Polars DataFrame width Usage & Examples

In Polars, the DataFrame.width property returns the total number of columns in the DataFrame. It provides a quick and efficient way to examine the structure of your dataset without needing to load or view the full DataFrame.

In this article, I will explain the Polars DataFrame width Property by using syntax. Through clear examples, you’ll learn how it returns an integer representing the number of columns in a Polars DataFrame.

Key Points –

width is a property (not a method) of Polars DataFrame.
It returns an integer representing the column count.
It works on both non-empty and empty DataFrames.
When a DataFrame has no columns, width returns 0.
It does not include the number of rows, only columns.
width can be used in loops or scripts to check for column consistency across multiple DataFrames.
Adding or dropping columns will change the width value dynamically.
If a DataFrame has columns but no rows, width still reflects the column count.

Syntax of Polars DataFrame width Property

Let’s know the syntax of the width property.


# Syntax of width
property DataFrame.width: int

Return Value

This function returns an integer (int) representing the number of columns in the Polars DataFrame.

Usage of Polars DataFrame width Property

The width property in Polars is a simple and efficient way to get the number of columns in a DataFrame. It is particularly useful for checking the structure of your data quickly.

To run some examples of the Polars DataFrame width Property, let’s create a Polars DataFrame.


import polars as pl

technologies= {
    'Courses':["Spark","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000, 25000, 23000, 24000, 35000],
    'Discount':[1000, 2300, 1000, 1200, 2200],
    'Duration':['35days','40days','65days','50days','60days']
          }

df = pl.DataFrame(technologies)
print("Original DataFrame:\n", df)

Yields below output.

To get the number of columns in a Polars DataFrame, you can use the width property.


# Get number of columns
print("Number of columns (width):", df.width)

Here,

df.width returns an int representing the number of columns in the DataFrame.
In this case, the DataFrame includes the columns: 'Courses', 'Fee', 'Discount', and 'Duration'.

Using df.width After Dropping a Column

When you drop a column from a Polars DataFrame, the width property immediately reflects the reduced column count. This is consistent whether the DataFrame contains data or is empty.


# Drop the 'Discount' column
df_dropped = df.drop("Discount")
print("After dropping 'Discount', number of columns:", df_dropped.width)

# Output:
# After dropping 'Discount', number of columns: 3

Here,

Initially, df has 4 columns: 'Courses', 'Fee', 'Discount', and 'Duration'.
After df.drop("Discount"), the new DataFrame df_dropped has only 3 columns.
Using width confirms the column count before and after the drop.

Conditional Logic Based on Width Property

Applying conditional logic based on the width (i.e., number of columns) of a Polars DataFrame can be useful when handling dynamic data, allowing you to perform different actions depending on how many columns are present.


# Check number of columns and act accordingly
if df.width > 2:
    print("This DataFrame has more than 2 columns.")
elif df.width == 2:
    print("This DataFrame has exactly 2 columns.")
else:
    print("This DataFrame has fewer than 2 columns.")
    
# Output:
# This DataFrame has more than 2 columns.

Here,

df.width gives the number of columns.
You can use standard Python if, elif, and else to apply conditional logic.
This helps in situations where you want to: Drop columns if too many exist. Combine columns conditionally. Trigger different processing logic.

Using width on an Empty DataFrame

Using the width property on an empty Polars DataFrame (with 0 rows) still returns the correct number of defined columns. This makes it helpful for validating the structure of a DataFrame, even when it contains no data.


import polars as pl

# Create an empty DataFrame
empty_df = pl.DataFrame()

# Get the number of columns
print("Number of columns in empty DataFrame:", empty_df.width)

# Output:
# Number of columns in empty DataFrame: 0

Here,

pl.DataFrame() creates an empty DataFrame with no columns and no rows.
width returns the number of columns, so for an empty DataFrame, this will be 0.

Adding a new column Using width

When you add a new column to a Polars DataFrame, the width property automatically updates to reflect the increased column count.


import polars as pl

df = pl.DataFrame({
    'Courses': ["Spark", "PySpark", "Hadoop"],
    'Fee': [22000, 25000, 23000]
    
})

# Add a new column
df = df.with_columns(
    pl.Series('Discount',[1000, 2300, 1000]) )
print("New width:", df.width)  

# Output:
# New width: 3

Here,

with_columns() returns a new DataFrame (Polars is immutable by default).
The original df remains unchanged unless reassigned.

Conclusion

In conclusion, the DataFrame.width property in Polars is a simple yet powerful tool to quickly determine the number of columns in your dataset. Whether you're validating schema, building dynamic data pipelines, or applying conditional logic, width offers a lightweight and efficient way to understand and work with the structure of your DataFrame.

Happy Learning!!

References

https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.schema.html

Polars DataFrame width Usage & Examples

Syntax of Polars DataFrame width Property

Return Value

Usage of Polars DataFrame width Property

Using df.width After Dropping a Column

Conditional Logic Based on Width Property

Using width on an Empty DataFrame

Adding a new column Using width

Conclusion

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References