The code generated by the agent modifies the original data (dfs)

[XJTU-JP]

System Info

pandasai 2.4.0

🐛 Describe the bug

This is the data that is input to the agent

then the agent run this code:

# Convert the 'date' column to datetime format and set it as the index
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

# Sort the DataFrame by date
df.sort_index(inplace=True)

# Drop any rows with missing values
df = df.dropna()

# Extract the 'close' prices for analysis
close_prices = df['close']

The original data is modified.

How to avoid this problem

[dosubot[bot]]

To avoid modifying the original DataFrame when using PandasAI, you can create a copy of the DataFrame before performing any operations. This ensures that the original data remains unchanged. Here's how you can modify the code to work with a copy:

# Create a copy of the original DataFrame
df_copy = df.copy()

# Convert the 'date' column to datetime format and set it as the index
df_copy['date'] = pd.to_datetime(df_copy['date'])
df_copy.set_index('date', inplace=True)

# Sort the DataFrame by date
df_copy.sort_index(inplace=True)

# Drop any rows with missing values
df_copy = df_copy.dropna()

# Extract the 'close' prices for analysis
close_prices = df_copy['close']

By using df.copy(), you ensure that all subsequent operations are performed on df_copy, leaving the original df unchanged.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[XJTU-JP]

To avoid modifying the original DataFrame when using PandasAI, you can create a copy of the DataFrame before performing any operations. This ensures that the original data remains unchanged. Here's how you can modify the code to work with a copy:

# Create a copy of the original DataFrame
df_copy = df.copy()

# Convert the 'date' column to datetime format and set it as the index
df_copy['date'] = pd.to_datetime(df_copy['date'])
df_copy.set_index('date', inplace=True)

# Sort the DataFrame by date
df_copy.sort_index(inplace=True)

# Drop any rows with missing values
df_copy = df_copy.dropna()

# Extract the 'close' prices for analysis
close_prices = df_copy['close']

By using df.copy(), you ensure that all subsequent operations are performed on df_copy, leaving the original df unchanged.

To continue talking to Dosu, mention @dosu.

Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other

but the code generated by agent , not me

[gdcsinaptik]

@XJTU-JP can you share more details about the context in which the agent generates this code?
Which kind of user queries is it processing, whether you have trained the agent with instructions training, etc. The more the details, the better. It would be very helpful to investigate what is happening. Thanks in advance!