())
    result = {
        "type": "number",
        "value": num_locations
    }
    return result
```

2023-10-07 00:53:48 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-10-07 00:53:48 [ERROR] Failed with error: Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''
. Retrying
2023-10-07 00:53:48 [INFO] Using prompt: 
You are provided with the following pandas DataFrames with the following metadata:

<dataframe>
Dataframe dfs[0], with 335982 rows and 67 columns.
This is the metadata of the dataframe dfs[0]:
iso_code,continent,location,date,total_cases,new_cases,new_cases_smoothed,total_deaths,new_deaths,new_deaths_smoothed,total_cases_per_million,new_cases_per_million,new_cases_smoothed_per_million,total_deaths_per_million,new_deaths_per_million,new_deaths_smoothed_per_million,reproduction_rate,icu_patients,icu_patients_per_million,hosp_patients,hosp_patients_per_million,weekly_icu_admissions,weekly_icu_admissions_per_million,weekly_hosp_admissions,weekly_hosp_admissions_per_million,total_tests,new_tests,total_tests_per_thousand,new_tests_per_thousand,new_tests_smoothed,new_tests_smoothed_per_thousand,positive_rate,tests_per_case,tests_units,total_vaccinations,people_vaccinated,people_fully_vaccinated,total_boosters,new_vaccinations,new_vaccinations_smoothed,total_vaccinations_per_hundred,people_vaccinated_per_hundred,people_fully_vaccinated_per_hundred,total_boosters_per_hundred,new_vaccinations_smoothed_per_million,new_people_vaccinated_smoothed,new_people_vaccinated_smoothed_per_hundred,stringency_index,population_density,median_age,aged_65_older,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,hospital_beds_per_thousand,life_expectancy,human_development_index,population,excess_mortality_cumulative_absolute,excess_mortality_cumulative,excess_mortality,excess_mortality_cumulative_per_million
AFG,Asia,Afghanistan,2020-01-04,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-07,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-03,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-05,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-06,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
</dataframe>

The user asked the following question:
User 1: How many locations are there?

You generated this python code:
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    num_locations = len(dfs[0]['location'].unique())
    result = {
        "type": "number",
        "value": num_locations
    }
    return result

It fails with the following error:
Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''


Correct the python code and return a new python code that fixes the above mentioned error. Do not generate the same code again.

2023-10-07 00:53:57 [WARNING] Failed to execute code with a correction framework [retry number: 2]
2023-10-07 00:53:57 [ERROR] Failed with error: Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''
. Retrying
2023-10-07 00:53:57 [INFO] Using prompt: 
You are provided with the following pandas DataFrames with the following metadata:

<dataframe>
Dataframe dfs[0], with 335982 rows and 67 columns.
This is the metadata of the dataframe dfs[0]:
iso_code,continent,location,date,total_cases,new_cases,new_cases_smoothed,total_deaths,new_deaths,new_deaths_smoothed,total_cases_per_million,new_cases_per_million,new_cases_smoothed_per_million,total_deaths_per_million,new_deaths_per_million,new_deaths_smoothed_per_million,reproduction_rate,icu_patients,icu_patients_per_million,hosp_patients,hosp_patients_per_million,weekly_icu_admissions,weekly_icu_admissions_per_million,weekly_hosp_admissions,weekly_hosp_admissions_per_million,total_tests,new_tests,total_tests_per_thousand,new_tests_per_thousand,new_tests_smoothed,new_tests_smoothed_per_thousand,positive_rate,tests_per_case,tests_units,total_vaccinations,people_vaccinated,people_fully_vaccinated,total_boosters,new_vaccinations,new_vaccinations_smoothed,total_vaccinations_per_hundred,people_vaccinated_per_hundred,people_fully_vaccinated_per_hundred,total_boosters_per_hundred,new_vaccinations_smoothed_per_million,new_people_vaccinated_smoothed,new_people_vaccinated_smoothed_per_hundred,stringency_index,population_density,median_age,aged_65_older,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,hospital_beds_per_thousand,life_expectancy,human_development_index,population,excess_mortality_cumulative_absolute,excess_mortality_cumulative,excess_mortality,excess_mortality_cumulative_per_million
AFG,Asia,Afghanistan,2020-01-04,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-07,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-03,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-05,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-06,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
</dataframe>

The user asked the following question:
User 1: How many locations are there?

You generated this python code:
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    num_locations = len(dfs[0]['location'].unique())
    result = {
        "type": "number",
        "value": num_locations
    }
    return result

It fails with the following error:
Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''


Correct the python code and return a new python code that fixes the above mentioned error. Do not generate the same code again.

2023-10-07 00:58:54 [INFO] Question: How many locations are there?
2023-10-07 00:58:54 [INFO] Running PandasAI with openai LLM...
2023-10-07 00:58:54 [INFO] Prompt ID: d044e7e2-d467-4c8e-a9a2-600006c6d160
2023-10-07 00:58:54 [INFO] Using cached response
2023-10-07 00:58:54 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    num_locations = len(dfs[0]['location'].unique())
    result = {
        "type": "number",
        "value": num_locations
    }
    return result
```

2023-10-07 00:58:54 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-10-07 00:58:54 [ERROR] Failed with error: Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''
. Retrying
2023-10-07 00:58:54 [INFO] Using prompt: 
You are provided with the following pandas DataFrames with the following metadata:

