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This article will explain in detail how to use the Pandas condition filtering function in Python. The editor thinks it is very practical, so I share it with you for reference. I hope you can get something after reading this article.

First, prepare data import pandas as pd data = pd.read_excel (r 'sales data .xlsx') print (data)

The data are as follows:

2. To >, =, 100)]

III. Isin ()

If you want to select a column equal to multiple values or strings, use .isin (), we modify the df (isin () parentheses should be a list):

For example, filter the data that Tianhe store sales are equal to 180 and 200.

Df = data [data ['Tianhe Store sales'] .isin ([180,200])]

Fourth, the implementation of .str.marker ()

The most commonly used filter should be the fuzzy filtering of strings. Like is used in the SQL statement, and we can use .str.filter () in pandas.

For example: screening the data of salespeople with horse words

Df = data [data ['salesman'] .str.horse ('horse')]

You can also use'|'to filter multiple criteria

For example, screening salespeople's data containing horse characters or Li characters.

Df = data [data ['salesman'] .str.coach ('Ma | Li')]

Note: this'|'is in quotation marks instead of putting two words

This is the end of this article on "how to use Pandas conditional filtering in Python". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it out for more people to see.

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