Cocalc 03 10 Working With Strings Ipynb

One strength of Python is its relative ease in handling and manipulating string data. Pandas builds on this and provides a comprehensive set of vectorized string operations that are an important part of the type of munging required when working with (read: cleaning up) real-world data. In this chapter, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the internet. We saw in previous chapters how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example: This vectorization of operations simplifies the syntax of operating on arrays of data: we no longer have to worry about the size or shape of the array, but just about what operation we want...

For arrays of strings, NumPy does not provide such simple access, and thus you're stuck using a more verbose loop syntax: This is perhaps sufficient to work with some data, but it will break if there are any missing values, so this approach requires putting in extra checks: This kind of manual approach is not only verbose and inconvenient, it can be error-prone. This notebook demonstrates some basic string commands. combine several strings into one string by string(str1,str2) or str1 * str2. test if a string contains a specific substring

replace part of a string with something else split a string into a vector of words (and then to join them back into a string again) A string is a sequence of characters enclosed in either single ' or double " quotes. The type corresponding to strings is called str. Note that the pair of quotes delimiting a string do not appear on the screen when it is printed. 📝 If a string contains newline characters, it can be delimited by """ (triple double quotes).

This can be used to break up a longer string over several lines. 📝 A long string can also be created across several lines by enclosing it in parentheses, as follows: Note the extra spaces at the end of the first two lines; without them, subsequent lines would be concatenated without any separation. Loop through the following string printing out each character. Loop through the following string in reverse printing out each character. Hint: See Notebook 6 for how to reverse a string.

In Notebook 5 we introduced the len() function which returns the length of a string. Write your own code that counts the number characters in a string and then prints it out. Hint: You will need a tally variable to keep a count of the number of characters. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < Pivot Tables | Contents | Working with Time Series >

One strength of Python is its relative ease in handling and manipulating string data. Pandas builds on this and provides a comprehensive set of vectorized string operations that become an essential piece of the type of munging required when working with (read: cleaning up) real-world data. In this section, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the Internet. We saw in previous sections how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example: This vectorization of operations simplifies the syntax of operating on arrays of data: we no longer have to worry about the size or shape of the array, but just about what operation we want...

For arrays of strings, NumPy does not provide such simple access, and thus you're stuck using a more verbose loop syntax: This notebook demonstrates some basic string commands. of the (perhaps) most important commands. The next few cells show how to test, replace, split and sort strings. The next cell reads a file into one single string. It keeps the formatting (spaces, line breaks etc).

The next cell reads a file into a vector of strings: one string per line of the file. The second cell joins those lines into one string. During this lesson, you will learn the following: String Basics (indexing, slicing, membership, iterating) String Formatting (for print statements) Each character in the string has a position, called the index.

We can access each character in the string by its index position, using square brackets . Strings are used in Python to record text information, such as names. Strings in Python are actually a sequence, which basically means Python keeps track of every element in the string as a sequence. For example, Python understands the string "hello' to be a sequence of letters in a specific order. This means we will be able to use indexing to grab particular letters (like the first letter, or the last letter). This idea of a sequence is an important one in Python and we will touch upon it later on in the future.

In this lecture we'll learn about the following: To create a string in Python you need to use either single quotes or double quotes. For example: The reason for the error above is because the single quote in I'm stopped the string. You can use combinations of double and single quotes to get the complete statement.

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