How to solve the issue of “ valueerror: input contains nan, infinity or a Value too large for dtyp float ?

In the context of data science, here are the appropriate approaches given for the above scenario:-

Identifying the problematic values

Firstly, you should try to identify which Columns or even rows contain NaN values, infinity, or extremely large values. You can use pandas method such as ISNA(), using() and conditional filtering for this:-

Import pandas as pd

Import numpy as np

# Load the dataset

Df = pd.read_csv(‘stock_prices.csv’)

# Identify NaN values

Nan_rows = df[df.isna().any(axis=1)]

Print(“Rows with NaN values:”)

Print(nan_rows)

# Identify infinity values

Inf_rows = df[np.isinf(df).any(axis=1)]

Print(“Rows with infinity values:”)

Print(inf_rows)

# Identify extremely large values

Large_value_threshold = np.finfo(np.float64).max

Large_value_rows = df[(df > large_value_threshold).any(axis=1)]

Print(“Rows with extremely large values:”)

Print(large_value_rows)

Handling NaN values

Strategies:

Removing NaNa

# Drop rows with any NaN values

Df_cleaned = df.dropna()

Filling NaNs with a default value

# Fill NaN values with 0

Df_filled = df.fillna(0)

Filling NaNs with statistical values

# Fill NaN values with the mean of the column

Df_filled_mean = df.fillna(df.mean())

Handling infinity values

# Replace infinity values with NaN

Df.replace([np.inf, -np.inf], np.nan, inplace=True)

# Optionally fill NaN values with the maximum value of the column

Df_filled = df.apply(lambda x: x.fillna(x.max()), axis=0)

Handling large values

# Identify and cap large values to a specified threshold

Large_value_threshold = np.finfo(np.float64).max

Df_capped = df.applymap(lambda x: large_value_threshold if x > large_value_threshold else x)

Ensuring data integrity

# Check for any remaining NaN values

If df_cleaned.isna().sum().sum() == 0:

    Print(“No NaN values found.”)

# Check for any remaining infinity values

If not np.isinf(df_cleaned).values.any():

    Print(“No infinity values found.”)

# Check for values exceeding float64 limits

If not (df_cleaned > large_value_threshold).values.any():

    Print(“No values exceed float64 limits.”)

Preventing future issues

Data validation

You should try to ensure the data validation at the point of entry or even ingestion to catch NaN, infinity, and excessively large values early.

Regularly monitoring

You should try to implement regular data quality to check and monitor such issues.

Use robust data type

Where possible, you can use the data type to handle larger ranges or even specific values such as float 128 in scientific computing libraries.

Comprehensive testing

You can write unit tests to ensure the data transformation and even calculations for handling edge cases correctly and effectively.