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Pandas is a powerful and user-friendly library that plays a vital role in the data science workflow. From cleaning messy datasets to performing complex transformations, Pandas provides efficient tools that simplify these tasks.
In this blog, you’ll learn 15 essential Pandas tricks that every data enthusiast should know to boost productivity in 2025. Whether you’re just starting or an experienced data scientist, these tips will make working with Pandas faster and more effective.
1. Selecting Columns and Rows
Selecting rows and columns is one of the most basic yet essential operations in Pandas.
Selecting Columns
- Access a single column:
df['column_name']
- Access multiple columns:
df[['column1', 'column2']]
- Using dot notation (works only for column names without spaces):
df.column_name
Selecting Rows
- By index position:
df.iloc[0] # First row
df.iloc[:5] # First 5 rows
- By index label:
df.loc['row_label']
- Conditional selection: