diff --git a/featuretools/utils/entity_utils.py b/featuretools/utils/entity_utils.py
index 3fb6e9499b..495b5eeea4 100644
--- a/featuretools/utils/entity_utils.py
+++ b/featuretools/utils/entity_utils.py
@@ -27,14 +27,6 @@ def infer_variable_types(df, link_vars, variable_types, time_index, secondary_ti
     if len(list(secondary_time_index.keys())):
         vids_to_assume_datetime.append(list(secondary_time_index.keys())[0])
     inferred_type = vtypes.Unknown
-    df_len = len(df)
-    if df_len:
-        sample_df = df.sample(frac=min(10000 / df_len, 1))
-        if isinstance(df, dd.core.DataFrame):
-            sample_df = sample_df.compute()
-    else:
-        sample_df = df
-
     for variable in df.columns:
         if variable in variable_types:
             continue
@@ -43,7 +35,7 @@ def infer_variable_types(df, link_vars, variable_types, time_index, secondary_ti
                   'use variable_types to provide type metadata for entity'
             raise ValueError(msg)
         elif variable in vids_to_assume_datetime:
-            if col_is_datetime(sample_df[variable]):
+            if col_is_datetime(df[variable]):
                 inferred_type = vtypes.Datetime
             else:
                 inferred_type = vtypes.Numeric
@@ -52,15 +44,15 @@ def infer_variable_types(df, link_vars, variable_types, time_index, secondary_ti
             inferred_type = vtypes.Categorical
 
         elif df[variable].dtype == "object":
-            if not df_len:
+            if not len(df[variable]):
                 inferred_type = vtypes.Categorical
-            elif col_is_datetime(sample_df[variable]):
+            elif col_is_datetime(df[variable]):
                 inferred_type = vtypes.Datetime
             else:
                 inferred_type = vtypes.Categorical
 
                 # heuristics to predict this some other than categorical
-                sample = sample_df[variable]
+                sample = df[variable].sample(min(10000, len(df[variable])))
 
                 # catch cases where object dtype cannot be interpreted as a string
                 try:
@@ -79,10 +71,10 @@ def infer_variable_types(df, link_vars, variable_types, time_index, secondary_ti
         elif pdtypes.is_numeric_dtype(df[variable].dtype):
             inferred_type = vtypes.Numeric
 
-        elif col_is_datetime(sample_df[variable]):
+        elif col_is_datetime(df[variable]):
             inferred_type = vtypes.Datetime
 
-        elif df_len:
+        elif len(df[variable]):
             sample = df[variable] \
                 .sample(min(10000, df[variable].nunique(dropna=False)))