How Does Python help in Data Analytics

What is data analysis?


Data Analysis – Analyze source data to find trends. Data analysis is a broad field with a set purpose. There are four main types of data analysis.


Technical analysis: Technical analysis is the process of answering questions about what happened. It is best used to explain the results to stakeholders. In the process, Guest Posting collects that data and analyzes the data to present the data visually. Technical analysis gives you an idea of ​​the experience.

Diagnostic analysis: Diagnostic analysis is the process of answering the question of why something happened. They gather descriptive analysis and then dig deeper into the data to find the root cause.

Predictive analytics: Predictive analytics is the process of answering questions about what will happen in the future. This method is used to verify that previous data is returned or returning or returning. The expected analysis ensures the understanding of the future tendency. This method uses a variety of statistical and machine learning methods, such as regression, neural networks, and solution trees.

Educational analysis: Receptive analysis is the process of answering questions. Data-driven decision-making is done using predictive analytics. Helps to evaluate results based on previous data. Data analysis is essential in banking and finance.

Data analysis helps financial institutions become more efficient by detecting and preventing fraud. Data analysis is divided into 4 stages.


data generation

data management

static analysis

Provides information

What is Python?


Python is a growing programming language used in both web development and design and software design. Python is very easy to learn and implement. Python has many advantages. It is used for artificial learning, machine learning, and deep learning.


How can Python help you with data analysis? Using Python for data analysis requires a good knowledge of Matplotlib and CSV. Then you need to install panda, which can be installed using the following command:


pip install panda


Create a data frame in panda. Then read the data stored in the CSV file. Then import the data into the panda. I use symbolic and mathematical pandas to index the data. That’s how pandas are planned.

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