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Box plot

A box plot shows data spread and outliers.

Box plot

A box plot summarises a distribution with five numbers: the box is the first and third quartile (Q1, Q3), the middle line the median, the whiskers reach up to the 1.5·IQR limit; points outside are outliers.

fig, ax = plt.subplots()
ax.boxplot([X[y == 0, 0], X[y == 1, 0], X[y == 2, 0]])
ax.set_xticks([1, 2, 3], ["setosa", "versicolor", "virginica"])

It is tidier than a histogram for comparing several groups side by side.

Computing outliers

The outliers the plot marks can be found by hand with the same rule. This decides which examples to examine in the cleaning step:

q1, q3 = np.percentile(x, [25, 75])
iqr = q3 - q1
aykiri = x[(x < q1 - 1.5 * iqr) | (x > q3 + 1.5 * iqr)]

The plot and the calculation should agree; if they don't, the percentile method usually differs.

Practice Box and outliers

Draw a box plot of x and save it. Print the median, then the outliers by the 1.5·IQR rule, on separate lines.

Starter code

model.py
import numpy as np
import matplotlib.pyplot as plt

x = np.array([12, 14, 15, 15, 16, 17, 18, 19, 40])

# buraya yaz

Sample solution

model.py
import numpy as np
import matplotlib.pyplot as plt

x = np.array([12, 14, 15, 15, 16, 17, 18, 19, 40])

fig, ax = plt.subplots()
ax.boxplot(x)
fig.savefig("kutu.png")
print(np.median(x))
q1, q3 = np.percentile(x, [25, 75])
iqr = q3 - q1
print(x[(x < q1 - 1.5 * iqr) | (x > q3 + 1.5 * iqr)])

Expected output

16.0
[40]

Questions and answers

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

1 What does the line in the middle of the box show in a box plot?

  • The median Correct answer
  • The mean
  • The mode
  • The standard deviation

Explanation: A box plot is based on quartiles; the middle line is the 50th percentile.

2 Draw the groups as a box plot.

Question 
ax.____([a, b, c])
Answer 
ax.boxplot([a, b, c])

Explanation: boxplot takes a list of arrays; each becomes a separate box.

3 Match the box plot parts.

Term Meaning
box Between Q1 and Q3
middle line Median
whisker 1.5·IQR limit
outlier point Outlier

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