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Cluster Analysis in Python
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Cluster Analysis in Python

Cluster Analysis in Python

1. K-Means Algorithm
Exploratory Data AnalysisWhat is a K-Means Algorithm?Defining the Number of ClustersCase 1: Three Distinct ClustersCase 2: Four Distinct ClustersClustering Weather DataFebruary vs July Average TemperaturesVisualizing the Dynamics Across Clusters
2. K-Medoids Algorithm
What is a K-Medoids Algorithm?Cluster CentersSilhouette ScoresK-Medoids and the Weather DataMean Yearly Temperatures Across ClustersComparing the Dynamics
3. Hierarchical Clustering
What is a Hierarchical Clustering?DendrogramsSetting Parameters: LinkageRand IndexThe Weather Data and LinkagesDeciding the Number of ClustersWeather Data: Complete and Ward LinkagesHow Similar are the Results?
4. Spectral Clustering
What is a Spectral Clustering?Peculiarity of Spectral ClusteringSetting Parameters: AffinityDefining the Number of ClustersIs 4 the Optimal Number of Clusters?Comparing the Trends Across Clusters

Clustering Weather Data

Let's work with some real data. If you passed the 'Visualization in Python with matplotlib' course you might remember the USA cities' weather data. We will use an expanded version of this dataset there.

First, let's describe our dataset. It contains 15 columns: Country, City, All the 12 months, and Continent. Obviously, the months' columns are numerical and contain the average monthly temperature in Fahrenheit. For example, each row of this DataFrame looks like this.

We may guess that it would be logical to cluster by continent. But let's remind, that all the continents combine different climate types, which depend on proximity to the sea, to the ocean, to mountains, and so on. So, let's find out how will K-Means algorithm divide the observations.

Task

Given DataFrame data. Watch out, that numerical columns have indices 2 - 13! [object Object]

  1. Import the pandas, seaborn libraries with their standard aliases (pd and sns respectively), and KMeans from sklearn.clusters.
  2. Create range object with integers from 2 to 9 assigned to clusters variable.
  3. Iterate over clusters values. At each step:
  • Initialize KMeans model with new number of clusters (i).
  • Fit the model to 2-13 (indices) columns of data. Remember .iloc[] method of DataFrame. You can pass the first parameter to access certain rows, and the second to access columns.
  • Add model total within sum of squares value (value of .inertia_ attribute of model) to variances list.
  1. Display the seaborn lineplot "number of clusters vs total within sum of squares" (clusters - x-axis vs variances - y-axis).

Task

Given DataFrame data. Watch out, that numerical columns have indices 2 - 13! [object Object]

  1. Import the pandas, seaborn libraries with their standard aliases (pd and sns respectively), and KMeans from sklearn.clusters.
  2. Create range object with integers from 2 to 9 assigned to clusters variable.
  3. Iterate over clusters values. At each step:
  • Initialize KMeans model with new number of clusters (i).
  • Fit the model to 2-13 (indices) columns of data. Remember .iloc[] method of DataFrame. You can pass the first parameter to access certain rows, and the second to access columns.
  • Add model total within sum of squares value (value of .inertia_ attribute of model) to variances list.
  1. Display the seaborn lineplot "number of clusters vs total within sum of squares" (clusters - x-axis vs variances - y-axis).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 6
toggle bottom row

Clustering Weather Data

Let's work with some real data. If you passed the 'Visualization in Python with matplotlib' course you might remember the USA cities' weather data. We will use an expanded version of this dataset there.

First, let's describe our dataset. It contains 15 columns: Country, City, All the 12 months, and Continent. Obviously, the months' columns are numerical and contain the average monthly temperature in Fahrenheit. For example, each row of this DataFrame looks like this.

We may guess that it would be logical to cluster by continent. But let's remind, that all the continents combine different climate types, which depend on proximity to the sea, to the ocean, to mountains, and so on. So, let's find out how will K-Means algorithm divide the observations.

Task

Given DataFrame data. Watch out, that numerical columns have indices 2 - 13! [object Object]

  1. Import the pandas, seaborn libraries with their standard aliases (pd and sns respectively), and KMeans from sklearn.clusters.
  2. Create range object with integers from 2 to 9 assigned to clusters variable.
  3. Iterate over clusters values. At each step:
  • Initialize KMeans model with new number of clusters (i).
  • Fit the model to 2-13 (indices) columns of data. Remember .iloc[] method of DataFrame. You can pass the first parameter to access certain rows, and the second to access columns.
  • Add model total within sum of squares value (value of .inertia_ attribute of model) to variances list.
  1. Display the seaborn lineplot "number of clusters vs total within sum of squares" (clusters - x-axis vs variances - y-axis).

