We are faced with umpteen events in our day to day life. All observations in life are a set of nascent data with immense possibilities of information. Since every pattern follows some behavioural psychology thus studying a large amount of sample data can help us build solutions based on those historical data. Python Data Analysis is the study of these data events for deriving insights or patterns from the data using Python. It is one of the most robust data analysis tool currently in use. Python Data Analysis is thus used to manipulate, process and clean data in Python.
Python Data Analysis : Types
There are many ways to analyze the data for arriving at a conclusion. Here we are concerned with doing data analysis with Python. Why we are using python in data analysis? This question could stem since we have other ways and programming languages to analyze data as well.
There are many reasons out of which one of the reason could be is its high code readability and easy to learn property which enables it a less steep learning curve. It has large libraries and is one of the best general purpose programming languages. These are the libraries of important and useful functions and classes we can use. Three of the most powerful packages that we can use in python data analysis and will discuss here are Numpy, Pandas and Matplotlib.
- Numpy is used in large mathematical functions on multidimensional arrays and matrices.
- used in Linear algebra operations and random data generation.
- we can use it as glue code as it has tools for integrating with c and c++.
- Pandas are used to handle a large amount of data for data exploration in a better and efficient way. They combine the usability of excel and the relational database.
- It helps in working with structured data fast and easy enabling space and efficiency conundrum.
- We use a Dataframe as the prime pandas object for data analysis.
- It provides functionality to perform aggregations, slice and dice and selecting subsets of data for data manipulation.
- It also has time series analysis functionality.
- Matplotlib is a library for plotting very good visualization to further explore the data only with few lines of code.
We can start data analysis by following different phases in succession.
Python Data Analysis : Phases
1) Business Problem / Question: This is the first phase of any data analysis phase where we are looking for some question to answer or some problem to solve.
2) Data Wrangling: This is where after defining the question or problem thereafter we are looking for acquiring the data relevant to the problem. Data Wrangling has three phases which are:
- Collect, where we will collect data from different sources relevant to the solution we are looking for.
- Access, to check for data quality and data tidiness issues
- Cleanse, where we clean the data by modifying, replacing or moving data to make the data proper and high quality for extended analysis.
- After acquiring the data, we need to clean the data to keep it ready for the next phase which is exploring the data.
3) Data exploration: This phase, popularly known as EDA (Exploratory Data Analysis) is about studying the data by analyzing, exploring and augmenting the data. It involves finding the relevant pattern in data, visualizing the data by finding a relationship in the data and thus arriving at some conclusions about the data. Relevant since we should keep it on track with the business problem or the question we are asking. We can also do feature engineering the data which could improve the quality of the analysis.
4) Conclusions/Predictions: In this phase, we will look to draw out some conclusions from the explored data. Hence we can make machine learning models to predict some output out of the data. Similarly, we can use inferential and descriptive statistics to arrive at some conclusions from the data.
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5) Communicate: Analysis is as good as we are able to communicate. Above all, it is the ability to communicate our findings in an understandable form to the stakeholders. Finally, this phase is used to communicate the findings to relevant stakeholders to justify and convey meaning to our observations from the analysis done in the previous steps. We can use reports, presentations, blogs, slides or emails to communicate the findings in the analysis done above.
Python Data Analysis : Accessibility
These processes could be iterative and consequently may not always follow a sequence and thus these are the non-linear process in nature. For example, sometimes we could have the source data and afterward, we analyze the data to derive insights and patterns out of it and subsequently deriving the questions out of it. We can execute the data wrangling and data exploration processes interchangeably too.
Data can be accessed by downloading the files that are available from different sources. We can get the data from API or scrape the data from a web page too. Also, we can combine data from multiple different formats. The data accessed then can proceed with all the steps discussed. Finally, we make a story with a nice happy ending by following these steps in python data analysis.