1. Aprender
    2. /
  2. Cursos
    3. /
  3. Dealing with Missing Data in Python
  • 1

    The Problem With Missing Data

    Gratuito

    Get familiar with missing data and how it impacts your analysis! Learn about different null value operations in your dataset, how to find missing data and summarizing missingness in your data.

  • 2

    Does Missingness Have A Pattern?

    Analyzing the type of missingness in your dataset is a very important step towards treating missing values. In this chapter, you'll learn in detail how to establish patterns in your missing and non-missing data, and how to appropriately treat the missingness using simple techniques such as listwise deletion.

  • 3

    Imputation Techniques

    Embark on the world of data imputation! In this chapter, you will apply basic imputation techniques to fill in missing data and visualize your imputations to be able to evaluate your imputations' performance.

  • 4

    Advanced Imputation Techniques

    Finally, go beyond simple imputation techniques and make the most of your dataset by using advanced imputation techniques that rely on machine learning models, to be able to accurately impute and evaluate your missing data. You will be using methods such as KNN and MICE in order to get the most out of your missing data!

Initializing

Exercicio

Visualize missingness

In the previous exercise, you calculated the number of missing values and the percentage of missingness for each column. However, this is usually not enough and preference is to visualize them graphically.

You'll use the misssingno package which is built for visualizing missing values. The airquality DataFrame has already been imported, and the pandas library as pd.

You will visualize the missingness by plotting a bar chart and a nullity matrix of the missing values.

Note that we've used a proprietary display() function instead of plt.show() to make it easier for you to view the output.

Instruções 1/4

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  • 1
    • Plot a bar chart of the missing values in airquality.
  • 2
    • Plot the nullity matrix of airquality.
  • 3
    • Plot the nullity matrix of airquality across a monthly frequency.
  • 4
    • Splice airquality from 'May-1976' to 'Jul-1976' and plot its nullity matrix.