Encoding in python

If your name is José, you are in good company. José is a very common name. Yet, when dealing with text files, sometimes José will appear as José, or other mangled array of symbols and letters. Or, in some cases, Python will fail to convert the file to text at all, complaining with a UnicodeDecodeError. Unless only dealing with numerical data, any data jockey or software developer needs to face the problem of encoding and decoding characters. Ever heard or asked the question, "why do we need character encodings?" Indeed, character encodings cause heaps of confusion for software developer and end user alike. But ponder for a moment, and we all have to admit that the "do we need character encoding?" question is nonsensical. If you are dealing with text and computers, then there has to be encoding. The letter "a", for instance, must be recorded and processed like everything else: as a byte (or multiple bytes). Most likely (but not necessarily), your text editor or terminal will encode "a" as the number 97. Without the encoding, you aren't dealing with text and strings. Just bytes. Think of character encoding like a top secret substitution cipher, in which every letter has a corresponding number when encoded. No one will ever figure it out! Enter fullscreen mode Exit fullscreen mode The above 4 character codes are hexadecimal: 73, 70, 61, 6d (the escape code \x is Python's way of designating a hexadecimal literal character code). In decimal, that's 115, 112, 97, and 109. Try t...

I have this string that has been decoded from Quoted-printable to ISO-8859-1 with the email module. This gives me strings like "\xC4pple" which would correspond to "Äpple" (Apple in Swedish). However, I can't convert those strings to UTF-8. >>> apple = "\xC4pple" >>> apple '\xc4pple' >>> apple.encode("UTF-8") Traceback (most recent call last): File "", line 1, in UnicodeDecodeError: 'ascii' codec can't decode byte 0xc4 in position 0: ordinal not in range(128) What should I do? This is a common problem, so here's a relatively thorough illustration. For non-unicode strings (i.e. those without u prefix like u'\xc4pple'), one must decode from the native encoding ( iso8859-1/ latin1, unless sys.setdefaultencoding function) to unicode, then encode to a character set that can display the characters you wish, in this case I'd recommend UTF-8. First, here is a handy utility function that'll help illuminate the patterns of Python 2.7 string and unicode: >>> def tell_me_about(s): return (type(s), s) A plain string >>> v = "\xC4pple" # iso-8859-1 aka latin1 encoded string >>> tell_me_about(v) (, '\xc4pple') >>> v '\xc4pple' # representation in memory >>> print v ?pple # map the iso-8859-1 in-memory to iso-8859-1 chars # note that '\xc4' has no representation in iso-8859-1, # so is printed as "?". Decoding a iso8859-1 string - convert plain string to unicode >>> uv = v.decode("iso-8859-1") >>> uv u'\xc4pple' # decoding iso-8859-1 becomes unicode, in memory >>> tell_me_about(uv) (, u'\...

• sklearn.preprocessing.OneHotEncoder • OneHotEncoder • OneHotEncoder.fit • OneHotEncoder.fit_transform • OneHotEncoder.get_feature_names_out • OneHotEncoder.get_params • OneHotEncoder.infrequent_categories_ • OneHotEncoder.inverse_transform • OneHotEncoder.set_output • OneHotEncoder.set_params • OneHotEncoder.transform • sklearn.preprocessing.OneHotEncoder sklearn.preprocessing.OneHotEncoder class sklearn.preprocessing. OneHotEncoder ( *, categories='auto', drop=None, sparse='deprecated', sparse_output=True, dtype=, handle_unknown='error', min_frequency=None, max_categories=None ) [source] Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka ‘one-of-K’ or ‘dummy’) encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the sparse_output parameter) By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the categories manually. This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels. Note: a one-hot encoding of y labels should use a LabelBinarizer instead. Read more in the User Guide. Parameters : categories ‘auto’ or a list of array-like, default=’auto’ Categories ...

Warning Be cautious when parsing JSON data from untrusted sources. A malicious JSON string may cause the decoder to consume considerable CPU and memory resources. Limiting the size of data to be parsed is recommended. json exposes an API familiar to users of the standard library marshal and pickle modules. Encoding basic Python object hierarchies: >>> import json >>> json . dumps ([ 'foo' , >>> from io import StringIO >>> io = StringIO () >>> json . dump ([ 'streaming API' ], io ) >>> io . getvalue () '["streaming API"]' Compact encoding: >>> import json >>> json . loads ( '["foo", ] >>> json . loads ( '" \\ "foo \\ bar"' ) '"foo\x08ar' >>> from io import StringIO >>> io = StringIO ( '["streaming API"]' ) >>> json . load ( io ) ['streaming API'] Specializing JSON object decoding: >>> import json >>> def as_complex ( dct ): ... if '__complex__' in dct : ... return complex ( dct [ 'real' ], dct [ 'imag' ]) ... return dct ... >>> json . loads ( '' , ... object_hook = as_complex ) (1+2j) >>> import decimal >>> json . loads ( '1.1' , parse_float = decimal . Decimal ) Decimal('1.1') Extending JSONEncoder: >>> import json >>> class ComplexEncoder ( json . JSONEncoder ): ... def default ( self , obj ): ... if isinstance ( obj , complex ): ... return [ obj . real , obj . imag ] ... # Let the base class default method raise the TypeError ... return json . JSONEncoder . default ( self , obj ) ... >>> json . dumps ( 2 + 1 j , cls = ComplexEncoder ) '[2.0, 1.0]' >>> ComplexEncoder () ...

