Python - Display True for infinite values in a Pandas DataFrame

When working with numerical data in Pandas, you may encounter infinite values. You can identify and display True for infinite values using the isin() method or np.isinf() function.

Creating a DataFrame with Infinite Values

First, let's create a DataFrame containing some infinite values using np.inf −

import pandas as pd
import numpy as np

# Create DataFrame with infinite values
data = {"Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000, 900, np.inf]}
dataFrame = pd.DataFrame(data)
print("DataFrame...")
print(dataFrame)

DataFrame...
   Reg_Price
0   7000.506
1        inf
2   5000.000
3        inf
4   9000.758
5   6000.000
6    900.000
7        inf

Method 1: Using isin() Method

The isin() method checks if values are contained in the provided list and returns True for infinite values −

import pandas as pd
import numpy as np

data = {"Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000, 900, np.inf]}
dataFrame = pd.DataFrame(data)

# Display True for infinite values
result = dataFrame.isin([np.inf, -np.inf])
print("Boolean mask for infinite values...")
print(result)

Boolean mask for infinite values...
   Reg_Price
0      False
1       True
2      False
3       True
4      False
5      False
6      False
7       True

Method 2: Using np.isinf() Function

You can also use NumPy's isinf() function to detect infinite values directly −

import pandas as pd
import numpy as np

data = {"Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000, 900, np.inf]}
dataFrame = pd.DataFrame(data)

# Using np.isinf() to detect infinite values
result = np.isinf(dataFrame)
print("Boolean mask using np.isinf()...")
print(result)

# Count total infinite values
count = np.isinf(dataFrame).values.sum()
print(f"\nTotal infinity values count: {count}")

Boolean mask using np.isinf()...
   Reg_Price
0      False
1       True
2      False
3       True
4      False
5      False
6      False
7       True

Total infinity values count: 3

Comparison

Conclusion

Both isin([np.inf, -np.inf]) and np.isinf() effectively identify infinite values in DataFrames. Use np.isinf() for simplicity or isin() when you need more control over specific values to check.