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For float values that are already whole numbers and fit the target range, use astype("int64"). If values may be missing, use pandas’ nullable Int64 dtype instead. If the input is text, parse it with pd.to_numeric first—and decide explicitly what should happen to invalid text and fractional values.
Choose the conversion that fits your data
| Input or requirement | Approach | What to watch |
|---|---|---|
| Numeric values are whole numbers; no missing values | astype("int64") |
Values must fit the selected integer range. |
| Numeric values may include missing entries | astype("Int64") |
Capital-I Int64 is pandas’ nullable integer extension dtype; missing entries remain <NA>. |
| Values are text or mixed input | pd.to_numeric, then cast |
Choose whether invalid text should raise an error or become missing. |
| Smaller integer storage is needed | pd.to_numeric(..., downcast="integer") |
This selects a smaller signed integer dtype when values fit; it does not round fractional values. |
Convert whole-number floats in a Series or column
When every value is already a whole number, cast the Series directly:
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s_int = s.astype("int64")For a DataFrame column, assign the converted Series back to that column:
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11df["count"] = df["count"].astype("int64")This is a dtype conversion, not a rule for handling fractions. Confirm the values are whole numbers before casting, and ensure they fit in the chosen integer type. Pandas documents astype for converting objects to a specified dtype: DataFrame.astype reference.
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Keep missing values with nullable integers
Ordinary NumPy-style int64 cannot represent a missing value as an integer. Use pandas’ capitalized nullable dtype when integer values and missing entries need to coexist:
s_int = s.astype("Int64")Missing values are represented as <NA>. Pandas’ documentation recommends nullable-integer extension dtypes when integers may be missing: Nullable integer data type.
Parse text and decide how invalid values behave
Use pd.to_numeric when a Series contains numeric text or mixed input. With errors="raise", unparseable values cause an error; with errors="coerce", they become missing numeric values:
numeric = pd.to_numeric(s, errors="raise") # stop if a value cannot be parsedIf invalid entries should become missing, then convert to nullable integers so those missing values can remain in the result:
numeric = pd.to_numeric(s, errors="coerce")
integer = numeric.astype("Int64")Inspect the resulting missing entries after coercion: it changes invalid input into missing data rather than correcting it. Also check large identifiers or precision-sensitive values before parsing. Pandas warns that values outside supported integer bounds may be converted to floating point, potentially losing precision. See the to_numeric reference.
Choose what happens to fractional values
A float such as 7.8 is not an integer value. Decide whether your data should retain fractions, be rounded according to a chosen convention, be floored, or be truncated. Apply that rule before casting, rather than relying on a cast to express your intent.
For example, to round first, make the rounding step explicit and then convert:
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rounded = s.round() # pandas' rounding method
s_int = rounded.astype("int64")Check representative values—including positive and negative fractions—against the policy your application requires. Do not assume a rounding convention without verifying that it matches your intended treatment of ties and negative values.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use downcasting only to reduce integer storage
If a smaller signed integer dtype is useful, request it after numeric parsing:
small = pd.to_numeric(s, downcast="integer")Pandas chooses the smallest signed integer type that can hold the values; the selected dtype therefore depends on the data. This is a storage choice, not a way to remove fractions. The pandas basics guide notes that numeric downcasting applies to one-dimensional inputs, so select a column rather than passing a whole DataFrame to this option: Downcasting in the basics guide.
Check the result before using it
- Confirm fractional values have been handled according to an explicit rule.
- Check whether missing values should remain missing; use
Int64when they must coexist with integers. - If you used
errors="coerce", inspect which entries became missing. - Verify that identifiers and other large or precision-sensitive numbers have not exceeded safe conversion limits.
- After downcasting, inspect the resulting dtype and confirm it has the range your later calculations require.
The cited API references are from pandas 3.0.6 documentation surfaced on October 7, 2026; consult the current documentation if using a different pandas version.
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