split() to break the string using delimiter and options to manage number of breaks, expand , order in Pandas.str methods

import pandas as pd 
my_dict={'name':['Ravi King','Raju Queen','Alex Jack']}
df = pd.DataFrame(data=my_dict)
print(df.name.str.split()) # without delimiter

Output

0     [Ravi, King]
1    [Raju, Queen]
2     [Alex, Jack]

Returns Series, Index, DataFrame

Options

Break email address using @ to separate userid and domain part of the email address.

import pandas as pd 
my_dict={'email':['Ravi@example.com','Raju@example.com','Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.split('@'))

Output

0    [Ravi, example.com]
1    [Raju, example.com]
2    [Alex, example.com]

By using the option expand=True, we can get data in columns ( DataFrame ). We can use columns to get our data.

print(df.email.str.split('@',expand=True))

Output

      0            1
0  Ravi  example.com
1  Raju  example.com
2  Alex  example.com

The userid part can be collected like this

print(df.email.str.split('@',expand=True)[0])

Change the column name to 1 ( [1] ) to get domain part.

Handling NaN

import numpy as np
import pandas as pd 
my_dict={'email':['Ravi@example.com','Raju@example.com',np.nan,'Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.split('@'))

Output

0    [Ravi, example.com]
1    [Raju, example.com]
2                    NaN
3    [Alex, example.com]

Using get() to get the columns

print(df.email.str.split('@').str.get(0))

Output

0    Ravi
1    Raju
2     NaN
3    Alex

n= int ( default =-1) all

We can specify number of splits to apply, by default all matching occurrences are used ( n=-1 ). We have changed our sample data to include more number of delimiters.

import numpy as np
import pandas as pd 
my_dict={'email':['id.Ravi@example.co.in','id.Raju@example.co.in',np.nan,'id.Alex@example.co.in']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.split('.',expand=True,n=1))

Output

     0                   1
0   id  Ravi@example.co.in
1   id  Raju@example.co.in
2  NaN                 NaN
3   id  Alex@example.co.in

rsplit()

We can break or split the string starting from right side or from end by using rsplit()

import numpy as np
import pandas as pd 
my_dict={'email':['id.Ravi@example.co.in','id.Raju@example.co.in',np.nan,'id.Alex@example.co.in']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.rsplit('.',expand=True,n=1))

Output

                    0    1
0  id.Ravi@example.co   in
1  id.Raju@example.co   in
2                 NaN  NaN
3  id.Alex@example.co   in

Uses of split()

One of the common requirement is to separate directory and file from the path. Here are some sample data where some addresses ( URLs) are given. Let us try to collect directory name and file name from the data.

import pandas as pd 
my_dict={'Page':['https://www.plus2net.com/html_tutorial/button-linking.php',
                 'https://www.plus2net.com/c-tutorial/grade.php',
                 'https://www.plus2net.com/sql_tutorial/between-date.php',
                 'https://www.plus2net.com/php_tutorial/variables2.php',
                 'https://www.plus2net.com/sql_tutorial/sql_like.php',
                 'https://www.plus2net.com/sql_tutorial/sql_sum-multiple.php',
                 'https://www.plus2net.com/sql_tutorial/date-lastweek.php',
                 'https://www.plus2net.com/sql_tutorial/sql_max.php',
                 'https://www.plus2net.com/sql_tutorial/sql_count.php',
                 'https://www.plus2net.com/html_tutorial/html_marquee_behvr.php',
                 'https://www.plus2net.com/javascript_tutorial/clock.php',
                 'https://www.plus2net.com/php_tutorial/php_drop_down_list.php'
]}
df = pd.DataFrame(data=my_dict)
print(df.Page.str.split('/',expand=True)[3])

Output is here

0           html_tutorial
1              c-tutorial
2            sql_tutorial
3            php_tutorial
4            sql_tutorial
5            sql_tutorial
6            sql_tutorial
7            sql_tutorial
8            sql_tutorial
9           html_tutorial
10    javascript_tutorial
11           php_tutorial

To get the file name we can use like this

print(df.Page.str.split('/',expand=True)[4])

After split we will add to columns

df[['id','prototcal','url','dir','file']]=df.Page.str.split('/',expand=True)

For multi level matching of columns we can use like this. This will help when we are not sure about the number of columns we will get in return. Sometime 2 columns sometime more than 2 columns.

df3 = df['page'].str.split('/', expand=True)
df3.columns = ['page_id{}'.format(x+1) for x in df3.columns]
df = df.join(df3)

Pandas contains() Converting char case slice() cat()

Numpy arrays Python & MySQL Python- Tutorials

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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str.split(): Break the string using delimiters

Options

Handling NaN

n= int ( default =-1) all

rsplit()

Uses of split()

Subhendu Mohapatra