Pandas is a powerful Python library widely used for data manipulation and analysis. Most people are familiar with the basic functions like read_csv, head(), and groupby(). But Pandas has many lesser-known functions that can make your work easier, faster, or cleaner when dealing with data. These hidden gems often go unnoticed, yet knowing them can improve how you handle complex tasks. This article introduces some of these rarely used Pandas functions in 2025 that are worth adding to your toolbox.

15 Rarely Used Pandas Functions

1. query() for Cleaner Data Filtering

Filtering rows based on a condition is common and usually done with Boolean indexing. However, query() lets you write conditions as a string expression, making your code more readable, especially with multiple conditions.

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df.query('age > 30 and income < 50000')

This function works well when your filtering involves several columns. It can also handle variable substitution inside the query string, which helps keep your code tidy.

2. explode() to Flatten Lists in Columns

Sometimes, a column contains lists or arrays, and you want to convert each element in those lists into separate rows. The explode() function does exactly that.

For example, if a cell has a list of tags, explode() will create a new row for each tag, repeating the other column values.

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df.explode('tags')

This is particularly useful when working with nested data or JSON imports that have list fields.

3. get_dummies() for Quick One-Hot Encoding

When preparing data for machine learning, categorical variables often need conversion into numeric form. Instead of writing custom code, get_dummies() automatically converts categorical columns into dummy/indicator variables.

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pd.get_dummies(df['category'])

You can also apply it to the whole DataFrame and choose whether to drop one category to avoid multicollinearity.

4. pivot_table() Beyond Simple Pivoting

While many know pivot(), fewer use pivot_table(), which is more powerful. It allows aggregation during pivoting, handling duplicates gracefully.

For example, you can create a table showing average sales by region and product:

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df.pivot_table(values='sales', index='region', columns='product', aggfunc='mean')

pivot_table() supports multiple aggregation functions and can fill missing values, too, making it flexible for summarizing data.

5. mask() and where() to Conditionally Replace Data

These two functions are similar but serve different purposes. mask() replaces values where a condition is true, while where() keeps values where the condition is true and replaces others.

For instance, to replace negative values with zero:

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df['column'] = df['column'].mask(df['column'] < 0, 0)

These functions offer a clear way to apply conditional changes without complex loops or lambda functions.

6. assign() to Add or Modify Columns Cleanly

Rather than adding columns one by one, assign() lets you chain column creation or modification in a readable way.

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df.assign(new_col=df['old_col'] * 2, another_col=lambda x: x['new_col'] + 5)

This keeps transformations concise and readable, which is handy in data pipelines.

7. pipe() to Build Clear Data Processing Chains

Pandas operations often chain together, but when you have custom functions, pipe() helps insert them smoothly.

Example:

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df.pipe(custom_function).pipe(another_function)

It improves readability by reducing nested calls and clarifying the data flow through your processing steps.

8. convert_dtypes() for Better Data Type Inference

When loading data, Pandas guesses data types, but sometimes you want more precise types, like string instead of object.

Using:

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df = df.convert_dtypes()

helps Pandas select the best possible dtypes, improving performance and consistency, especially with nullable data types.

9. style for Quick DataFrame Visualization

Although not a data transformation function, style lets you apply visual formatting to DataFrames in Jupyter Notebooks.

For example:

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df.style.highlight_max(axis=0)

You can highlight max values, apply color gradients, or format numbers. This helps in quickly spotting trends or anomalies during data exploration.

10. memory_usage() to Check DataFrame Size

When working with large datasets, knowing memory usage is important. memory_usage() shows how much memory each column consumes.

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df.memory_usage(deep=True)

This lets you identify heavy columns and consider downcasting or converting types to save memory.

11. factorize() for Label Encoding

factorize() turns categorical values into numeric codes quickly.

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codes, uniques = pd.factorize(df['category'])

It’s faster than LabelEncoder from scikit-learn and useful when you want a simple numeric representation without external dependencies.

12. explode() with Multiple Columns

A newer Pandas feature allows exploding multiple list-like columns at once. This can flatten complex nested structures in fewer steps.

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df.explode(['col1', 'col2'])

This is handy for cleaning up data from APIs or files with nested arrays.

13. at[] and iat[] for Fast Scalar Access

When you need to access or set a single value in a DataFrame, at[] and iat[] are faster alternatives to loc[] and iloc[].

• at[] uses label-based indexing:

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df.at[row_label, column_label] = new_value

• iat[] uses integer position indexing:

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df.iat[row_index, column_index] = new_value

These are useful when performance matters and you’re working with individual cells.

14. explode() with ignore_index=True for Clean Result

By default, after exploding a list column, the index repeats old labels, which can cause confusion. Using the parameter ignore_index=True resets the index:

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df.explode('list_col', ignore_index=True)

This results in a clean DataFrame with sequential index values, making downstream operations easier.

15. melt() to Transform Wide Data into Long Format

melt() is great when you want to reshape data from wide to long format, which is common for plotting or statistical analysis.

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pd.melt(df, id_vars=['id'], value_vars=['var1', 'var2'])

This stacks selected columns into two: one for variable names and one for values, simplifying aggregation or filtering.

Wrapping It Up

The functions listed here aren't usually the first ones that come to mind when working with Pandas, but they can save time and reduce code complexity. Adding them to your Pandas skill set will help you handle complex data problems more efficiently in 2025. Whether it’s cleaning nested data, managing data types, or improving code readability, these lesser-known features can make a real difference. Next time you work with data, try one of these and see how it fits your workflow.