If you are preparing your precious move towards the journey of data scientist, you’ll undoubtedly meet Python in your way. What is the reason? Because data science experts and professional broadly use it as a data language. The main reasons are:

- Python smoothly handles various data structures.
- It is easy to understand and interpret.
- Python offers a robust open source set of statistical and visualization libraries.

In this article, I’ll present everything you need to learn about python in data science. My primary concern will be on python enabled data science part.

Here we go!

**Why Python?**

When we talk about coding in data, we generally prefer four languages:

- Python
- Structured Query Language (SQL)
- R Programming
- Bash

Learning all these four languages can be very beneficial in data science, but if you are a beginner at it, I’ll always prefer to start your journey with Python programming and SQL. Only these two languages deal with 99% of data science and analytics queries you will meet in the future. Now, whats the use of Python in the data science journey?

- It’s easy to learn and use.
- It contains useful packages to handle simple to complex analytics projects such as segmentation, explorative and cohort analysis, machine learning models etc.
- The digital world is looking for data professionals with powerful Python understanding. It simply means putting Python in your CV will make you stand among others.

**What is Python? **

Python is a high-level programing language used in various fields including data science. It means you don’t need to finish the whole **Data Science with Python**. Learning basics and useful tools will be suitable enough to make it worth.

**How to install Python? **

Python can be installed in two ways:

- You can directly download and install each component from the official website.
- Or, you can download and install pre-installed packages with libraries such as NumPy, Requests, Twisted, anaconda etc.

I think the second method will be more comfortable for beginners.

**Selecting a Development Environment: **

After you are done with Python installation, you can choose your environment from various options such as:

- Through terminal
- Use IDLE (default python environment)
- iPython notebook

Depending on your need, you can go for a suitable environment.

**Python Data Structure and Libraries: **

Data structures used in Python are:

**Lists: **

Python lists are mutable in nature where any specific element can be altered. Lists contain various items with the same data type. It is written by the list of separated commas within square brackets.

E.g.,

1 |
list1 = [1,4,9,16,25,36] |

**Strings: **

Strings in Python are defined by single, double or triple inverted commas. The triple inverted commas enclosed strings are spanned over various lines with the use of escape character. These strings are immutable.

E.g.,

1 |
Call = ‘Hello’ |

**Tuples: **

Tuple represents a group of values separated by commas and are immutable. They serve faster processing than lists.

E.g.,

1 |
Tuple_a = (0,1,3,5,6,7,8,9) |

**Dictionary: **

Dictionary represents an unordered set of key and value pairs. Each key is unique. {} represents an empty dictionary.

E.g.,

1 |
ext = {‘kapi’ : 7809, ‘japi’ : 6789, ‘tapi’ : 9999} |

**Python Libraries: **

Various useful Python Libraries are:

**NumPy:**

NumPy or Numerical Python contains features like an n-dimensional array, basic algebra function, Fourier transformation, random number etc.

**Pandas: **

It is used for structured data operations, munging and preparation.

**Blaze: **

It is used for dealing distributed datasets to boost capabilities of NumPy and Pandas.

**SciPy: **

Scientific Python is built on NumPy for dealing high-level science modules such as Fourier transformation, optimization of matrices etc.

**Matplotlib: **

It is used for plotting graphs such as histograms, heat or line plotting etc.

**Statsmodels:**

It is used for modeling statistics with data exploration, estimation, and testing capacity. It contains descriptive statistics, plotting functions, statistical tests, and results.

**Requests: **

It allows web access.

**Scrapy: **

It is used for web crawling as it gets specific data patterns.

**SymPy: **

It is used for symbolic arithmetics such as calculus, algebra, quantum maths etc.

**Bokeh: **

used for designing interactive plots, data-enabled applications, dashboards etc., for web browsers.

**Seaborn: **

It is used in statistical data visualization.

**Scikit Learn: **

It is used to implement machine learning etc.

**Let’s Explore: **

- Let’s implement the Pandas solution for this work. Pandas is a robust library. We can use it to read the data set from analytics. It has two data structures- Series (1-D indexed array) and DataFrames (Similar to Excel).
- Let’s download the dataset available online for the analysis.
- Start python interface and import necessary libraries and data sets such as pandas, numpy, matplotlib etc.
- Once imported, read data set with function read_csv().
- Explore data such as look at the top with head() function.
- Show the summary with describe() function.
- Perform distribution analytics by plotting graphs such as for histogram df[‘variable’].hist(‘bins=40’), df.boxplot(column = ‘variable’ )
- After plotting, check for the missing values in the data set and fill them.

command: df.apply(lambda y: sum(y.isnull()), axis=0)

- Now we have made this data useful for modeling then go and build a predictive model. Scikit-Learn is the broadly used python library for modeling.
- A decision tree can be also used for model building.

**Final Words: **

Python is converting dreams into reality with its incredible use among data scientists. The reason is simple: its easy use, implementation, libraries, computational intensity and powerful data analytics capability. So a complete package for python can help you to read, analyze, visualize and make predictions.

## The Brain behind this Post.

Vixit Raj is a digital marketer and guest post outreach expert, holding 2 years of experience in digital marketing. He is well aware of the technicalities of SEO, Google Adwords, and email marketing. By understanding the vision, goals, and requirements of clients across the world, he offers lucrative solutions that help them obtain the desired results.