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Creating a DataFrame

A DataFrame is created with pd.DataFrame() from a dictionary or list.

DataFrame from a dict

The easiest way is a dict whose keys are column names and whose values are lists:

df = pd.DataFrame({
    "ad": ["Ali", "Ece", "Can"],
    "yas": [34, 28, 45],
})
print(df.shape)   # (3, 2)

All lists must have the same length; otherwise a ValueError occurs.

From a list of rows and from NumPy

If the data comes row by row, a list of lists and columns are used:

satirlar = [["Ali", 34], ["Ece", 28]]
df = pd.DataFrame(satirlar, columns=["ad", "yas"])

import numpy as np
df2 = pd.DataFrame(np.zeros((2, 3)), columns=["a", "b", "c"])

The other direction is easy too: df.to_numpy() turns a numeric table into a NumPy array.

Practice Build a table

Create a DataFrame with urun and fiyat columns (with a dict). Print its shape and its column names with columns.tolist().

Starter code

model.py
import pandas as pd

# buraya yaz

Sample solution

model.py
import pandas as pd

df = pd.DataFrame({"urun": ["kalem", "defter", "silgi"], "fiyat": [10, 25, 5]})
print(df.shape)
print(df.columns.tolist())

Expected output

(3, 2)
['urun', 'fiyat']

Questions and answers

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

1 What is the shape of a DataFrame built from a dict with three keys and 5-element lists?

  • (5, 3) Correct answer
  • (3, 5)
  • (15,)
  • (5,)

Explanation: Each key is a column and each list element is a row.

2 Specify the column names.

Question 
df = pd.DataFrame(satirlar, ____=["ad", "yas"])
Answer 
df = pd.DataFrame(satirlar, columns=["ad", "yas"])

Explanation: The columns parameter gives the column names in order.

3 Match the conversions.

Term Meaning
pd.DataFrame(dict) Table from a dict
columns= Column names
df.to_numpy() To a NumPy array
df.shape Row and column count

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