Tanvi Bugdani, Author at AskPython

RSA Algorithm: Theory and Implementation in Python

Cryptography is the practice of securing communication by using codes and ciphers. It includes a variety of techniques for converting plaintext into ciphertext, enabling secure communication, and protecting the confidentiality and integrity of data. Banking, email, e-commerce, and other industries all employ cryptography extensively. In this article you will learn about asymmetric encryption and the […]

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HashSets and HashTables in Python

There are many ways in which data can be stored and retrieved. Let us say you want to store the data related to a cricket match. One way to do it is to create a list of lists for each attribute and its value as shown below: Output: Here, the data is stored in contiguous memory

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How does tuple comparison work in Python?

Python has four built-in data types that are used to store collections of data. These Python data types are: A tuple in Python is a collection of data items or objects stored inside a single variable. The data items inside a tuple are separated by commas. Syntactically, tuples store data inside round brackets i.e. (<insert

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Pandas to_sql(): Write records from a DataFrame to a SQL Database

Pandas is the preferred library for the majority of programmers when working with datasets in Python since it offers a wide range of functions for data cleaning, analysis, and manipulation. You’ll have to use SQL if you incorporate a database into your program. A Pandas DataFrame can be loaded into a SQL database using the

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Pandas merge(): Merging DataFrame or Series objects with a join

Two Pandas DataFrame or Series objects can be joined together in a database-style operation using the Pandas merge() function. When merging items based on one or more keys, it is utilized. You will learn about the Pandas merge() method in this lesson as well as how to use its various available parameters to put it

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Pandas read_sql: Read SQL query/database table into a DataFrame

For working with datasets, Pandas is the most widely used Python library. You have learned how to use Pandas to read data from files in a variety of formats, including JSON, CSV, Excel, and others, and how to change data in our previous tutorials. One such way is Pandas read_sql(), which enables you to read

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Pandas to_pickle(): Pickle (serialize) object to File

Pickle is generally used in Python to serialize and deserialize Python object structures. To put it another way, it is the procedure of transforming a Python object into a byte stream so that it can be stored in a file or database, have its state preserved across sessions, or be used to transfer data over

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Pandas eval(): Evaluate a Python expression as a string

Hello and welcome to our tutorial! You are aware that the most widely used Python library for data analysis and manipulation is Pandas. It makes use of a data structure known as a DataFrame to represent data as rows and columns. You could frequently need to do various actions involving two or more columns over

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