Python Pandas Tutorial: Different ways to filter Pandas DataFrame #9
In this video lecture you will learn different ways to filter the data from Pandas DataFrame.
Introducing Pandas Query — Dynamic Data Filtering
What to do when the filtering criteria move too much?
Pandas has easy-to-use tools to filter your DataFrames, however the basic methods — like loc, iloc, or passing a boolean expression — lack the ability to use them comfortably when the filtering parameters are not static. The query DataFrame method offers a solution for this problem.
Data Filtering is one of the most frequent data manipulation operation. It is similar to WHERE clause in SQL or you must have used filter in MS Excel for selecting specific rows based on some conditions. In terms of speed, python has an efficient way to perform filtering and aggregation. It has an excellent package called pandas for data wrangling tasks.
You will learn:
1.) Apply filter single value.
2.) How to use Boolean value in Filter?
3.) Filter the criteria based on multiple columns.
4.) Filter the multiple cells in single column or multiple column.
5.) Understanding the logical operators in filters.
6.) How to apply nested criteria in filter?
7.) Apply the filter using ~ (tild) operator.
8.) Filter with string data.
9.) Apply the filter using isin method.
10.) How to use Lambda within the filter?
11.) Filter with Query method, nlargest, nsmallest and loc.
12.) Examples....
Jupyter Notebook: https://github.com/AbhisheakSaraswat/...
DataSet: https://github.com/AbhisheakSaraswat/...
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