How to CLEAN DATA: 5 steps in Python
I show how to clean data before applying machine learning algorithms.
If you apply data science techniques to original data directly, you can receive a biased result that you can not rely on!
Watch this video to understand basic concepts of data cleaning and to be more efficient in data analysis.
Steps to cleaned data:
1. Explore data
2. Fix syntax and grammar errors
3. Handle null values
4. Remove outliers
5. Remove duplicates
Functions I recommend to use:
head https://pandas.pydata.org/pandas-docs...
info https://pandas.pydata.org/pandas-docs...
groupby https://pandas.pydata.org/pandas-docs...
dropna https://pandas.pydata.org/pandas-docs...
fillna https://pandas.pydata.org/pandas-docs...
replace https://pandas.pydata.org/pandas-docs...
describe https://pandas.pydata.org/pandas-docs...
drop_duplicates https://pandas.pydata.org/pandas-docs...
#datacleaning #datacleaningPYTHON
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