Product Design

Jan. 15, 2019

/ code & tools

What you should know about Python Pandas?

Luiza Sadowska

Data analysis was always important, especially for scientists. However, data collection and analysis plays a significant role in business as well. Nowadays, with tools like Pandas or NumPy, it is becoming easier to discover opportunities in the data. Today we are going to talk about Pandas, which is a Python library with pre-built methods for many applications. Pandas seems to be quite useful for data science operations and additionally, easy to use which means time and effort efficient. Let’s check if it is true!

Data Analysis with Pandas and Python

If you wonder where the name comes from, unfortunately, it is not because the creators liked pandas as a species so much - it is a combination of panel data which has roots in econometry and Python data analysis. Pandas deals with the data processing and analysis in five steps: load, prepare, manipulate, model and analyze. It is a widely used tool, particularly in data wrangling and munging. Available for everyone as an open source project and free to use (BSD license). The credits for its creation goes to Wes McKinney. Some call it a Python’s answer to R, which is a data analysis and statistical programming language.  However, Pandas makes data analysis easier - out of data (no matter if it is saved in CSV, TSV, Excel, HDF, JSON, THML or even an SQL database) it creates a data frame (Python object with rows and columns) which looks familiar to Excel or SPSS (or even to R). So, out of a bunch of data, you get a clean and not that complicated data frame instead of lists managed by loops and so on. Additionally, Pandas reads from the cache and loads Python objects (serialized in files), can deal with ordered and unordered time series data, arbitrary matrix data and any kind of statistical data sets. 

Python’s data analysis toolkit: pros and cons of using Pandas

Pandas features are the best advantages of the library: 

  • data representation - easy to read, suited for data analysis. In comparison with Java or C/C++, it doesn’t require lines of sophisticated code; 
  • easy handling of missing data - representing it as NaNs;
  • data alignment - intelligent automatic label-based alignment, deals with messy data (and puts it in order);
  • easy to convert data structures to DataFrame objects;
  • easy syntax and fast operations;
  • easy to add/delete columns from DataFrame -  efficient object with integrated indexing for data manipulation, easy data frame management;
  • tools for reading and writing data between in-memory data structures and different file formats;
  • data subsetting and filtering;
  • label-based slicing, fancy indexing, and subsetting of large data sets, as well as data set merging and joining;
  • data from flat files;
  • time efficient - because it is easy to use and powerful enough to handle a lot of qualitative data;
  • flexible - enables to reshape and pivot data sets with ease;
  • handles large datasets;
  • native to Python;
  • extensive file format compatibility;
  • optimized for performance;
  • powerful grouping.

Cons of using Pandas and other Python’s libraries

As every library to a programming language it has some disadvantages but let’s agree first on that, it does its job quite well. However, Pandas was made for structured data operations and manipulations, used particularly for data munging and preparation. Though it is rather new into the data science community, those features boosted Python’s usage in it a lot! 

On the other hand, there are more suitable tools for different types of operations. If you are working in Python and can’t handle n-dimensional arrays in Pandas or the statistical modeling doesn’t get well, try these: 

  • NumPy
  • SciPy
  • Matplotlib
  • Scikit Learn
  • Statsmodels
  • Seaborn
  • Bokeh
  • Blaze
  • Scapy
  • SymPy

If you want to see some real-life examples of Pandas in action, you can check them on this GitHub profile. However, the community around it is not as large as for e.g. NumPy or SciPy which is rather a low-level abstraction library but has its footprint. Looking at the GitHub commits and contributors, Pandas is the 3rd mostly used Python’s library in data science.

Where to use data analysis library?

Whenever you need to collect and analyze data, Pandas come to the rescue. Depending on the project’s needs, choosing the right library or toolset is crucial. However, the popularity of Python as a programming language is constantly growing, especially in the startup environment,  and because Pandas is a Python’s library, the availability of resources as well as a community around it is a huge advantage. Moreover, Pandas’ has the ability to handle a huge amount of data which is necessary in Machine Learning applied in many daily-use applications like GoogleMaps, Siri, Gmail, Uber and many more.

As a flexible and powerful library for Python, Pandas provides labeled data structures and statistical functions for companies like: 

  • Vital Labs, Inc. 
  • Astronomer
  • mappable
  • Instacart
  • SendGrid
  • Supply.AI
  • Secret Excapes
  • Narrative Science
  • Toucan Toco
  • Sighten
  • LotoData
  • QpidHealth
  • VizyDrop
  • ScrapingHub
  • Narrative Science

Python development and Data Science

For any kind of scientific computations and data analysis, no matter which language you use, you can make your life easier by applying a ready-made library and let some part of your job automated. Even if you have already mastered Python, learning new tools will be useful and make your time spent at work more efficient. Pandas is just one of Python’s libraries! In case it doesn’t suit your needs, there is always another one which might be more helpful. Obviously, this is not the only reason why data scientists love Python, but it definitely has some impact! You can start using Pandas by reading its documentation and following tutorials like this introduction to Data Analysis with Pandas,  and later on, check out the 12 useful Pandas techniques for Data Manipulation. 

 

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