encode

How to display a categorical dataframe in a pairplot

How to display a categorical dataframe in a pairplot Question: I am getting the following error: No variables found for grid columns? In the console it prints the dataframe ok, but when I want to display it in seaborn, it doesn’t work. %matplotlib inline import seaborn as sns from ipywidgets import interact import matplotlib.pyplot as …

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How to label-encode comma separated text in a Dataframe column in Python?

How to label-encode comma separated text in a Dataframe column in Python? Question: I have dataframe(df) that looks like something like this: Shape Weight Colour Circle 5 Blue, Red Square 7 Yellow, Red Triangle 8 Blue, Yellow, Red Rectangle 10 Green I would like to label encode the "Colour" column so that the dataframe looks …

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Emoji to UTF-8 converter

Emoji converter Question: How can I convert this string in python3 ‘ ‘ into this ‘%F0%9F%91%80%F0%9F%A5%B6’ ? Would like to do this with any emojis Asked By: rdy4sv || Source Answers: If percentage encoding is what you are after, urllib module in Python could help. Ref: https://docs.python.org/3/library/urllib.parse.html#urllib.parse.quote Python 3: text = ‘ ‘ import urllib.parse …

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How to use BeautifulSoup to extract the element and decode to human language

How to use BeautifulSoup to extract the element and decode to human language Question: <div class="read-content j_readContent" id="j_721885204"> <script id="enContentLoader"> window.enContent = 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" window.cuChapterId = "721885204" window.cES = "2" window.fEnS = "1" var el = document.querySelector("#enContentLoader") el.parentNode.removeChild(el) </script> </div> I have used below two lines of code to get the <class ‘bs4.element.Tag’> above. soup = …

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Why print returns \, not a escape character in Python

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python encoding chinese to special character

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Python HTTPSConnection encryption and Name or service not found error

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Total answers: 2

Python encoded message with HMAC-SHA256

Python encoded message with HMAC-SHA256 Question: I try to encoded message with HMAC-SHA256 in python according to instructions import hmac import hashlib nonce = 1234 customer_id = 123232 api_key = 2342342348273482374343434 API_SECRET = 892374928347928347283473 message = nonce + customer_id + api_key signature = hmac.new( API_SECRET, msg=message, digestmod=hashlib.sha256 ).hexdigest().upper() but I get this Traceback (most recent …

Total answers: 2

Delete every non utf-8 symbols from string

Delete every non utf-8 symbols from string Question: I have a big amount of files and parser. What I Have to do is strip all non utf-8 symbols and put data in mongodb. Currently I have code like this. with open(fname, “r”) as fp: for line in fp: line = line.strip() line = line.decode(‘utf-8’, ‘ignore’) …

Total answers: 4