CodeBlanks Logo Icon CodeBlanks
Login Register

Loading data

Load and inspect a dataset with pandas.

Project: weather readings

In this project you'll process five days of temperature and humidity readings step by step: load, clean, compute statistics, write a report. The data is CSV text and some cells are empty:

gun,sicaklik,nem
1,20,40
2,22,
3,,60
4,26,50
5,24,45

In real projects data comes from a file; here we'll read the text like a file with io.StringIO.

np.genfromtxt

np.genfromtxt turns a text table into a numeric array and makes empty cells nan:

veri = np.genfromtxt(io.StringIO(csv), delimiter=",", skip_header=1)
print(veri.shape)              # (5, 3)
print(np.isnan(veri).sum())    # number of empty cells

skip_header=1 skips the header line; if it isn't skipped, the header is also read as a row of nan.

Practice Load the data

Read the csv text with np.genfromtxt (delimiter=",", skip_header=1). Print the array's shape and the number of empty cells 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"

# 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)
print(veri.shape)
print(np.isnan(veri).sum())

Expected output

(5, 3)
2

Questions and answers

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

1 How many data rows does this CSV have (excluding the header)?

  • 5 Correct answer
  • 6
  • 3
  • 4

Explanation: The first line holds the column names; below it are the readings of five days.

2 Read while skipping the header line.

Question 
veri = np.genfromtxt(io.StringIO(csv), delimiter=",", ____=1)
Answer 
veri = np.genfromtxt(io.StringIO(csv), delimiter=",", skip_header=1)

Explanation: skip_header is the number of lines to skip at the start.

3 Match the pieces.

Term Meaning
io.StringIO Reads text like a file
delimiter="," Separator character
skip_header=1 Skips the header
np.isnan Finds empty cells

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