str.contains to search string or pattern matching in columns series using Pandas

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Pandas

Searches for string or pattern matching with different options.
Returns boolean searies

We will read data from one excel file ( student.xlsx ) by using read_excel() to create a DataFrame.

import pandas as pd 
my_data = pd.read_excel('student.xlsx')
print(my_data)

This will return all the rows

We will use contains() to get only rows having Al in name column. We used the option case=False so this is a case insensitive matching. You can make it case sensitive by changing case option to case=True

import pandas as pd 
my_data = pd.read_excel('student.xlsx')
my_data=my_data[my_data['name'].str.contains('Al',case=False)]
print(my_data)

Output

    id       name class  mark     sex
5    6  Alex John  Four    55    male
10  11     Ronald   Six    89  female

Check this Exercise on how to use str.contains(), dataframe.max(), min() to analyse search queries and cliks

regex=True | False

We can use regual expression pattern matching by setting the option regex=True.
We will collect rows where name column is starting with A or B

import pandas as pd 
my_data = pd.read_excel('student.xlsx')
my_data=my_data[my_data['name'].str.contains('^[AB]',case=True,regex=True)]
print(my_data)

Name column ending with d

my_data=my_data[my_data['name'].str.contains('d$',regex=True)]

Name column ending with a or d

my_data=my_data[my_data['name'].str.contains('[ad]$',regex=True)]

Name column not having Al

my_data=my_data[my_data['name'].str.contains('^((?!Al).)*$',case=True,regex=True)]

my_data=my_data[~my_data['name'].str.contains('Al',case=False)]

str.contains.sum()

Pandas read_csv() read_excel() to_excel()

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