Task

Given DataFrame data. Watch out, that numerical columns have indices 2 - 13! [object Object]

  1. Import the pandas, seaborn libraries with their standard aliases (pd and sns respectively), and KMeans from sklearn.clusters.
  2. Create range object with integers from 2 to 9 assigned to clusters variable.
  3. Iterate over clusters values. At each step:
  • Initialize KMeans model with new number of clusters (i).
  • Fit the model to 2-13 (indices) columns of data. Remember .iloc[] method of DataFrame. You can pass the first parameter to access certain rows, and the second to access columns.
  • Add model total within sum of squares value (value of .inertia_ attribute of model) to variances list.
  1. Display the seaborn lineplot "number of clusters vs total within sum of squares" (clusters - x-axis vs variances - y-axis).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 6
toggle bottom row

Clustering Weather Data

Let's work with some real data. If you passed the 'Visualization in Python with matplotlib' course you might remember the USA cities' weather data. We will use an expanded version of this dataset there.

First, let's describe our dataset. It contains 15 columns: Country, City, All the 12 months, and Continent. Obviously, the months' columns are numerical and contain the average monthly temperature in Fahrenheit. For example, each row of this DataFrame looks like this.

We may guess that it would be logical to cluster by continent. But let's remind, that all the continents combine different climate types, which depend on proximity to the sea, to the ocean, to mountains, and so on. So, let's find out how will K-Means algorithm divide the observations.

Task

Given DataFrame data. Watch out, that numerical columns have indices 2 - 13! [object Object]

  1. Import the pandas, seaborn libraries with their standard aliases (pd and sns respectively), and KMeans from sklearn.clusters.
  2. Create range object with integers from 2 to 9 assigned to clusters variable.
  3. Iterate over clusters values. At each step:
  • Initialize KMeans model with new number of clusters (i).
  • Fit the model to 2-13 (indices) columns of data. Remember .iloc[] method of DataFrame. You can pass the first parameter to access certain rows, and the second to access columns.
  • Add model total within sum of squares value (value of .inertia_ attribute of model) to variances list.
  1. Display the seaborn lineplot "number of clusters vs total within sum of squares" (clusters - x-axis vs variances - y-axis).

Task

Given DataFrame data. Watch out, that numerical columns have indices 2 - 13! [object Object]

  1. Import the pandas, seaborn libraries with their standard aliases (pd and sns respectively), and KMeans from sklearn.clusters.
  2. Create range object with integers from 2 to 9 assigned to clusters variable.
  3. Iterate over clusters values. At each step:
  • Initialize KMeans model with new number of clusters (i).
  • Fit the model to 2-13 (indices) columns of data. Remember .iloc[] method of DataFrame. You can pass the first parameter to access certain rows, and the second to access columns.
  • Add model total within sum of squares value (value of .inertia_ attribute of model) to variances list.
  1. Display the seaborn lineplot "number of clusters vs total within sum of squares" (clusters - x-axis vs variances - y-axis).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Let's work with some real data. If you passed the 'Visualization in Python with matplotlib' course you might remember the USA cities' weather data. We will use an expanded version of this dataset there.

First, let's describe our dataset. It contains 15 columns: Country, City, All the 12 months, and Continent. Obviously, the months' columns are numerical and contain the average monthly temperature in Fahrenheit. For example, each row of this DataFrame looks like this.

We may guess that it would be logical to cluster by continent. But let's remind, that all the continents combine different climate types, which depend on proximity to the sea, to the ocean, to mountains, and so on. So, let's find out how will K-Means algorithm divide the observations.

Task

Given DataFrame data. Watch out, that numerical columns have indices 2 - 13! [object Object]

  1. Import the pandas, seaborn libraries with their standard aliases (pd and sns respectively), and KMeans from sklearn.clusters.
  2. Create range object with integers from 2 to 9 assigned to clusters variable.
  3. Iterate over clusters values. At each step:
  • Initialize KMeans model with new number of clusters (i).
  • Fit the model to 2-13 (indices) columns of data. Remember .iloc[] method of DataFrame. You can pass the first parameter to access certain rows, and the second to access columns.
  • Add model total within sum of squares value (value of .inertia_ attribute of model) to variances list.
  1. Display the seaborn lineplot "number of clusters vs total within sum of squares" (clusters - x-axis vs variances - y-axis).

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 6
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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