Table of Contents
- Introduction: Why Discover Hidden Pandas Functions?
- Why Most Developers Miss These Pandas Functions
- Function 1:
query()– SQL-like Data Filtering - Function 2:
eval()– Fast and Readable Calculations - Function 3:
pipe()– Building Clean Data Pipelines - Function 4:
explode()– Expanding List Values into Rows - Function 5:
nsmallest()andnlargest()– Quick Top & Bottom Selection - Function 6:
melt()– Reshaping Data for Analysis - Function 7:
squeeze()– Simplifying Single Columns - Function 8:
cut()andqcut()– Binning Continuous Data - Function 9:
factorize()– Encoding Categorical Data - Function 10:
sample()– Random Sampling Made Easy - Why These Hidden Functions Matter
- Practical Use Cases in Real Projects
- Final Thoughts
1. Introduction: Why Discover Hidden Pandas Functions?
If you work with Python and data, Pandas is probably one of the first libraries you’ve used. It is the foundation of almost every data analysis project, from small CSV explorations to large machine learning pipelines.
Most tutorials focus on basic Pandas functions like read_csv(), merge(), and groupby(). But beyond the basics, Pandas hides a lot of underrated functions that can make your work more productive, readable, and efficient.
In this blog, we’ll explore 10 hidden Pandas functions you probably never used, but once you do, you’ll wonder how you ever worked without them.
2. Why Most Developers Miss These Pandas Functions
Many developers rely heavily on Stack Overflow snippets or YouTube tutorials. As a result, they only see the common Pandas usage patterns. But Pandas has been actively developed for over a decade, and its documentation includes dozens of functions designed for specific scenarios.
Some functions are hidden simply because:
- They solve niche problems.
- They are new additions in recent versions.
- They don’t appear in beginner tutorials.
By learning them, you’ll improve both speed and clarity in your data workflows.
3. Function 1: query() – SQL-like Data Filtering
Instead of writing df[df['Age'] > 30], you can filter data using a clean SQL-like syntax.
import pandas as pd
df = pd.DataFrame({'Name': ['Ali', 'Sara', 'John', 'Emma'],
'Age': [25, 30, 22, 28],
'Salary': [50000, 60000, 45000, 70000]})
print(df.query("Age > 25"))
✅Cleaner, shorter, and perfect for users coming from a SQL background.
4. Function 2: eval() – Fast and Readable Calculations
Instead of manually creating new columns with multiple steps, use eval().
df.eval("Bonus = Salary * 0.1", inplace=True)
This avoids writing df['Bonus'] = df['Salary'] * 0.1 and is optimized for large datasets.
5. Function 3: pipe() – Building Clean Data Pipelines
When you chain many operations, your code can become unreadable. pipe() lets you structure it better.
def add_tax(data):
data['Tax'] = data['Salary'] * 0.15
return data
df = df.pipe(add_tax)
This makes your workflow modular and reusable.
6. Function 4: explode() – Expanding List Values into Rows
If a cell contains a list, explode() will split each item into its own row.
df2 = pd.DataFrame({'Name': ['Ali', 'Sara'],
'Hobbies': [['Cricket', 'Music'], ['Reading', 'Travel']]})
print(df2.explode('Hobbies'))
Very useful when working with nested JSON data or survey results.
7. Function 5: nsmallest() and nlargest() – Quick Top & Bottom Selection
Need top 5 salaries or youngest 3 employees?
print(df.nlargest(2, 'Salary'))
print(df.nsmallest(2, 'Age'))
Faster than sorting the entire DataFrame.
8. Function 6: melt() – Reshaping Data for Analysis
Transforms wide-format data into long-format (tidy data).
df3 = pd.DataFrame({'Name': ['Ali', 'Sara'],
'Math': [90, 85],
'Science': [88, 92]})
print(pd.melt(df3, id_vars=['Name'], value_vars=['Math', 'Science']))
Useful for Seaborn, Plotly, or Power BI visualizations.
9. Function 7: squeeze() – Simplifying Single Columns
When your DataFrame has only one column, squeeze() converts it to a Series:
df4 = pd.DataFrame([1, 2, 3], columns=['Numbers'])
print(df4.squeeze())
Saves memory and makes calculations easier.
10. Function 8: cut() and qcut() – Binning Continuous Data
Great for turning continuous values into categories.
ages = [15, 25, 32, 40, 60, 75]
print(pd.cut(ages, bins=[0,18,35,50,100]))
Used often in customer segmentation (e.g., “young”, “adult”, “senior”).
11. Function 9: factorize() – Encoding Categorical Data
Quickly encode categorical values without manually mapping.
df['Gender'] = ['M', 'F', 'M', 'F']
codes, uniques = pd.factorize(df['Gender'])
print(codes)
print(uniques)
Common in machine learning preprocessing.
12. Function 10: sample() – Random Sampling Made Easy
Need a random subset of data for testing?
print(df.sample(2))
Perfect for working with large datasets during experimentation.
13. Why These Hidden Functions Matter
- They make your code cleaner and faster.
- They are especially useful in real-world scenarios like cleaning messy data, preparing machine learning inputs, or quickly testing subsets.
- They reduce dependency on external libraries for simple transformations.
14. Practical Use Cases in Real Projects
explode()is used in processing survey data where one person may select multiple options.melt()is widely used in reshaping sales reports for visualization.query()helps finance teams filter transactional data like SQL.factorize()simplifies feature engineering in machine learning.
For more advanced case studies, you can also explore Pandas official documentation.
15. Final Thoughts
The future of data analysis with Pandas lies not in mastering only the common commands but in discovering these hidden gems. By incorporating query(), pipe(), explode(), and others into your daily workflow, you can dramatically improve both your productivity and the readability of your code.
Whether you are a beginner data analyst or an experienced machine learning engineer, these functions will make your Pandas experience much more powerful.