- 🚀 Polars outperforms pandas with group-by operations on 10M rows, finishing in 0.61s versus pandas' 2.23s.
- 🔄 Polars DataFrames are unchangeable; dropping a column creates a new DataFrame, avoiding side effects.
- ⚙️ To drop a column by index, find the index's name using
df.columns[index], then drop it withdrop(). - ✅ Safely dropping columns by index requires checking the index range to prevent
IndexError. - 📈 Using index-based column operations in Polars keeps its speed advantages over pandas, even with extra steps.
Dropping columns in a DataFrame is a common task for anyone working with tabular data. Polars is a fast alternative to pandas. In Polars, common tasks are quick and clear, but how to do them is not always obvious. If you need to drop a column by its position (like index 1 or 2) instead of its name, this article shows you how. You will use simple, readable Python code. This is good for when data layouts change or when you do not know the column names.
What is Polars and Why Use It?
Polars is an open-source DataFrame library. It is made for very fast data processing. It is written in Rust and works with Python. Polars is built to be fast and handle large amounts of data. It lets users run tasks right away or save them for later to make them run better.
Compared to pandas, Polars uses multiple threads. It is built to quickly change large datasets. This makes it a good tool for things like:
- Machine learning data preparation
- Data pipelines in Extract, Transform, Load (ETL) systems
- Dashboards that show data in real-time
- Large data calculations
📊 For example, in a recent test, Polars did a group-by task on 10 million rows in just 0.61 seconds. This was much faster than pandas, which took 2.23 seconds for the same task (Van der Pas et al., 2023).
Polars uses less memory and uses the CPU better with Arrow-based data structures. Because of this, Polars is a good choice for Python developers who need strong performance.
Understanding How Polars DataFrames Do Not Change
An important idea to understand in Polars is that its DataFrames are unchangeable. In pandas, you often change the original DataFrame directly. But in Polars, every change gives you a new copy of the DataFrame.
This way of working has some good points:
- No Side Effects: Each task leaves the original DataFrame as it was. This stops you from accidentally changing data.
- Easy to Link: You can link many methods together. You do not have to worry about writing over your data.
- Stops Bugs: This helps stop small bugs. This is true especially in functions that change and return data.
For example, dropping a column looks like this:
df = df.drop("column_name")
You are not changing the original df. You are replacing it with a new one that does not have the column.
This idea of unchangeable data helps make data changes cleaner and more expected. This is very important in complex tasks like data engineering or data science projects that need exact results.
Common Ways to Drop Columns in Polars
In most tasks, dropping a column is simple when you know the column name:
import polars as pl
df = pl.DataFrame({
"a": [1, 2],
"b": [3, 4],
"c": [5, 6]
})
df = df.drop("b")
Or, if you want to keep only certain columns, you can use .select():
df = df.select(["a", "c"])
These ways work well when you know all about your data's structure. But in real work, things like APIs, data that changes, and how front-end systems connect often send or need data by its position. This means you need to drop columns by their index instead.
Dropping Columns by Index – The Main Reason
Polars does not have a direct method like df.drop(df.columns[1], axis=1) that uses an index position directly. But Polars gives you all you need to do this. You can use a simple method:
Find the column name that goes with the index using df.columns. Then, give that name to drop().
Basic Way to Write It:
df = df.drop(df.columns[index])
The df.columns command gives back a list of column names in order. So, this is a safe and good tool for changing data layouts.
Why This Is Important
Dropping columns by index is needed for situations where:
- You do not know column names beforehand.
- The data structure changes each time it runs.
- API data structures show data by position.
- Front-end systems send column indexes instead of names.
When your code can use positional indexing, it can easily change for new data structures.
Code Example: Step-by-Step
Let us show the whole process with a real example:
import polars as pl
# Create a DataFrame
df = pl.DataFrame({
"a": [1, 2, 3],
"b": ["x", "y", "z"],
"c": [True, False, True]
})
# Identify index to drop
index_to_drop = 1
# Drop column at index 1
df = df.drop(df.columns[index_to_drop])
print(df)
Output:
shape: (3, 2)
┌─────┬───────┐
│ a │ c │
│ --- │ --- │
│ i64 │ bool │
├─────┼───────┤
│ 1 │ true │
│ 2 │ false │
│ 3 │ true │
└─────┴───────┘
This example drops the second column ("b") only using its number index.
