[Solved] Pandas ValueError: cannot convert float NaN to integer (3 solutions)

Understanding the Error

When working with numerical data in Pandas, encountering a ValueError: cannot convert float NaN to integer is a common stumbling block for many. This error often emerges during data cleaning or preprocessing, particularly when you are trying to convert a column from float to integer. Understanding the core reasons behind this error and knowing how to address it effectively is crucial for anyone delving into data science with Python. This tutorial will guide you through the common causes of this error and provide detailed solutions to overcome it.

Common Causes

The ‘cannot convert float NaN to integer’ error typically arises when Pandas encounters a NaN (Not a Number) value in a float column that you are trying to convert to an integer type. Since NaN values are inherently float, attempting direct conversion without handling these NaNs will lead to a ValueError. This situation often occurs in real-world data sets which can have missing or corrupt values.

Solutions to the Error

Solution #1 – Ignoring NaNs with Downcasting

Pandas provides the option to downcast when converting data types. This means NaNs can be ignored during the conversion process, allowing the conversion to proceed without error. However, it’s important to note that the result will still contain NaN values, which are float.

• Step 1: Identify the column or columns you wish to convert.
• Step 2: Use the astype method with the argument downcast='integer'.

Example:

import pandas as pd

# Sample DataFrame with NaN values
df = pd.DataFrame({'A': [1.0, 2.0, float('nan'), 4.0]})

# Downcasting to ignore NaN and prevent ValueError
df['A'] = df['A'].astype('float').astype('Int64', errors='ignore')

# Display the modified DataFrame
df

Output:

     A
0    1
1    2
2  NaN
3    4

Notes: This method is straightforward but leaves NaNs in your data, which may not be desirable for all applications. It works best when retaining NaNs in the dataset is acceptable.

Solution #2 – Fill NaN with a Placeholder before Conversion

Another approach is to fill NaN values with a placeholder integer before attempting the conversion. This method requires you to decide on an appropriate placeholder value that suits your dataset’s context (e.g., -1 for missing values).

• Step 1: Use the fillna() method to replace all NaN values with your chosen placeholder.
• Step 2: Convert the column to an integer data type.

Example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1.0, 2.0, float('nan'), 4.0]})

# Filling NaN values with -1
df['A'] = df['A'].fillna(-1).astype(int)

# Resulting DataFrame
df

Output:

   A
0  1
1  2
2 -1
3  4

Notes: This solution modifies the original data by replacing NaNs, which may not always be appropriate depending on the use case. It is crucial to choose a placeholder value that does not conflict with existing data.

Solution #3 – Convert to Nullable Integer Type

A recent feature in Pandas is the introduction of Nullable Integer data types, which support the presence of NaN values within integer columns. This allows for a more direct approach to converting float columns with NaN values to integers. What you need to do here is just directly convert the float column to a Nullable Integer type (Int64, Int32, etc.) using the astype function.

Example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1.0, 2.0, float('nan'), 4.0]})

# Converting to a Nullable Integer Type
df['A'] = df['A'].astype('Int64')

# Displaying the modified DataFrame
df

Output:

     A
0    1
1    2
2  NaN
3    4

Notes: This method allows for the smooth handling of NaNs without alteration of the data. However, you should ensure that your codebase or any downstream systems are compatible with pandas’ Nullable Integer data type as it is a relatively new feature.