Polars DataFrame.rename() Method

In Polars, the rename() method is used to rename the column names of a DataFrame by providing a mapping of the current column names to the new names. This method requires a dictionary where the keys represent the current column names, and the values specify the desired names. It returns a new DataFrame with the renamed columns.

In this article, I will explain the Polars DataFrame.rename() method, including its syntax, parameters, and usage, to demonstrate how it can be used to create a new DataFrame with updated column names.

Key Points –

• The rename() method is used to change the names of columns in a Polars DataFrame.
• It accepts a dictionary where the keys are current column names, and the values are the new column names.
• By default, rename() returns a new DataFrame with renamed columns, without modifying the original DataFrame.
• You can rename only a subset of columns, leaving others unchanged, by specifying only the columns you want to rename in the dictionary.
• You can rename multiple columns in one operation by passing multiple key-value pairs in the dictionary.
• You can chain the rename() method with other Polars DataFrame methods to apply additional transformations in a single expression.

Syntax of Polars DataFrame.rename()

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

# Syntax of rename()
DataFrame.rename(
    mapping: dict[str, str] | Callable[[str], str],
    *,
    strict: bool = True,
) → DataFrame

Parameters of the Polars DataFrame.rename()

Following are the parameters of the polars DataFrame.rename() method.

• mapping – Specifies how the column names should be renamed.
  • If a dictionary is provided, it maps old column names to new column names.
  • If a callable is provided, it applies the function to each column name.
• strict (optional) – bool (default = True)
  • If True, raises an error if any column in the mapping does not exist in the DataFrame.
  • If False, ignores missing columns in the mapping.

Return Value

This function returns a new DataFrame with renamed columns.

Usage of Polars DataFrame.rename()

The rename() method is used to change the names of one or more columns in a Polars DataFrame by providing a dictionary that maps existing column names to new ones.

To run some examples of the Polars DataFrame.rename() method, let’s create a Pandas 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','40days'],
    'Discount':[1000,1500,1200,2500]
}

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

Yields below output.

To rename columns in a Polars DataFrame using a dictionary, you can use the .rename() method. The dictionary will map the old column names to the new ones.

# Rename columns using a dictionary
df2 = df.rename({
    'Courses': 'Course_List',
    'Fees': 'Course_Fee',
    'Duration': 'Course_Duration',
    'Discount': 'Course_Discount'
})

print("Renamed DataFrame:\n", df2)

In the above example, The rename() method is used with a dictionary where the keys represent the old column names, and the values represent the new column names.

Rename Only a Subset of Columns

To rename only a subset of columns in a Polars DataFrame, you can specify a dictionary with only the columns you want to rename. The columns that are not included in the dictionary will remain unchanged.

# Rename only a subset of columns
df2 = df.rename({
    'Courses': 'Course_Name',
    'Fees': 'Course_Fee'
})
print("Renamed Subset DataFrame:\n", df2)

# Output:
# Renamed Subset DataFrame:
# shape: (4, 4)
┌─────────────┬────────────┬──────────┬──────────┐
│ Course_Name ┆ Course_Fee ┆ Duration ┆ Discount │
│ ---         ┆ ---        ┆ ---      ┆ ---      │
│ str         ┆ i64        ┆ str      ┆ i64      │
╞═════════════╪════════════╪══════════╪══════════╡
│ Spark       ┆ 22000      ┆ 30days   ┆ 1000     │
│ Hadoop      ┆ 25000      ┆ 50days   ┆ 1500     │
│ Python      ┆ 20000      ┆ 40days   ┆ 1200     │
│ Pandas      ┆ 26000      ┆ 40days   ┆ 2500     │
└─────────────┴────────────┴──────────┴──────────┘

Here,

• The rename() method is used, but only the columns 'Courses' and 'Fees' are renamed ('Course_Name' and 'Course_Fee').
• The columns 'Duration' and 'Discount' remain unchanged.

Rename Columns Using a Function

To rename columns using a function in Polars, you can use the .columns attribute to access the column names and apply a function to modify them.

# Rename columns using a function (e.g., converting column names to lowercase)
df2 = df.rename({col: col.lower() for col in df.columns})
print("Renamed DataFrame:\n", df2)

# Output:
# Renamed DataFrame:
# shape: (4, 4)
┌─────────┬───────┬──────────┬──────────┐
│ courses ┆ fees  ┆ duration ┆ discount │
│ ---     ┆ ---   ┆ ---      ┆ ---      │
│ str     ┆ i64   ┆ str      ┆ i64      │
╞═════════╪═══════╪══════════╪══════════╡
│ Spark   ┆ 22000 ┆ 30days   ┆ 1000     │
│ Hadoop  ┆ 25000 ┆ 50days   ┆ 1500     │
│ Python  ┆ 20000 ┆ 40days   ┆ 1200     │
│ Pandas  ┆ 26000 ┆ 40days   ┆ 2500     │
└─────────┴───────┴──────────┴──────────┘

Here,

• The df.columns gives you the list of current column names.
• The dictionary comprehension {col: col.lower() for col in df.columns} creates a new dictionary that maps the original column names to the lowercased versions.
• The .rename() method then uses this dictionary to rename the columns in the DataFrame.

Add a Prefix to Column Names

To add a prefix to column names in a Polars DataFrame, you can use the .columns attribute and apply a function that adds the prefix to each column name.

# Add a prefix to all column names
df2 = df.rename({col: "Tech_" + col for col in df.columns})
print("DataFrame with Prefix:\n", df2)

# Output:
# DataFrame with Prefix:
# shape: (4, 4)
┌──────────────┬───────────┬───────────────┬───────────────┐
│ Tech_Courses ┆ Tech_Fees ┆ Tech_Duration ┆ Tech_Discount │
│ ---          ┆ ---       ┆ ---           ┆ ---           │
│ str          ┆ i64       ┆ str           ┆ i64           │
╞══════════════╪═══════════╪═══════════════╪═══════════════╡
│ Spark        ┆ 22000     ┆ 30days        ┆ 1000          │
│ Hadoop       ┆ 25000     ┆ 50days        ┆ 1500          │
│ Python       ┆ 20000     ┆ 40days        ┆ 1200          │
│ Pandas       ┆ 26000     ┆ 40days        ┆ 2500          │
└──────────────┴───────────┴───────────────┴───────────────┘

Here,

• The rename() method is used to rename the columns.
• The dictionary comprehension {"Tech_" + col for col in df.columns} adds the prefix "Tech_" to each column name.
• This method modifies all the column names in the DataFrame.

Add a Suffix to Column Names

To add a suffix to column names in a Polars DataFrame, you can use a similar approach as with adding a prefix. By accessing the .columns attribute, you can apply a function that adds a suffix to each column name.

# Add a suffix to all column names
df2 = df.rename({col: col + "_info" for col in df.columns})
print("DataFrame with Suffix:\n", df2)

# Output:
# DataFrame with Suffix:
 shape: (4, 4)
┌──────────────┬───────────┬───────────────┬───────────────┐
│ Courses_info ┆ Fees_info ┆ Duration_info ┆ Discount_info │
│ ---          ┆ ---       ┆ ---           ┆ ---           │
│ str          ┆ i64       ┆ str           ┆ i64           │
╞══════════════╪═══════════╪═══════════════╪═══════════════╡
│ Spark        ┆ 22000     ┆ 30days        ┆ 1000          │
│ Hadoop       ┆ 25000     ┆ 50days        ┆ 1500          │
│ Python       ┆ 20000     ┆ 40days        ┆ 1200          │
│ Pandas       ┆ 26000     ┆ 40days        ┆ 2500          │
└──────────────┴───────────┴───────────────┴───────────────┘

Here,

• The rename() method is used to rename the columns.
• The dictionary comprehension {col: col + "_info" for col in df.columns} adds the suffix "_info" to each column name.
• This approach applies the suffix to all columns in the DataFrame.

Replace Spaces in Column Names

Similarly, to modify column names in a Polars DataFrame by replacing spaces, you can use the .rename() method or opt for a programmatic approach such as a list comprehension. This allows you to substitute spaces with another character (e.g., underscores _) or eliminate them altogether.

import polars as pl

# Create a DataFrame with spaces in column names
df = pl.DataFrame({
    "Course Name": ["Spark", "Hadoop", "Python", "Pandas"],
    "Course Fees": [22000, 25000, 20000, 26000],
    "Course Duration": ["30days", "50days", "40days", "40days"]
})

# Replace spaces in column names dynamically
df_renamed = df.rename({
    col: col.replace(" ", "_") for col in df.columns
})
print("DataFrame with Spaces Replaced Dynamically:\n", df_renamed)

# Output:
# DataFrame with Spaces Replaced Dynamically:
# shape: (4, 3)
┌─────────────┬─────────────┬─────────────────┐
│ Course_Name ┆ Course_Fees ┆ Course_Duration │
│ ---         ┆ ---         ┆ ---             │
│ str         ┆ i64         ┆ str             │
╞═════════════╪═════════════╪═════════════════╡
│ Spark       ┆ 22000       ┆ 30days          │
│ Hadoop      ┆ 25000       ┆ 50days          │
│ Python      ┆ 20000       ┆ 40days          │
│ Pandas      ┆ 26000       ┆ 40days          │
└─────────────┴─────────────┴─────────────────┘

Here,

• Replace spaces in column names explicitly using a dictionary in .rename().
• Use a dictionary comprehension to programmatically update all column names, replacing " " with "_" or any other preferred character.

Conclusion

In this article, I will explain the Polars DataFrame.rename() method and by using its syntax, parameters, and usage how we can return a new DataFrame with the updated column names. The original DataFrame remains unchanged, ensuring a non-destructive workflow that allows you to make transformations without modifying the original data.

Happy Learning!!

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• Polars DataFrame.explode() Method
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• Polars DataFrame.join() Explained With Examples
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References

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