Data analyst is one of the hottest professions of the time. Learning Python is easy for any IT based student. Here in this article you are going to learn how Python is helpful for data analysis. Python is in trend these days and its community support is tremendous. Once you are a Python expert, you will be able to solve any data analysis problem with an ease. All you need is get complete knowledge of Python and study Python with complete dedication.

**Learning Python for Data Analysis**

Python is gaining interest in IT sector and the top IT students opt to learn Python as their choice of language for learning data analysis. The candidates want to jump into the career of a data analyst must have knowledge about some language and if we compare Python with other languages, Python is much more interesting and easy to learn as compared to other programming languages. Thus, it has become a common language for data analysis. Python is easy to learn and use whether you are new to the language or you are an experienced professional of information technology. Python helps you serve the company as a great data analyst.

learn-data-analytics-using-python, CCBot/2.0 (https://commoncrawl.org/faq/)

**Installing Python**

Python can be installed in two different ways in your desktop or laptop.

Firstly, you need to go to the project site of Python and get Python directly downloaded from there or you have the choice to install the elements and libraries you wish to use.

Secondly, you can also download the package that include pre-installed libraries and install the package with no disturbances at all.

The top experts and professionals recommend second method for the beginners who are new to this field as well as for seasoned pro.

After the installation process is done, you are required to choose the environment for your work field. The options for choosing the environment include terminal or shell based environment, IDLE and iPython notebook. IDLE is set as default environment and can be used as the most common environment for the users. The environment you choose depends on the requirements you need for coding.

**Python Libraries**

Libraries are very helpful for the ones who wish to learn Python. Before using any library, you need to import that library into your environment. Let us learn some important libraries used in Python for scientific calculations and data analysis.

**NumPy**

Numerical Python is the most dominant library in Python. Its most commanding characteristic is its n-dimensional array with the help of which n-dimensional quantities can be solved. Basic linear algebra functions, fourier transforms, advanced random number capabilities and tools for integration are also present in NumPy just like the features of low level languages such as Fortran, C and C++.

**SciPy**

Scientific Python is an important and useful library for you if you want to use various high level engineering modules such as discrete Fourier transform, linear algebra, optimization and Sparse matrices.

**Matplotlib**

Matplotlib is the library used for the purpose of plotting large number of graphs whether they are from histograms or from heat plots. The important feature that iPython notebook include for plotting is Pylab feature to use inline plotting. If you don’t use inline option in iPython environment, The Pylab will convert iPython environment to Matlab environment. In order to add math in your plot, you can use Latex commands.

**Pandas**

Pandas are most commonly used libraries in Python for data munging and preparing data operations. Pandas are used for structured data procedures and planning. The usage of Python is increased after addition of Pandas into it. Pandas help in enhancing Python among data scientists for further research and analysis.

**Scikit** **Learn**

This library is used for machine learning and a lot of important useful tools are added in this library in order to make the calculations, statistical modelling, regression, and clustering, dimensional reduction easy to work with.

**Statsmodels**

Statsmodels is used for statistical modelling. This module helps in searching data, guess statistical models and carry out statistical tests. The list of widespread statistics, statistical tests, plotting functions and result statistics is provided for different data types.

**Seaborn**

Seaborn is used for statistical data visualization mainly used for creating eye-catching and knowledge able statistical graphics in Python. The main purpose of Seaborn is to centralize the visualization and make efforts in exploring and understanding data.

**Bokeh**

Advanced web browsers use Bokeh for designing interactive plots and dashboards and data applications. The performance in the interaction of about huge datasets can be done with this library. Bokeh allow the users to create stylish and concise graphics.

**Blaze**

Blaze is used to access data from various sources such as Bclz, MongoDB, Apache Spark, PyTables etc and is an important library that creates interesting visualizations and dashboards for large amounts of data.

**Scrapy**

Scrapy is used to get detailed patterns of data. It also allows the users to go to website home link and gather appropriate information from different web pages.

**SymPy**

SymPy is used to do various symbolic calculations and has efficiency to perform basic arithmetic calculations, calculus, algebra discrete mathematics and quantum physics. The result of the calculations is formatted into Latex code.

**Requests**

This library type is used to access the web. It is easy to use and code and relates to urllib2 with a little difference in them.

**Os**

Os is used for operation system and execution of files.

**Networkx**

Data manipulations that need to be done on graphs are being done by networkx.

