cannot convert float NaN to integer" pandas fix

TL;DR

Plain int dtype cannot represent NaN, so astype(int) on a column with missing values raises. Fix it with the nullable integer dtype: df['c'].astype('Int64') (capital I), which holds real integers plus pandas NA. It happens because numpy int64 has no NaN slot, while float64 does.

ValueError: cannot convert float NaN to integer

Use this when

• df['c'].astype(int) raises on a column with NaN
• Converting a float column with missing values to integer fails
• int(x) on a NaN value inside apply/loop code

Not for

• General astype conversions that work fine
• SQL NULL to integer handling
• Float rounding/precision questions

Steps

1. Confirm NaN is the cause:

df['c'].isna().sum()

Expected output: a count greater than 0.

1. Use the nullable integer dtype (capital I):

df['c'] = df['c'].astype('Int64')

Expected output: dtype Int64, integers intact, NaN shown as NA.

1. If you need plain numpy int64 downstream, fill first:

df['c'] = df['c'].fillna(0).astype('int64')

Expected output: no NaN left, plain int64 dtype. Pick the fill value deliberately, 0 vs -1 vs drop matters.

1. Same fix inside apply-style code:

df['c'] = df['c'].apply(lambda x: 0 if pd.isna(x) else int(x)).astype('Int64')

Expected output: conversion with explicit NaN handling instead of a crash.

Variant phrasings

pandas astype int with NaN fails

The canonical case. Nullable Int64 (or fillna first) is the fix.

Int64 vs int64 pandas

Lowercase int64 is the numpy dtype: no missing values allowed. Capital-I Int64 is pandas nullable extension dtype: integers plus NA. They print almost identically, which is why the error confuses people.

cannot convert NaN to integer in numpy

Numpy has the same limitation. Use masked arrays, or fill NaN before converting, same idea.

Why it happens

Numpy's int64 stores raw 64-bit integers with no reserved bit pattern for missing, while float64 reserves NaN. A float column can hold NaN; converting it to int64 has nowhere to put those NaNs, so pandas raises instead of silently dropping them.

Edge cases

• Nullable Int64 propagates NA through arithmetic (1 + NA = NA); fillna before math if you want zeros.
• Some downstream tools (older sklearn, parquet writers) dont accept Int64; convert to int64 with fillna at the boundary.
• pd.to_numeric(downcast='integer') gives numpy int types and will still choke on NaN; downcast after handling missingness.
• String 'nan' vs real NaN: check with isna(), not string comparison.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_D7VLc2xZPbd2AsFpw7RM6g