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Python Articles
Page 92 of 852
Statistical Simulation in Python
Statistical simulation is the task of making use of computer based methods in order to generate random samples from a probability distribution so that we can model and analyse complex systems which exhibit random behaviour. In this article we are going to see how to make use of this powerful tool in Python to make predictions, generate insights as well as evaluate the performance of statistical algorithms. There are different types of statistical simulations, which are as follows: Monte Carlo simulations − Generation of random samples from a probability distribution in order to estimate the expected value of a ...
Read MoreNetwork Analysis in Python
A network is a collection of nodes and edges that represent the relationships or connections between those nodes. The nodes can represent various entities, such as individuals, organizations, genes, or websites, while the edges represent the connections or interactions between them. Network analysis is the study of the relationships between these entities are node represented as a network. In this article, we are going to see how to implement network analysis using python. It involves the use of many mathematical, statistical and computational techniques. Network analysis can provide insights into the behaviour of complex systems and help to make ...
Read MoreIntroduction to Financial Concepts using Python
Python provides us with a variety of tools as well as libraries that help us work with the foundations of probability. Probability has a wide scale use case from AI content detection to card games. The random module is often used for probability related problem statements. This combined with libraries like numpy and scipy (and matplotlib and seaborn for visualization) can be of great advantage when the data is large scale and mainly in the form of csv files. Probability problem statements can further be clubbed with statistics to gain more insights. It doesn’t matter if you are a beginner ...
Read MoreFoundations of Probability in Python
Probability deals with the study of random events as well as their outcomes. It is an essential concept in various fields like finance, physics, engineering and data science. It is defined as the likelihood of an event occurring as no event can be predicted with 100% certainty. Hence probability is just a guide. In this article, we are going to be seeing the foundations of probability in Python. Python offers a number of libraries that allow us to work with probability distributions and perform statistical computations as well as generate random numbers. The basic concepts and keywords of probability ...
Read MoreForecasting Using ARIMA Models in Python
ARIMA is a statistical model used for time series forecasting that combines three components: autoregression (AR), integration (I), and moving average (MA). Autoregression (AR) − This component models the dependence between an observation and a number of lagged observations. It's based on the idea that past values of a time series can be used to predict future values. The order of autoregression, denoted by "p", specifies the number of lagged observations to use as predictors. Integration (I) − This component handles non-stationarity of the time series data by removing trends and seasonality. The order of integration, denoted by "d", ...
Read MoreCleaning Data with Apache Spark in Python
In today's time, when we have high volume and velocities of data flowing, Apache Spark, an open source big data processing framework, is a common choice as it allows parallel and distributed processing of data. Cleaning of such data is an important step and Apache Spark provides us with a variety of tools and methods for the cleaning of data. In this method, we are going to be seeing how to clean data with Apache Spark in Python and the steps to do so are as follows: Loading the data into a Spark DataFrame − The SparkSession.read method allows ...
Read MoreBuilding Chatbots in Python
A chatbot is a computer program designed to simulate conversations with human users via text or voice. It uses AI and NLP techniques to help understand and interpret user’s messages and provide relevant responses. In this article, we will see how to create a chatbot with the help of Python. Chatbots like chatGPT have become popular since the end of 2022 and have a wide-scale use case for people of different fields. Chatbots are also integrated with mobile apps like Swiggy and Zomato to provide faster resolution to customer complaints. Chatbots are of multiple types which are as follows: ...
Read MoreModelling Steady Flow Energy Equation in Python
Steady Flow Energy Equation (SFEE) is the application of conservation of energy on to an open system. Figure shown below is a schematic of open system in which fluid enters at 𝑖 and exits at 𝑒. The red broken line represents the control surface (CS) of the control volume (CV). The inlet and exit parameters are mentioned in the table shown below − Parameter Inlet Exit Pressure pi pe Velocity Vi Ve Density Pi Pe Specific volume vi ve Enthalpy hi he Area Ai Ae ...
Read MoreModelling Two Dimensional Heat Conduction Problem using Python
In this tutorial, we will see how to model 2D heat conduction equation using Python. A 2D, steady, heat conduction equation with heat generation can be written in Cartesian coordinates as follows − $$\mathrm{\triangledown^{2} T \: + \: \frac{q_{g}}{k} \: = \: \frac{\partial^{2}T}{\partial x^{2}} \: + \: \frac{\partial^{2}T}{\partial y^{2}} \: + \: \frac{q_{g}}{k} \: = \: 0 \:\:\dotso\dotso (1)}$$ This has to be discretized to obtain a finite difference equation. Let us consider a rectangular grid as shown below. The index 𝑖 runs vertically i.e. row wise whereas the index 𝑗 runs horizontally i.e. column wise. Any inside node ...
Read MoreModelling the Taylor Table Method in Python
The Taylor Table method is a very efficient and elegant method of obtaining a finite difference scheme for a particular derivative considering a specific stencil size. To understand it one should be very much clear about what is a stencil. Suppose one wants to evaluate $\mathrm{\frac{d^{2}f}{dx^{2}}}$ then in finite difference method the starting point is the Taylor series. Consider the figure shown below for a better understanding of the method. The Taylor series expansion at the point $\mathrm{x_{i} \: + \: h}$ will be: $$\mathrm{f(x_{i} \: + \: h) \: = \: f(x_{i}) \: + \: hf'(x_{i}) \: + ...
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