String Operations in NumPy

Introduction

To start with this article, let us refresh some of the critical concepts of strings in computer science.

Python uses strings to store text information like names of people or email addresses. Python treats strings as sequences, which implies that it keeps track of each character in a string as a separate sequence. For instance, Python recognizes the string "hello" as a specific set of characters. This suggests we can use indexing to find specific characters (like the first or last character).

Let us learn about the various string operations we can perform using NumPy in Python without further ado.

String Operation in NumPy

Numpy offers a wide range of NumPy string functions for use with NumPy strings. For example, two strings can be added, the content of a string can be changed, the characters in a string can be lowercase, the extra space to the left or right of a string can be trimmed, and so on. Let us examine each of the NumPy string functions individually.

1. Add Function

The add function is employed to join two strings. Let us now examine its syntax.

Syntax :

It takes two strings (x1 and x2) as input and returns the concatenated string as output. Consider the following example :

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2. Multiply Function

If we want to concatenate a given string n times, the numpy.char.add() function would be redundant, as we would have to use it n times, which would be much more tedious; this is where numpy.char.multipy() comes into play. However, before proceeding, let us first understand its syntax.

Syntax :

It accepts a string and an integer representing the number of times the given string should be replicated. It should be noted that if we pass a negative value to the function, it will interpret it as zero and return an empty string.

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3. Mod Function

In Python, the char.mod() method returns ( a % I ) string formatting for a pair of array elements. The char.mod() method accepts two parameters : a, which symbolizes an array object of strings or Unicode, and a value, which expresses the values that will be interpolated into the string element by element. Let us have a peek at the syntax.

Syntax :

Let us try to grasp the mod function with an example.

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4. Capitalize Function

We utilize the 'numpy.char.capitalize function' to capitalize the first letter of our string. So let's begin with the syntax.

Syntax :

It accepts a string or an array of strings and returns the string with upper case letters as the first character. Let us try to grasp this with an example.

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5. Center Function

It is employed to center a string in a specific length or width. Padding is performed with the supplied fillchar, if none is provided, padding is done with whitespaces.

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6. Decode Function

The NumPy string function decode() deciphers the string using the registered encoding codec. It uses the standard string encoding of 'UTF-8' by default. Let's study its syntax :

Syntax :

It accepts an input that indicates how our failures should be handled in addition to the array and the encoding name. Consider the following example to help you understand it better.

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7. Encode Function

It is the inverse of the decode function and is used to encrypt our string. NumPy string encode() returns bytes object containing an encrypted version of the string. The default encoding is 'utf-8', and errors can be used to specify an alternative error-handling method. The standard for errors is 'strict', which means that encoding problems result in a UnicodeError.

Consider the following syntax :

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8. Expandtabs Function

NumPy string expandtabs() returns a duplicate of each string element with all tab characters substituted by one or more spaces. Let us now investigate its syntax.

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Consider the following example :

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9. Join Function

The string method in NumPy join() returns a string by combining all of the elements of a numpy string array that are partitioned by a string separator. First, let us take a closer look at its syntax.

Syntax :

Its parameter is a Numpy string array with a string separator.

For example :

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10. Left Justified Function

The string function ljust() in Python NumPy produces the string left justified in a length width specified by us. Padding is performed using the provided fillchar, if no fillchar is specified, the standard is a space. If the width is smaller than the string length, the original string is produced.

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11. Right Justified Function

It is comparable to the NumPy string function ljust. Whereas ljust() produces the left-justified text, the NumPy string function rjust() returns the right justified string in a string of length that we choose. The "width" parameter we supply to the rjust function essentially represents the string length we define. Padding is performed using the fillchar supplied; if none is specified, the standard is a space. The actual string is returned if the width is smaller than the string length. Let us now look at its syntax.

Syntax :

Let us learn about the Numpy rjust() function with an example.

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12. Lowercase Function

The lower() function in NumPy produces the lowercase string from a provided string. First, all capital letters are converted to lowercase. If no capital characters are found, the original string is returned. Let us look at the syntax now.

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Consider the following example.

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13. Uppercase Function

The upper() function of the NumPy char class produces the capitalized string from the provided string. All lowercase letters are converted to uppercase. Unless lowercase letters are found, the original string is returned. The upper() function of the NumPy char class has the following syntax :

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14. Strip Function

Based on the parameter defining the set of letters to be deleted, the NumPy strip() function eliminates letters from both the left and right sides. The string's starting and ending whitespaces are eliminated if the chars parameter is not specified. Let us examine its syntax

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15. Left Strip Function

Based on the string parameter supplied, the NumPy lstrip() function duplicates the string with the leading characters deleted. The lstrip() function strips letters from the left using the parameter that specifies the letters to be deleted. Let us now look at the syntax.

