Built with Tutify.io

    AIStudyLabs

    Convert Polars Cast String to Float

    Convert Polars Cast String to Float

    In Polars, you can cast a column from a string type to a float type using the cast() function. This is particularly useful when your data is stored as strings (e.g., numbers in string format) and you need to perform numeric operations or analyses. Converting these string values to a numeric type like Float64 enables you to carry out calculations and further analysis. In this article, I will explain how to convert polars cast string to float.

    Key Points –

    <a id="h-usage-of-polars-cast-string-to-float"></a>

    Usage of Polars Cast String to Float

    To convert a column from string to float in Polars, you can use the cast() method to change the data type of the column to Float64. This is especially useful when you have numeric values stored as strings and want to perform numerical operations.

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

    
import polars as pl

technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :["20000","25000","22000", "30000"],
    'Discount':["1000","2300","1200","2000"]
              }
df = pl.DataFrame(technologies)
print("Original DataFrame:\n", df)

    Yields below output.

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

    
# Convert Fee and Discount columns to float
df2 = df.with_columns([
    pl.col("Fee").cast(pl.Float64).alias("Fee"),
    pl.col("Discount").cast(pl.Float64).alias("Discount")
])
print("DataFrame after conversion:\n", df2)

    Here,

    <a id="h-casting-a-single-column"></a>

    Casting a Single Column

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

    
# Cast the 'Fee' column to float
df2 = df.with_columns([
    pl.col("Fee").cast(pl.Float64).alias("Fee")
])
print("DataFrame after casting a single column:\n", df2)

# Output:
# DataFrame after casting a single column:
# shape: (4, 3)
┌─────────┬─────────┬──────────┐
│ Courses ┆ Fee     ┆ Discount │
│ ---     ┆ ---     ┆ ---      │
│ str     ┆ f64     ┆ str      │
╞═════════╪═════════╪══════════╡
│ Spark   ┆ 20000.0 ┆ 1000     │
│ PySpark ┆ 25000.0 ┆ 2300     │
│ Python  ┆ 22000.0 ┆ 1200     │
│ pandas  ┆ 30000.0 ┆ 2000     │
└─────────┴─────────┴──────────┘

    Here,

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

    Casting Multiple Columns

    You can apply the cast() function to each column individually to cast multiple columns in a Polars DataFrame, and then use with_columns() to update the DataFrame

    
# Cast 'Fee' and 'Discount' columns to Float64
df2 = df.with_columns([
    df['Fee'].cast(pl.Float64).alias('Fee'),
    df['Discount'].cast(pl.Float64).alias('Discount')
])
print("DataFrame after casting 'Fee' and 'Discount' to Float64:\n", df2)

# Output:
# DataFrame after casting 'Fee' and 'Discount' to Float64:
# shape: (4, 3)
┌─────────┬─────────┬──────────┐
│ Courses ┆ Fee     ┆ Discount │
│ ---     ┆ ---     ┆ ---      │
│ str     ┆ f64     ┆ f64      │
╞═════════╪═════════╪══════════╡
│ Spark   ┆ 20000.0 ┆ 1000.0   │
│ PySpark ┆ 25000.0 ┆ 2300.0   │
│ Python  ┆ 22000.0 ┆ 1200.0   │
│ pandas  ┆ 30000.0 ┆ 2000.0   │
└─────────┴─────────┴──────────┘

    In the above example, both the Fee and Discount columns are cast from strings (str) to floats (f64). The with_columns() method updates both columns in one step.

    <a id="h-converting-a-subset-of-columns"></a>

    Converting a Subset of Columns

    To convert a subset of columns from string to float in a Polars DataFrame, you can specify the columns you want to convert and apply the cast() method only to those columns.

    
# Convert only the 'Fee' and 'Discount' columns to float
df2 = df.with_columns([
    pl.col("Fee").cast(pl.Float64).alias("Fee_float"),
    pl.col("Discount").cast(pl.Float64).alias("Discount_float")
])

print("DataFrame After Conversion (Subset of Columns):\n", df2)

# Output:
# DataFrame After Conversion (Subset of Columns):
# shape: (4, 5)
┌─────────┬───────┬──────────┬───────────┬────────────────┐
│ Courses ┆ Fee   ┆ Discount ┆ Fee_float ┆ Discount_float │
│ ---     ┆ ---   ┆ ---      ┆ ---       ┆ ---            │
│ str     ┆ str   ┆ str      ┆ f64       ┆ f64            │
╞═════════╪═══════╪══════════╪═══════════╪════════════════╡
│ Spark   ┆ 20000 ┆ 1000     ┆ 20000.0   ┆ 1000.0         │
│ PySpark ┆ 25000 ┆ 2300     ┆ 25000.0   ┆ 2300.0         │
│ Python  ┆ 22000 ┆ 1200     ┆ 22000.0   ┆ 1200.0         │
│ pandas  ┆ 30000 ┆ 2000     ┆ 30000.0   ┆ 2000.0         │
└─────────┴───────┴──────────┴───────────┴────────────────┘

    Here,

    <a id="h-conversion-with-none-values"></a>

    Conversion with None Values

    When converting a string column to a float in Polars, any invalid or non-numeric string values will be converted to None (null) in the resulting column.

    
import polars as pl

# Sample DataFrame with strings and None values
data = {
    'Courses': ["Spark", "PySpark", "Python", "pandas"],
    'Fee': ["20000", "25000", None, "30000"],   # None in 'Fee'
    'Discount': ["1000", "2300", None, "2000"]  # None in 'Discount'
}

df = pl.DataFrame(data)

# Casting 'Fee' and 'Discount' 
# To float while handling None values
df = df.with_columns([
    pl.col("Fee").cast(pl.Float64),      
    pl.col("Discount").cast(pl.Float64)
])
print("DataFrame After Casting:\n", df)

    Here,

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

    Conclusion

    In conclusion, the cast() function in Polars provides a simple yet powerful way to convert string columns to float, enabling seamless numerical operations and analysis. It effectively handles diverse scenarios, such as scientific notations, missing or invalid values, and multiple-column conversions.

    Happy Learning!!

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

    Related Articles

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

    References