When working with Seaborn heatmaps, encountering the error “TypeError: ufunc ‘isnan’ not supported for the input types” is frustrating but easily resolved. This error occurs when your DataFrame contains non-numeric data that Seaborn attempts to process as numbers. The solution requires identifying the problematic columns and converting them to appropriate numeric types.
Understanding the Error
Seaborn’s heatmap function expects numeric data to visualize. When the function receives string, object, or mixed-type data, it tries to convert values to numbers internally. If conversion fails, NumPy’s isnan() function (used to detect missing values) throws a TypeError because it cannot evaluate whether text strings are “not a number.”
The error typically manifests when you load data from CSV files where numeric columns are imported as strings, when your DataFrame contains categorical data mixed with numbers, or when date/time columns haven’t been properly converted.
Solution 1: Check and Convert Data Types
The most direct fix involves inspecting your DataFrame’s data types and converting columns appropriately. Use the dtypes attribute to see what type each column contains. If numeric columns appear as ‘object’ type, convert them using pd.to_numeric().
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load your data
df = pd.read_csv('data.csv')
# Check data types
print(df.dtypes)
# Convert object columns to numeric
df['column_name'] = pd.to_numeric(df['column_name'], errors='coerce')
# Now create the heatmap
sns.heatmap(df)
plt.show()
Solution 2: Select Only Numeric Columns
If your DataFrame contains both numeric and non-numeric columns, explicitly select only the numeric columns before creating the heatmap. This approach prevents the error by excluding problematic data types.
import pandas as pd
import seaborn as sns
df = pd.read_csv('data.csv')
# Select only numeric columns
numeric_df = df.select_dtypes(include=['number'])
# Create heatmap with numeric data only
sns.heatmap(numeric_df)
Solution 3: Handle Missing Values Properly
Sometimes the error relates to improperly formatted missing values. Seaborn expects NaN (numpy.nan) for missing numeric values. If your data contains strings like ‘N/A’, ‘None’, or empty strings representing missing data, convert them to NaN first.
import pandas as pd
import numpy as np
df = pd.read_csv('data.csv')
# Replace various missing value representations with NaN
df = df.replace(['N/A', 'None', '', 'null'], np.nan)
# Then convert to numeric (missing values become NaN)
df = df.apply(pd.to_numeric, errors='coerce')
Prevention Strategies
When loading data, specify data types explicitly during import to prevent this error from occurring initially. The dtype parameter in pd.read_csv() allows you to declare column types upfront, ensuring NumPy processes them correctly.
Additionally, establish data validation routines in your scripts. Before attempting visualization, explicitly check that your DataFrame contains the expected data types. This defensive programming approach catches errors early and prevents wasted debugging time.
The “ufunc ‘isnan’ not supported” error signals a data type mismatch. By converting columns to numeric types, selecting only numeric columns, or replacing invalid missing value representations, you resolve the error quickly.