CodeBlanks Logo Icon CodeBlanks
Login Register

Computing statistics

Compute a statistical summary of the data.

Column statistics

We'll compute a summary for each column of the clean data. The day column (0) is only a sequence number and its statistics are meaningless, so it is skipped with veri[:, 1:]:

olcum = veri[:, 1:]          # temperature and humidity
print(olcum.mean(axis=0))    # [23.   48.75]
print(olcum.max(axis=0))     # [26. 60.]

A per-feature summary lets you see the data's range and catch faulty values (for example 99 °C).

argmax: the hottest day

argmax gives the position of the largest value. With that position, the matching value is read from another column:

i = veri[:, 1].argmax()
print(i)              # 3  (0-based)
print(veri[i, 0])     # 4.0  -> day 4

argmin does the same for the smallest value.

Practice Compute the summary

Print the mean, standard deviation, minimum and maximum of the temperature column (veri[:, 1]) in a single print. Then find the hottest day with argmax and print it as an integer (int).

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)
veri = np.where(np.isnan(veri), np.nanmean(veri, axis=0), veri)

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

s = veri[:, 1]
print(s.mean(), s.std(), s.min(), s.max())
print(int(veri[s.argmax(), 0]))

Expected output

23.0 2.0 20.0 26.0
4

Questions and answers

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

1 How does filling with the mean affect the column's mean?

  • It decreases it
  • It makes it nan
  • It doesn't change it Correct answer
  • It increases it

Explanation: Adding a value equal to the mean keeps the mean; it does shrink the standard deviation slightly.

2 Find the position of the lowest humidity.

Question 
i = veri[:, 2].____()
Answer 
i = veri[:, 2].argmin()

Explanation: argmin returns the position, not the value.

3 Match the expressions.

Term Meaning
veri[:, 1:] Columns except the day
max(axis=0) Column maxima
argmax() Position of the maximum
veri[i, 0] That row's day

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