Data cleaning is not the most attractive part of data science but that’s where good marks (and models) come from. In class and assignments, having messy data can easily throw off your analysis before you even actually get to analyze it! Messy datasets often mix up different data types.
Missing values and outliers are two of the biggest troublemakers. Manage them properly, and all the chaos will turn into clarity. Do it badly and your resulting data is useless.
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In this guide, you will learn how to tackle these issues in Python and get your assignments right.
TL;DR
Topic | Summary |
Why Data Cleaning Matters? | Because messy data lead to bad analysis–you can clean up your data and improve the accuracy and therefore the accuracy of your grades. |
Missing Values | Missing values can be caused by errors, missing data collection, or missing data being irrelevant; the missing value must be dealt with before an analysis. |
Detecting Missing Values | You can find where your missingness is with the following Pandas command `df.isnull().sum() |
Handling Missing Values | You can drop the rows if a few are missing, fill in a constant for categories, use the mean/median/mode for numbers, or use some other predictive way to fill in values. |
Outliers | Outliers are extreme values that can generally be defined as errors or rare cases, which can distort your results. |
Detecting Outliers | Outliers can be detected with Inter-Quartile Range (IQR) based on spread, or Z-score, being the deviation from the mean. |
Handling Outliers | Outliers can be dealt with easily (deleting entries that are obvious errors, winsorizing (capping the extremes), or transforming your data to remove skew. |
Best Practices | You want to explain your methods, use visuals, show before and after, don’t over-clean, and match your cleaning method with your data type. |
Relevance of Data Cleaning in Assignments
In real life data is never perfect. This is also true for datasets that professors provide for you. This can be null values, abnormal entries or even numbers that are completely out of range.
Cleaning data helps in making sure that your analysis is based on reality. Skipping it leads to decisions being made based on mistakes, not real patterns.
When it comes to students, after they end up with these datasets in their hands, cleaning of the data is a step that demonstrates you not only know how to run a model but understand the whole data science process.
It’s the difference between an average grade and an impressive one.
Step 1: Understanding Missing Values
The absence of a data point is simply a missing value. This could either be missing or blank cells, NaN values or simply a placeholder like ‘N/A’ or -999.
Why do they happen?
It is possible that no data was captured, the sensor has gone bad, or the information was not important at the time. The issue is — a lot of algorithms cannot use missing data directly. That’s the reason you have to determine whether to remove or replace.
Detecting Missing Values in Python
Before you do anything, establish what is missing.
Using Pandas, this is a couple of lines:
import pandas as pd df =pd.read_csv('data.csv') print(df.isnull().sum())
This will show you how many missing values there are in each column, and once you know where you have holes, you can start to plan how you will approach them.
Step 2: Dealing with Missing Values
There are no ironclad rules to follow. Your approach will vary depending on the type of data you have, how much is missing, and what is important for your analysis.
Dropping Missing Values
If it is only a few rows, a quick option is to delete them.
This avoids making assumptions about the dataset.
df = df.dropna()
However, be careful — deleting too many rows may leave you with inadequate data.
If you lose more than 20% of the dataset, it might be worthwhile exploring the other options.
Fill with Simple Values
In some cases, missing values can be filled in with simple values such as 0 or “Unknown”.
This is a quick option, but it tends to work better for categorical data or columns where the fill-in simply works.
df['Category'] = df['Category'].fillna('Unknown')
Predictive Imputation
For some advanced assignment, you can replace the missing values in the data with predictions from machine learning models.
For instance, predicting what is missing using KNN (K-Nearest Neighbors). This is a good way to show professors you can go beyond basic techniques — but take time and process.
Trying predictive imputation with KNN? It’s also one of the cool project ideas you can explore in Python.
Step 3: Outliers
An outlier is a value that is far removed from other observations (data). Outliers can be mistakes, rare events, or sincere extreme situations.
Example: The Data on student ages showed that most students were between 18-25 years old. One record has 120; this would be suspicious.
Why Outliers are Important?
Outliers can skew averages, standard deviations, and regression models. If we do not scout outliers first, we may find our analysis suggests trends when none exist in the data set.
In assignments, scouting and justifying your treatment of outliers is critical for obtaining good marks.
Step 4: Identifying Outliers in Python.
There are many ways to identify outliers; the two most common are:
1. Using IQR (interquartile range)
The IQR focuses on the middle 50% of your data. Any observations lying beyond that range are considered outliers.
Q1 = df['Value'].quantile(0.25)
Q3 = df['Value'].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df['Value'] < Q1 - 1.5 * IQR) | (df['Value'] > Q3 + 1.5 * IQR)]
2. Using Z-Scores
Z scores tell you how far away a value is from the mean in standard deviations.
Usually, a value that has an absolute z-score greater than 3 is considered an outlier.
from scipy import stats
df['z_score'] = stats.zscore(df['Value'])
outliers = df[abs(df['z_score']) > 3]
Best Practices for Assignments
- Always document your version of reasoning – Professors appreciate that you think about your choices as much as the code.
- Always include a visualization – Boxplots and histograms make it apparent where the data is unusual.
- Always include a before and after – Describe how your cleaning procedure improved the data.
- Don’t “over clean” – Removing too much may be removing important information.
- Always choose a method related to your data type – There are different methods to clean at the time of the analysis for numbers, categories, and dates.
Conclusion
Managing missing values and treating outliers is the minimum requirement for every Python data cleaning assignment. It shows you are capable of preparing your data for accurate analysis, and models.
Following these procedures – detecting, deciding, and documenting – means that you’ll not only create cleaner datasets, you’ll also receive better grades.
In the end, data cleaning is more than a labor of love, it’s the essential ingredient in producing reliable, professional-quality results.
