diff --git a/docs/general.rst b/docs/general.rst
index d6eaf01..4209d16 100644
--- a/docs/general.rst
+++ b/docs/general.rst
@@ -12,8 +12,6 @@ Most functions take a parameter **nan_policy** to specify how to handle *nan* va
 * **raise** – Raise an error on *nan* values
 * **omit** – Silently drop rows with *nan* values
 
-In determining whether there is *nan* in the data, only the columns specified in the function (if applicable) are considered.
-
 .. autofunction:: yli.utils.check_nan
 
 General result classes
diff --git a/yli/utils.py b/yli/utils.py
index 937a36b..678821c 100644
--- a/yli/utils.py
+++ b/yli/utils.py
@@ -26,7 +26,7 @@ from .config import config
 # ----------------------------
 # Data cleaning and validation
 
-def check_nan(df, nan_policy):
+def check_nan(df, nan_policy, *, cols=None):
 	"""
 	Check df against *nan_policy* and return cleaned input
 	
@@ -34,22 +34,25 @@ def check_nan(df, nan_policy):
 	:type df: DataFrame
 	:param nan_policy: Policy to apply when encountering NaN values (*warn*, *raise*, *omit*)
 	:type nan_policy: str
+	:param cols: Columns to check for NaN, or *None* for all columns
+	:type cols: List[str]
 	
 	:return: Data with NaNs removed, which may or may not be copied
 	:rtype: DataFrame
 	"""
 	
 	if nan_policy == 'raise':
-		if pd.isna(df).any(axis=None):
+		df_to_check = df if cols is None else df[cols]
+		if pd.isna(df_to_check).any(axis=None):
 			raise ValueError('NaN in input, pass nan_policy="warn" or "omit" to ignore')
 		return df
 	elif nan_policy == 'warn':
-		df_cleaned = df.dropna()
+		df_cleaned = df.dropna(subset=cols)
 		if len(df_cleaned) < len(df):
 			warnings.warn('Omitting {} rows with NaN'.format(len(df) - len(df_cleaned)))
 		return df_cleaned
 	elif nan_policy == 'omit':
-		return df.dropna()
+		return df.dropna(subset=cols)
 	else:
 		raise Exception('Invalid nan_policy, expected "raise", "warn" or "omit"')