Organizations across all industries now heavily rely on data-driven insights to make decisions and transform their business operations. Effective data analysis is one essential part of this transformation.
But for effective data analysis, it is
important that the data used is clean, consistent, and accurate. The real-world
data that data science professionals collect for analysis is often messy. These
data are often collected from social media, customer transactions, sensors,
feedback, forms, etc. And therefore, it is normal for the datasets to be
inconsistent and with errors.
This is why data cleaning is a very
important process in the data science project lifecycle. You may find it
surprising that 83% of data scientists are using machine learning methods
regularly in their tasks, including data cleaning, analysis, and data visualization
(source: market.us).
These advanced techniques can, of course,
speedup the data science processes. However, if you are a beginner, then you
can use Panda’s one-liners to correct a lot of inconsistencies and missing
values in your datasets.
In the following infographic, we explore
the top 10 Pandas one-liners that you can use for:
·
Dropping rows with missing
values
·
Extracting patterns with
regular expressions
·
Filling missing values
·
Removing duplicates, and more
The infographic also guides you on how to
create a sample dataframe from GitHub to work on.
Check out this infographic and master Panda’s
one-liners for data cleaning
