Pandas DataFrame Exercise 1: Basic Data Handling & Queries

Build muscle memory with core Pandas methods. In this hands-on exercise, load the benchmark 35-row student dataset (student.xlsx), inspect dimensions and data types, slice records, filter by conditional thresholds, calculate summary statistics, and query unique categorical values.

Dataset Overview & Excel Download 🔝

The 35-row student benchmark dataset contains five columns: id, name, class, mark, and gender. You can download the workbook or copy the dictionary generator from the Sample Student DataFrame guide:

Download student.xlsx (35 Rows) ↓ View Dataset Guide →

Video Guide: Solving DataFrame Basic Questions 🔝

Practice Problems Overview (Q1 to Q10) 🔝

1. Read the Excel file download/student.xlsx into a DataFrame using id as the index.
2. Find the dimensions (shape) and total row count.
3. Display the first 4 rows using head().
4. Display the list of column names and their data types.
5. Extract the first 5 records of the name column.
6. Display the top 5 highest-scoring students based on the mark column using sort_values().
7. List all unique classes in the class column and count total unique classes (nunique()).
8. Filter and display students where mark > 60.
9. Filter students enrolled in class 'Three' with marks greater than 50 using combined boolean criteria.
10. Compute the average, maximum, and total sum of marks.

Step-by-Step Code Solutions & Assertions 🔝

import pandas as pd

# 1. Read Excel file
df = pd.read_excel("download/student.xlsx", index_col="id")

# 2. Dimensions & Row Count
print("Shape:", df.shape)  # (35, 4)
print("Total Rows:", len(df))
assert df.shape[0] == 35
assert len(df) == 35

# 3. First 4 Rows
print("
First 4 Rows:
", df.head(4))

# 4. Column Names
print("
Columns:", df.columns.tolist())

# 5. First 5 Names
print("
First 5 Names:
", df["name"].head(5))

# 6. Top 5 Marks
top5_marks = df.sort_values(by="mark", ascending=False).head(5)
print("
Top 5 Highest Marks:
", top5_marks)
assert top5_marks.iloc[0]["name"] == "Kenn Rein"
assert top5_marks.iloc[0]["mark"] == 96

# 7. Unique Classes
classes = df["class"].unique()
print("
Unique Classes:", classes)
print("Class Count:", df["class"].nunique())
assert df["class"].nunique() == 7

# 8. Mark > 60 Filter
high_scorers = df[df["mark"] > 60]
print(f"
Students with Mark > 60: {len(high_scorers)}")
assert len(high_scorers) > 0

# 9. Multiple Conditions: Class Three and Mark > 50
class3_passed = df[(df["class"] == "Three") & (df["mark"] > 50)]
print(f"Class Three with Mark > 50: {len(class3_passed)}")

# 10. Summary Metrics
print(f"Average Mark: {df['mark'].mean():.2f}")
print(f"Highest Mark: {df['mark'].max()}")
print(f"Total Mark Sum: {df['mark'].sum()}")
assert df["mark"].max() == 96

Exercise Series Navigation 🔝

• Exercise 1 (Current Page): Basic data queries, marks, and unique values.
• Exercise 1-1: Data binning with cut(), groupby distributions, and charts.
• Exercise 2: Search Console query analytics, string length, and click analysis.
• Exercise 3 Series: Time-series maintenance logs and datetime queries.

Practice in Google Colab & GitHub 🔝

Open in Google Colab View on GitHub
Run all 10 student dataset query solutions interactively in Google Colab. The 35-row benchmark dataset is directly embedded.

More Details & Related Tutorials 🔝

• Sample Student DataFrame — Full dictionary code and CSV/Excel downloads.
• read_excel() — Ingesting spreadsheet files into Pandas DataFrames.
• head() and tail() — Inspecting row slices and boundary records.
• sort_values() — Sorting tables by single or multiple columns.
• unique() and nunique() — Identifying distinct category elements.
• SQL Practice Queries — Cross-practice relational queries on the same student dataset.

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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