Python Pandas Features

Features of Pandas:

  1. Handling of data: Pandas library has a fast and efficient way to manage and explore data. It can be done by providing us with sereis and data frames, which help us to represent data efficiently and we can also manipulate it in various ways.This feature make Pandas an attractive library for data scientists.

  2. Alignment and indexing: Labelling the data is most important thing. Another most important thing is organising the data without which it is impossible to read the data.The organising and labelling of data can be done perfectly in pandas by using  pandas method alignment and indexing. 

  3. Handling missing data: As we know data are quite confusing to read. It is also curde in nature another problem associated with data is occurrence of missing data or values. Therefore,  it's necessary  to handle the missing values properly so that they do not adulterate study results.

  4. Cleaning up data: As we know data are very crude. Therefore it is really messy, so much that performing any analysis over such data will give wrong results.Thus it is very important to clean our data and this in pandas are provided.They help a lot in cleaning a data and make them tidies up. The cleaner the data, the better the result.

  5. Input and output tools: In Pandas there are wide array of built-in tools which is used for the purpose of reading and writing data. It makes data analysis very simple with the help of Pandas' in-built tools.

  6. Multiple file formats supported: There are so many types of different file formats in which data are found, that it become crucial that libraries used for data analysis can read various file formats.Pandas can support it all, like JSON or CSV ,Excel and HDF5. It is one of the most appealing Python Pandas features. 

  7. Merging and joining of datasets: While doing analysis we need to merge and join multiple datasets to create a new and final datasets to do analysis properly. If data sets aren't merged properly it will affect the result which we don't want. Pandas does this very efficiently so that we don't face any problems while analysis. 

  8. A lot of time series: The features in Pandas don't make any sense for beginners but they have great use in future. So the deeper we go into learning Pandas we will see how essential and useful these features are, for a data scientist.  It include feature  likes of moving window statistics and frequency conversion.

  9. Optimized performance: This feature of pandas make it fast and suitable for data science.The critical codes of Pandas are generally written in C or Cython which make it extremely responsive and fast.

  10. Python support: Pandas begin a part of Python also allow us to acees the other libraries like  NumPy and MatPlotLib.This feature of Python is a deal closer.

  11. Visualize: Visualisation plays an important role in Data Science. It makes the results understandable by human eyes. Pandas has an in-built feature which allow us to plot data and see various kinds of graphs formed.

  12. Grouping: Pandas has the feature GroupBy which is used to split data into categories of our choice according to the criteria we set. The GroupBy function splits the data, implements a function and then combines the results.

  13. Mask data:  Pandas has the feature in which it can filter the data according to the data needed for analysis. By using mask function we can do this. Whenever it finds the data which meets the criteria we set for elimination, it turns the data into a missing value.

  14. Unique data: As we know that there is a lot of repetition, therefore we need to analyse the data which has unique values.So there is a feature present in python pandas which allow us to do so. dataset.column.unique(). Where “dataset” and “column” are the names of your dataset and column, respectively.

  15. Perform mathematical operations on the data: The apply function in pandas allow us to implement various mathematical operation on the data. This is one of the most attractive features of Pandas.

Tue, 02/09/2021 - 22:11

Authored by

Devanshi, is working as a Data Scientist with iVagus. She has expertise in Python, NumPy, Pandas and other data science technologies.