Pandas is the most widely used Python library for data analysis and manipulation. But the data that you read from the source often requires a series of data cleaning steps—before you can analyze it to gain insights, answer business questions, or build machine learning models. This guide breaks...
If you’re already comfortable with the math, then the scikit-learn documentation has a great list of tutorials to get you up and running in Python. If not, then the Math for Data Science Learning Path is a good place to start. Additionally, there’s also an entire learning path for ma...
When I first started using Python to analyze data, the first line of code that I wrote was ‘importpandasas pd’. I was very confused about whatpandaswas and struggled a lot with the code. Many questions were in my mind: Why does everyone apply ‘importpandasas pd’ in their first lin...
While the specifics of the structuring stage may vary for structured and unstructured data, it is a crucial step in the data wrangling process for both. A well-structured dataset enables more efficient data manipulation. Cleaning Data cleaning is often confused with data wrangling. The first ...
Data cleansing.The aim here is to find the easiest way to rectify quality issues, such as eliminating bad data, filling in missing data and otherwise ensuring the raw data is suitable for feature engineering. Data reduction.Raw data sets often include redundant data that comes from characterizing...
Create a complete ETL pipeline using Docker, working with SuperStore sales data. Clean raw data with Python, model a relational database in MySQL, and analyze the data using Jupyter Notebook. This project guides you through traditional ETL steps, from data cleaning to database loading and analys...
Essentially, you’ll need to master SQL for querying and manipulating databases, but you’ll then need to choose between R and Python for your next programming language. You can find a comparison of Python vs R for data analysis in a separate post. You can also learn to become a data ...
No-Code Solution: Easily connect your Excel data without writing a single line of code. Flexible Transformations: Use drag-and-drop tools or custom scripts for data transformation. Real-Time Sync: Keep your destination database updated in real time. ...
Machine Learning is a must-have ability for any Data Scientist. Predictive Models are created using Machine Learning. For example, if you want to forecast how many clients you’ll have in the coming month based on the previous month’s Data, you’ll need to employ Machine Learning techniques...
"Objects are Python's abstraction for data. All data in a Python program is represented by objects or by relations between objects." We'll take a closer look at Python objects in Chapter 6, Advanced Concepts – OOP, Decorators, and Iterators. For now, all we need to know is that every...