Pyspark enables processing of big data sets, at the same time enable processing of complex queries as well. Machine learning algorithm, statistical algorithms are easy to deploy with the help of Pyspark. Before running an algorithm, cleaning of data is
Category: Pyspark
Pyspark programming language enables easy deployment of complex ML algorithm on Big Data. Before working on larger dataframes, it becomes crucial to process data well. To process data, removing duplicate records is one important aspect. Many a time data quality
Quality of data can be good or can some time not be good enough as per expectations. There may be some data cleaning requirement for many cases. Sometime the column names are not up to the mark and can have
Bigger datafiles are generally stored in text format, csv format. But Excel file i.e. XLSX file also remains an important format of storage, as it can save formats and other features along with the data as well. Importing an Excel
Comma Separated Value files (CSV) remains one of the main format to store data. It can store smaller number of rows, as well as large datasets. Most of the analysis starts with reading data into the coding environment. Reading CSV
Python and Pyspark are two key coding languages popular for data processing. When working on a Pandas Dataframe, it becomes sometimes necessary to convert the file into Pyspark Dataframe. After then further processing can be done in Pyspark environment. This
Pyspark is becoming popular among Data Scientists. For doing data processing for large datasets, running machine learning algorithms etc. Pyspark has many use cases. Of course, for any Pyspark learning enthusiast having the coding language installed in local laptop becomes