python NaN

NaN Python - The handling of NaN values in Python

Florian Zyprian

NaN, which stands for "Not a Number", is a special floating point value in Python (and many other programming languages) that is used to represent undefined or unrepresentable values, such as the result of 0/0 or the square root of a negative number.

Here are important points you should know about NaN in Python should know:

Representation in Python

In Python you can NaN through float('nan') or np.nan from the numpy library. math.nan is also available from the math library.

NaN is equal to nothing

This includes itself. Therefore gives float('nan') == float('nan') False back. To check whether a value NaN you can use the function math.isnan() or numpy.isnan() when working with numpy arrays.

Dealing with NaN-values during data processing

When processing data, especially with Pandas DataFrames or Numpy arrays, you often have to work with NaN-values. They represent missing or corrupted data. There are several ways to handle them - you can delete the rows or columns with NaN-values or NaN with a specific value, such as the average or median of the data, using the method fillna() in pandas.

NaN in mathematical operations

Any mathematical operation with NaN results in NaN. For example there are 5 + float('nan') float('nan') back.

NaN and None are different

NaN is a numeric value used specifically in mathematical and numerical contexts, while None more commonly used in Python to represent the absence of a value.

Here is an example of how to NaN used in Python:

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The understanding and correct handling of NaN-values is very important in data analysis and other scientific calculation tasks.


Excursion - Dealing with NaN

'nan' in Python

It is important to know that Python, even without using pandas, knows NaN values. We can obtain these by using float() Generate:

n1 = float("nan")
n2 = float("Nan")
n3 = float("NaN")
n4 = float("NAN")
print(n1, n2, n3, n4)
print(type(n1))

Furthermore nan since Python 3.5 part of the math-module:

import math
n1 = math.nan
print(n1)
print(math.isnan(n1))

Comparisons with "NaN" values and regular number values should be avoided as they may cause problems:

print(n1 == n2)
print(n1 == 0)
print(n1 == 100)
print(n2 < 0)

Use of NaN in Pandas

In Pandas, there are several ways to handle NaN values. In a hypothetical scenario where we are evaluating a file of temperature measurements that contains sporadic NaN values, we could use the function read_csv to read in the data:

import pandas as pd
df = pd.read_csv("data1/temperatures.csv",
                 sep=";",
                 index_col=0,
                 decimal=",")
print(df.head())

In this DataFrame we could then calculate the average temperatures and add them as a new column:

average_temp_series = df.mean(axis=1)
df = df.assign(temperature=average_temp_series)

If NaN values are present, they could falsify the result. Therefore, it is important to handle them sensibly. One possibility would be to use the dropna() Method to remove all rows where NaN values occur:

df = df.dropna()

Other methods to deal with missing data include replacing it with a specified number or filling it with upstream or downstream values (fillna(method='ffill') respectively fillna(method='bfill')). However, this depends strongly on the context and the specific data.

Conclusion

The handling of NaN-values is an essential part of working with numeric data in Python. These values often represent missing or undefined data and their correct handling is crucial for accurate and meaningful results.

Continuing topics

Now that you have a basic understanding of NaN-values in Python, you might want to look further into the following topics:

  • Error handling in Python: Learn how to handle errors and exceptions in your code.
  • Data cleaning with Pandas: Learn more about data cleaning and preparation methods with the powerful Pandas library.
  • Advanced Numpy Techniques: Deepen your understanding of the Numpy library and its application to numerical data.

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