CodeBlanks Logo Icon CodeBlanks
Login Register

What is Pandas?

Pandas is a Python library for data analysis, providing DataFrame and Series data structures.

What is pandas?

pandas is the standard Python library for working with tabular data. It has two core structures:

  • Series: a single labelled column.
  • DataFrame: a table made of named columns.
import pandas as pd
s = pd.Series([3, 5, 8], name="puan")
print(s.mean())   # 5.333...

Reading CSV, filling missing values, grouping and so on are all a few lines in pandas; then the data becomes a NumPy array via df.values and is given to the model.

Reading CSV

Real data usually comes from a file:

df = pd.read_csv("evler.csv")
print(df.shape)            # (rows, columns)
print(df.columns.tolist()) # column names
df.to_csv("temiz.csv", index=False)

index=False stops the row numbers from being written to the file as a separate column.

Practice Series mean

Create a pd.Series from the puan list; print its mean and its maximum separated by a space.

Starter code

model.py
import pandas as pd

puan = [70, 85, 90, 75]

# buraya yaz

Sample solution

model.py
import pandas as pd

puan = [70, 85, 90, 75]

s = pd.Series(puan)
print(s.mean(), s.max())

Expected output

80.0 90

Questions and answers

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

1 What type is a single column of a DataFrame?

  • ndarray
  • Series Correct answer
  • list
  • dict

Explanation: df["column"] returns a Series; a Series carries a NumPy array inside.

2 Read the CSV file.

Question 
df = pd.____("veri.csv")
Answer 
df = pd.read_csv("veri.csv")

Explanation: read_csv loads the file as a DataFrame.

3 Match the structures.

Term Meaning
Series Single column
DataFrame Table
read_csv Reading from a file
to_csv Writing to a file

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