What Type of Data Is a DataFrame?
You may have heard a lot of data and its type if you try to analyze data in a detailed manner. Data scientists, analysts, strategists, and data miners who are experts in handling data use various ways to sort, add, edit, manipulate, update, and delete data. One of the most used ways that millions of people are using is Pandas DataFrame. Panda is a well-known library of Python that is ideally used for data science, machine learning, and data analysis. It is used for transforming, cleaning, visualizing, exploring, and learning data in a fast and flexible way. Moreover, pandas is an expressive data structure designed to make data analysis easier.
There are several ways out there to analyze data, and that makes beginners confused. When beginners plan to start moving towards data science or data analysis, they start learning about the best possible ways to do so, and that’s when they get confused because there are infinite ways for data analysis, and trust me most of them are not the right ones to even try.
When I started the research to pursue a career in data analysis, I came up with so many options that don’t even work. I tried many ways, but when it comes to working with data in detail, Pandas is the best as it has DataFrames to store data and handle it the way you want. This is the best I found that works, so if you want to start working with data, Pandas DataFrame is the best one to try.
Many questions arise in your mind about what is DataFrame and what type of data is in a DataFrame. You will have all the answers you are looking for. Let’s check out what DataFrame is:
What is a DataFrame and What Does It Store?
DataFrame, or better say, Pandas DataFrame is a two-dimensional table in rows and columns. It is a data structure, which is somewhat similar to a spreadsheet that all of you have already seen. DataFrame is best known as a data structure that is used in modern data analysis as it is highly intuitive and flexible.
DataFrame follows a blueprint called schema, which works to define data type as well as the name of each column. We also have Spark DataFrame that has universal data types, such as IntegerType, and StringType, and data types, particularly for Spark like StructType. In the case of incomplete or missing values stored, it stores as null values in your DataFrame.
The logic of a DataFrame is simple as it is similar to a spreadsheet with columns having names. Though, there is a difference between them. Spreadsheets remain on a system just in a specific location only, while DataFrame is different as it spans millions of systems.
A DataFrame is ideal for handling big data for analysis through distributed computing clusters. So, whether you want to use it for big data or small data, it is perfect for both. It must be intuitive if data is put on two or more computers, and the reason can be either data that is huge to store in one system, or it is time-consuming to perform the analysis on one system.
A DataFrame is quite common in different frameworks and languages. It is the main data type that is used in Pandas, which is a data analysis Python library. Other languages like Scala and R also have DataFrame. DataFrame is also named Pandas DataFrame.
And that’s all about DataFrame and its types. It is a perfect way to handle data in different ways. Be it large or small data, you can easily handle it with Pandas DataFrame.