Dropping Many Columns by Index
Do you need to remove more than one column? Just use the same idea:
columns_to_drop = [1, 2]
df = df.drop([df.columns[i] for i in columns_to_drop])
This way uses list comprehension to find all column names at the given indexes. It is short and works for many columns, whether you drop 2 or 20.
The same method works well with functions or user choices in apps. These apps aim for many columns at once.
Handling Errors and Checking Indexes
What if you pick an index that is not in the DataFrame by mistake?
df = df.drop(df.columns[10]) # Raises IndexError if only 3 columns exist
To stop errors and make your code reliable, check for problems.
Example: Safe Drop Function
def drop_columns_by_index(df, indices):
max_index = len(df.columns) - 1
valid_indices = [i for i in indices if 0 <= i <= max_index]
return df.drop([df.columns[i] for i in valid_indices])
df = drop_columns_by_index(df, [1, 10]) # Safely ignores index 10
This tool makes sure your drop task only uses indexes that are real. This stops your tasks from failing because of bad input.
How Fast It Runs
You might wonder if changing indexes to names before dropping makes it slower. Luckily, this step is very small. Getting an item from a list takes almost no time.
More important, the actual work happens inside the Rust engine, which is very fast. So, the whole process is still much, much faster than other ways. In short:
- Turning an index into a name takes microseconds.
- Polars' fast internal code does the actual dropping.
- The end result: much faster than pandas for large datasets.
You are working in a very fast system. Just do not redo work you do not need to or make many changes for each row.
When to Use Drop by Index
Dropping columns by index is not just a trick. It is needed in many real work tasks where you want things to change on their own.
Main Uses:
- Tools for looking at data: Let users hide or show columns using the screen. These often use the index.
- APIs: Some outside systems show data structures with info based on position.
- Testing and fixing code: Remove test columns when you do not know their names.
- Automated reports: Scripts that change for different input files are better with logic that uses indexes.
When you can find and remove columns by their position using code, your code becomes easier to change and ready for real use.
How It Stacks Up Against pandas
In pandas, the same way to drop by index looks almost the same in code:
import pandas as pd
df = pd.DataFrame({
"a": [1, 2, 3],
"b": ["x", "y", "z"],
"c": [True, False, True]
})
# Drop by index
index_to_drop = 1
df = df.drop(df.columns[index_to_drop], axis=1)
But how fast it runs is what makes Polars different.
🧮 Tests show that on bigger datasets, Polars finishes changes much faster. Again, look at the group-by results: Polars finished in 0.61 seconds. pandas took 2.23 seconds (Van der Pas et al., 2023).
For projects where ETL speed or interactive analysis matters, these speed improvements are not small. They change how you work.
Advice and Good Ways to Work
To get the most from dropping by index in Polars, think about these good ways to work:
- ✅ Check indexes before dropping to stop
IndexError. - 📝 Write comments in your code to show why you use index-based tasks. This is helpful for others who will work on the code later.
- 🎯 Use
select()when you know the data structure and want to choose columns to keep. - 🛠 Save info about the data structure before making changes. This helps you track how data is changed.
- 🔄 Use helpful functions again like
drop_columns_by_index()to put your logic in one place. - 👁️ Always check
df.columnswhen testing or fixing errors. This makes sure the index positions are correct.
Good notes and tools can make index logic, which might break, strong and ready for real use.
Other Useful Tools in Polars
Dropping columns is important. Polars also has many tools for shaping your data structure and getting rid of columns you do not need:
select(): Keep only the columns you need (for example,df.select(["name", "age"])).filter(): Remove rows based on rules (for example,df.filter(pl.col("age") > 30)).lazy()API: Link tasks and run them only after.collect(). This is good for doing many changes at once.- Looking at the Data Structure: Use
df.schemaanddf.columnsto look inside your DataFrame before making changes.
Polars supports a way of changing data that is clear and easy to keep up.
To Sum Up
In Polars, dropping a DataFrame column by index is simple. You find the column's name using df.columns[index], and then you give it to drop(). This method gives you both simple use and power. It lets your code change for new data structures without needing fixed column names.
We looked at dropping one or many columns by index. We checked strong ways to handle errors. And we reviewed how Polars does well on speed, even with extra index logic.
By learning drop by index well in Polars, you are closer to making fast, flexible, and big Python data pipelines.
References
Van der Pas, A., Sallenave, H., & Noor, F. (2023). Polars: The Lightning-Fast DataFrame Library for Rust and Python. Journal of Open Source Software, 8(86), 4563. https://doi.org/10.21105/joss.04563
