Slice Twice – Chained Slicing Techniques in Pandas 2026
"Slice twice" refers to performing two consecutive slicing operations on a DataFrame — first on rows, then on columns (or vice versa). This pattern is very common in data manipulation when you need to filter rows and then select specific columns, or narrow down both dimensions step by step.
TL;DR — Recommended "Slice Twice" Patterns
df.loc[row_condition, column_list]– Single line (best)- Chained slicing:
df.loc[row_condition].loc[:, column_list] - Use
.iloc[]for position-based double slicing
1. Basic "Slice Twice" – Filter Rows then Select Columns
import pandas as pd
df = pd.read_csv("sales_data.csv", parse_dates=["order_date"])
# Method 1: Recommended - Do both in one .loc[]
high_value_north = df.loc[
(df["region"] == "North") & (df["amount"] > 1000),
["order_date", "customer_id", "amount", "category"]
]
# Method 2: Slice twice (chained)
high_value = df.loc[df["amount"] > 1000] # First slice: rows
north_high = high_value.loc[:, ["customer_id", "amount"]] # Second slice: columns
2. Advanced Chained Slicing with Multiple Conditions
# Slice 1: Filter high-value orders in 2026
recent_high = df.loc[
(df["order_date"].dt.year == 2026) & (df["amount"] > 1500)
]
# Slice 2: Select only specific columns and sort
final = recent_high.loc[:, ["region", "category", "amount", "customer_id"]].sort_values("amount", ascending=False)
print(final.head(10))
3. Position-based Double Slicing with .iloc[]
# First 100 rows, then columns 2 to 6
subset = df.iloc[:100, 2:7]
# Last 50 rows, first 4 columns
recent = df.iloc[-50:, :4]
4. Best Practices for "Slice Twice" in 2026
- Prefer single
.loc[]with row condition + column list when possible — it's faster and cleaner - Use chained slicing when the logic is complex or you want to break it into steps for readability
- Always put row filtering first, then column selection
- Be careful with chained slicing on copies — it can trigger SettingWithCopyWarning
- Use method chaining with parentheses for complex filters
Conclusion
"Slice twice" is a very common pattern in Pandas: filter rows first, then select the columns you need. In 2026, the best practice is to do both operations in a single .loc[] call when possible. However, breaking it into two explicit steps (chained slicing) can improve readability when your filtering logic becomes complex. Mastering this pattern will make your data manipulation code cleaner and more efficient.
Next steps:
- Review your current code and replace separate row filtering + column selection with a single well-written
.loc[]statement where appropriate