String Functions in Python with Examples

This tutorial outlines various string (character) functions used in Python. To manipulate strings and character values, python has several in-built functions. It means you don't need to import or have dependency on any external package to deal with string data type in Python. It's one of the advantage of using Python over other data science tools. Dealing with string values is very common in real-world. Suppose you have customers' full name and you were asked by your manager to extract first and last name of customer. Or you want to fetch information of all the products that have code starting with 'QT'.
Table of Contents

Python String Functions

List of frequently used string functions

The table below shows many common string functions along with description and its equivalent function in MS Excel. We all use MS Excel in our workplace and familiar with the functions used in MS Excel. The comparison of string functions in MS EXCEL and Python would help you to learn the functions quickly and mug-up before interview.
Function Description MS EXCEL FUNCTION
mystring[:N] Extract N number of characters from start of string. LEFT( )
mystring[-N:] Extract N number of characters from end of string RIGHT( )
mystring[X:Y] Extract characters from middle of string, starting from X position and ends with Y MID( )
str.split(sep=' ') Split Strings -
str.replace(old_substring, new_substring) Replace a part of text with different sub-string REPLACE( )
str.lower() Convert characters to lowercase LOWER( )
str.upper() Convert characters to uppercase UPPER( )
str.contains('pattern', case=False) Check if pattern matches  (Pandas Function) SQL LIKE Operator
str.extract(regular_expression) Return matched values (Pandas Function) -
str.count('sub_string') Count occurence of pattern in string -
str.find( ) Return position of sub-string or pattern FIND( )
str.isalnum() Check whether string consists of only alphanumeric characters -
str.islower() Check whether characters are all lower case -
str.isupper() Check whether characters are all upper case -
str.isnumeric() Check whether string consists of only numeric characters -
str.isspace() Check whether string consists of only whitespace characters -
len( ) Calculate length of string LEN( )
cat( ) Concatenate Strings (Pandas Function) CONCATENATE( )
separator.join(str) Concatenate Strings CONCATENATE( )

LEFT, RIGHT and MID Functions

If you are intermediate MS Excel users, you must have used LEFT, RIGHT and MID Functions. These functions are used to extract N number of characters or letters from string.
1. Extract first two characters from beginning of string
mystring = "Hey buddy, wassup?"
mystring[:2]
Out[1]: 'He'
  1. string[start:stop:step] means item start from 0 (default) through (stop-1), step by 1 (default).
  2. mystring[:2] is equivalent to mystring[0:2]
  3. mystring[:2] tells Python to pull first 2 characters from mystring string object.
  4. Indexing starts from zero so it includes first, second element and excluding third.
2. Find last two characters of string
mystring[-2:]
The above command returns p?.The -2 starts the range from second last position through maximum length of string.
3. Find characters from middle of string
mystring[1:3]
Out[1]: 'ey'
mystring[1:3] returns second and third characters. 1 refers to second character as index begins with 0.
4. How to reverse string?
mystring[::-1]
Out[1]: '?pussaw ,yddub yeH'
-1 tells Python to start it from end and increment it by 1 from right to left.
5. How to extract characters from string variable in Pandas DataFrame?
Let's create a fake data frame for illustration. In the code below, we are creating a dataframe named df containing only 1 variable called var1
import pandas as pd
df = pd.DataFrame({"var1": ["A_2", "B_1", "C_2", "A_2"]})

  var1
0  A_2
1  B_1
2  C_2
3  A_2
To deal text data in Python Pandas Dataframe, we can use str attribute. It can be used for slicing character values.
df['var1'].str[0]
In this case, we are fetching first character from var1 variable. See the output shown below.
Output
0    A
1    B
2    C
3    A

Extract Words from String

Suppose you need to take out word(s) instead of characters from string. Generally we consider one blank space as delimiter to find words from string.
1. Find first word of string
mystring.split()[0]
Out[1]: 'Hey'
How it works?
  1. split() function breaks string using space as a default separator
  2. mystring.split() returns ['Hey', 'buddy,', 'wassup?']
  3. 0 returns first item or word Hey
2. Comma as separator for words
mystring.split(',')[0]
Out[1]: 'Hey buddy'
3. How to extract last word
mystring.split()[-1]
Out[1]: 'wassup?'
4. How to extract word in DataFrame
Let's build a dummy data frame consisting of customer names and call it variable custname
mydf = pd.DataFrame({"custname": ["Priya_Sehgal", "David_Stevart", "Kasia_Woja", "Sandy_Dave"]})
        custname
0   Priya_Sehgal
1  David_Stevart
2     Kasia_Woja
3     Sandy_Dave
#First Word
mydf['fname'] = mydf['custname'].str.split('_').str[0]

#Last Word
mydf['lname'] = mydf['custname'].str.split('_').str[1]
Detailed Explanation
  1. str.split( ) is similar to split( ). It is used to activate split function in pandas data frame in Python.
  2. In the code above, we created two new columns named fname and lname storing first and last name.
  3. Output
            custname  fname    lname
    0   Priya_Sehgal  Priya   Sehgal
    1  David_Stevart  David  Stevart
    2     Kasia_Woja  Kasia     Woja
    3     Sandy_Dave  Sandy     Dave
    

