The Poisson distribution is a probability distribution that models the occurrence of events in a fixed time or space interval, given the average rate of occurrence. It is commonly used in fields such as physics, engineering, and economics to model the arrival of particles, failures of components, or customer arrivals. One way to visualise the Poisson distribution is to plot its probability mass function (PMF), which shows the probability of each possible number of events occurring in a given interval. In Python, we can use the SciPy library to generate the PMF of a Poisson distribution and then use Matplotlib ... Read More

In data analysis, it's often necessary to count the number of unique values in a pandas Groupby object. Pandas Groupby object is a powerful tool for grouping data based on one or more columns and performing aggregate functions on each group. By counting the number of unique values in a Groupby object, we can gain insights into the diversity and distribution of the data within each group. To count unique values in a pandas Groupby object, we need to use the nunique() method. This method returns the number of unique values in each group of the Groupby object. We can ... Read More

Pandas is a popular Python library used for data manipulation and analysis. One common task in data analysis is to count the number of duplicate values in a Pandas DataFrame. Duplicates can occur when multiple rows have the same values in all columns, or in a subset of columns. There are different ways to count duplicates in a Pandas DataFrame, depending on the specific requirements of the analysis. One common approach is to use the duplicated() method, which returns a Boolean Series indicating whether each row is a duplicate of a previous row. By default, the method considers all columns ... Read More

In this article, we will learn and explore how we can create beautiful command line interfaces in Python. First, let's talk a little about Python and then we will talk about command line interfaces. Why Python? Python is a popular high−level programming language known for its simplicity, readability, and versatility. Created in the late 1980s by Guido van Rossum, Python has since become one of the most widely used languages for web development, scientific computing, data analysis, and machine learning. Python's syntax is designed to be intuitive and easy to understand, with a focus on reducing the amount ... Read More

In Python, counting the number of instances of a class is a common task that can be accomplished using various techniques. One straightforward approach is to use a class variable to keep track of the number of instances created. To implement this method, you can define a class variable, such as "count, " and increment it each time a new instance of the class is created. This variable can be accessed from both the class and its instances, allowing you to easily retrieve the total number of instances created. Another approach is to use the built−in function "len()" along with ... Read More

Counting the number of occurrences of a specific value in a column is a common task in data analysis. Fortunately, the pandas library in Python provides a quick and easy way to do this with the value_counts() method. This method returns a Pandas series that contains the count of each unique value in the column. You can then access the count for a specific value by using square brackets and the value you want to count. In this article, we will walk through the steps of counting the occurrences of a specific value in a pandas column. We will cover ... Read More

PyTorch is a popular open−source machine learning framework that provides efficient tensor operations on both CPUs and GPUs. A tensor is a multi−dimensional array in PyTorch, and it is the fundamental data structure used for storing and manipulating data in PyTorch. In this context, a 3D tensor is a tensor with three dimensions, and it can be represented as a cube−like structure with rows, columns, and depth. To access elements in a 3D PyTorch tensor, you need to know its dimensions and the indices of the elements you want to access. The indices of a tensor are specified using square ... Read More

Unstructured data is data that does not follow any specific data model or format, and it can come in different forms such as text, images, audio, and video. Converting unstructured data to structured data is an important task in data analysis, as structured data is easier to analyse and extract insights from. Python provides various libraries and tools for converting unstructured data to structured data, making it more manageable and easier to analyse. In this article, we will explore how to convert unstructured biometric data into a structured format using Python, allowing for more meaningful analysis and interpretation of the ... Read More

Pandas is a popular data manipulation library in Python, used for cleaning and transforming data. It provides various functionalities for converting data types, such as the astype() method. However, manually converting data types can be time−consuming and prone to errors. To address this, Pandas introduced a new feature in version 1.0 called convert_dtypes(), which allows automatic conversion of columns to their best−suited data types based on the data in the column. This feature eliminates the need for manual type conversion and ensures that the data is appropriately formatted. Converting the Datatype of a Pandas Series Consider the code shown below ... Read More

Scikit−learn (sklearn) is one of the most popular machine learning libraries for Python. It provides a range of efficient tools for machine learning and statistical modelling, including a variety of datasets. These datasets are provided in the form of numpy arrays, which can be difficult to work with for certain tasks, such as exploratory data analysis. Pandas is a popular data manipulation library that provides powerful tools for data analysis and manipulation. It provides data structures for efficiently storing and manipulating large datasets, and provides a wide range of tools for data cleaning, transformation, and analysis. Below are the two ... Read More