Python complete tutorial
  • Python Complete Tutorial
  • About this book
  • What you need to prepare
  • 1️⃣Try python for the first time
    • Install python
    • Hello world!
    • Hello world in a nutshell
    • The first simple python project
    • most useful libraries
    • Recommended websites
  • 2️⃣Data structure and basic operations
    • Python data structure
    • Data structure without hash table
    • Data structure with hash table
    • Variability and address
    • basic python programming
    • basic python programming 2
    • basic python programming 3
    • some additions
    • Fibonacci sequence
    • Judging prime numbers
    • txt/csv file operation
  • 🐍Practice program
    • 🚩fancy print
    • 🚩Remove duplicate elements
    • 🚩Palindrome detection
  • 😎leetcode
    • what is leetcode
  • 3️⃣Data mining and machine learning
    • What is data mining
    • iris data set
    • Mean median mode
    • Harmonic mean
    • Histogram
    • Correlation algorithm
    • Gaussian distribution data set
    • projection
    • PCA
    • MDS
    • Bayesian and Frequentist
    • Data normalization
    • binary SVM
    • One Hot Encoding
    • Multi-class SVM
    • Accuracy and error rate
    • Confusion matrix & Accuracy, Precision, Recall
    • F1 score
    • ROC and AUC
  • 4️⃣big data and data visualization
    • line chart
    • Parallel coordinates
    • Histogram chart
  • 5️⃣Mathematical algorithm and digital signal processing
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    • Normal distribution
    • Permutation and combination
    • Bernoulli distribution
    • Chaotic system
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    • Classes and design patterns
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    • MySQL
      • Install MySQL
      • First try MySQL
      • MySQL Architecture
      • database operations
      • database
  • 8️⃣Cryptography
    • beginning of Cryptography
  • 9️⃣deep learning
    • What is Deep Learning
    • basic
  • 💔algorithm
    • Algorithms and Data Structures
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Parallel coordinates

Parallel coordinates

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Last updated 3 years ago

When you are trying to find the relationship between multiple variables, Parallel coordinates is a good choice.

Parallel coordinates can clearly observe the distribution of data. For example, we can run Parallel coordinates on the iris data set.

import seaborn
iris_data = seaborn.load_dataset('iris')
 
from pandas.plotting import parallel_coordinates
parallel_coordinates( iris_data, 'species',
                      color = ('#55627080', '#4ECDC480', '#C7F46480')
                      )

import matplotlib.pyplot as plt
plt.title('iris dataset with parallel_coordinates')
# Mandatory run display interface
plt.show()

After running Parallel coordinates on the iris data set, you can clearly observe the relationship between sepal_length, sepal_width, petal_length, petal_width and species.

For example, the petal_length of virginica is larger than the other two varieties. Then when you encounter an iris plant in the future, if its petal_length is larger, then it is likely to be a virginica.

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Start time of this page: December 20, 2021

Completion time of this page: December 20, 2021

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iris dataset with parallel_coordinates