This tutorial explains various string (character) functions used in Python, with examples.
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'.
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.
mystring = "Hey buddy, wassup?" mystring[:2]
Out[1]: 'He'
string[start:stop:step]
means item start from 0 (default) through (stop-1), step by 1 (default).mystring[:2]
is equivalent tomystring[0:2]
mystring[:2]
tells Python to pull first 2 characters frommystring
string object.- Indexing starts from zero so it includes first, second element and excluding third.
mystring[-2:]The above command returns
p?
.The -2 starts the range from second last position through maximum length 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.
mystring[::-1]
Out[1]: '?pussaw ,yddub yeH'-1 tells Python to start it from end and increment it by 1 from right to left.
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_2To 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.mystring.split()[0]
Out[1]: 'Hey'
split()
function breaks string using space as a default separatormystring.split()
returns['Hey', 'buddy,', 'wassup?']
0
returns first item or wordHey
mystring.split(',')[0]
Out[1]: 'Hey buddy'
mystring.split()[-1]
Out[1]: 'wassup?'
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]
str.split( )
is similar tosplit( )
. It is used to activate split function in pandas data frame in Python.- In the code above, we created two new columns named
fname
andlname
storing first and last name.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 usingstr.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
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)
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 X2Suppose 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--2We 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 3To 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
str.strip()
removes both leading and trailing spaces.str.lstrip()
removes leading spaces (at beginning).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 ofstr( )
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
DeepanshuBhallaIn 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 useisin(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
~
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 NaNTo remove missing values, we can use
dropna( )
function.
df2['var1'].str.extract(r'(^[A-Z]_)').dropna()
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