DataFrame is a data structure that is two-dimensional and aligned in a tabular manner in columns and rows. It is somewhat similar to a spreadsheet. It is ideal for data analysts and data miners to work in a modern way as it is flexible enough to store and work on data in a unique way. This allows you to store many datasets in a DataFrame. When it comes to Panda DataFrame, there are infinite options to declare, store, add, edit, delete, etc. If you are using Panda DataFrame and want to look for an alternative, or are a beginner planning to create a DataFrame, but other than using Pandas. Both of you must be looking for a better substitute than Pandas.
Beginners and learners often find themselves in sheer confusion when it comes to finding what is better than Pandas Dataframe. It happens to everyone, including me. I found myself in utter confusion as to whether use the pandas dataframe or use any other alternative. After thorough research, I learned what can be used. Beginners should need to have enough information to decide which one to choose to create a dataframe; pandas or PySpark.
What is Panda?
It is an amazing Python library that is most frequently used for working with structured tabular data ideal for analysis. This open-source library is widely used for machine learning, data analysis, data science projects, etc. in order to create a dataframe, Pandas has the ability to load the data by reading JSON, CSV, SQL, and other formats.
Pandas dataframe consists of rows and columns. Distributed processing is not supported by Pandas. Thus, resources need to be increased when you want to support growing data. In that case, you require additional horsepower to tackle increasing data.
Dataframe in Panda is not lazy but mutable with each column having statistical functions applied by default. Pandas is imported using Pandas as pd to be passed with import.
If you don’t want to use Pandas Dataframe, then the best option is to use PySpark.
What is PySpark?
PySpark has the capability to run operations on multiple machines, whereas Pandas run on a single machine. When you want to work on machine learning applications for dealing with large datasets is the motive, then PySpark is just the right and better alternative to using. It is 100 times faster than Pandas. It is a Spark library to execute Python applications, which is also written in Python. To run applications parallelly, it uses Apache Spark. To do that, you can simply run apps on the distributed cluster on single or multiple nodes.
With PySpark, you can efficiently and effectively process data in a distributed manner as it has a distributed processing engine. Moreover, it is a popular in-memory, and general-purpose library that makes creating and working on dataframes easier. SpySpark features built-in optimization for using DataFrames. It is fault-tolerant, immutable, and offers lazy evaluation.
How to decide between PySpark and Pandas
Now that the concept of both is cleared. Let’s check out when you need to prefer PySpark over Pandas
- If you have large data that seems to be growing and you want to boost the processing time.
- You need fault-tolerant
- Stream data and real-time processing
- If you need to have capabilities of machine learning
- Compatibility with ANSI SQL
- Choose different languages as sparks also support Scala, R, and Java along with Python.
I have discussed the difference between Pandas DataFrame and PySpark DataFrame. Both are good for DataFrames. I leave the decision to you to decide which one is better for you and your project’s needs. I answered what is better than Pandas DataFrame, so if you want to use another one, then PySpark is the better option.