Pyspark can read CSV file directly to create Pyspark Dataframe. In situation where the CSV file does not has header available in the data, it becomes difficult to read it the right way. It may happen that the first row
CSV files widely used for storing datasets, can sometime have some challenges. Many a times header is not present in the data file, this can pose challenges in importing the file. It can result into importing the first row as
Aggregation of data is necessary to summarize and analyze the results. Groupby function in Pandas helps in grouping the data and further aggregation. Summarization can be done for counting rows, getting sum, maximum value, minimum value etc. Challenge comes in
Python provides various modules and function to sort Dataframe. Sort_values in Pandas helps in sorting Pandas Dataframe. One key challenge with sorting is presence of missing or NA values. Na values are grouped into one category and placed in the
Sorting a dataframe is very often done during data processing steps. To know the best performing observation we can sort the dataset by specific column. Similarly, to know the worst performing observation, sorting can help. Sorting can help to have
Pandas provide various functions to clean data before analyzing it. Dropping rows remains one such operation which is very important during cleaning stage. There can various rows, or uncleaned rows which are note useful for analysis. Also, there can be
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
Microsoft Excel provide very useful tool of “Remove Duplicates”. This is present in the Data tab. It can have various use cases, and help working on data in a much easier fashion. In case the requirement is to remove duplicates
Data processing in Pandas can require various stages in between. There can be a need to drop certain rows in the datafile as well. Dropping rows in Pandas is comparatively easier when done at index level. This article explains Pandas
Remove duplicate tool is very handy option in Excel. It can make life easy for anyone working on data analysis. There can be a need to remove duplicates at a single column level, or remove duplicate at multiple column level.