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In this chapter, we will discuss the string operations with our basic Series/Index. In the subsequent chapters, we will learn how to apply these string functions on the DataFrame.
Pandas provides a set of string functions which make it easy to operate on string data. Most importantly, these functions ignore (or exclude) missing/NaN values.
Almost, all of these methods work with Python string functions (refer: https://docs.python.org/3/library/stdtypes.html#string-methods). So, convert the Series Object to String Object and then perform the operation.
Let us now see how each operation performs.
| Sr.No | Function & Description |
|---|---|
| 1 |
lower() Converts strings in the Series/Index to lower case. |
| 2 |
upper() Converts strings in the Series/Index to upper case. |
| 3 |
len() Computes String length(). |
| 4 |
strip() Helps strip whitespace(including newline) from each string in the Series/index from both the sides. |
| 5 |
split(” ”) Splits each string with the given pattern. |
| 6 |
cat(sep=” ”) Concatenates the series/index elements with given separator. |
| 7 |
get_dummies() Returns the DataFrame with One-Hot Encoded values. |
| 8 |
contains(pattern) Returns a Boolean value True for each element if the substring contains in the element, else False. |
| 9 |
replace(a,b) Replaces the value a with the value b. |
| 10 |
repeat(value) Repeats each element with specified number of times. |
| 11 |
count(pattern) Returns count of appearance of pattern in each element. |
| 12 |
startswith(pattern) Returns true if the element in the Series/Index starts with the pattern. |
| 13 |
endswith(pattern) Returns true if the element in the Series/Index ends with the pattern. |
| 14 |
find(pattern) Returns the first position of the first occurrence of the pattern. |
| 15 |
findall(pattern) Returns a list of all occurrence of the pattern. |
| 16 |
swapcase Swaps the case lower/upper. |
| 17 |
islower() Checks whether all characters in each string in the Series/Index in lower case or not. Returns Boolean |
| 18 |
isupper() Checks whether all characters in each string in the Series/Index in upper case or not. Returns Boolean. |
| 19 |
isnumeric() Checks whether all characters in each string in the |
Let us now create a Series and see how all the above functions work.
import pandas as pd import numpy as np s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'', np.nan, ''1234'',''SteveSmith'']) print s
Its output is as follows −
0 Tom 1 William Rick 2 John 3 Alber@t 4 NaN 5 1234 6 Steve Smith dtype: object
lower()
import pandas as pd import numpy as np s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'', np.nan, ''1234'',''SteveSmith'']) print s.str.lower()
Its output is as follows −
0 tom 1 william rick 2 john 3 alber@t 4 NaN 5 1234 6 steve smith dtype: object
upper()
import pandas as pd import numpy as np s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'', np.nan, ''1234'',''SteveSmith'']) print s.str.upper()
Its output is as follows −
0 TOM 1 WILLIAM RICK 2 JOHN 3 ALBER@T 4 NaN 5 1234 6 STEVE SMITH dtype: object
len()
import pandas as pd import numpy as np s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'', np.nan, ''1234'',''SteveSmith'']) print s.str.len()
Its output is as follows −
0 3.0 1 12.0 2 4.0 3 7.0 4 NaN 5 4.0 6 10.0 dtype: float64
strip()
import pandas as pd
import numpy as np
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print s
print ("After Stripping:")
print s.str.strip()
Its output is as follows −
0 Tom 1 William Rick 2 John 3 Alber@t dtype: object After Stripping: 0 Tom 1 William Rick 2 John 3 Alber@t dtype: object
split(pattern)
import pandas as pd
import numpy as np
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print s
print ("Split Pattern:")
print s.str.split('' '')
Its output is as follows −
0 Tom 1 William Rick 2 John 3 Alber@t dtype: object Split Pattern: 0 [Tom, , , , , , , , , , ] 1 [, , , , , William, Rick] 2 [John] 3 [Alber@t] dtype: object
cat(sep=pattern)
import pandas as pd import numpy as np s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t'']) print s.str.cat(sep=''_'')
Its output is as follows −
Tom _ William Rick_John_Alber@t
get_dummies()
import pandas as pd import numpy as np s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t'']) print s.str.get_dummies()
Its output is as follows −
William Rick Alber@t John Tom 0 0 0 0 1 1 1 0 0 0 2 0 0 1 0 3 0 1 0 0
contains ()
import pandas as pd
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print s.str.contains('' '')
Its output is as follows −
0 True 1 True 2 False 3 False dtype: bool
replace(a,b)
import pandas as pd
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print s
print ("After replacing @ with $:")
print s.str.replace(''@'',''$'')
Its output is as follows −
0 Tom 1 William Rick 2 John 3 Alber@t dtype: object After replacing @ with $: 0 Tom 1 William Rick 2 John 3 Alber$t dtype: object
repeat(value)
import pandas as pd s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t'']) print s.str.repeat(2)
Its output is as follows −
0 Tom Tom 1 William Rick William Rick 2 JohnJohn 3 Alber@tAlber@t dtype: object
count(pattern)
import pandas as pd
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print ("The number of ''m''s in each string:")
print s.str.count(''m'')
Its output is as follows −
The number of ''m''s in each string: 0 1 1 1 2 0 3 0
startswith(pattern)
import pandas as pd
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print ("Strings that start with ''T'':")
print s.str. startswith (''T'')
Its output is as follows −
0 True 1 False 2 False 3 False dtype: bool
endswith(pattern)
import pandas as pd
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print ("Strings that end with ''t'':")
print s.str.endswith(''t'')
Its output is as follows −
Strings that end with ''t'': 0 False 1 False 2 False 3 True dtype: bool
find(pattern)
import pandas as pd
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print s.str.find(''e'')
Its output is as follows −
0 -1 1 -1 2 -1 3 3 dtype: int64
“-1” indicates that there no such pattern available in the element.
findall(pattern)
import pandas as pd
s = pd.Series([''Tom '', '' William Rick'', ''John'', ''Alber@t''])
print s.str.findall(''e'')
Its output is as follows −
0 [] 1 [] 2 [] 3 [e] dtype: object
Null list([ ]) indicates that there is no such pattern available in the element.
swapcase()
import pandas as pd s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'']) print s.str.swapcase()
Its output is as follows −
0 tOM 1 wILLIAM rICK 2 jOHN 3 aLBER@T dtype: object
islower()
import pandas as pd s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'']) print s.str.islower()
Its output is as follows −
0 False 1 False 2 False 3 False dtype: bool
isupper()
import pandas as pd s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'']) print s.str.isupper()
Its output is as follows −
0 False 1 False 2 False 3 False dtype: bool
isnumeric()
import pandas as pd s = pd.Series([''Tom'', ''William Rick'', ''John'', ''Alber@t'']) print s.str.isnumeric()
Its output is as follows −
0 False 1 False 2 False 3 False dtype: bool
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