How to Fix SettingWithCopyWarning in Pandas

The SettingWithCopyWarning you’re seeing after upgrading to pandas 0.13.0rc1 is a new safety mechanism introduced to prevent common data manipulation errors. This warning occurs when pandas cannot determine whether you’re modifying the original DataFrame or a copy/slice of it.

Contents

• What the Warning Means
• Why It Occurs in Your Code
• Recommended Solutions
• Suppressing the Warning
• Best Practices
• Conclusion

What the Warning Means

The SettingWithCopyWarning tells you that your code is attempting to modify a value on what might be a copy of a DataFrame slice rather than the original DataFrame. As pandas documentation explains, “Warning raised when trying to set on a copied slice from a DataFrame.”

This warning was introduced in pandas 0.13.0rc1 to help developers avoid situations where they think they’re modifying their original DataFrame but are actually working with a copy, which can lead to unexpected behavior and data inconsistencies.

Why It Occurs in Your Code

In your _decode_stock_quote function, the warning appears in several places:

1. quote_df = quote_df.ix[:,[0,3,2,1,4,5,8,9,30,31]] - This line creates a view of the original DataFrame using the deprecated .ix indexer
2. quote_df['TVol'] = quote_df['TVol']/TVOL_SCALE - This assignment targets what pandas may think is a copy
3. Similar assignments for TAmt and TDate

The issue stems from chained indexing operations. When you use .ix to select columns and then try to assign values to columns, pandas can’t determine if you’re modifying the original DataFrame or a copy.

Recommended Solutions

Solution 1: Use .loc for Assignments

Replace your column assignments with .loc syntax:

def _decode_stock_quote(list_of_150_stk_str):
    """decode the webpage and return dataframe"""
    
    from cStringIO import StringIO
    
    str_of_all = "".join(list_of_150_stk_str)
    
    quote_df = pd.read_csv(
        StringIO(str_of_all),
        sep=',',
        names=list('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefg')) 
    quote_df.rename(
        columns={
            'A':'STK', 'B':'TOpen', 'C':'TPCLOSE', 'D':'TPrice',
            'E':'THigh', 'F':'TLow', 'I':'TVol', 'J':'TAmt',
            'e':'TDate', 'f':'TTime'},
        inplace=True)
    
    # Use .loc for column selection and assignment
    quote_df = quote_df.loc[:, [0,3,2,1,4,5,8,9,30,31]]
    
    # Use .loc for all assignments
    quote_df.loc[:, 'TClose'] = quote_df['TPrice']
    quote_df.loc[:, 'RT'] = 100 * (quote_df['TPrice']/quote_df['TPCLOSE'] - 1)
    quote_df.loc[:, 'TVol'] = quote_df['TVol']/TVOL_SCALE
    quote_df.loc[:, 'TAmt'] = quote_df['TAmt']/TAMT_SCALE
    quote_df.loc[:, 'STK_ID'] = quote_df['STK'].str.slice(13,19)
    quote_df.loc[:, 'STK_Name'] = quote_df['STK'].str.slice(21,30)
    quote_df.loc[:, 'TDate'] = quote_df.TDate.map(lambda x: x[0:4]+x[5:7]+x[8:10])
    
    return quote_df

Solution 2: Explicitly Create Copies

If you want to work with a separate DataFrame, explicitly create a copy:

# After your column selection
quote_df = quote_df.ix[:,[0,3,2,1,4,5,8,9,30,31]].copy()

# Then you can use your original syntax
quote_df['TClose'] = quote_df['TPrice']
quote_df['RT'] = 100 * (quote_df['TPrice']/quote_df['TPCLOSE'] - 1)
# ... etc

Solution 3: Replace Deprecated .ix Indexer

The .ix indexer is deprecated and can cause unpredictable behavior. Replace it with .iloc (for integer-based indexing) or .loc (for label-based indexing):

# Replace .ix with .iloc for integer-based indexing
quote_df = quote_df.iloc[:, [0,3,2,1,4,5,8,9,30,31]]

Suppressing the Warning

If you prefer to keep your current syntax, you can suppress the warning by modifying pandas settings:

import pandas as pd

# Suppress the warning
pd.options.mode.chained_assignment = None

# Or use these alternatives:
# pd.options.mode.chained_assignment = 'ignore'  # Also suppresses the warning
# pd.options.mode.chained_assignment = 'raise'   # Raises an exception instead of warning

However, as Real Python warns, “You want to modify df and not some intermediate data structure that isn’t referenced by any variable. That’s why pandas issues a SettingWithCopyWarning and warns you about this possible mistake.”

Best Practices

1. Use .loc for assignments: It’s the most explicit and reliable way to modify DataFrames
2. Avoid chained indexing: Use single-step operations instead of multiple indexing operations
3. Be explicit about copies: If you need a copy, use .copy() method
4. Replace deprecated methods: Update .ix to .iloc or .loc

Important Note: The Copy-on-Write (CoW) mode in pandas will eventually become the default behavior in pandas 3.0. As mentioned in the Stack Overflow discussion, “One consequence is SettingWithCopyWarning will never be raised. Another is chained assignment never works.” Planning for this future change now is recommended.

Conclusion

The SettingWithCopyWarning in pandas 0.13.0rc1 and later versions is a helpful feature that prevents potential data manipulation errors. While you can suppress the warning if needed, the best approach is to modify your code to use explicit DataFrame operations like .loc for assignments and avoid chained indexing. This will not only eliminate the warnings but also make your code more reliable and easier to understand.

For your specific case, I recommend using .loc syntax for all assignments and replacing the deprecated .ix indexer with .iloc or .loc. This approach will resolve the warnings while maintaining the same functionality.