Sometime column name of dataframe is given with space, to remove this space we will rename the column name.
Before Rename
income_evaluation.csv(Check dataset On Kaggle)
Example:
col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship',\ 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
df.columns = col_names
df.columnsOutput:
Index(['age', 'workclass', 'fnlwgt', 'education', 'education_num',
'marital_status', 'occupation', 'relationship', 'race', 'sex',
'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',
'income'],
dtype='object')
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Great example! Renaming columns in a DataFrame is super important when you’re preparing data for machine learning models. Clean and consistent column names make it easier to preprocess, analyze, and build accurate pipelines without errors. I’ve faced similar cases where spaces or special characters in column names caused issues during feature selection or model training — so defining col_names like in your snippet is definitely the way to go. ✅
On a related note, if anyone here is also looking for tutorials outside of Python/ML and into streaming setups, I put together this detailed guide: Cómo descargar Magis TV gratis en Firestick: guía paso a paso. It’s a different niche, but just like renaming columns simplifies your ML workflow, following a clear step-by-step process makes setting up apps on Firestick much smoother.
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Great point! In Python, especially when working with machine learning, it’s common to encounter column names with spaces. You can easily rename them using:
import pandas as pd # Example DataFrame df = pd.DataFrame({ "Student Name": ["John", "Emma"], "Total Marks": [85, 92] }) # Rename columns by replacing spaces with underscores df.columns = df.columns.str.replace(" ", "_") print(df)This makes the DataFrame much cleaner and avoids errors while building ML models.
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