Generating Wrong Code

[harshvardhanchand]

System Info

python version 3.11
pandasai 2.28
OS:Mac OS

🐛 Describe the bug

Python

def get_all_commodities_trend():
df = dfs[14]
filtered_df = df[df['Sourcemonth'].notna()]
filtered_df['All commodities'] = pd.to_numeric(filtered_df['All commodities'])
try:
plt.figure(figsize=(10, 6))
plt.plot(filtered_df['Sourcemonth'], filtered_df['All commodities'])
plt.xlabel('Month')
plt.ylabel('Value')
plt.title("Trend for 'All commodities'")
plt.savefig('/Users/harsh/Desktop/NDAP-Analyzer-/Final Version/exports/charts/temp_chart.png')
except Exception as e:
print(f'An error occurred: {e}')

writes python at the beginning of code making the code incorrect

[dosubot]

To address the issue with your code for generating a trend plot for "All commodities," you can follow these steps to ensure it works correctly:

1. Ensure DataFrame is Correctly Loaded: Make sure dfs[14] contains the correct DataFrame.
2. Convert Columns to Correct Data Types: Ensure the columns are in the correct data types for plotting.
3. Handle Missing Values: Filter out rows with missing values in the relevant columns.
4. Plot the Data: Use matplotlib to plot the data and save the plot.

Here is the corrected version of your code:

import matplotlib.pyplot as plt
import pandas as pd

def get_all_commodities_trend():
    df = dfs[14]
    
    # Ensure 'Sourcemonth' and 'All commodities' columns exist
    if 'Sourcemonth' not in df.columns or 'All commodities' not in df.columns:
        print("Required columns are missing in the DataFrame.")
        return
    
    # Filter out rows with missing values in 'Sourcemonth' and 'All commodities'
    filtered_df = df[df['Sourcemonth'].notna() & df['All commodities'].notna()]
    
    # Convert 'All commodities' to numeric
    filtered_df['All commodities'] = pd.to_numeric(filtered_df['All commodities'], errors='coerce')
    
    try:
        plt.figure(figsize=(10, 6))
        plt.plot(filtered_df['Sourcemonth'], filtered_df['All commodities'])
        plt.xlabel('Month')
        plt.ylabel('Value')
        plt.title("Trend for 'All commodities'")
        plt.savefig('/Users/harsh/Desktop/NDAP-Analyzer-/Final Version/exports/charts/temp_chart.png')
        plt.show()
    except Exception as e:
        print(f'An error occurred: {e}')

# Call the function to generate the plot
get_all_commodities_trend()

Key Changes:

1. Column Existence Check: Added a check to ensure the required columns exist in the DataFrame.
2. Filter Missing Values: Filtered out rows where either 'Sourcemonth' or 'All commodities' is missing.
3. Convert to Numeric: Used pd.to_numeric with errors='coerce' to handle any non-numeric values gracefully.
4. Show Plot: Added plt.show() to display the plot after saving it.

This should resolve the issues and generate the correct trend plot for "All commodities."

To continue talking to Dosu, mention @dosu.

[harshvardhanchand]

To address the issue with your code for generating a trend plot for "All commodities," you can follow these steps to ensure it works correctly:

1. Ensure DataFrame is Correctly Loaded: Make sure dfs[14] contains the correct DataFrame.
2. Convert Columns to Correct Data Types: Ensure the columns are in the correct data types for plotting.
3. Handle Missing Values: Filter out rows with missing values in the relevant columns.
4. Plot the Data: Use matplotlib to plot the data and save the plot.

Here is the corrected version of your code:

import matplotlib.pyplot as plt
import pandas as pd

def get_all_commodities_trend():
    df = dfs[14]
    
    # Ensure 'Sourcemonth' and 'All commodities' columns exist
    if 'Sourcemonth' not in df.columns or 'All commodities' not in df.columns:
        print("Required columns are missing in the DataFrame.")
        return
    
    # Filter out rows with missing values in 'Sourcemonth' and 'All commodities'
    filtered_df = df[df['Sourcemonth'].notna() & df['All commodities'].notna()]
    
    # Convert 'All commodities' to numeric
    filtered_df['All commodities'] = pd.to_numeric(filtered_df['All commodities'], errors='coerce')
    
    try:
        plt.figure(figsize=(10, 6))
        plt.plot(filtered_df['Sourcemonth'], filtered_df['All commodities'])
        plt.xlabel('Month')
        plt.ylabel('Value')
        plt.title("Trend for 'All commodities'")
        plt.savefig('/Users/harsh/Desktop/NDAP-Analyzer-/Final Version/exports/charts/temp_chart.png')
        plt.show()
    except Exception as e:
        print(f'An error occurred: {e}')

# Call the function to generate the plot
get_all_commodities_trend()

Key Changes:

1. Column Existence Check: Added a check to ensure the required columns exist in the DataFrame.
2. Filter Missing Values: Filtered out rows where either 'Sourcemonth' or 'All commodities' is missing.
3. Convert to Numeric: Used pd.to_numeric with errors='coerce' to handle any non-numeric values gracefully.
4. Show Plot: Added plt.show() to display the plot after saving it.

This should resolve the issues and generate the correct trend plot for "All commodities."

To continue talking to Dosu, mention @dosu.

No You are wrong what I does is write Python at the beginning of python code making the code incorrect every time I use it

[dosubot]

To prevent the word "Python" from being added at the beginning of your code and ensure it runs as intended, you can use the _polish_code method from the LLM class. This method removes the leading "python" or "py" from the code string.

Here is the relevant part of the code:

def _polish_code(self, code: str) -> str:
    """
    Polish the code by removing the leading "python" or "py", 
    removing surrounding '`' characters and removing trailing spaces and new lines.

    Args:
        code (str): A string of Python code.

    Returns:
        str: Polished code.
    """
    if re.match(r"^(python|py)", code):
        code = re.sub(r"^(python|py)", "", code)
    if re.match(r"^`.*`$", code):
        code = re.sub(r"^`(.*)`$", r"\1", code)
    code = code.strip()
    return code

You can call this method with your code string to remove the leading "Python":

llm_instance = LLM()
clean_code = llm_instance._polish_code(your_code_string)

This will ensure that any leading "Python" or "py" is removed from your code, allowing it to run as intended ^[1].

To continue talking to Dosu, mention @dosu.