Python

There is little to introduce to Python. It is well known by all developers.

Anything you want can be done with Python and this, together with its simplicity and simplicity, has made it one of the star programming languages today. It is a strongly typed object-oriented language in which it is especially important to maintain code readability.

It is the star language in data science, machine learning, deep learning, and everything related.

But you can still build web applications, or any other tool you can think of.

There are bookstores for everything!!!

In this section we solve some of the main problems that the Python developer often faces. In this way the way to become a ninja dev in python is assured.

Solved: convert birth date column to age pandas

In today’s world, data analysis has become increasingly important, and one of the most popular tools used by data analysts and data scientists is Python with the pandas library. Pandas is a powerful, open-source data analysis and manipulation tool that allows for easy manipulation of data structures and series. One common problem that users encounter is converting birth dates to ages for more accurate and practical analysis. In this article, we will delve into how to tackle this issue with clear examples and explanations of the code implementation.

Pandas is a versatile tool that often involves working with DateTime objects – this is the case when dealing with birth dates. The first step to convert birth dates into age requires simple arithmetic with the DateTime library. This will enable us to find the age of individuals by calculating the difference between their birth date and the current date

Solved: To convert Date dtypes from Object to ns%2CUTC with Pandas

Pandas is an essential tool in the world of data manipulation and analysis when working with Python. Its flexibility and ease-of-use make it suitable for a wide range of tasks related to handling and analyzing data. One common problem faced when working with Pandas is converting date dtypes from Object to ns with UTC timezone. This conversion is necessary because, in some datasets, date columns are not recognized as date dtypes by default and are instead considered objects.

Solved: pandas unique value each column

Pandas is a powerful and widely-used Python library for data manipulation and analysis. One common task when working with datasets is the need to find unique values in each column. This can be helpful in understanding the diversity and distribution of values in your data, as well as identifying potential outliers and errors. In this article, we will explore how to accomplish this task using Pandas and provide a detailed, step-by-step explanation of the code involved. We will also discuss some related libraries and functions that may be useful when working with unique values and other data analysis tasks.

Solved: pandas read parquet from s3

In today’s fashion-driven world, dealing with large data sets is quite common, and pandas is a popular library in Python that provides powerful, easy-to-use data manipulation tools. Among the great variety of data formats, Parquet is widely used for its efficient columnar storage and lightweight syntax. Amazon S3 is a popular storage option for your files, and integrating it with pandas can significantly improve your workflow. In this article, we will explore how to read Parquet files from Amazon S3 using the powerful pandas library.

Solved: max deviation in pandas

Max deviation in Pandas is an interesting topic when it comes to data analysis and manipulation using the popular Python library Pandas. One of the key aspects of analysing data is identifying the variability within the data, which can be done by calculating the maximum deviation. In this article, we will learn how to compute max deviation in Pandas, explore different approaches and delve deeper into some relevant libraries and functions that can be used to solve this problem.

Solved: Convert a Pandas Column of Timestamps to Date

In the world of data analysis, it is common to encounter datasets containing timestamps. Sometimes, we may want to simplify and only consider the date, which can be useful for various purposes such as trends analysis, forecasting, or visualization. In this article, we’ll show you how to **convert a Pandas column of timestamps to date** using Python, making it easier for you to work with and understand your data. We’ll walk you through a solution, provide a step-by-step explanation of the code, as well as delve into some related libraries and functions that can further benefit your data manipulation skills.

Solved: pandas mean and sum

Pandas is a powerful Python library for data analysis and manipulation, widely used in various domains, including the world of fashion. Utilizing Pandas, fashion experts and developers can spot trends, patterns, and insights by analyzing datasets related to the fashion industry. In this article, we’ll delve into the powerful Pandas functions, mean and sum, and their applications in the analysis of fashion data.

Solved: pandas iloc include header

Pandas is a widely-used Python library for data manipulation and analysis, and iloc is a crucial function within the library that allows users to select and manipulate data by integer-based indexing. This can be particularly useful when working with large datasets. In this article, we will explore the usage of pandas iloc in various scenarios and explain how the function works step-by-step to help you understand its significance and potential applications in data analysis.

Solved: pandas date difference in months

Pandas is a popular Python library that facilitates data manipulation and analysis, offering a wide range of functions for handling dates and times. One common use case in data analysis is calculating the difference between dates in months. In this article, we’ll explore an approach to achieve this using Pandas, along with a step-by-step explanation of the code. Moreover, we’ll discuss some other relevant libraries and functions to enhance our understanding of the problem.