The elements of a list can be added or removed after it has been created.
To add new elements at the end of a list, Py
Python uses class for object-oriented programming. A class represents the general behavior or information that the programmer or data analyst focuses on. When a class is created, particular objects belonging to this class can be created. This process is called instantiation. Class contains attributes, methods, or functions for general purpose. Attributes for instances can be modified by directly assigning new values, or by using methods defined in a class.
When we do data analysis, random variables in the dataset are usually mutually correlated. Sometimes, we may want to measure the pure relationship between two variables, and the influence from other variables being controlled. A partial correlation calculation could fulfill this purpose.
Normal distribution is describing random variables with bell-shaped probability density functions. Normal distribution is widely used in data science because large sample random variates have a mean value which follows approximate normal distribution if variates are independently drawn from any distributions. The probability density function for normal distribution is determined by two parameters: mean(miu) and standard deviation(sigma).
When a correlation, usually Person type correlation, is calculated, two variables have to be continuous. But this requirement does not excludes the situation when one of the two variables is a dichotomous (binary) distributed. Say if we want to measure the correlations between height and gender for a group of people, the variable gender has clear dichotomous values. This kind of Pearson correlation is called point-biserial correlation, because the value for gender variable is strictly 0 or 1.
dplyr is a package that belongs to tidyverse framework. dplyr allows usage of pipeline structure (%>%), which can chain multiple functions together into one statement to make data management more effective. spread() of dplyr is a function that spreads the values of columns from the current data frame, and make them as column labels in the resulting data frame.
R language provides several useful functions for importing delimited files and creating data frames. These delimited files are often stored in local computer directory, with extensions ‘txt’, ‘csv’, ‘dat’. The mostly widely used functions for importing these files in R are read.table() and read.csv().
List is the simplest type of data structure in Python programming. A list is used to store a collection of elements of same type (numeric, string, etc.). In Python, a pair of brackets [] indicates the data object is a list type. For example, the following two statements create two lists, in which one is numeric and the other is of string type.
Python is among the most popular programming language for data science nowadays, and getting started with Python is quite easy. You can just install e.g. a free platform like Anaconda, then you can get direct access to Python as well as most of its preinstalled modules (Numpy, pandas, matplotlib, etc.), its IDE (Spyder, etc) and its easygoing package management tools.
Categorical variables, including nominal and ordinal variables in R programming language are called factor variables. For example, gender(male/female) is nominal, and survey results (excellent, good, normal, bad) have ordinal values. Categorical variables are useful because many data analysis operations are related to values in different categories, such as contingency tables between two categorical variables for independence analysis, hypothesis testing of homogeneity of variances, just name a few.