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Column operations

DataFrame columns are accessed with brackets and statistics are computed on them.

Selecting and adding columns

A single column is selected with df["ad"], several with df[["ad", "yas"]]. A new column is added by computing it from existing ones:

df["yillik"] = df["maas"] * 12
df["genc"] = df["yas"] < 30

The operation is applied to the whole column at once; it is the same as vectorisation in NumPy.

Dropping and renaming

Column operations return a new DataFrame; the original table doesn't change:

df2 = df.drop(columns=["genc"])
df3 = df.rename(columns={"maas": "ucret"})
print(df["maas"].sum())   # column total

To use the result, assign it to a new variable or write df = df.drop(...).

Practice Computed column

Add a yillik column to df equal to 12 times maas. Print the total of this column.

Starter code

model.py
import pandas as pd

df = pd.DataFrame({
    "ad": ["Ali", "Ece", "Can", "Su"],
    "yas": [34, 28, 45, 22],
    "maas": [5000, 4200, 7100, 3000],
})

# buraya yaz

Sample solution

model.py
import pandas as pd

df = pd.DataFrame({
    "ad": ["Ali", "Ece", "Can", "Su"],
    "yas": [34, 28, 45, 22],
    "maas": [5000, 4200, 7100, 3000],
})

df["yillik"] = df["maas"] * 12
print(df["yillik"].sum())

Expected output

231600

Questions and answers

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

1 What is the difference between <code>df[["ad"]]</code> and <code>df["ad"]</code>?

  • There is no difference
  • The first errors
  • The second returns a list
  • The first returns a one-column DataFrame, the second a Series Correct answer

Explanation: Giving names inside a list always returns a DataFrame.

2 Remove the maas column from the table.

Question 
df2 = df.____(columns=["maas"])
Answer 
df2 = df.drop(columns=["maas"])

Explanation: drop(columns=[...]) returns a new table without the given columns.

3 Match the expressions.

Term Meaning
df["yas"] Series
df[["yas", "ad"]] Two-column table
df.rename Renaming
df["y"] = ... New column

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