Stringo

An Open Source Python Library that includes some pre-coded functions which can be applied for different type of string/text operations accordingly.

The main idea behind coding this library was to facilitate the programmer to perform simple strings operations without have to worry about the datatype such as simple single string, list of strings, Series or DataFrame.

Available Functions

• apply_function_typewise
• lemmatize_object
• stem_object
• remove_extra_spaces
• split_sentences_spacy
• remove_punctuations
• remove_empty_from_list
• sort_list_of_strings
• tokenize_string_spacy

Usage

For Demo Purpose I am going to use the following examples:

simple_string = "I am a Programmer  !"
list_of_strings = ["I am a Programmer  !", "@I love to Code"]
dataframe = pd.DataFrame(["I am a Programmer  ", "I love to Code."], columns=['strings'])
series = pd.Series(["I am a Programmer!!!", "I love to Code...  ..."])

stem_object

print ('Simple String : \n', stem_object(simple_string))
print ('---------- \nList of Strings : \n', stem_object(list_of_strings))
print ('---------- \nDataFrame : \n', stem_object(dataframe))
print ('---------- \nSeries: \n', stem_object(series))

======== OUTPUT =============>

Simple String: 
 i am a programmer  !
---------- 
List of Strings: 
 ['i am a programmer  !', '@i love to cod']
---------- 
DataFrame: 
                strings
0  i am a programmer  
1      i love to code.
---------- 
Series: 
0      i am a programmer!!!
1    i love to code...  ...

remove_extra_spaces

print ('Simple String: \n', remove_extra_spaces(simple_string))
print ('---------- \nList of Strings: \n', remove_extra_spaces(list_of_strings))
print ('---------- \nDataFrame: \n', remove_extra_spaces(dataframe))
print ('---------- \nSeries: \n', remove_extra_spaces(series))

Simple String: 
 I am a Programmer !
list
---------- 
List of Strings: 
 ['I am a Programmer !', '@I love to Code']
df
---------- 
DataFrame: 
              strings
0  I am a Programmer
1    I love to Code.
---------- 
Series: 
0     I am a Programmer!!!
1    I love to Code... ...

remove_punctuations

print ('Simple String: \n', remove_punctuations(simple_string))
print ('---------- \nList of Strings: \n', remove_punctuations(list_of_strings))
print ('---------- \nDataFrame: \n', remove_punctuations(dataframe))
print ('---------- \nSeries: \n', remove_punctuations(series))

Simple String: 
 I am a Programmer
---------- 
List of Strings: 
 ['I am a Programmer', 'I love to Code']
---------- 
DataFrame: 
              strings
0  I am a Programmer
1     I love to Code
---------- 
Series: 
0    I am a Programmer
1       I love to Code

sort_list_of_strings

print ('Simple String: \n', sort_list_of_strings(simple_string))
print ('---------- \nList of Strings: \n', sort_list_of_strings(list_of_strings, reverse=False))
print ('---------- \nDataFrame: \n', sort_list_of_strings(dataframe))
print ('---------- \nSeries: \n', sort_list_of_strings(series))

Simple String: 
 I am a Programmer  !
---------- 
List of Strings: 
 ['', '@I love to Code', 'I am a Programmer  !'] ## HERE !!!!
---------- 
DataFrame: 
                strings
0  I am a Programmer  
1      I love to Code.
---------- 
Series: 
0      I am a Programmer!!!
1    I love to Code...  ...

tokenize_string_spacy

print ('Simple String: \n', tokenize_string_spacy(simple_string))
print ('---------- \nList of Strings: \n', tokenize_string_spacy(list_of_strings))
print ('---------- \nDataFrame: \n', tokenize_string_spacy(dataframe))
print ('---------- \nSeries: \n', tokenize_string_spacy(series))

Simple String: 
 ['I', 'am', 'a', 'Programmer', ' ', '!']
---------- 
List of Strings: 
 [['I', 'am', 'a', 'Programmer', ' ', '!'], ['@I', 'love', 'to', 'Code'], []]
---------- 
DataFrame: 
                      strings
0  [I, am, a, Programmer,  ]
1     [I, love, to, Code, .]
---------- 
Series: 
0     [I, am, a, Programmer, !, !, !]
1    [I, love, to, Code, ...,  , ...]