Syntax :

Let's learn about the Numpy lstrip() function with an example.

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16. Right Strip Function

The rstrip() function of the NumPy char module eliminates any trailing characters or characters at the end of a string, space is the default trailing character to remove. It takes two parameters : an array of text and char and a string defining the list of characters to be eliminated. If omitted or None, the chars option removes whitespace by default. The chars parameter is not a suffix. Rather, all of its value combinations are removed. Let us now look at its syntax.

Syntax :

Consider the following example:

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17. Partition Function

The NumPy partition() function looks for a given string and separates it into a tuple with three components. The segment before the supplied string is included in the first component. The provided string is contained in the second component. The component after the string is contained in the third component. Let us now examine its syntax before proceeding.

Syntax :

Let us examine the Numpy partition() method using an example.

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18. Replace Function

The replace() function in NumPy produces a replica of the string with the old substring replaced by the new substring. The original string remains unaltered. If the old substring cannot be retrieved, the replica of the actual string is returned. Let us now study its syntax before proceeding.

Syntax :

Let's learn about the Numpy replace() method with an illustration.

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19. Split Function

The NumPy split() function divides a string by the provided separator and produces a NumPy array object containing string elements. Any whitespace character is used as the separator by default. Let us now examine its syntax.

Syntax :

To further understand it, consider the following example.

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20. Right Split Function

Starting from the right, the NumPy rsplit() function separates a string into a list. When no maxsplit is supplied, this function provides the same value as NumPy split(). Whenever maxsplit is provided, the list will have the number of elements supplied plus one. Let's have a look at its syntax

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21. Split Lines Function

NumPy splitlines() separate a string at line breaks and produces a NumPy array object containing string elements. "\n" is the default separator. Let us now examine its syntax.

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For a better understanding, consider the example below.

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22. Swapcase Function

The NumPy Char class's swapcase() function turns alphabetic capital letters from a string to lowercase letters and lowercase alphabetic letters from the same input to capital letters. Let us now look at the syntax of NumPy's swapcase function.

Syntax :

Consider the following example,

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23. Title Function

The title() function of the NumPy Char class returns a string in which the initial character of every single word in our text is capital letters, exactly like a header or title. If the term includes either a number or a symbol as its initial letter, the following letter will be transformed into capital letters. Let us now examine the syntax of NumPy's title method.

Syntax :

For a better understanding, consider the example below.

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24. Translate Function

The translate() function of the NumPy char class returns a string in which each letter is assigned to its associated letter in the translation table (or a dictionary). The translation table is used by the translate() function to substitute characters in the provided string according to the mapping table/ dictionary. The character will not be replaced if the substitute character is not provided in the mapping dictionary/table. The translate() function has the following syntax :

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Consider the following example Code :

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25. Zfill Function

The zfill() function of the NumPy char class returns a duplicate of the string with a "0" character filled to the left side of the provided string. It requires length as well as an array of strings as an input. "width" specifies the length of the string returned by zfill() with "0" digits added to the left. The zfill() function in NumPy has the following syntax :

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For a better understanding, consider the example below.

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Convenience Classes in NumPy

As the name implies, convenience classes are NumPy library classes that may be used to work around NumPy strings. All of the functions stated above, for string operations on NumPy, such as adding two strings, capitalizing the string's content, changing the string's content, lowercasing the characters in the strings, trimming the extra space to the left or right of a string, and so on or be it extracting relevant information from a string, such as whether all of the letters in a string are alphanumeric or not, counting a single character in a string, finding a substring from a string, calculating the length of a string, and so on and finally, whether it be string comparisons, we were able to do since we used the convenience class to generate a NumPy object from the provided string. Let's look at some convenience classes supplied by the NumPy library.

1. numpy.char.array Class

To build a chararray, use the numpy.char.array class. Let us now look into its syntax.

Syntax :

Please visit this page to learn more about its parameter

Consider the following example :

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2. numpy.char.asarray Class

The numpy.char.array class is employed to transform the input to a chararray while only duplicating the data when necessary.

Syntax :

Consider the following example :

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3. numpy.char.chararray Class

The numpy.char.array class is employed to offer a simple view of arrays containing string and unicode data. Before proceeding, let us take a closer look at its syntax.

Syntax :

The following example will help you better grasp the numpy.char.chararray class.

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See Also

string information in numpy

string comparison in numpy