Convert Polars Cast Float to String

In Polars, you can convert a float column to a string type by using the cast() method with pl.Utf8. This function allows you to change a column’s data type, and to convert a float column to a string, you specify the Utf8 type, which represents strings in Polars. In this article, I will explain how to convert polars cast float to string.

Key Points –

• Use .cast(pl.Utf8) to convert float columns to string in a Polars DataFrame.
• Casting floats to strings helps in converting numeric data to a textual format for processing or presentation.
• Apply .cast() on specific columns using pl.col("column_name").
• Use .with_columns() to add the converted string column back to the DataFrame.
• Converting floats to strings enables the use of string-specific operations on the data.
• Use .alias("new_column_name") to rename the resulting string column.
• Casted strings retain the original float’s precision unless explicitly formatted beforehand.
• Casting can be combined with other transformations in a single .with_columns() or .select() pipeline.

Usage of Polars Float to String

Converting a float column to a string in Polars is simple with the cast() method. This method facilitates type conversion, allowing you to change a column’s data type to any desired type, including strings. To convert a float column to a string, apply .cast(pl.Utf8) on the target column.

To run some examples of converting polars cast float to string, let’s create a Polars DataFrame.

import polars as pl

technologies= {
    'Courses':["Spark","PySpark","Hadoop","Python","Pandas"],
    'Fees' :[22000.30,25000.40,23000.20,24000.50,26000.10],
    'Duration':['30days','50days','35days', '40days','55days'],
    'Discount':[1000.10,2300.15,1000.5,1200.22,2500.20]
          }
df = pl.DataFrame(technologies)
print("Original DataFrame:\n", df)

Yields below output.

To perform a basic conversion of float values to string in the Fee and Discount columns of the DataFrame, you can use the cast() function to convert those columns from float to string.

# Casting 'Fees' and 'Discount' from float to string
df2 = df.with_columns([
    pl.col("Fees").cast(pl.Utf8).alias("Fees"),
    pl.col("Discount").cast(pl.Utf8).alias("Discount")
])
print("DataFrame after conversion:\n", df2)

In the above example, both the Fees and Discount columns are cast to string types using the with_columns() method in one step.

Cast Single Column from Float to String

To cast a single column from float to string in Polars, you can use the cast() function specifically on that column and then update the DataFrame using the with_columns() method.

# Using Casting a Single Column from Float to String
df_casted = df.with_columns(
    df['Fees'].cast(pl.Utf8).alias('Fees'))
print("DataFrame with 'Fees' casted to String:\n", df_casted)

# Output:
# DataFrame with 'Fees' casted to String:
# shape: (5, 4)
┌─────────┬─────────┬──────────┬──────────┐
│ Courses ┆ Fees    ┆ Duration ┆ Discount │
│ ---     ┆ ---     ┆ ---      ┆ ---      │
│ str     ┆ str     ┆ str      ┆ f64      │
╞═════════╪═════════╪══════════╪══════════╡
│ Spark   ┆ 22000.3 ┆ 30days   ┆ 1000.1   │
│ PySpark ┆ 25000.4 ┆ 50days   ┆ 2300.15  │
│ Hadoop  ┆ 23000.2 ┆ 35days   ┆ 1000.5   │
│ Python  ┆ 24000.5 ┆ 40days   ┆ 1200.22  │
│ Pandas  ┆ 26000.1 ┆ 55days   ┆ 2500.2   │
└─────────┴─────────┴──────────┴──────────┘

Here,

• df['Fees'] – Selects the column Fees that you want to cast.
• cast(pl.Utf8) – Casts the column to a string (UTF-8 encoding).
• alias('Fees') – Keeps the column’s name as 'Fees' after casting.

Cast Multiple Columns from Float to String

To cast multiple columns from float to string in Polars, you can utilize the with_columns() method along with the cast() method for each column you want to convert. Here’s how you can cast both the Fees and Discount columns from float to string.

# Casting multiple columns from float to string
df_casted = df.with_columns([
        df['Fees'].cast(pl.Utf8).alias('Fees'),
        df['Discount'].cast(pl.Utf8).alias('Discount')])
print("DataFrame with 'Fees' and 'Discount' casted to String:\n", df_casted)

# Output:
# DataFrame with 'Fees' and 'Discount' casted to String:
 shape: (5, 4)
┌─────────┬─────────┬──────────┬──────────┐
│ Courses ┆ Fees    ┆ Duration ┆ Discount │
│ ---     ┆ ---     ┆ ---      ┆ ---      │
│ str     ┆ str     ┆ str      ┆ str      │
╞═════════╪═════════╪══════════╪══════════╡
│ Spark   ┆ 22000.3 ┆ 30days   ┆ 1000.1   │
│ PySpark ┆ 25000.4 ┆ 50days   ┆ 2300.15  │
│ Hadoop  ┆ 23000.2 ┆ 35days   ┆ 1000.5   │
│ Python  ┆ 24000.5 ┆ 40days   ┆ 1200.22  │
│ Pandas  ┆ 26000.1 ┆ 55days   ┆ 2500.2   │
└─────────┴─────────┴──────────┴──────────┘

Here,

• df['Fees'] and df['Discount'] – Select the columns you want to cast.
• cast(pl.Utf8) – Casts both Fees and Discount columns to string (UTF-8 encoding).
• alias('Fees') and .alias('Discount') – Keep the column names after casting.

Cast Float to String After Mathematical Operation

To cast a float to a string after performing a mathematical operation in Polars, you can first apply the operation on the column and then use cast() to convert the result into a string.

# Cast float to string after mathematical operation
df_casted = df.with_columns(
    (df['Fees'] * 1.1).cast(pl.Utf8).alias('Fees'))
print("DataFrame with 'Fees' after multiplication and casted to String:\n", df_casted)

# Output:
# DataFrame with 'Fees' after multiplication and casted to String:
# shape: (5, 4)
┌─────────┬────────────────────┬──────────┬──────────┐
│ Courses ┆ Fees               ┆ Duration ┆ Discount │
│ ---     ┆ ---                ┆ ---      ┆ ---      │
│ str     ┆ str                ┆ str      ┆ f64      │
╞═════════╪════════════════════╪══════════╪══════════╡
│ Spark   ┆ 24200.33           ┆ 30days   ┆ 1000.1   │
│ PySpark ┆ 27500.440000000002 ┆ 50days   ┆ 2300.15  │
│ Hadoop  ┆ 25300.22           ┆ 35days   ┆ 1000.5   │
│ Python  ┆ 26400.550000000003 ┆ 40days   ┆ 1200.22  │
│ Pandas  ┆ 28600.11           ┆ 55days   ┆ 2500.2   │
└─────────┴────────────────────┴──────────┴──────────┘

Here,

• df['Fees'] * 1.1 – This is a mathematical operation, multiplying the Fees column by 1.1 (for example, applying a 10% increase).
• cast(pl.Utf8) – Casts the result of the operation (now a float) into a string (UTF-8 encoding).
• alias('Fees') – Retains the column name as 'Fees' after the operation and cast.

Cast Float Column to String with Precision Handling

To cast a float column to a string with precision handling in Polars, you can round the float values to a specific number of decimal places before converting them to strings.

# Cast float column to string with precision handling
df_casted = df.with_columns(
    (df['Fees'].round(2)).cast(pl.Utf8).alias('Fees'))
print("DataFrame with 'Fees' rounded to 2 decimal places and casted to String:\n", df_casted)

# Output:
# DataFrame with 'Fees' rounded to 2 decimal places and casted to String:
#  shape: (5, 4)
┌─────────┬─────────┬──────────┬──────────┐
│ Courses ┆ Fees    ┆ Duration ┆ Discount │
│ ---     ┆ ---     ┆ ---      ┆ ---      │
│ str     ┆ str     ┆ str      ┆ f64      │
╞═════════╪═════════╪══════════╪══════════╡
│ Spark   ┆ 22000.3 ┆ 30days   ┆ 1000.1   │
│ PySpark ┆ 25000.4 ┆ 50days   ┆ 2300.15  │
│ Hadoop  ┆ 23000.2 ┆ 35days   ┆ 1000.5   │
│ Python  ┆ 24000.5 ┆ 40days   ┆ 1200.22  │
│ Pandas  ┆ 26000.1 ┆ 55days   ┆ 2500.2   │
└─────────┴─────────┴──────────┴──────────┘

Here,

• df['Fees'].round(2) – Rounds the values in the Fees column to 2 decimal places.
• cast(pl.Utf8) – Casts the rounded float values to a string.
• alias('Fees') – Keeps the column name as 'Fees' after rounding and casting.

Conclusion

In conclusion, converting a float column to a string in Polars is straightforward using the cast() method with pl.Utf8. This process allows you to change the column’s data type, making the float values into string representations. By using the with_columns() method, you can create a new column or update an existing one with the converted values.

Happy Learning!!

Related Articles

• Convert Polars Cast String to Float
• Polars DataFrame.rename() Method
• Polars Cast Multiple Columns
• Polars DataFrame drop() Method
• Polars DataFrame select() Method
• How to Transpose DataFrame in Polars
• Polars Filter DataFrame with Multilple Conditions
• Polars DataFrame.pivot() Explained with Examples

References

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