Course Content

Cluster Analysis in Python

1. K-Means Algorithm

Exploratory Data Analysis What is a K-Means Algorithm?Defining the Number of Clusters Case 1: Three Distinct Clusters Case 2: Four Distinct Clusters Clustering Weather Data February vs July Average Temperatures Visualizing the Dynamics Across Clusters

2. K-Medoids Algorithm

What is a K-Medoids Algorithm?Cluster Centers Silhouette Scores K-Medoids and the Weather Data Mean Yearly Temperatures Across Clusters Comparing the Dynamics

3. Hierarchical Clustering

What is a Hierarchical Clustering?Dendrograms Setting Parameters: Linkage Rand Index The Weather Data and Linkages Deciding the Number of Clusters Weather Data: Complete and Ward Linkages How Similar are the Results?

4. Spectral Clustering

What is a Spectral Clustering?Peculiarity of Spectral Clustering Setting Parameters: Affinity Defining the Number of Clusters Is 4 the Optimal Number of Clusters?Comparing the Trends Across Clusters

Visualizing the Dynamics Across Clusters

The selective pair of months on the scatter plot looked good, didn't it? Maybe there were no key differences between 'areas' on the plot, but at least there were no outliers outside the respective groups, and in general, all groups were disjoint.

Finally, let's find out the yearly dynamics for each cluster, i.e. let's build the line plot representing the monthly averages for each group of points.

Task

Extract the necessary columns (month's names and temperatures) within the col variable:

Firstly, extract the 2-13 column names as list type, and save them within the col variable.
Then add the 'prediction' string to the list col.

Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:

Firstly group the observations of col column of data by 'prediction'.
Then calculate .mean() of grouped table.
Then apply .stack() to stack the table (already done).
Finally reset the indices using .reset_index() method.

Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.
Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

Task

Extract the necessary columns (month's names and temperatures) within the col variable:

Firstly, extract the 2-13 column names as list type, and save them within the col variable.
Then add the 'prediction' string to the list col.

Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:

Firstly group the observations of col column of data by 'prediction'.
Then calculate .mean() of grouped table.
Then apply .stack() to stack the table (already done).
Finally reset the indices using .reset_index() method.

Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.
Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

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

Everything was clear?

Section 1. Chapter 8

Visualizing the Dynamics Across Clusters

Finally, let's find out the yearly dynamics for each cluster, i.e. let's build the line plot representing the monthly averages for each group of points.

Task

Extract the necessary columns (month's names and temperatures) within the col variable:

Firstly, extract the 2-13 column names as list type, and save them within the col variable.
Then add the 'prediction' string to the list col.

Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:

Firstly group the observations of col column of data by 'prediction'.
Then calculate .mean() of grouped table.
Then apply .stack() to stack the table (already done).
Finally reset the indices using .reset_index() method.

Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.
Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

Task

Extract the necessary columns (month's names and temperatures) within the col variable:

Firstly, extract the 2-13 column names as list type, and save them within the col variable.
Then add the 'prediction' string to the list col.

Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:

Firstly group the observations of col column of data by 'prediction'.
Then calculate .mean() of grouped table.
Then apply .stack() to stack the table (already done).
Finally reset the indices using .reset_index() method.

Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.
Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

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

Everything was clear?

Section 1. Chapter 8

Visualizing the Dynamics Across Clusters

Finally, let's find out the yearly dynamics for each cluster, i.e. let's build the line plot representing the monthly averages for each group of points.

Task

Extract the necessary columns (month's names and temperatures) within the col variable:

Firstly, extract the 2-13 column names as list type, and save them within the col variable.
Then add the 'prediction' string to the list col.

Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:

Firstly group the observations of col column of data by 'prediction'.
Then calculate .mean() of grouped table.
Then apply .stack() to stack the table (already done).
Finally reset the indices using .reset_index() method.

Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.
Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

Task

Extract the necessary columns (month's names and temperatures) within the col variable:

Firstly, extract the 2-13 column names as list type, and save them within the col variable.
Then add the 'prediction' string to the list col.

Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:

Firstly group the observations of col column of data by 'prediction'.
Then calculate .mean() of grouped table.
Then apply .stack() to stack the table (already done).
Finally reset the indices using .reset_index() method.

Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.
Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

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

Everything was clear?

Finally, let's find out the yearly dynamics for each cluster, i.e. let's build the line plot representing the monthly averages for each group of points.

Task

Extract the necessary columns (month's names and temperatures) within the col variable:

Firstly, extract the 2-13 column names as list type, and save them within the col variable.
Then add the 'prediction' string to the list col.

Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:

Firstly group the observations of col column of data by 'prediction'.
Then calculate .mean() of grouped table.
Then apply .stack() to stack the table (already done).
Finally reset the indices using .reset_index() method.

Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.
Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

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

Section 1. Chapter 8

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