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Working with Text Data
  • 时间：2025-02-11

Python Pandas - Working with Text Data

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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/pbrary/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 newpne) from each string in the Series/index from both the sides.
5

sppt( )

Sppts 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 pst 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 Series/Index are numeric. Returns Boolean.

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 ,  Wilpam Rick ,  John ,  Alber@t , np.nan,  1234 , SteveSmith ])

print s

Its output is as follows −

0            Tom
1   Wilpam 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 ,  Wilpam Rick ,  John ,  Alber@t , np.nan,  1234 , SteveSmith ])

print s.str.lower()

Its output is as follows −

0            tom
1   wilpam 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 ,  Wilpam 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 ,  Wilpam 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  ,   Wilpam Rick ,  John ,  Alber@t ])
print s
print ("After Stripping:")
print s.str.strip()

Its output is as follows −

0            Tom
1   Wilpam Rick
2           John
3        Alber@t
dtype: object

After Stripping:
0            Tom
1   Wilpam Rick
2           John
3        Alber@t
dtype: object

sppt(pattern)

import pandas as pd
import numpy as np
s = pd.Series([ Tom  ,   Wilpam Rick ,  John ,  Alber@t ])
print s
print ("Sppt Pattern:")
print s.str.sppt(   )

Its output is as follows −

0            Tom
1   Wilpam Rick
2           John
3        Alber@t
dtype: object

Sppt Pattern:
0   [Tom, , , , , , , , , , ]
1   [, , , , , Wilpam, Rick]
2   [John]
3   [Alber@t]
dtype: object

cat(sep=pattern)

import pandas as pd
import numpy as np

s = pd.Series([ Tom  ,   Wilpam Rick ,  John ,  Alber@t ])

print s.str.cat(sep= _ )

Its output is as follows −

Tom _ Wilpam Rick_John_Alber@t

get_dummies()

import pandas as pd
import numpy as np

s = pd.Series([ Tom  ,   Wilpam Rick ,  John ,  Alber@t ])

print s.str.get_dummies()

Its output is as follows −

   Wilpam 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  ,   Wilpam 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  ,   Wilpam Rick ,  John ,  Alber@t ])
print s
print ("After replacing @ with $:")
print s.str.replace( @ , $ )

Its output is as follows −

0   Tom
1   Wilpam Rick
2   John
3   Alber@t
dtype: object

After replacing @ with $:
0   Tom
1   Wilpam Rick
2   John
3   Alber$t
dtype: object

repeat(value)

import pandas as pd

s = pd.Series([ Tom  ,   Wilpam Rick ,  John ,  Alber@t ])

print s.str.repeat(2)

Its output is as follows −

0   Tom            Tom
1   Wilpam Rick   Wilpam Rick
2                  JohnJohn
3                  Alber@tAlber@t
dtype: object

count(pattern)

import pandas as pd
 
s = pd.Series([ Tom  ,   Wilpam 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  ,   Wilpam 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  ,   Wilpam 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  ,   Wilpam 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  ,   Wilpam Rick ,  John ,  Alber@t ])

print s.str.findall( e )

Its output is as follows −

0 []
1 []
2 []
3 [e]
dtype: object

Null pst([ ]) indicates that there is no such pattern available in the element.

swapcase()

import pandas as pd

s = pd.Series([ Tom ,  Wilpam 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 ,  Wilpam 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 ,  Wilpam 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 ,  Wilpam 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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