Data structure that makes data preparation easy, quick and efficient
Data analytics / data science requires alot of time spent on data preparation. It can be a very tedious task but it is also a crucial part of your project. Pandas dataframes helps make it much easier for you.
To make inferences from your data, it has to describe the events that you are analysing in the most accurate way possible. To get to this state usually involves some merging, slicing, aggregation or transposing of data.
Advantages of using Pandas Dataframes:
- Pandas Dataframes are able to load data from various databases and data formatsWith just few lines of code:
- merge seperate dataframes that contain a common key (common identifying value e.g customer account number) to obtain a complete view
- segment records within a dataframe
- aggregate, summarize or obtain descriptive stats from your data by accessing in-built functions within Pandas dataframes
- define your own Python functions with specific computational tasks and apply them on your dataframe records