As I delved into my week two learning at Lux Tech Academy, we were introduced to Python as a beginner language. Here is an overview of an introduction to Python as a data analytics tool. I've gone through numerous videos of data analysts and most of them recommend using Python for data analysis, why? Python is a popular programming language for data analytics as it offers versatility, flexibility, vast libraries, and visualization capabilities to work with large datasets. It's also suitable for data analytics as it blends well with BI tools and databases.

Advantages of Python in Data Analytics

Python is a preferred choice in data analytics since:

1. It has a syntax that is simple and easy to learn.
2. Numerous libraries such as NumPy, Pandas, Seaborn, SciPy, and Matplotlib aid in data analysis.
3. The available libraries such as Matplotlib and Seaborn help in data visualization, aiding in understanding data trends and patterns.
4. It is flexible and works well with various data sources and databases, allowing for analysis.
5. It helps to deploy analytical solutions into production using frameworks such as Django and Flask. This helps to apply theoretical concepts to the real business world.

Below are some of the ways in which Python is applied in data analytics

• Data Wrangling What exactly is data wrangling? This is the process of gathering raw data, assessing and cleaning it to make it useful. Python is used to import data from various sources, handle missing values, and process large datasets. This helps to reshape data for analysis. Pandas Library is mostly used for data wrangling and manipulation. To use pandas, an environment has to be available i.e., Vs Code or Jupyter Notebook. If pandas isn't installed, run the command:

Pip install pandas

Then run:

import pandas as pd

From there, load a dataset into a pandas data frame.

Exploratory Data Analysis (EDA)
Python helps in data exploration and visualization using libraries like Matplotlib, Seaborn, and Pandas. Coding with Python helps to understand data distribution and relationships using descriptive statistics, pivot tables etc.

Machine Learning
Python offers libraries that aid in machine learning such as Scikit-Learn and Tensor Flow.

How do data analysts use Python in every day life?

Importing data
Various Python libraries such as NumPy, Pandas, BeautifulSoup help in data importation from various sources such as: Csv files and excel spreadsheets, SQL databases, Web APIs and scraping HTML/XML pages and cloud storage. The choice of a library to be used depends on the type and structure of data.

EDA in Python
In the real world, data often contain errors such as missing values and sometimes can be inaccurate thus the need for some cleaning. Exploratory Data Analysis in Python helps in data cleaning by: Identifying anomalies, handling missing data, checking and fixing data types, transforming raw data into a useful and reliable set for analysis.

Transforming data for insights
After cleaning data, python enables various transformation like Pivot tables, concatenating datasets and changing data types and formats.

Descriptive Analysis using Python
SciPy Library has descriptive and inferential statistical functions whereas Matplotlib and Seaborn aid in data visualization.
Python is an interesting language and I can attest it's beginner-friendly. Feel free to leave any question/comment in the comment section.