These five pandas scripts catch common problems in CSV and DataFrame inputs: missing or unexpected columns, type mismatches, excessive nulls, duplicate keys, and invalid values. They report issues without silently changing the data, so you can decide whether each failure should stop a pipeline, raise a warning, or trigger investigation.
Data quality is not one score. Schema, completeness, uniqueness, and value validity are separate checks, as reflected in Great Expectations’ data-quality use cases and Pandera’s DataFrame schemas. Treat the rules below as examples to adapt to your data contract—not universal thresholds.
Set up a DataFrame to validate
The functions below accept a pandas DataFrame. For a CSV, load it first and make parsing choices explicit where they matter; for example, an identifier with leading zeros should generally be read as text rather than inferred as a number.
import pandas as pd
df = pd.read_csv("input.csv")These checks identify issues; they do not repair them. Keep the original input available, inspect failures, and choose deliberately whether a check is fatal or informational in your pipeline.
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1. Profile columns and missingness
Start with a compact diagnostic report. This function shows each column’s inferred dtype, count and fraction of missing values, and number of distinct non-null values. It helps reveal what rules may be needed, but it does not decide whether a column is valid.
def profile_dataframe(df: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame({
"dtype": df.dtypes.astype(str),
"missing_count": df.isna().sum(),
"missing_fraction": df.isna().mean(),
"distinct_non_null": df.nunique(dropna=True),
}).sort_values("missing_fraction", ascending=False)
print(profile_dataframe(df))A result might show middle_name with a high missing fraction and order_id with none. The first is a prompt to ask whether the field is optional; the second does not by itself prove that identifiers are unique. Missingness only becomes a defect when the field’s meaning or contract says it should be present. Pandera and Great Expectations likewise treat schema and missingness as distinct concerns (Pandera schemas; Great Expectations schema checks).
2. Check required columns and types
Compare the incoming structure with an explicit expectation instead of assuming CSV type inference always matches. This example requires three named columns and exact pandas dtype strings.
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EXPECTED_DTYPES = {
"customer_id": "int64",
"email": "object",
"amount": "float64",
}
def check_schema(df: pd.DataFrame) -> dict:
missing = sorted(set(EXPECTED_DTYPES) - set(df.columns))
wrong_types = {
col: {"actual": str(df[col].dtype), "expected": expected}
for col, expected in EXPECTED_DTYPES.items()
if col in df.columns and str(df[col].dtype) != expected
}
return {"missing_columns": missing, "wrong_types": wrong_types}
print(check_schema(df))A failure could look like {'missing_columns': ['email'], 'wrong_types': {'customer_id': {'actual': 'float64', 'expected': 'int64'}}}. A numeric identifier may become floating point when a CSV contains blanks, and nullable integer columns may use a different pandas dtype. Set parsing rules at ingestion and select expected types accordingly; do not silently coerce values, since malformed input can be concealed by coercion.
Decide separately whether additional columns are acceptable. This function permits them; for a strict contract, compare the full set of actual and expected names and report unexpected columns too. Pandera lets schemas specify columns and types, with options for strictness, coercion, nullable columns, and optional columns (DataFrame Schemas). Great Expectations also documents schema checks for column names and data types (Validate data schema with GX).
3. Enforce per-column missing-value limits
Once the profile informs a contract, set a maximum missing fraction for each field. The denominator here is the total number of rows in the DataFrame; the fraction is the number of null values divided by that row count.
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MAX_MISSING_FRACTION = {
"order_id": 0.0,
"middle_name": 0.80,
}
def check_missingness(df: pd.DataFrame) -> dict:
results = {}
for col, limit in MAX_MISSING_FRACTION.items():
if col not in df.columns:
results[col] = {"pass": False, "reason": "missing column"}
continue
if df.empty:
results[col] = {
"pass": False,
"reason": "cannot evaluate missing fraction for empty input",
"limit": limit,
}
continue
fraction = float(df[col].isna().mean())
results[col] = {
"pass": fraction <= limit,
"missing_fraction": fraction,
"limit": limit,
}
return results
print(check_missingness(df))For example, a result of {'order_id': {'pass': False, 'missing_fraction': 0.02, 'limit': 0.0}} means 2% of rows lack an order ID, against a rule allowing none. The 0.80 allowance for middle_name is illustrative, not a recommended standard. Set thresholds by field meaning and downstream use. This function explicitly treats an empty DataFrame as unevaluable and failing; change that behavior if your pipeline’s contract says empty inputs are acceptable.
4. Find duplicate identifiers or composite keys
Repeated values are a problem only where uniqueness is part of the contract. A key can be one column or a combination, such as country_code and customer_number. This function returns every row involved in a duplicate key group, making it easier to inspect both copies.
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missing = [col for col in key_columns if col not in df.columns]
if missing:
raise ValueError(f"Missing key columns: {missing}")
if not key_columns:
raise ValueError("Provide at least one key column")
return df[df.duplicated(subset=key_columns, keep=False)]
repeats = duplicate_rows(df, ["country_code", "customer_number"])
print(f"Duplicate key rows: {len(repeats)}")
print(repeats)keep=False marks every member of a duplicate group, not just later occurrences. Report the key columns and duplicate-row count with the result. Then decide whether any duplicate is an error, a warning, or acceptable up to a documented threshold. Great Expectations describes both single-column and compound-column uniqueness checks, including proportion-based alternatives when a contract allows some duplicates (Validate data uniqueness with GX).
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5. Validate numeric ranges and allowed categories
Check domain rules explicitly, and distinguish invalid values from nulls. This example flags negative amounts and non-null statuses outside an allowed set; missing values are left for the completeness rule.
ALLOWED_STATUS = {"new", "paid", "cancelled"}
def value_issues(df: pd.DataFrame) -> dict:
issues = {}
if "amount" in df.columns:
bad = df["amount"].notna() & (df["amount"] < 0)
issues["negative_amount_rows"] = df.index[bad].tolist()
if "status" in df.columns:
bad = df["status"].notna() & ~df["status"].isin(ALLOWED_STATUS)
issues["unexpected_status_rows"] = df.index[bad].tolist()
return issues
print(value_issues(df))A result such as {'negative_amount_rows': [14], 'unexpected_status_rows': [22]} identifies the DataFrame index labels to inspect. If the input has a non-default index, these are labels rather than necessarily row positions.
Choose bounds and categories from the domain contract, not from convenience. Negative amounts may be valid for refunds; a new status may require updating the allowed set. For date rules, parse with an explicit error policy first, then compare parsed dates with the permitted interval. Pandera supports built-in comparisons, allowed-set checks, and custom series checks that can express such constraints declaratively (Validating with Checks).
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Use functions or a validation framework?
For a few checks on one DataFrame, small Python functions are easy to read and fit into an existing script. A schema framework becomes worth evaluating when you need to declare rules once, reuse them across DataFrames, or report failures consistently. Pandera documents DataFrame schemas and checks for multiple DataFrame backends (schemas; checks); Great Expectations documents schema, uniqueness, and broader data-quality use cases (schema; uniqueness; use cases).
Choose based on the DataFrame engine or backend you use, how rules are declared and reused, how failures and offending rows are reported, how nulls and coercion are handled, and whether you need local scripts or a managed validation workflow. These sources describe capabilities, not a universal ranking for setup effort, speed, or deployment fit. Check the documentation for the installed library version before adopting framework APIs.
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