# Cleaning Student Performance Data with Pandas – My First Practical Data Cleaning Case Study 📊🧹

Hey there 👋  
I’m **Avadhoot Kamble**, and this blog is a breakdown of one of my first real-world data cleaning projects using **Pandas** in Python.

I recently downloaded a dataset from Kaggle on student performance and decided to **clean it thoroughly** using only code — no Excel this time! In this post, I’ll walk you through everything I did, step-by-step, including the challenges I faced and how I solved them.

Whether you're a beginner learning Pandas or someone curious about how data is cleaned before analysis or modeling, this post will give you a solid, realistic picture.

## 📁 Dataset Overview

The dataset I worked on contains student information — not just their test scores, but also family, education, and lifestyle-related features. Here’s what each column means:

| Column Name | Description |
| --- | --- |
| `Gender` | Gender of the student (`Male`/`Female`) |
| `EthnicGroup` | Ethnic group (`Group A` to `Group E`) |
| `ParentEduc` | Parent's education background |
| `LunchType` | Type of school lunch (`Standard`, `Free/Reduced`) |
| `TestPrep` | Whether they completed test preparation |
| `ParentMaritalStatus` | Marital status of the parents |
| `PracticeSport` | How often they practice sports |
| `IsFirstChild` | Is this student the first child in the family? (`Yes`/`No`) |
| `NrSiblings` | Number of siblings |
| `TransportMeans` | Means of transport to school |
| `WklyStudyHours` | Weekly self-study hours |
| `MathScore`, `ReadingScore`, `WritingScore` | Academic scores (0–100) |

![Raw dataset](https://cdn.hashnode.com/res/hashnode/image/upload/v1751959967278/493455af-9a8c-4055-aede-a69a4e869328.png align="center")

One weird thing I spotted was an `Unnamed: 0` column — probably an index from an earlier save. I dropped it immediately:

```python
data = data.drop(columns=['Unnamed: 0'])
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1751960147405/e5434ce2-0f72-460c-8006-6bbc9ef7beed.png align="center")

## 🛠️ Step 2: Handling Null Values

Several columns had missing values. Here’s how I tackled them:

### 🔹 `EthnicGroup` & `ParentEduc`

I filled their missing values using the **most frequent value (mode)**:

```python
data['EthnicGroup'] = data['EthnicGroup'].fillna(data['EthnicGroup'].mode()[0])
data['ParentEduc'] = data['ParentEduc'].fillna(data['ParentEduc'].mode()[0])
```

### 🔹 `TestPrep`, `ParentMaritalStatus`, `PracticeSport`

I looped through these related columns and applied mode-filling as well:

```python
col_to_fill = ['TestPrep', 'ParentMaritalStatus', 'PracticeSport']
for col in col_to_fill:
    data[col] = data[col].fillna(data[col].mode()[0])
```

## 🎯 Step 3: Fixing `IsFirstChild` (Binary Encoding)

This column had `"yes"`/`"no"` values. I filled nulls with mode and mapped it to 1s and 0s for binary classification:

```python
data['IsFirstChild'] = data['IsFirstChild'].fillna(data['IsFirstChild'].mode()[0])
data['IsFirstChild'] = data['IsFirstChild'].map({'yes': 1, 'no': 0})
```

## 📊 Step 4: Numerical Fixes – `NrSiblings`, `WklyStudyHours`

For `NrSiblings`, I filled nulls with the **median** and converted it to integer:

```python
data['NrSiblings'] = data['NrSiblings'].fillna(data['NrSiblings'].median())
data['NrSiblings'] = data['NrSiblings'].astype('int64')
```

For `WklyStudyHours`, I:

* Removed spaces
    
* Lowercased all entries
    
* Mapped ranges (`<5`, `5-10`, `>10`) to numerical midpoints: 2.5, 7.5, 12.5
    
* Filled any missing values with the **median**
    

```python
data['WklyStudyHours'] = data['WklyStudyHours'].str.replace(" ", "").str.lower()
data['WklyStudyHours'] = data['WklyStudyHours'].map({'<5': 2.5, '5-10': 7.5, '>10': 12.5})
data['WklyStudyHours'] = data['WklyStudyHours'].fillna(data['WklyStudyHours'].median())
```

Column “WklyStudyHours” before mapping:

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1751960440732/cda575aa-093e-488b-b601-e799b4d73678.png align="center")

Column “WklyStudyHours” after mapping:

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1751960518990/8d263912-8851-448b-aa6a-68b71993f00d.png align="center")

## ✂️ Step 5: Categorical Cleaning – Lowercasing & Trimming

I applied `.str.strip().str.lower()` on a few selected columns to clean casing and whitespace:

```python
pythonCopyEditcol_to_clean = ['TestPrep', 'Gender', 'PracticeSport']
for col in col_to_clean:
    data[col] = data[col].str.strip().str.lower()
```

---

## 🔎 Step 6: Checking for Outliers in Score Columns

Before finalizing the dataset, I wanted to make sure there were **no unusual or invalid scores** in the three academic columns: `MathScore`, `ReadingScore`, and `WritingScore`.

I used the `.unique()` function to inspect the values in each column:

```python
data['ReadingScore'].unique() #checking for outliers
data['MathScore'].unique()
data['WritingScore'].unique()
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1751961055070/8e49e3a1-99e3-480a-afc3-afdcc1e01714.png align="center")

## 🧹 Step 7: Removing Duplicates

Finally, I checked for duplicate rows and dropped them if any were found:

```python
pythonCopyEditdata.duplicated().sum()
data = data.drop_duplicates()
```

---

## 💾 Final Step: Exporting Cleaned Dataset

Once everything looked clean and consistent, I saved the dataset:

```python
pythonCopyEditdata.to_csv("Cleaned student exam data.csv", index=False)
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1751961402630/3fcd1b7a-a1c0-4395-8914-8cd5a6329371.png align="center")

✅ Done! I now had a cleaned, structured dataset ready for EDA or modeling.

## 💡 What I Learned from This Project

* **Real data isn’t clean** — even well-structured datasets need attention.
    
* **Pandas is powerful** — chaining small functions (`.map()`, `.fillna()`, `.str.strip()`) creates big improvements.
    
* **Data cleaning is decision-making** — context matters when choosing between median, mode, or dropping.
    

## 🚀 What’s Next?

This was my first real experience cleaning a dataset using only code. Next, I’ll be working on:

* 🔎 **Exploratory Data Analysis** with Pandas and Seaborn
    
* 📈 Visualizations comparing scores by gender, lunch type, parental education
    
* 🤖 My first **machine learning project**: predicting test outcomes
    

## 📂 Project Files on GitHub

You can find the full project here, including:

* The **Jupyter Notebook** (`StudentsExamScores.ipynb`)
    
* The **Raw and Cleaned CSVs**
    

🔗 [View the Project on GitHub](https://github.com/AvadhootKamble24/Data-Cleaning-Projects/tree/main/Student%20Exam%20Data)

## 🙌 Let’s Connect

If you're on a similar path or want to share tips, feedback, or connect — I’d love to hear from you!

* 💻 [GitHub – Projects](https://github.com/AvadhootKamble24)
    
* 🌐 [LinkedIn – Say hi](https://linkedin.com/in/avadhootkamble)
    
* 📝 [More Blogs on Hashnode](https://avadhootkamble24.hashnode.dev)
    

Thanks for reading 🙏

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#Pandas #Python #DataCleaning #DataScience #KaggleDataset #LearningInPublic #BeginnerProjects #JupyterNotebook #PortfolioProject