**Igraph**

The working of igraph is same as network that is data manipulations for graph based data is done with this library.

**Regular expressions**

This library is used to find patterns in data written in the form of text.

learn-data-analytics-using-python, CCBot/2.0 (https://commoncrawl.org/faq/)

**BeautifulSoup**

Scrapy gather information from different web-pages but unlike Scrapy BeautifulSoup gather information from a single web-page.

These are the different libraries that are used in Python for better performance and results of the code. Different libraries have different features and all the features help in learning data analysis.

**Python Data Structures**

All the languages have their own data structures and libraries. Similar is in the case of Python. Python has different data structures that help in making the code. Some of the data structures are:

**Lists** – Lists are flexible data structures of Python that has the features to change each element of the list. A list can be described by writing a list of elements or values separated by comma within the square brackets.

**Strings** – Strings in Python are defined by commas. It may be single, double or triple inverted comma. Triple comma quotes are used for docstrings for multiple lines. Once the value is added into the strings, it cannot be changed.

**Tuples** – Tuples are described by the elements or values separated by commas. The values in the tuple cannot be changed or modified. They work much quicker than lists.

**Dictionary** – Dictionary is an unordered set of keys. The keys need to be unique to make the set as dictionary. A dictionary contains a set of unique values. An empty dictionary is made up of a pair of braces.

The above data structures play an important role in Python whether it addition of elements or values into the program or any other operations.

**Data Analysis with Python with the help of Pandas**

Data can be explored more in detail with the help of the most important library known as Panda. Panda is used to read data sets and perform exploratory analysis to solve any problem.

In order to begin with data exploration, first of all choose the environment you want to work in. You can choose any of the above environments that we discussed in the above discussion. After selecting the appropriate environment, import the libraries you want and read the dataset.

After you read the dataset, go through the top rows of the dataset. You can also view more rows by printing the dataset.

Describe function is used to view the summary of numerical values. The function also offers count, mean value, standard deviation, quartiles etc in the output. If you wish to view non-numerical values, you can view frequency distribution for more detailed knowledge.

**Data Munging with Python**

During the process of data exploration, there are some problems found that needs to be solved before data to be ready for a good data model. The process of cleaning all the errors and solving all the problems from the data is known as data munging.

Sometimes problems arise when there are some missing values in some of the variables. The missing values need to be estimated honestly so as to fill the missing spaces according to the expected values of variables.

Sometimes some datasets include extreme values that need to be adjusted appropriately.

To look into the missing values, you need to have inputting done because models with missing number of values don’t work.

In order to fill the missing values, one must reconsider the estimated value by approaching the nearby values. Let us consider that the value of loan amount is missing in the model. Now by taking a hypothesis such as if a person is educated or employed, he is able to give an estimated amount of loan.

We can also make a predictive model that will help to make data helpful for modelling. Some of the most important libraries used in creating datasets for a good model are Skicit-Learn. All the values need to be numeric if you are using this library. If not, the library will automatically convert all the variables into numeric values by encoding.

**Practising **

Once you gather all the knowledge and technical skills of using Python, what you all need is get a deep study of the terms and techniques being used in Python. GO through all the Python libraries, data structures and functions and practice each of them by your own implementation and coding. Practice more and more and you will be proficient in the programming language named as Python. Once you nailed it, you will get any data analyst job with highly paid salary. Your skills required to be a data analysts will be fulfilled once you learn Python with complete dedication.

The best way to practice your skills is to compete with your competitors and fellow data scientists via live competitions and search for other great ways to practice and excel in Python.

Try to solve as much Python tutorial questions as you can and use all the brain to solve those brain storming questions. This will lead to get you more knowledge about the concepts as well as help you get some new things in your pocket. Keep your brain working on solving problems and coding.

Data analysis can be learnt if you learn Data Science Course with your whole heart. And once you excel in data analysis, you will be counted among the top IT professionals of the times. Any company will be happy to pay you high amounts of salary if they see your technical skills in data analysis. Data analysis is the running course in the IT filed nowadays and getting efficient in it makes you the most wanted IT professional and expert in the market. Now is the time when big data is used worldwide on the large extent by almost all the IT companies. To handle those enormous data, companies need data analysts who are efficient in analysing data and providing appropriate solutions to their problems as well as ways to boost the businesses.

The data analysts who have deep knowledge about Python have complete knowledge about data sets and data structures and are capable enough to get any data analytics job in any renowned company.

## 0 Comments