SQL LIKE Operator in Pandas DataFrame

In SQL, LIKE Statement is used to find out if a character string matches or contains a pattern. We can implement similar functionality in python using str.contains( ) function.
df2 = pd.DataFrame({"var1": ["AA_2", "B_1", "C_2", "a_2"],
                    "var2": ["X_2", "Y_1", "Z_2", "X2"]})
   var1 var2
0  AA_2  X_2
1   B_1  Y_1
2   C_2  Z_2
3   a_2   X2
How to find rows containing either A or B in variable var1?
df2['var1'].str.contains('A|B')
str.contains(pattern) is used to match pattern in Pandas Dataframe.
Output
0     True
1     True
2    False
3    False
The above command returns FALSE against fourth row as the function is case-sensitive. To ignore case-sensitivity, we can use case=False parameter. See the working example below.
df2['var1'].str.contains('A|B', case=False)
How to filter rows containing a particular pattern?
In the following program, we are asking Python to subset data with condition - contain character values either A or B. It is equivalent to WHERE keyword in SQL.
df2[df2['var1'].str.contains('A|B', case=False)]
Output 
 var1 var2
0  AA_2  X_2
1   B_1  Y_1
3   a_2   X2
Suppose you want only those values that have alphabet followed by '_'
df2[df2['var1'].str.contains('^[A-Z]_', case=False)]
^ is a token of regular expression which means begin with a particular item.
  var1 var2
1  B_1  Y_1
2  C_2  Z_2
3  a_2   X2

Find position of a particular character or keyword

str.find(pattern) is used to find position of sub-string. In this case, sub-string is '_'.
df2['var1'].str.find('_')
0    2
1    1
2    1
3    1

Replace substring

str.replace(old_text,new_text,case=False) is used to replace a particular character(s) or pattern with some new value or pattern. In the code below, we are replacing _ with -- in variable var1.
df2['var1'].str.replace('_', '--', case=False)
Output
0    AA--2
1     B--1
2     C--2
3     A--2
We can also complex patterns like the following program. + means item occurs one or more times. In this case, alphabet occurring 1 or more times.
df2['var1'].str.replace('[A-Z]+_', 'X', case=False)
0    X2
1    X1
2    X2
3    X2

Find length of string

len(string) is used to calculate length of string. In pandas data frame, you can apply str.len() for the same.
df2['var1'].str.len()
Output
0    4
1    3
2    3
3    3
To find count of occurrence of a particular character (let's say, how many time 'A' appears in each row), you can use str.count(pattern) function.
df2['var1'].str.count('A')

Convert to lowercase and uppercase

str.lower() and str.upper() functions are used to convert string to lower and uppercase values.
#Convert to lower case
mydf['custname'].str.lower()

#Convert to upper case
mydf['custname'].str.upper()

Remove Leading and Trailing Spaces

  1. str.strip() removes both leading and trailing spaces.
  2. str.lstrip() removes leading spaces (at beginning).
  3. str.rstrip() removes trailing spaces (at end).
df1 = pd.DataFrame({'y1': [' jack', 'jill ', ' jesse ', 'frank ']})
df1['both']=df1['y1'].str.strip()
df1['left']=df1['y1'].str.lstrip()
df1['right']=df1['y1'].str.rstrip()
        y1   both    left   right
0     jack   jack    jack    jack
1    jill    jill   jill     jill
2   jesse   jesse  jesse    jesse
3   frank   frank  frank    frank

Convert Numeric to String

With the use of str( ) function, you can convert numeric value to string.
myvariable = 4
mystr = str(myvariable)

Concatenate or Join Strings

By simply using +, you can join two string values.
x = "Deepanshu"
y ="Bhalla"
x+y
DeepanshuBhalla
In case you want to add a space between two strings, you can use this - x+' '+y returns Deepanshu Bhalla Suppose you have a list containing multiple string values and you want to combine them. You can use join( ) function.
string0 = ['Ram', 'Kumar', 'Singh']
' '.join(string0)
Output
'Ram Kumar Singh'
Suppose you want to combine or concatenate two columns of pandas dataframe.
mydf['fullname'] = mydf['fname'] + ' ' + mydf['lname']
OR
mydf['fullname'] = mydf[['fname', 'lname']].apply(lambda x: ' '.join(x), axis=1)
     custname  fname    lname       fullname
0   Priya_Sehgal  Priya   Sehgal   Priya Sehgal
1  David_Stevart  David  Stevart  David Stevart
2     Kasia_Woja  Kasia     Woja     Kasia Woja
3     Sandy_Dave  Sandy     Dave     Sandy Dave

SQL IN Operator in Pandas

We can use isin(list) function to include multiple values in our filtering or subsetting criteria.
mydata = pd.DataFrame({'product': ['A', 'B', 'B', 'C','C','D','A']})
mydata[mydata['product'].isin(['A', 'B'])]
  product
0       A
1       B
2       B
6       A
How to apply NOT criteria while selecting multiple values?
We can use sign ~ to tell python to negate the condition.
mydata[~mydata['product'].isin(['A', 'B'])]

Extract a particular pattern from string

str.extract(r'regex-pattern') is used for this task.
df2['var1'].str.extract(r'(^[A-Z]_)')
r'(^[A-Z]_)' means starts with A-Z and then followed by '_'
0    NaN
1     B_
2     C_
3    NaN
To remove missing values, we can use dropna( ) function.
df2['var1'].str.extract(r'(^[A-Z]_)').dropna()
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About Author:

Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 8 years of experience in data science. During his tenure, he has worked with global clients in various domains like Banking, Insurance, Telecom and Human Resource.

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