Most real-life datasets we encounter during our data science project development have columns of mixed data type. These datasets consist of both Gender column with categorical elements like Male and Female. These labels have no specific order of preference and also since the data is string labels, machine learning models misinterpreted that there is some sort of hierarchy in them. One approach to solve this problem can be label encoding where we will assign a numerical value to these labels for example Male and Female mapped to 0 and 1. But this can add bias in our model as it will start giving higher preference to the Female parameter as 1>0 but ideally, both labels are equally important in the dataset. To deal with this issue we will use the One Hot Encoding technique. • It allows the use of categorical variables in models that require numerical input. • It can improve model performance by providing more information to the model about the categorical variable. • It can help to avoid the problem of ordinality, which can occur when a categorical variable has a natural ordering (e.g. “small”, “medium”, “large”). The disadvantages of using one hot encoding include: • It can lead to increased dimensionality, as a separate column is created for each category in the variable. This can make the model more complex and slow to train. • It can lead to sparse data, as most observations will have a value of 0 in most of the one-hot encoded columns. • It can lead to overfitting, espec...

I'm having some brain failure in understanding reading and writing text to a file (Python 2.4). # The string, which has an a-acute in it. ss = u'Capit\xe1n' ss8 = ss.encode('utf8') repr(ss), repr(ss8) ("u'Capit\xe1n'", "'Capit\xc3\xa1n'") print ss, ss8 print >> open('f1','w'), ss8 >>> file('f1').read() 'Capit\xc3\xa1n\n' So I type in Capit\xc3\xa1n into my favorite editor, in file f2. Then: >>> open('f1').read() 'Capit\xc3\xa1n\n' >>> open('f2').read() 'Capit\\xc3\\xa1n\n' >>> open('f1').read().decode('utf8') u'Capit\xe1n\n' >>> open('f2').read().decode('utf8') u'Capit\\xc3\\xa1n\n' What am I not understanding here? Clearly there is some vital bit of magic (or good sense) that I'm missing. What does one type into text files to get proper conversions? What I'm truly failing to grok here, is what the point of the UTF-8 representation is, if you can't actually get Python to recognize it, when it comes from outside. Maybe I should just JSON dump the string, and use that instead, since that has an asciiable representation! More to the point, is there an ASCII representation of this Unicode object that Python will recognize and decode, when coming in from a file? If so, how do I get it? >>> print simplejson.dumps(ss) '"Capit\u00e1n"' >>> print >> file('f3','w'), simplejson.dumps(ss) >>> simplejson.load(open('f3')) u'Capit\xe1n' Rather than mess with .encode and .decode, specify the encoding when opening the file. The io module, added in Python 2.6, provides an io.open function, ...

Unicode HOWTO Release 1.12 This HOWTO discusses Python’s support for the Unicode specification for representing textual data, and explains various problems that people commonly encounter when trying to work with Unicode. Introduction to Unicode Definitions Today’s programs need to be able to handle a wide variety of characters. Applications are often internationalized to display messages and output in a variety of user-selectable languages; the same program might need to output an error message in English, French, Japanese, Hebrew, or Russian. Web content can be written in any of these languages and can also include a variety of emoji symbols. Python’s string type uses the Unicode Standard for representing characters, which lets Python programs work with all these different possible characters. Unicode ( A character is the smallest possible component of a text. ‘A’, ‘B’, ‘C’, etc., are all different characters. So are ‘È’ and ‘Í’. Characters vary depending on the language or context you’re talking about. For example, there’s a character for “Roman Numeral One”, ‘Ⅰ’, that’s separate from the uppercase letter ‘I’. They’ll usually look the same, but these are two different characters that have different meanings. The Unicode standard describes how characters are represented by code points. A code point value is an integer in the range 0 to 0x10FFFF (about 1.1 million values, the U+265E to mean the character with value 0x265e (9,822 in decimal). The Unicode standard contains a...

Python Tutorials → In-depth articles and video courses Learning Paths → Guided study plans for accelerated learning Quizzes → Check your learning progress Browse Topics → Focus on a specific area or skill level Community Chat → Learn with other Pythonistas Office Hours → Live Q&A calls with Python experts Podcast → Hear what’s new in the world of Python Books → Round out your knowledge and learn offline Unlock All Content → • Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Working With JSON Data in Python Since its inception, Luckily, this is a pretty common task, and—as with most common tasks—Python makes it almost disgustingly easy. Have no fear, fellow Pythoneers and Pythonistas. This one’s gonna be a breeze! So, we use JSON to store and exchange data? Yup, you got it! It’s nothing more than a standardized format the community uses to pass data around. Keep in mind, JSON isn’t the only format available for this kind of work, but As you can see, JSON supports primitive types, like Wait, that looks like a Python dictionary! I know, right? It’s pretty much universal object notation at this point, but I don’t think UON rolls off the tongue quite as nicely. Feel free to discuss alternatives in the comments. Whew! You survived your first encounter with some wild JSON. Now you just need to learn how to tame it. Python Supports JSON Natively! Python comes with a built-...