Polars DataFrame width Usage & Examples

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  • Post category:Polars
  • Post last modified:April 6, 2025
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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.

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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.

polars width

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'.
polars width

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

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