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    Polars DataFrame drop() Method

    Polars DataFrame drop() Method

    In Polars, the drop() method is used to remove one or more columns from a DataFrame. It offers a flexible and efficient approach to manipulating the structure of a DataFrame, allowing columns to be removed based on their names, patterns, or selectors. While this function is similar to the drop() function in other libraries like Pandas, it is specifically designed for use with Polars' DataFrame.

    In this article, I will cover the Polars DataFrame drop() method, including its syntax, parameters, and usage, as well as how it returns a new DataFrame with the specified columns removed.

    Key Points –

    <a id="h-syntax-of-polars-dataframe-drop"></a>

    Syntax of Polars DataFrame drop()

    Let’s know the syntax of the Polars DataFrame drop() method.

    
# Syntax of drop()
DataFrame.drop(
    *columns: ColumnNameOrSelector | Iterable[ColumnNameOrSelector],
    strict: bool = True,
) -> DataFrame

    <a id="h-parameters-of-the-polars-dataframe-drop"></a>

    Parameters of the Polars DataFrame.drop()

    Following are the parameters of the drop() method.

    <a id="h-return-value"></a>

    Return Value

    This function returns a new DataFrame with the specified columns removed. The original DataFrame is not modified.

    <a id="h-usage-of-polars-dataframe-drop-method"></a>

    Usage of Polars DataFrame drop() Method

    The drop() method in Polars is used to remove one or more columns from a DataFrame. This operation returns a new DataFrame with the specified columns removed.

    First, let’s create a Polars DataFrame.

    
import polars as pl

# Creating a new Polars DataFrame
technologies = {
    'Courses': ["Spark", "Hadoop", "Python", "Pandas"],
    'Fees': [22000, 25000, 20000, 26000],
    'Duration': ['30days', '50days', '40days', '60days'],
    'Discount': [1000, 1500, 1200, 2000]
}

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

    Yields below output.

    To drop a single column from a Polars DataFrame, use the drop() method. Here’s how you can drop one column from your DataFrame.

    
# Dropping the 'Discount' column
df2 = df.drop("Discount")
print("DataFrame after dropping 'Discount':\n", df2)

    Here,

    <a id="h-dropping-multiple-columns"></a>

    Dropping Multiple Columns

    To drop multiple columns from a Polars DataFrame, use the drop() method with a list of column names.

    
# Dropping the 'Fees' and 'Duration' columns
df2 = df.drop(["Fees", "Duration"])
print("DataFrame after dropping 'Fees' and 'Duration':\n", df2)

# Output:
# DataFrame after dropping 'Fees' and 'Duration':
# shape: (4, 2)
┌─────────┬──────────┐
│ Courses ┆ Discount │
│ ---     ┆ ---      │
│ str     ┆ i64      │
╞═════════╪══════════╡
│ Spark   ┆ 1000     │
│ Hadoop  ┆ 1500     │
│ Python  ┆ 1200     │
│ Pandas  ┆ 2000     │
└─────────┴──────────┘

    Here,

    <a id="h-ignoring-non-existing-columns-with-strict-false"></a>

    Ignoring Non-Existing Columns (with strict=False)

    When using the drop() method to remove columns, you can handle non-existing columns more gracefully by setting the strict parameter to False. By default, strict=True, which raises an error if any of the specified columns are not found. However, setting strict=False allows the method to ignore columns that don't exist and proceed without raising an error.

    
# Attempting to drop 'Fees' and a non-existing column 'NonExistent' with strict=False
df2 = df.drop(["Fees", "NonExistent"], strict=False)
print("DataFrame after dropping 'Fees' and ignoring non-existing columns:\n", df2)

# Output:
# DataFrame after dropping 'Fees' and ignoring non-existing columns:
# shape: (4, 3)
┌─────────┬──────────┬──────────┐
│ Courses ┆ Duration ┆ Discount │
│ ---     ┆ ---      ┆ ---      │
│ str     ┆ str      ┆ i64      │
╞═════════╪══════════╪══════════╡
│ Spark   ┆ 30days   ┆ 1000     │
│ Hadoop  ┆ 50days   ┆ 1500     │
│ Python  ┆ 40days   ┆ 1200     │
│ Pandas  ┆ 60days   ┆ 2000     │
└─────────┴──────────┴──────────┘

    Here,

    <a id="h-dropping-all-columns-leaving-empty-dataframe"></a>

    Dropping All Columns (Leaving Empty DataFrame)

    To drop all columns in a Polars DataFrame, effectively leaving an empty DataFrame with no columns, you can pass all column names to the drop() method.

    
# Dropping all columns from the DataFrame
df2 = df.drop(df.columns)
print("DataFrame after dropping all columns:\n", df2)

# Output:
# DataFrame after dropping all columns:
# shape: (0, 0)
┌┐
╞╡
└┘

    Here,

    <a id="h-dropping-columns-using-a-list-of-column-names"></a>

    Dropping Columns Using a List of Column Names

    You can drop specific columns from a Polars DataFrame by passing a list of column names to the drop() method. This method will remove the columns corresponding to the names provided in the list.

    
# List of columns to drop
columns_to_drop = ["Fees", "Discount"]

# Dropping the specified columns
df2 = df.drop(columns_to_drop)
print("DataFrame after dropping columns using a list:\n", df2)

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

    Here,

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

    Conclusion

    In this article, I have explained the Polars DataFrame drop() method, by using its syntax, parameters, usage, and how it returns a new DataFrame with the specified columns removed while leaving the original DataFrame unchanged.

    Happy Learning!!

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

    References