Python for data science – Implementing Python libraries
The usage and importance of Python have been growing year after year, especially with the data analytics and data science community. In this article, Disha Gupta offers a quick demo of how to implement Python libraries.
Data science and Python are the two most hyped trends we hear about all the time. And a combination of these two can give a huge advantage to tech aspirants. The usage and importance of Python have been growing year after year, especially with the data analytics and data science community.
Every day, a huge amount of weather forecasts are issued covering almost all the regions and cities. You probably notice the forecast was wrong when it starts to rain in the middle of your outing when it was supposed to be sunny, but did you ever wonder just how accurate those forecasts really are?
The forecasts are gathered every day, put in a database, and are compared to the actual conditions encountered. These results are then used to improve forecast models for the next round. All this collection, analysis, and reporting do take a lot of heavy analytical horsepower, but it can all be made simpler with one programming language: Python.
Big brands like Google, NASA, and CERN are using Python for almost every programming purpose under the sun.
According to the TIOBE Index, there are more than 250 programming languages currently in existence. These programming languages are sophisticated languages that build applications, programs, and environments used by people. The most popular amongst these languages is the Python programming language. Python is an open-source language that has been around since February 1991. And data scientists have been using Python for years now. Hence, Python for Data Science is a must learn for Data Analytics professionals.
Let us take a closer look at why Python is so popular among data scientists.
Why is data science using Python?
Python is multifaceted and flexible with easy readability, making it an obvious language of choice in the field. Python libraries like Pandas help clean up data and perform advanced manipulation.
The growth of Python in data science has increased because of its libraries like Pandas. Pandas has opened the use of Python for data analysis to a broader audience enabling it to deal with row-and-column datasets, import CSV files, and much more.
Just like Pandas, there are hundreds of other specialized libraries available in Python that serve a similar purpose. These libraries aid in everything from machine learning to data preprocessing to neural networks. Some of these Python libraries are:
- Numpy – provides fundamental scientific computing.
- Matplotlib – used for plotting and visualization.
- Pandas – applied for data analysis and manipulation.
- Scikit-learn – designed for machine learning and data mining.
- StatsModels – statistical modeling, testing, and analysis.
- Scipy – a bunch of mathematical algorithms built on the Numpy extension of Python.
- Seaborn – used for visualization of statistical models.
- Plotly – web-based toolbox used for visualizations.
- Theano – defines multi-dimensional arrays.
The main benefit of Python is that it is flexible in nature which enables data scientists to use one tool every step of the way.
Another plus point is the large community of data scientists, machine learning experts, and programmers who go out of their way not only to make Python learning easy but also to provide datasets to test a Python student’s mastery of their newfound skills. So, whether you are a social scientist who needs Python for advanced data analysis or maybe an experienced developer, the Python community is always ready to help you out.
Now that we know why data scientists are obsessing over Python, let us look at a demo of its practical implementation.
A quick demo
Problem statement: There is a dataset comprising of comprehensive statistics on a range of aspects like distribution & nature of prison institutions, overcrowding in prisons, type of prison inmates etc. Use this dataset to perform descriptive statistics and derive useful insights out of the data. Below are a few tasks:
Data loading: Load a dataset “prisoners.csv” using pandas and display the first and last five rows. Find the number of columns using the describe method in Pandas.
Data manipulation: Create a new column -“total benefitted”, that is the sum of inmates benefitted through all modes.
Data visualization: Create a bar plot with each state name on the x-axis and their total benefitted inmates as their bar heights.
For data loading, write the below code:
import pandas as pd import matplotlib.pyplot as plot %matplotlib inline file_name = "prisoners.csv" prisoners = pd.read_csv(file_name) prisoners
Now to use the describe method in Pandas, just type the below statement:
Next, in Python with data science article, let us perform data manipulation.
And finally, let us perform some visualization in Python for data science article. Refer to the below code:
import numpy as np xlabels = prisoners['STATE/UT'].values plot.figure(figsize=(20, 3)) plot.xticks(np.arange(xlabels.shape), xlabels, rotation = 'vertical', fontsize = 18) plot.xticks plot.bar(np.arange(prisoners.values.shape),prisoners['total_benefited'],align = 'edge')
When we thought that Python could not get any cooler, we discovered that it is named after Monty Python’s Flying Circus which is a classic comedy series. Python documentation is littered with comedic references to Monty Python.
As Python is still under development, it receives regular updates and releases. So, you can be assured that the time and investment spent in learning Python for data science would be a time well spent.