In data analysis, it's often necessary to extract only a portion of your data — whether it’s a few rows, specific columns, or a combination of both. This process is called slicing, and in Pandas, it’s fast, flexible, and powerful.

In this article, you'll learn:

• ✅ What slicing means in Pandas

• ✅ How to slice rows and columns

• ✅ Using .loc[], .iloc[], and direct slicing

• ✅ Slicing with conditions

• ✅ Full working examples

• ✅ Tips and common pitfalls

What is Slicing in Pandas?

Slicing refers to extracting parts (or slices) of a Pandas DataFrame or Series. It’s similar to slicing lists in Python, but with more flexibility and structure due to labels and indexing.

Step 1: Import Pandas and Create Sample Data

import pandas as pd

# Sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 35, 40, 45],
    'Score': [88, 92, 85, 90, 95]
}
df = pd.DataFrame(data)

Output:

      Name  Age  Score
0    Alice   25     88
1      Bob   30     92
2  Charlie   35     85
3    David   40     90
4      Eva   45     95

Slicing Rows

Slice by index range:

df[1:4]

Output:

      Name  Age  Score
1      Bob   30     92
2  Charlie   35     85
3    David   40     90

Just like Python lists, slicing with df[start:stop] includes start and excludes stop.

Slicing Columns

Select specific columns:

df[['Name', 'Score']]

Output:

      Name  Score
0    Alice     88
1      Bob     92
2  Charlie     85
3    David     90
4      Eva     95

Always wrap column names in a list (e.g., [['Name']]) for multiple columns.

.loc[] – Label-based Slicing

.loc[] is used to slice based on labels (i.e., row index names and column names).

Slice rows by label and select columns:

df.loc[1:3, ['Name', 'Score']]

Output:

      Name  Score
1      Bob     92
2  Charlie     85
3    David     90

loc[] includes both start and end index when slicing.

.iloc[] – Position-based Slicing

.iloc[] slices using integer positions (like list slicing).

df.iloc[0:3, 1:3]

Output:

   Age  Score
0   25     88
1   30     92
2   35     85

iloc[] works like regular Python slices: start is inclusive, end is exclusive.

Conditional Slicing (Boolean Indexing)

You can slice data based on conditions:

df[df['Score'] > 90]

Output:

   Name  Age  Score
1   Bob   30     92
4   Eva   45     95

Combine conditions:

df[(df['Score'] > 90) & (df['Age'] < 40)]

✅ Full Working Example

import pandas as pd

# Create sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 35, 40, 45],
    'Score': [88, 92, 85, 90, 95]
}
df = pd.DataFrame(data)

# Slice rows from index 1 to 3
rows = df[1:4]

# Select 'Name' and 'Score' columns for rows 0 to 2
subset = df.loc[0:2, ['Name', 'Score']]

# Slice by position using iloc
position_slice = df.iloc[2:5, 0:2]

# Filter with condition
filtered = df[df['Score'] > 90]

print("Row Slice:\n", rows)
print("\nLabel-based Slice:\n", subset)
print("\nPosition-based Slice:\n", position_slice)
print("\nFiltered by Condition:\n", filtered)

Tips & Best Practices

• Use .loc[] when working with labeled indexes.

• Use .iloc[] when positions are more reliable than labels.

• Always double-check if slicing is inclusive or exclusive.

• Combine slicing and filtering for powerful subsetting.

• For performance, avoid unnecessary copies when slicing large datasets.

⚠️ Common Pitfalls

Summary

Pandas slicing is an essential tool for data manipulation and analysis. Whether you're extracting rows, columns, or complex subsets — understanding slicing techniques will make your workflow more efficient.

Key Takeaways:

• Use df[start:end] for basic row slicing

• Use .loc[] for label-based slicing

• Use .iloc[] for position-based slicing

• Combine slicing with conditions for flexible filtering