Taming Infinity: Techniques for Cleaning Infinite Values in Pandas

1. Replacing with NaN and Dropping:

   • You can replace infinite values with NaN (Not a Number) using the replace method. NaN is a special missing data value that pandas can handle more gracefully than infinities.
   • After replacing infinities with NaN, you can use the dropna method to remove rows containing NaN values (including the ones you just replaced the infinities with).

Here's an example:

import pandas as pd
import numpy as np

np.random.seed(2)
data = {}
for i in [0, 1, 2]:
  data['col'+str(i)] = np.random.rand(5)
  data['col'+str(i)][i] = np.inf 

df = pd.DataFrame(data)
print(df)

# Replace inf with NaN and drop rows with NaN
df = df.replace([np.inf, -np.inf], np.nan).dropna(axis=0)
print(df)

This will print the original DataFrame with infinities, and then a new DataFrame with the infinities replaced with NaN and the rows containing NaN (including the former infinity rows) dropped.

1. Using isin and any:

   • You can use the isin method to check for rows that contain infinite values.
   • Then, you can chain the any method to check if any value in a row is infinite.
   • Finally, use boolean indexing to select and keep only the rows that don't contain any infinities.

import pandas as pd
import numpy as np

np.random.seed(2)
data = {}
for i in [0, 1, 2]:
  data['col'+str(i)] = np.random.rand(5)
  data['col'+str(i)][i] = np.inf 

df = pd.DataFrame(data)

# Keep rows without any infinities
df = df[~df.isin([np.inf, -np.inf]).any(1)]
print(df)

This approach directly selects rows that don't contain any infinities, keeping the DataFrame intact without introducing NaN values.

Choosing which method to use depends on your specific needs. If you need to explicitly replace infinities with NaN for further analysis, the first approach is better. If you just want to remove rows with infinities and don't need to replace them, the second approach is more concise.

import pandas as pd
import numpy as np

# Create a DataFrame with random data and introduce infinities
np.random.seed(2)
data = {}
for i in [0, 1, 2]:
  data['col'+str(i)] = np.random.rand(5)
  data['col'+str(i)][i] = np.inf

df = pd.DataFrame(data)

# Print the original DataFrame with infinities
print("Original DataFrame:")
print(df)

# Replace infinities with NaN
df_replaced = df.replace([np.inf, -np.inf], np.nan)

# Drop rows with NaN (including former infinity rows)
df_filtered = df_replaced.dropna(axis=0)

# Print the DataFrame after processing
print("\nDataFrame after dropping rows with infinities:")
print(df_filtered)

This code first creates a DataFrame with sample data and injects infinities. Then, it replaces both positive and negative infinities with NaN. Finally, it drops all rows containing NaN (including the rows where infinities were replaced).

import pandas as pd
import numpy as np

# Create a DataFrame with random data and introduce infinities
np.random.seed(2)
data = {}
for i in [0, 1, 2]:
  data['col'+str(i)] = np.random.rand(5)
  data['col'+str(i)][i] = np.inf

df = pd.DataFrame(data)

# Print the original DataFrame with infinities
print("Original DataFrame:")
print(df)

# Find rows containing any infinities
inf_rows = df.isin([np.inf, -np.inf]).any(1)

# Keep rows without any infinities using boolean indexing
df_filtered = df[~inf_rows]

# Print the DataFrame after processing
print("\nDataFrame after dropping rows with infinities:")
print(df_filtered)

This code achieves the same result as the first method, but in a more concise way. It checks for rows containing any infinities (np.inf or -np.inf) and then uses boolean indexing to keep only the rows that don't have any infinities.

1. Setting use_inf_as_na:

   • Pandas offers a global setting pd.set_option('use_inf_as_na', True). This tells pandas to treat infinities as missing values (NaN) by default.
   • With this option set, any operations on the DataFrame will automatically convert infinities to NaN.

import pandas as pd
import numpy as np

np.random.seed(2)
data = {}
for i in [0, 1, 2]:
  data['col'+str(i)] = np.random.rand(5)
  data['col'+str(i)][i] = np.inf

df = pd.DataFrame(data)

# Set pandas to treat infinities as NaN
pd.set_option('use_inf_as_na', True)

# Any operation on the DataFrame will now convert inf to NaN
df_filtered = df.mean(axis=0)  # Using mean for demonstration

# Print the DataFrame after processing (infinities converted to NaN)
print(df_filtered)

# Reset the option after use (optional)
pd.set_option('use_inf_as_na', None)

Important Note: This approach affects all DataFrames you work with after setting the option. Make sure to reset it (pd.set_option('use_inf_as_na', None)) if you don't want infinities treated as NaN globally.

import pandas as pd
import numpy as np

np.random.seed(2)
data = {}
for i in [0, 1, 2]:
  data['col'+str(i)] = np.random.rand(5)
  data['col'+str(i)][i] = np.inf

df = pd.DataFrame(data)

def filter_infinities(df):
  """Filters a DataFrame to remove rows with infinities."""
  return df[~df.isna().any(axis=1)]  # Check for any NaN (including inf)

# Filter infinities using the custom function
df_filtered = filter_infinities(df.copy())  # Copy to avoid modifying original df

# Print the DataFrame after processing
print(df_filtered)

This approach defines a function filter_infinities that checks for any NaN values (which includes infinities after setting use_inf_as_na to True temporarily within the function) and returns only the rows without them. It also uses .copy() to avoid modifying the original DataFrame.

Remember, the choice of method depends on your specific needs and workflow. Consider factors like whether you want to modify all DataFrames globally or keep specific filtering logic.