<dataframe>
Dataframe dfs[0], with 335982 rows and 67 columns.
This is the metadata of the dataframe dfs[0]:
iso_code,continent,location,date,total_cases,new_cases,new_cases_smoothed,total_deaths,new_deaths,new_deaths_smoothed,total_cases_per_million,new_cases_per_million,new_cases_smoothed_per_million,total_deaths_per_million,new_deaths_per_million,new_deaths_smoothed_per_million,reproduction_rate,icu_patients,icu_patients_per_million,hosp_patients,hosp_patients_per_million,weekly_icu_admissions,weekly_icu_admissions_per_million,weekly_hosp_admissions,weekly_hosp_admissions_per_million,total_tests,new_tests,total_tests_per_thousand,new_tests_per_thousand,new_tests_smoothed,new_tests_smoothed_per_thousand,positive_rate,tests_per_case,tests_units,total_vaccinations,people_vaccinated,people_fully_vaccinated,total_boosters,new_vaccinations,new_vaccinations_smoothed,total_vaccinations_per_hundred,people_vaccinated_per_hundred,people_fully_vaccinated_per_hundred,total_boosters_per_hundred,new_vaccinations_smoothed_per_million,new_people_vaccinated_smoothed,new_people_vaccinated_smoothed_per_hundred,stringency_index,population_density,median_age,aged_65_older,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,hospital_beds_per_thousand,life_expectancy,human_development_index,population,excess_mortality_cumulative_absolute,excess_mortality_cumulative,excess_mortality,excess_mortality_cumulative_per_million
AFG,Asia,Afghanistan,2020-01-07,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-05,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-04,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-03,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-06,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
</dataframe>

The user asked the following question:
User 1: How many locations are there?

You generated this python code:
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    num_locations = len(dfs[0]['location'].unique())
    result = {
        "type": "number",
        "value": num_locations
    }
    return result

It fails with the following error:
Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''


Correct the python code and return a new python code that fixes the above mentioned error. Do not generate the same code again.

2023-10-07 00:59:04 [WARNING] Failed to execute code with a correction framework [retry number: 2]
2023-10-07 00:59:04 [ERROR] Failed with error: Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''
. Retrying
2023-10-07 00:59:04 [INFO] Using prompt: 
You are provided with the following pandas DataFrames with the following metadata:

<dataframe>
Dataframe dfs[0], with 335982 rows and 67 columns.
This is the metadata of the dataframe dfs[0]:
iso_code,continent,location,date,total_cases,new_cases,new_cases_smoothed,total_deaths,new_deaths,new_deaths_smoothed,total_cases_per_million,new_cases_per_million,new_cases_smoothed_per_million,total_deaths_per_million,new_deaths_per_million,new_deaths_smoothed_per_million,reproduction_rate,icu_patients,icu_patients_per_million,hosp_patients,hosp_patients_per_million,weekly_icu_admissions,weekly_icu_admissions_per_million,weekly_hosp_admissions,weekly_hosp_admissions_per_million,total_tests,new_tests,total_tests_per_thousand,new_tests_per_thousand,new_tests_smoothed,new_tests_smoothed_per_thousand,positive_rate,tests_per_case,tests_units,total_vaccinations,people_vaccinated,people_fully_vaccinated,total_boosters,new_vaccinations,new_vaccinations_smoothed,total_vaccinations_per_hundred,people_vaccinated_per_hundred,people_fully_vaccinated_per_hundred,total_boosters_per_hundred,new_vaccinations_smoothed_per_million,new_people_vaccinated_smoothed,new_people_vaccinated_smoothed_per_hundred,stringency_index,population_density,median_age,aged_65_older,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,hospital_beds_per_thousand,life_expectancy,human_development_index,population,excess_mortality_cumulative_absolute,excess_mortality_cumulative,excess_mortality,excess_mortality_cumulative_per_million
AFG,Asia,Afghanistan,2020-01-07,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-05,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-04,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-03,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
AFG,Asia,Afghanistan,2020-01-06,,0.0,,,0.0,,,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,54.422,18.6,2.581,1.337,1803.987,,597.029,9.59,,,37.746,0.5,64.83,0.511,41128772.0,,,,
</dataframe>

The user asked the following question:
User 1: How many locations are there?

You generated this python code:
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    Analyze the data
    1. Prepare: Preprocessing and cleaning data if necessary
    2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
    3. Analyze: Conducting the actual analysis (if the user asks to plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    num_locations = len(dfs[0]['location'].unique())
    result = {
        "type": "number",
        "value": num_locations
    }
    return result

It fails with the following error:
Traceback (most recent call last):
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/smart_datalake/__init__.py", line 337, in chat
    result = self._code_manager.execute_code(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/code_manager.py", line 209, in execute_code
    code = add_save_chart(
  File "/Users/waizwafiq/Documents/Projects/langsphere/langsphere_env/lib/python3.9/site-packages/pandasai/helpers/save_chart.py", line 27, in add_save_chart
    makedirs(save_charts_path)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/os.py", line 225, in makedirs
    mkdir(name, mode)
FileNotFoundError: [Errno 2] No such file or directory: ''


Correct the python code and return a new python code that fixes the above mentioned error. Do not generate the same code again.