How To Deal With Missing Data in Pandas

Hey - Nick here! This page is a free excerpt from my $199 $99 course Python for Finance and Data Science, which is 50% off for the next 50 students.

If you want the full course, click here to sign up and create an account.

I have a 30-day satisfaction guarantee, so there's no risk (and a ton of upside!) in signing up for this course and leveling up your Python skills today!

In an ideal world we will always work with perfect data sets. However, this is never the case in practice. There are many cases when working with quantitative data that you will need to drop or modify missing data. We will explore strategies for handling this throughout this lesson.

The DataFrame We’ll Be Using In This Lesson

We will be using the np.nan attribute to generate NaN values throughout this lesson.


Np.nan

#Returns nan

In this lesson, we will make use of the following DataFrame:


df = pd.DataFrame(np.array([[1, 5, 1],[2, np.nan, 2],[np.nan, np.nan, 3]]))

df.columns = ['A', 'B', 'C']

df

A Pandas DataFrame With Missing Data

The Pandas dropna Method

Pandas has a built-in method called dropna. When applied against a DataFrame, the dropna method will remove any rows that contain a NaN value.

Let’s apply the dropna method to our df DataFrame as an example:


df.dropna()

A Pandas DataFrame After Using Dropna

Note that like the other DataFrame operations that we have explored, dropna does not modify the original DataFrame unless you either (1) force it to using the = assignment operator or (2) specify inplace=True.

We can also drop any columns that have missing values by passing in the axis=1 argument to the dropna method, like this:


df.dropna(axis=1)

A Pandas DataFrame After Using Dropna On Its Columns

The Pandas fillna Method

In many cases, you will want to replace missing values in a pandas DataFrame instead of dropping it completely. The fillna method is designed for this.

As an example, let’s fill every missing value in our DataFrame with the 🔥:


df.fillna('🔥')

A Pandas DataFrame After Using Fillna

Obviously, there is basically no situation where we would want to replace missing data with an emoji. This was simply an amusing example.

Instead, more commonly we will replace a missing value with either:

  • The average value of the entire DataFrame
  • The average value of that row of the DataFrame

We will demonstrate both below.

To fill missing values with the average value across the entire DataFrame, use the following code:


df.fillna(df.mean())

To fill the missing values within a particular column with the average value from that column, use the following code (this is for column A):


df['A'].fillna(df['A'].mean())

Moving On

In this lesson we explored the dropna and fillna methods for dealing with missing data in pandas. After working through some practice problems, we will discuss how to group a DataFrame’s elements according to a certain characteristic next.