how to handle mixed types in a pandas column

TL;DR

A column with mixed types sits at dtype object, which makes math fail and comparisons lie. Fix it by finding what the non-conforming values are, coercing with pd.to_numeric(errors='coerce'), and handling the NaT/NaN leftovers deliberately. Mixed types almost always come from dirty source data, not a pandas bug.

TypeError: unsupported format string passed to numpy.ndarray.__format__

Use this when

• A column is dtype object but should be numeric or datetime
• pd.to_numeric or pd.to_datetime raises on the column
• Sorting, max/min, or comparisons on the column give nonsense results

Not for

• Duplicate index labels, reindex errors
• Merge key type mismatches (int vs str keys), fix the key dtypes instead
• Memory blowups from object columns, use the chunking skill

Steps

1. See what types are actually in there:

df['c'].map(type).value_counts()

Expected output: the mix, e.g. 9500 float, 400 str, 100 NoneType.

1. Find the non-numeric offenders:

df[pd.to_numeric(df['c'], errors='coerce').isna() & df['c'].notna()]['c'].unique()[:20]

Expected output: the junk values: "N/A", "-", "12.5%", empty strings, whatever they are.

1. Coerce to numeric, junk becomes NaN:

df['c'] = pd.to_numeric(df['c'], errors='coerce')

Expected output: dtype float64, junk values now NaN.

1. Decide what the NaNs mean and handle them:

df['c'] = df['c'].fillna(0)   # or .dropna(), or keep as NaN

Expected output: no silent NaNs left unless you chose to keep them.

1. If the strings were meaningful (like "12.5%"), clean first, then convert:

df['c'] = pd.to_numeric(df['c'].astype(str).str.rstrip('%'), errors='coerce') / 100

Expected output: proper floats, e.g. 0.125 instead of the string "12.5%".

Variant phrasings

pandas column object dtype should be numeric

The column got object dtype because at least one value wasnt numeric. Steps 1-3 find and coerce them.

DtypeWarning columns with mixed types on read_csv

read_csv warns when a column mixes types across chunks. Pass an explicit dtype= for that column, or set low_memory=False, then clean with the steps above.

string and float mixed in pandas column

Usually numbers-as-strings plus real numbers. pd.to_numeric with errors='coerce' unifies them; check what became NaN.

Why it happens

Pandas assigns one dtype per column. The moment a single value cannot fit (a "N/A" string in a numeric column), the whole column falls back to object dtype, and every numeric operation on it either fails or silently does string things. Source files are the usual culprit: footers, placeholder text, locale-specific formats.

Edge cases

• Boolean-ish mixes ("yes"/"no"/1/0): map explicitly with a dict, dont rely on coercion.
• Datetime mixes: pd.to_datetime(errors='coerce') the same way, then inspect the NaT rows.
• infer_objects() only soft-converts; it wont fix real junk.
• After coercion, downcast (pd.to_numeric(..., downcast='integer')) if memory matters.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_D9SR2EZmbCqG88gPDcXAsQ