A simple and beginner-friendly Python project that uses NumPy to analyze numerical datasets.
You enter a list of numbers, and the program instantly computes useful statistics like mean, median, standard deviation, range values, and also provides a normalized version of your dataset.
This project is perfect for beginners learning:
- Python basics
- Exception handling
- NumPy fundamentals
- Data preprocessing concepts
- Takes user input safely (with error handling)
- Converts the list into a NumPy array
- Calculates:
- ✔ Count
- ✔ Mean
- ✔ Median
- ✔ Standard Deviation
- ✔ Max & Min
- ✔ Values above mean
- ✔ Normalized dataset
- Clean and well-formatted output
- Beginner-friendly and easy to expand
- Using
numpy.mean(),numpy.median(),numpy.std() - Boolean indexing (
array[array > mean]) - Normalization formula:
(x - min) / (max - min) - How to handle invalid user inputs using
try-except
============================================================
📊 NumPy Data Analyzer
============================================================
How many numbers do you want to enter?: 5
Enter value 1: 10
Enter value 2: 40
Enter value 3: 25
Enter value 4: 90
Enter value 5: 60
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Data Analysis report📊
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Count :- 5
Mean :- 45.00
Median :- 40.00
Standard deviation :- 27.38
Maximum value :- 90
Minimum value :- 10
Above Mean :- [60 90]
Normalized Data :- [0. 0. 0. 1. 1.]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Python 3.8+
- NumPy
Install NumPy using:
pip install numpy
python analyzer.py
(Replace analyzer.py with your filename.)
import numpy as np
print("="*60)
print("📊 NumPy Data Analyzer")
print("="*60)
while True:
try:
data_num = int(input("How many numbers do you want to enter?: "))
break
except ValueError:
print("Please enter numerical values only!")
while True:
try:
values =[int(input(f"Enter value {i + 1}: ")) for i in range(data_num)]
print("~" * 60)
break
except ValueError:
print("Please enter numerical values only!")
array = np.array(values)
print("Data Analysis report📊")
print("~" * 60)
print(f"Count :- {array.size}")
print(f"Mean :- {np.mean(array):.2f}")
print(f"Median :- {np.median(array):.2f}")
print(f"Standard deviation :- {np.std(array):.2f}")
print(f"Maximum value :- {np.max(array)}")
print(f"Minimum value :- {np.min(array)}")
print(f"Above Mean :- {np.sort(array[array > np.mean(array)])}")
print(f"Normalized Data :- {np.round((array - array.min()) / (array.max() - array.min()))}")
print("~" * 60)- Add file input/output (save reports)
- Graph visualizations using matplotlib
- CSV data support
- Outlier detection
- z-score normalization
- GUI version (Tkinter)
Pull requests are welcome!
This is a beginner-friendly project — feel free to build on top of it.
If you like this project, give it a ⭐ on GitHub!