Pandas In Python? The Story Of a Mysterious Data Structure

Pandas for Data Analysis

If you have ever used any data analysis tool, you will know how much time it takes to set up your data and get to work. This is especially true if you’re just getting started with your analytics project and don’t yet have a robust pipeline in place. Luckily, there are many packages available that make working with data more intuitive, efficient, and even fun. One of the most popular Python libraries for data analysis is Pandas. In this article we will explore what is pandas in python? Do you know any interesting story behind pandas? Let’s dive into it!

What is Pandas in Python?

Pandas is a Python library designed to make data analysis easier. Pandas has a number of data structures, including Series, Dataframe, Panel, and even Structures that handle data types such as dates, times, strings, numbers, and booleans. Pandas is also an extension of the Python standard library, so you don’t need to install anything to use it.

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Why We Need Pandas?

Every data analysis project starts with understanding your data. Pandas is one of the most popular Python libraries for data analysis for a good reason: it makes this step extremely easy. You can easily read and clean your data, create new columns, manage missing data, and much more with pandas. If you are not familiar with data structures and manipulation, pandas makes this super easy. Pandas has a large number of built-in functions that make manipulating data super simple. And if you want to create your own custom function, it has an easy-to-use API.

Python Data Structures and Libraries

Before you jump into pandas, you should have a basic understanding of other Python data structures and libraries. Pandas is just one of the many libraries that make up Python. Other popular libraries include SciPy, numpy, and matplotlib. – Data Structures: These are the building blocks of programming that allow you to define how data objects will be stored and organized. For example, a series is a way to store a string or a number, a dataframe is a way to store a collection of related data, etc. – Core Functions: These are the functions that make up the core functionality of Python. You will find them in many libraries, such as the function to return a sequence of strings, or a function to pull out a value from a list.

Pandas Dataframe Object

A dataframe is a Python object that contains multiple data columns, each of which can have different types of data. It is a structured way to store your data. You can also create a dataframe from existing Python objects like lists or tuples. A dataframe is similar to a spreadsheet where each column represents a different type of data. These columns can include strings, numbers, dates, times, or even other dataframes. You can string multiple columns together to create new insights. A dataframe can also have an additional column that stores metadata about the data. This includes information about the data set, such as the number of samples, the mean and standard deviation of each value, and the number of missing values.

Working with Series and columns in pandas

A pandas Series is a type of object that stores data as a sequence of tuples. A tuple is a Python object that contains a number of values. Series can have strings, numbers, datetime, or even objects as values. A Series can be created from a pandas Dataframe or from another Series. It can also be created from another pandas Series. You can use the Series() function to create a new Series from an existing one. You can also use the Series.map() function to transform or “map” the data in a Series. For example, if you have a Series with the number of visits per day, the Series.map() function can return a Series with the visits per hour for each day.

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Pandas columns

Pandas has a number of built-in functions that allow you to work with data in columns. For example, you can find the mean of each column, or find the median of each value. You can also sort the values in a column, group similar columns together, and much more.

Wrapping Things Up

In this article, we learned what is pandas in python? We talked about the basics of pandas, such as how the data is structured and how you can work with columns in pandas dataframe object. We also learnt how we can work with series and columns in pandas dataframe object. Once you become familiar with these basic concepts of pandas, you’ll find that data analysis is much simpler and more intuitive.

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