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Median

The median is the middle value when data is sorted.

Median

The median is the middle value when the values are sorted; with an even count it is the mean of the two middle ones.

x = np.array([3, 1, 100, 2, 4])
print(np.median(x))   # 3.0   sorted: 1 2 3 4 100
print(x.mean())       # 22.0

The median isn't affected by outliers; for skewed distributions like income or house prices it is better than the mean as a "typical value".

Percentiles and mode

The median is the 50th percentile. Other percentiles describe the shape of the distribution:

x = np.arange(1, 101)
print(np.percentile(x, [25, 50, 75]))   # [25.75 50.5  75.25]

vals, counts = np.unique([2, 3, 3, 5, 3], return_counts=True)
print(vals[counts.argmax()])            # 3  (mode: most frequent value)

The mode is used to fill missing values in categorical data (strategy="most_frequent").

Practice Outlier-robust centre

Print the mean and the median of the gelir array separated by a space; see the difference.

Starter code

model.py
import numpy as np

gelir = np.array([20, 22, 25, 23, 210])

# buraya yaz

Sample solution

model.py
import numpy as np

gelir = np.array([20, 22, 25, 23, 210])

print(gelir.mean(), np.median(gelir))

Expected output

60.0 23.0

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What is <code>np.median([5, 1, 3, 7])</code>?

  • 4.0 Correct answer
  • 3.0
  • 5.0
  • 4.5

Explanation: Sorted: 1 3 5 7; the mean of the middle two (3 and 5) is 4.

2 Compute the median.

Question 
m = np.____(x)
Answer 
m = np.median(x)

Explanation: For NumPy arrays median isn't a method; it is the np.median function.

3 Match the measures.

Term Meaning
mean Sum / count
median Middle sorted value
mode Most frequent value
75th percentile Upper quartile boundary

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4