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
Tag: Pandas
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
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
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
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
For doing data analysis, group by remains one of the key process to follow. Data preparation and exploration stage requires multiple level of aggregation. This article covers how to Group By data in Python. Aggregation can be a done at