Polars

Polars ComputeError: Cannot Cast - How to Fix It

Answer

This Polars error means you're trying to convert a column to a data type that's incompatible with its values. Fix it by cleaning the data first (removing non-numeric strings, handling nulls) or using strict=False to allow lossy conversions.

Why This Happens

Polars is strict about type casting by default. If you try to cast a string column containing "abc" to integers, or a float column with values too large for int32, Polars refuses rather than silently producing bad data. This is safer than pandas' permissive behavior but requires you to handle edge cases explicitly.

Solution

The rule: use strict=False to allow null results for failed casts, or clean your data first by replacing/filtering bad values. Polars is strict by design to prevent silent data corruption.

import polars as pl

df = pl.DataFrame({
    'numbers': ['1', '2', 'three', '4'],
    'prices': ['10.5', '20.0', 'N/A', '30.5'],
    'big_nums': [1e20, 2e20, 3e20, 4e20]
})

# ❌ Problematic: string 'three' can't become integer
df.with_columns(pl.col('numbers').cast(pl.Int64))
# ComputeError: cannot cast 'three' to Int64

# ✅ Fixed: use strict=False to get nulls for failures
df.with_columns(
    pl.col('numbers').cast(pl.Int64, strict=False)
)
# 'three' becomes null

# ❌ Problematic: 'N/A' can't become float
df.with_columns(pl.col('prices').cast(pl.Float64))
# ComputeError: cannot cast 'N/A' to Float64

# ✅ Fixed: replace bad values first, then cast
df.with_columns(
    pl.col('prices')
    .str.replace('N/A', '')
    .cast(pl.Float64, strict=False)
)

# ✅ Fixed: filter out non-numeric before casting
df.filter(
    pl.col('prices').str.contains(r'^\d+\.?\d*$')
).with_columns(
    pl.col('prices').cast(pl.Float64)
)

# ❌ Problematic: float too large for int32
df.with_columns(pl.col('big_nums').cast(pl.Int32))
# ComputeError: cannot cast without overflow

# ✅ Fixed: use larger int type or check range first
df.with_columns(pl.col('big_nums').cast(pl.Int64, strict=False))

# ✅ Debug: inspect problematic values
print(df.select(pl.col('numbers').filter(
    pl.col('numbers').cast(pl.Int64, strict=False).is_null()
)))

# ✅ Clean numeric strings before casting
df.with_columns(
    pl.col('prices')
    .str.strip_chars()
    .str.replace_all(r'[^\d.]', '')
    .cast(pl.Float64, strict=False)
)

Better Workflow

In Zerve, each block shows your data's actual state before you attempt risky casts. Dtypes, nulls, edge cases. No more guessing why pl.col("price").cast(pl.Float64) blows up. Unsure if strict=False or a custom fill_null() is the fix? Run both approaches side by side in separate blocks. Compare outputs instantly without rerunning your entire pipeline. Catch silent data corruption at the exact step it happens, not three transformations later.