Pandas is a popular Python library used for data analysis and manipulation. One of the most common tasks when working with Pandas DataFrames is sampling, which involves selecting a subset of rows from the original DataFrame.
Pandas provides three handy methods for sampling  head()
, tail()
, and sample()
. Sampling allows you to inspect a small portion of a large dataset, test models on sample data, and more. This comprehensive guide will explain Pandas’ sampling methods in detail with example code snippets.
We will cover:
 The basics of sampling and use cases
df.head()
 return first n rowsdf.tail()
 return last n rowsdf.sample()
 random sample of rows Parameters for finetuning sampling
 Sampling by group
 Best practices for effective sampling
By the end of this guide, you will have a solid understanding of how to use Pandas’ builtin sampling methods to work with subsets of large datasets efficiently.
Why Sample Datasets?
When working with large datasets in Pandas, it is often useful to take a sample or subset of the data for inspection, visualization, and model building. Some key reasons to sample data:

Understand data  Sample a few rows to quickly inspect data types, structure, and values.

Test models  Build and test models on a sample before applying to full dataset. Saves compute time.

Faster processing  Sampling reduces data size for faster loading, visualization and analysis.

Highlight patterns  Interesting patterns may emerge from sampling a diverse subset of data.

Data auditing  Take samples to audit data quality, check for anomalies and test hypotheses.

Prototype code  Run code on a small sample to debug issues faster before scaling up.
Pandas df.head()
Method
The most straightforward way to sample Pandas data is by using the df.head()
method. This returns the first n rows of the DataFrame, where n is the parameter specified.
import pandas as pd
df = pd.read_csv('data.csv')
df.head(10) #first 10 rows
The head()
method selects rows from the top of the DataFrame downwards. By default, it will return the first 5 rows if no parameter is specified.
Key properties of df.head()
:
 Selects the first n rows of the DataFrame.
 Parameter n specifies number of rows to return, default is 5.
 Rows are selected from the top of the DataFrame downwards.
 Useful for quickly inspecting initial rows and DataFrame structure.
 Returns a new DataFrame with sampled rows.
 Fast and convenient for small samples.
When to Use df.head()
 Quickly view top rows when loading data.
 Inspect beginning of data for sampling, quality check.
 Test run models, code on top portion of data.
 Understand DataFrame structure, data types of columns.
df.head()
gives you an easy way to inspect the first rows of the dataset. It is ideal for a fast sanity check on your data.
Pandas df.tail()
Method
The counterpart to head()
is the tail()
method. It returns the last n rows of the DataFrame, where n is the specified parameter.
df.tail(10) #last 10 rows
The tail()
method selects rows from the bottom of the DataFrame upwards. By default, it will return the last 5 rows if no parameter is passed.
Key properties of df.tail()
:
 Selects the last n rows of the DataFrame.
 Parameter n specifies number of rows to return, default is 5.
 Rows are selected from the bottom of the DataFrame upwards.
 Useful for quickly inspecting final rows.
 Returns a new DataFrame with sampled rows.
 Fast and convenient for small samples.
When to Use df.tail()
 Quickly view last rows when loading data.
 Inspect end of data for sampling, quality check.
 Test run models, code on last portion of data.
 Check bottom rows for anomalies, missing values.
 View summary stats by chaining
.describe()
.
df.tail()
enables you to conveniently peek at the tail end of your dataset. It complements head()
for basic data checks.
Pandas df.sample()
Method
For random sampling of rows, Pandas provides the sample()
method. This will return a randomly selected subset of rows from the DataFrame.
df.sample(10) #10 random rows
The sample()
method does not guarantee sorting and selects rows randomly. This makes it ideal for sampling a diverse subset of data.
Key properties of df.sample()
:
 Selects random rows from the DataFrame.
 Parameter n specifies number of rows to return.
 Rows are randomly selected, not ordered.
 Useful for sampling diverse data for inspection.
 Returns a new DataFrame with sampled rows.
 Use a seed for reproducible samples.
When to Use df.sample()
 Select a random subset to explore and visualize.
 Train and test models on random splits.
 Sample data for audits or quality testing.
 Get random rows matching criteria using
.query()
.  Shuffle data using
frac=1
and reset index.
df.sample()
gives you a simple way to grab random rows for sampling, training models, and more.
Key Parameters for Sampling Methods
The sampling methods head()
, tail()
, and sample()
all accept additional parameters to customize the sampled output.
Number of rows to return
Pass the n
parameter to specify the number of rows to return in the sample. For example:
df.head(10) #10 rows
df.tail(30) #30 rows
df.sample(200) #200 random rows
If no n
specified, a default value of 5 rows is returned.
Random state for reproducibility
For sample()
, set the random state seed to get reproducible results each run:
df.sample(20, random_state=42) #reproducible with seed 42
Sampling fraction of rows
Specify frac
as a float between 01 to sample a percentage of rows:
df.sample(frac=0.1) #10% random sample
Useful for large data where sample size is not known.
Sampling by group
To sample by categories in a column, use stratify
:
df.sample(100, stratify='category') #100 rows per category
More details on this method later.
Effective Practices for Pandas Sampling
Here are some key tips for effective sampling using Pandas:
Check sample size  Ensure your sample has enough rows to be representative, but not too large to slow down processing.
Use random sampling  sample()
gives more diverse samples than head()
/tail()
.
Specify seed  Set random state for reproducibility in sample()
.
Try different samples  Generate multiple samples with different parameters for robust results.
Sample fractions  Use frac
instead of n
for dynamic sampling of large datasets.
Sample groups  Use stratify
to sample representative rows from groups.
Visualize samples  Plot and visualize samples to spot trends and outliers.
Time operations  Compare time taken for full dataset vs. sample to determine optimal size.
Sample intelligently  Don’t purely random sample extremely large and diverse datasets.
Check for biases  Ensure sampling does not introduce selection bias.
Resample data  Generate new samples after data changes for updated results.
By following these best practices, you can effectively use Pandas’ sampling for a variety of tasks in data analysis and machine learning.
Sampling by Group with Pandas
An important sampling technique is to sample evenly across groups in your data. For example, you may want equal samples from different categories, time periods, or regions.
Pandas sample()
has a stratify
parameter to enable groupwise sampling:
df.sample(100, stratify='category')
This will grab 100 rows from each category group. The stratify
parameter can be:
 Column name to sample evenly across values
 List of column names to stratify jointly
 Boolean array matching DataFrame rows
Stratified sampling ensures that each group is represented in your sample proportional to its size in the full dataset.
Stratification Example
Say we have a retail store dataset with the columns city
, revenue
, and orders
. We want 100 random rows, evenly split by cities:
import pandas as pd
data = {'city': ['Austin', 'Dallas', 'Austin', 'Houston', 'Dallas'],
'revenue': [10000, 8000, 5000, 7000, 6000],
'orders': [100, 80, 50, 70, 60]}
df = pd.DataFrame(data)
df.sample(100, stratify='city')
This samples 100 rows, with approximately 33 from each city. Stratification ensures balanced groups in your sample.
Summary
In this comprehensive guide, we covered Pandas’ key sampling methods:
 head()  View first n rows, great for quick checks
 tail()  View last n rows, complements head()
 sample()  Random sampling of rows, useful for testing
 Parameters like n, frac, and stratify allow finetuning of samples
 Use best practices like random state and group sampling for robust results
Sampling is an essential skill for handling large datasets in Python. By mastering methods like head(), tail() and sample(), you can work with subsets of data for efficient exploration and analysis.
Happy sampling!