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This article is about how pandas uses str objects. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

The use of str objects

Series data type: str string

# define a Series sequence > addr = pd.Series ([... 'Washington, D.C. 20003. 'Brooklyn, NY 11211-1755. 'Omaha, NE 68154. 'Pittsburgh, PA 15211. ]) > addr.str.upper () 0 WASHINGTON, D.C. 200031 BROOKLYN, NY 11211-17552 OMAHA, NE 681543 PITTSBURGH, PA 15211dtype: object > addr.str.count (r'\ d') 051 92 53 5dtype: int64 copy Code

There are two method descriptions for the above str object:

Series.str.upper: uppercase all strings in Series

Series.str.count: counts the number of all strings in Series

In fact, it is not difficult to find that the use of this usage is very similar to string manipulation in Python. Yes, you can do the same in pandas, except that you are manipulating an entire column of string data. Still based on the above dataset, look at another operation of it:

> regex = (r'(? P [A-Za-z] +),'# one or more letters. R'(? P [Amurz] {2})'# two capital letters. R'(? P\ d {5} (?:-d {4})) # four optional extended digits. > addr.str.replace ('.',') .str.extract (regex) city state zip0 Washington DC 200031 Brooklyn NY 11211-17552 Omaha NE 681543 Pittsburgh PA 15211 copy code

There are two method descriptions for the above str object:

Series.str.replace: replaces the specified string in Series

Series.str.extract: extracting data information from a string through regular expressions

This usage is a little complicated, because it is obvious that it is a chain usage. Replace "." with "" through replace, which is empty, and then use three regular expressions (corresponding to city,state,zip) to extract the data through extract, and change the original Series data structure to DataFrame data structure.

Of course, in addition to the above usage, the common properties and methods are .rstrip, .strip, split, and so on. Let's take a look at the complete list of str properties with the following code:

> > [i for i in dir (pd.Series.str) if not i.startswith ('_')] ['capitalize',' cat', 'center',' contains', 'count',' decode', 'encode',' endswith', 'extract',' extractall', 'find',' findall', 'get',' get_dummies', 'index',' isalnum', 'isalpha',' isdecimal', 'isdigit',' islower', 'isnumeric' 'isspace', 'istitle',' isupper', 'join',' len', 'ljust',' lower', 'lstrip',' match', 'normalize',' pad', 'partition',' repeat', 'replace',' rfind', 'rindex',' rjust', 'rpartition',' rsplit', 'rstrip',' slice', 'slice_replace',' split', 'startswith',' strip', 'swapcase' 'title',' translate', 'upper',' wrap', 'zfill'] Thank you for reading! This is the end of this article on "how pandas uses str objects". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it for more people to see!

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