Appending is vertically stacking one Dataframe below the other. Appending can be easy if the Dataframes are well structured and has all column names present. In case column name is not present then appending Dataframes can be challenging. It is
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Pyspark has capacity to handle big data well. Many a times file can be present in multiple smaller files and not as one single file. Appending helps in creation of single file from multiple available files. Pyspark has function available
Pyspark has union function that helps in stacking one Dataframe below the other. Appending helps in creation of single file from the base multiple file. The variables present in both files should ideally be same and have same formats. This
Append is stacking datafiles one after another. It is a process of vertically stacking Dataframes to create a single large Dataframe. Many a times data is present not as a single file but can be available as multiple files. For
Appending is vertically stacking one Dataframe below the other. This process enables creation of single Dataframe from two Dataframes. The number of columns in the two files shall ideally be same. Also the formats of the columns should be same.
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