Working with dates and times is a common task in programming, and Python provides several modules to handle such operations. The datetime module is one of the most commonly used modules for manipulating dates and times. It offers a variety of functions and classes, including the strptime() method, which allows us to convert a string representation of a date and time into a datetime object. In this article, we will understand how we can use strptime with milliseconds in Python with the help of detailed examples. Understanding strptime() The strptime() method in the datetime module is used to parse a ... Read More

Seaborn is a popular Python library that provides a high-level interface for creating informative and aesthetically pleasing visualizations. One of the key features of Seaborn is its ability to customize the color palette of plots, allowing users to highlight specific aspects of the data. In this article, we will explore how to use Seaborn's color palette to color boxplots effectively. Understanding Boxplots Before understanding the customization options, it is essential to have a basic understanding of boxplots. A boxplot is a standardized way of displaying the distribution of a dataset, providing information about the median, quartiles, and potential outliers. It ... Read More

Pickle is a Python module that is used for data serialization ie. converting data into a byte stream. Pickle allows developers to save and load variables from memory to disk, ensuring data integrity and easy retrieval. In this article, we will explore how to utilize Pickle effectively to save and load variables in Python. Understanding Pickle Pickle is a built-in module in Python that enables object serialization, which refers to the process of converting objects into a byte stream. The byte stream can be stored as a file, transferred over the network, or even persisted in a database. Pickle allows ... Read More

Pandas is an open-source Python library used for data analysis and manipulation. Pandas provides functionality for data cleaning, transformation, and filtering. In large datasets, some extreme values called outliers can modify the data analysis result. In order to identify those outliers, a robust statistical measure called the Interquartile range (IQR) is used. In this article, we will understand how pandas filter with the IQR to identify and handle outliers in the dataset. Understanding the Interquartile Range (IQR) Before understanding how to use the Pandas filter with IQR, let’s briefly understand what is Interquartile range(IQR). Quartile divides a dataset into four ... Read More

Pandas is a Python library that is used for data manipulation and analysis of structured data. The cut() and qcut() methods of pandas are used for creating categorical variables from numerical data. The cut() and qcut() methods split the numerical data into discrete intervals or quantiles respectively and assign labels to each interval or quantile. In this article, we will understand the functionalities of the cut() and qcut() methods with the help of various examples. The cut() Function The cut() divides a continuous variable into discrete bins or intervals based on specified criteria. It creates groups or categories of ... Read More

The apply() function in pandas is used to apply a custom function to the data frame or series. The apply() function can be used to perform transformations, computation, and other operations on the data. The apply() function returns a new Data frame or series by default. We can also modify the dataframe or series by using the inplace parameter of the apply() function. In this article, we will understand how we can use apply() function inplace with the help of examples. Syntax of apply() Function df.apply(func, axis=0) Here, df is the dataframe on which we need to apply ... Read More

The Numpy where() function allows us to perform element-wise conditional operations on array. Numpy is a Python library that is used for numerical computation and data manipulation. To use where() method with multiple conditions in Python we can use logical operators like & (and), | (or) and ~ (not). In this article, we will explore some examples to use numpy where() with multiple method in Python. Syntax of where() Method numpy.where(condition, x, y) Here, the `condition` parameter is a boolean array or a condition that evaluates to a boolean array. The x and y are arrays which ... Read More

The paragraphs can be scraped using the Beautiful Soup library of Python. BeautifulSoup is a Python library that allows us to parse HTML and XML documents effortlessly. It provides a convenient way to navigate and search the parsed data, making it an ideal choice for web scraping tasks. By utilizing its robust features, we can extract specific elements, such as paragraphs, from web pages. In this article, we will scrape paragraphs using the Beautiful Soup library of Python. Installing the Required Libraries Before scraping the paragraph, we need to install the necessary libraries. Open your terminal or command prompt and ... Read More

The data of the local HTML file can be extracted using Beautiful Soup and Python file handling techniques. Beautiful Soup allows us to parse HTML documents and navigate their structure, while file handling enables us to fetch the HTML content from local files. By combining these tools, we can learn how to extract valuable data from HTML files stored on our computers. In this article, we will understand how we can scrape Data from Local HTML files using Python. Prerequisites Before understanding how to scrape data from local HTML files, make sure you have Python installed on your machine. ... Read More

Google Maps is a powerful tool that provides a vast amount of geospatial data, including locations, addresses, reviews, ratings, and more. Being able to extract this data programmatically can be immensely useful for various applications such as business analysis, research, and data-driven decision-making. In this article, we will explore how to scrape data from Google Maps using Python. Step 1: Install Required Libraries To begin with, we need to install the necessary Python libraries that will facilitate the web scraping process. Open your command prompt or terminal and run the following commands: pip install requests pip install beautifulsoup4 ... Read More