I was learning about models and how data is important for them and how crucial it is to clean the data before giving it to a model for its training. I myself then explored a dataset, I noticed that there are some missing values in the first few rows of the dataset. Is there a way to process the complete dataset at once and find all the missing values it contains? If there are methods and techniques for finding this, please provide them and give an example code if possible.
Hi @mubashir_rizvi! This would help you:
isnull()method is used to check if a value in a Pandas DataFrame or Series is null or missing. It returns a boolean array of the same shape as the input.
- Since we only want to check if there are missing values or not, we use the
any()method to check if there is at least one
Truevalue in the results provided by the
@mubashir_rizvi, there are many methods and approaches available for this purpose, but it totally depends on the data and needs.
To find missing values, you can use the
notna() method to return a boolean DataFrame where
True values represent non-missing values and
False represents missing values.
You can use the
all() method to check if all values in the DataFrame are
True, if the result is
False, it means you have missing values in the data.
info() method in Pandas DataFrame provides information such as the number of non-null values and total entries per column, making it easy to identify issues and inconsistencies in the data. Here is the code below for your better understanding: