Name of column plays key role in data analysis. Columns if not named correctly can cause challenges in later stages of analysis. For example some of the programming language cause challenges in case there is blank present in column name.
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
As the world of data is growing, corporation are maintaining detailed datasets. Number of columns are increasing day by day. It becomes sometime very difficult to work with data having multiple columns in it. So there exist a need of
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
Pandas helps in processing data to high extent. Many a times a user may need to drop rows based on specific column value. For example for a specific customer name there can be many rows, and the need would be
Pandas offers some great functions to process a dataset. In a data file there can be duplicates available at row level. Droping duplicates becomes very important, as the rows will create noise in any analysis. Some time the duplicates can
Python scripts saved in Jupyter notebooks are of ipynb formats. This is an interactive file, with charts data images all captured along with the codes. Due to its interactive nature, ipynb files is gathering popularity. Now python codes are mostly