Jammos Analytics

Dealing with missing data is an essential part of data analysis and machine learning. In real-world datasets, missing values are commonplace and can hinder the accuracy and reliability of your analysis. Fortunately, Python provides various tools and libraries to effectively handle missing data, allowing you to clean, pre-process, and analyse datasets with ease. In this beginner’s guide, we’ll explore some techniques to handle missing values using Python.

Identifying Missing Values

Before addressing missing values, it’s crucial to identify where they exist within your dataset. Common representations of missing data include NaN (Not a Number), None, NA, or simply blank cells. Pandas, a popular data manipulation library in Python, offers methods like isnull(), notnull(), or info() to detect missing values within a DataFrame:

import pandas as pd



# Load your dataset

data = pd.read_csv('your_dataset.csv')



# Check for missing values

print(data.isnull().sum())  # Show count of missing values

However, there is no one size fits all solution for missing values – depending on your dataset, there could be all sorts of values that mean nothing to you. This could be caused by poor source data or issues with the ingestion process itself.

In such cases, one idea is to simply use the replace() function in Pandas to replace those values that you find to be equivalent to a missing/blank value with a Null value, then repeat the above process:

import pandas as pd

# Load your dataset

data = pd.read_csv('your_dataset.csv')


# Replace 'missing' with NaN (null value)

data.replace('missing', pd.NA, inplace=True)

# Check for missing values

print(data.isnull().sum())  # Show count of missing values

Repeat the above as necessary or consider using Regex if those missing values are clearly identifiable and similar in nature.

Handling Missing Values

Dropping Missing Values

One approach is to eliminate rows or columns containing missing values. Pandas provides the dropna() method, allowing you to drop rows or columns with missing data.

# Drop rows with any missing values
data.dropna(axis=0, inplace=True)

# Drop columns with any missing values
data.dropna(axis=1, inplace=True)

Inputting Missing Values

Another strategy involves filling in missing values with estimates like the mean, median, or mode of the column. The fillna() method in Pandas is useful for this purpose.

# Fill missing values with the mean of the column
data.fillna(data.mean(), inplace=True)

# Fill missing values with a specific value (e.g., 0)
data.fillna(0, inplace=True)

Using Modelling Techniques

Advanced techniques, such as K-Nearest Neighbors (KNN) or predictive modelling, can be employed to impute missing values based on similar observations or models trained on the existing data. Those methods won’t be covered here, but feel free to research these methods if you are interested.

Summary

Handling missing values is a critical step in data pre-processing. Python, with libraries like Pandas and scikit-learn, offers numerous methods to handle missing data effectively. However, the choice of technique depends on the dataset and the nature of the missing values. Experiment with different approaches to find the most suitable method for your specific dataset.

By mastering these techniques, you can ensure cleaner, more reliable data for your analyses and machine learning models.

Remember, dealing with missing values is just the beginning of data pre-processing, but it’s a crucial step towards deriving meaningful insights from your data.