Built with Tutify.io

    AIStudyLabs

    Polars Delete a Column from DataFrame

    Polars Delete a Column from DataFrame

    To delete or remove a column from a Polars DataFrame, you can use the drop() method or select().exclude() by specifying the column name. This operation creates a new DataFrame that excludes the specified column(s), leaving the original DataFrame unchanged.

    Deleting a column from a Polars DataFrame means removing one or more columns from the table of data, resulting in a new DataFrame that no longer includes those columns. Since Polars DataFrames are immutable, this operation does not modify the original DataFrame but instead returns a new DataFrame without the specified column(s). In this article, I will explain the different methods to delete or drop a column from a polars DataFrame.

    Key Points –

    <a id="h-usage-of-polars-delete-a-column-from-dataframe"></a>

    Usage of Polars Delete a Column from DataFrame

    Deleting a column from a Polars DataFrame means removing that column entirely so it no longer appears in the DataFrame. Since Polars DataFrames are immutable by default, deleting a column returns a new DataFrame without the specified column(s).

    Now, let’s create a Polars DataFrame.

    
import polars as pl

# Creating a dictionary with course information
technologies = {
    'Courses': ["Spark", "Pandas", "Hadoop", "Python"],
    'Fees': [30000, 40000, 35000, 60000],
    'Duration': ['30days', '60days', '50days', '40days'],
    'Discount': [1000, 2000, 1500, 3000]
}

# Creating a Polars DataFrame from the dictionary
df = pl.DataFrame(technologies)
print("Original DataFrame:\n", df)

    Yields below output.

    To remove a single column by name from a Polars DataFrame, you can use the drop() method and pass the column name as a string.

    
# Drop a single column by name
df2 = df.drop("Duration")
print("DataFrame after dropping 'Duration' column:\n", df2)

    Yields below output.

    <a id="h-drop-all-numeric-columns-using-a-selector"></a>

    Drop All Numeric Columns Using a Selector

    To drop all numeric columns in a Polars DataFrame using a selector, you can use the select() or drop() methods combined with Polars' pl.col() and datatype selectors.

    
# Drop all numeric columns
df2 = df.drop(pl.col(pl.NUMERIC_DTYPES))
print("DataFrame after dropping all numeric columns:\n", df2)

# Output:
# DataFrame after dropping all numeric columns:
# shape: (4, 2)
┌─────────┬──────────┐
│ Courses ┆ Duration │
│ ---     ┆ ---      │
│ str     ┆ str      │
╞═════════╪══════════╡
│ Spark   ┆ 30days   │
│ Pandas  ┆ 60days   │
│ Hadoop  ┆ 50days   │
│ Python  ┆ 40days   │
└─────────┴──────────┘

    Here,

    Similarly, to drop all numeric columns from a Polars DataFrame using a selector.

    
# Drop all numeric columns (Fees and Discount)
df2 = df.drop(pl.selectors.numeric())
print("DataFrame after dropping all numeric columns:\n", df2)

# Output:
# DataFrame after dropping all numeric columns:
# shape: (4, 2)
┌─────────┬──────────┐
│ Courses ┆ Duration │
│ ---     ┆ ---      │
│ str     ┆ str      │
╞═════════╪══════════╡
│ Spark   ┆ 30days   │
│ Pandas  ┆ 60days   │
│ Hadoop  ┆ 50days   │
│ Python  ┆ 40days   │
└─────────┴──────────┘

    Here,

    <a id="h-drop-columns-using-select-exclude-single-column"></a>

    Drop Columns Using select().exclude() (Single Column)

    To drop a single column in Polars, you can use select().exclude() to select all columns except the one you want to remove. This approach is useful when you want to keep most columns but exclude specific ones.

    
# Drop columns Using select().exclude()
df2 = df.select(pl.all().exclude("Duration"))
print("DataFrame after dropping 'Duration' using select().exclude():\n", df2)

# Drop the 'Duration' column using select().exclude()
df2 = df.select(pl.exclude("Duration"))
print("DataFrame after dropping 'Duration' using select().exclude():\n", df2)

# Output:
# DataFrame after dropping 'Duration' using select().exclude():
# shape: (4, 3)
┌─────────┬───────┬──────────┐
│ Courses ┆ Fees  ┆ Discount │
│ ---     ┆ ---   ┆ ---      │
│ str     ┆ i64   ┆ i64      │
╞═════════╪═══════╪══════════╡
│ Spark   ┆ 30000 ┆ 1000     │
│ Pandas  ┆ 40000 ┆ 2000     │
│ Hadoop  ┆ 35000 ┆ 1500     │
│ Python  ┆ 60000 ┆ 3000     │
└─────────┴───────┴──────────┘

    Here,

    <a id="h-drop-columns-using-select-exclude-multiple-columns"></a>

    Drop Columns Using select().exclude() (Multiple Columns)

    To drop multiple columns in Polars using select().exclude(), pass a list of column names to pl.exclude().

    
# Drop multiple columns using select().exclude()
df2 = df.select(pl.all().exclude(["Duration", "Discount"]))
print("DataFrame after dropping 'Discount' and 'Duration' columns:\n", df2)

# Output:
# DataFrame after dropping 'Discount' and 'Duration' columns:
 shape: (4, 2)
┌─────────┬───────┐
│ Courses ┆ Fees  │
│ ---     ┆ ---   │
│ str     ┆ i64   │
╞═════════╪═══════╡
│ Spark   ┆ 30000 │
│ Pandas  ┆ 40000 │
│ Hadoop  ┆ 35000 │
│ Python  ┆ 60000 │
└─────────┴───────┘

    Here,

    <a id="h-conclusion"></a>

    Conclusion

    In summary, Polars provides flexible and efficient ways to drop columns, whether single or multiple, using methods like drop() and select().exclude(). By leveraging these techniques, you can easily manipulate your DataFrame to keep only the data you need.

    Happy Learning!!

    <a id="h-related-articles"></a>

    Related Articles

    <a id="h-references"></a>

    References