DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has two main hyperparameters: eps (epsilon) and MinPts (minimum number of points).
The eps parameter defines the radius for searching the neighboring points within a cluster, whereas MinPts defines the minimum number of points required to form a core point (dense regions). A core point has at least minPts data points within a eps radius.
This article describes the rules and methods for choosing the optimal values for MinPts and eps for forming the clusters.

In data analysis, calculating the mean of rows on selected columns is a common task, especially when dealing with large datasets with a large number of variables.
In R, you can use the rowMeans() function to calculate the mean of rows on selected columns.
rowMeans(subset(df, select = c(col1, col2))) The following step-by-step examples will explore how to calculate the mean of rows on selected columns in R
Example 1 (data frame without missing values) Create a sample data frame,

You can add incremental numbers to a new column in a pandas DataFrame by using various functions such as range(), insert(), and arange() functions.
Method 1: range() function df['new_col'] = range(1, len(df) + 1) Method 2: insert() function df.insert(0, 'new_col', range(1, 1 + len(df))) Method 3: arange() function df['new_col'] = np.arange(1, len(df) + 1) The following examples demonstrate how to use range(), insert(), and arange() functions to add incremental numbers to a new column in a Pandas DataFrame

In R, the summary() function is very useful for generating the summary statistics ( minimum, 1st quartile, median, mean, 3rd quartile, and maximum values) for numerical vector and data frame.
The output from a summary() function is in table format and is not convenient to access the values of the summary statistics for downstream analysis.
You can use the following methods to convert the output from the summary() function into a data frame format.

You can use the geom_histogram() function in ggplot2 to create a histogram in R.
Generally, the ggplot() accepts the data frame to create the histogram. But in this article, we will cover how to use the numeric vector to create a histogram using ggplot() and geom_histogram().
The following examples demonstrate how to use numeric vectors to create a histogram in ggplot2.
1 Convert vector to data frame Create a random numeric vector using the built-in rnorm() function,

geom_bar() is a function in the ggplot2 package which widely used for creating barplots.
Many times you need to visualize the mean of the data using the barplot. The geom_bar() is particularly useful for visualizing the mean of the data without manually calculating it.
The basic command of geom_bar() to visualize the mean using barplot is:
# load package library(ggplot2) ggplot(df, aes(group, value)) + geom_bar(stat='summary') By default, the stat='summary' argument in geom_bar() calculates the mean for each group in the data frame.