Hi everyone,

I’m new to working with Python and Pandas at my job, and I’ve started seeing the SettingWithCopyWarning while cleaning data. The confusing part is that my code still runs, so I’m not sure whether this is something I can ignore or if it’s actually causing problems.

I’ve read a few explanations online, but I’m still struggling to understand what this warning really means. Is it telling me that I’m modifying a copy instead of the original DataFrame? If so, what’s the recommended way to avoid this warning and make sure my changes are applied correctly?

I’d appreciate a beginner-friendly explanation, especially if someone can explain why this warning exists and the best practices for handling it in real projects. 

Hello! I’m considering switching to a career in data science and wanted to get advice from the community. I’m in my late 30s and looking for programs that provide practical experience in Python, machine learning, and data analytics.

Are there any bootcamps or online courses that stand out for career changers? Preferably ones that:

• Offer hands-on projects
• Are affordable
• Provide support in Spanish or with subtitles

Any experiences or recommendations would be really helpful!

I’m working on a system where numerical values inside content are updated dynamically. The issue is that the title often contains the same number, and when the value changes in the content, the title becomes inaccurate.

I’m looking for a reliable way to automatically detect these changes and update the title accordingly without manually editing each post. Has anyone solved a similar problem or found a scalable approach for this?

I came across this pipeline setup where feature engineering is being added before a ColumnTransformer, but the new features don’t seem to flow correctly through the pipeline:

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.base import BaseEstimator, TransformerMixin

class FeatureAdder(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self
    
    def transform(self, X):
        X['new_feature'] = X['col1'] * X['col2']
        return X

pipeline = Pipeline([
    ('feature_add', FeatureAdder()),
    ('preprocess', ColumnTransformer([
        ('num', StandardScaler(), ['col1', 'col2']),
        ('cat', OneHotEncoder(), ['col3'])
    ]))
])

The issue is:

• The newly created new_feature is not included in the ColumnTransformer

• This leads to it being dropped during transformation

In a setup like this:

• Should the ColumnTransformer be dynamically updated to include new features?

• Or is it better to handle feature engineering outside the pipeline altogether?

• How do you ensure feature consistency without breaking pipeline modularity?