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Cleaning data

Detect and clean missing data.

Filling the gaps

There are two empty cells: the humidity of day 2 and the temperature of day 3. Dropping the rows would lose 40% of the data; instead we'll fill each column with its own mean. np.nanmean takes the mean while ignoring nans:

print(np.nanmean(veri, axis=0))   # [ 3.   23.   48.75]
print(veri.mean(axis=0))          # [ 3.  nan  nan]  nan is contagious

Any operation involving nan gives nan, which is why nan-safe functions are needed.

Filling selectively with np.where

np.where(condition, a, b) takes from a where the condition is true and from b otherwise. Thanks to broadcasting, the column means are applied to every row:

ort = np.nanmean(veri, axis=0)
veri = np.where(np.isnan(veri), ort, veri)
print(np.isnan(veri).sum())   # 0

In scikit-learn, SimpleImputer(strategy="mean") does the same; here you saw its logic by hand.

Practice Clean

Fill the nan values in veri using np.nanmean(veri, axis=0) and np.where. Print the temperature column (veri[:, 1]) and the humidity column (veri[:, 2]) on separate lines.

Starter code

model.py
import io
import numpy as np

csv = "gun,sicaklik,nem\n1,20,40\n2,22,\n3,,60\n4,26,50\n5,24,45\n"
veri = np.genfromtxt(io.StringIO(csv), delimiter=",", skip_header=1)

# buraya yaz

Sample solution

model.py
import io
import numpy as np

csv = "gun,sicaklik,nem\n1,20,40\n2,22,\n3,,60\n4,26,50\n5,24,45\n"
veri = np.genfromtxt(io.StringIO(csv), delimiter=",", skip_header=1)

ort = np.nanmean(veri, axis=0)
veri = np.where(np.isnan(veri), ort, veri)
print(veri[:, 1])
print(veri[:, 2])

Expected output

[20. 22. 23. 26. 24.]
[40.   48.75 60.   50.   45.  ]

Questions and answers

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

1 What does <code>np.array([1, np.nan, 3]).mean()</code> give?

  • 2.0
  • 1.33
  • An error
  • nan Correct answer

Explanation: A sum containing nan becomes nan; nanmean would give 2.0.

2 Take the column means ignoring nan values.

Question 
ort = np.____(veri, axis=0)
Answer 
ort = np.nanmean(veri, axis=0)

Explanation: nanmean is the mean of the non-nan values.

3 Match the functions.

Term Meaning
np.nanmean Mean without nan
np.where Conditional selection
np.isnan nan mask
x.mean() Returns nan if nan exists

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