Pandas VS Flookup
Compare Pandas VS Flookup and see what are their differences
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
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Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Flookup
FuzzyWuzzy
Google Sheets + MonkeyLearn
SheetHacks by Polymer Search
Textalytic
SyncWith for Google Sheets
The Ultimate Google Sheet Toolkit
Logic Sheet
Flookup is lightweight data cleaning suite for Google Sheets. It can be used to:
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Flookup's answer:
Flookup's answer:
Flookup's answer:
Flookup was created out of necessity. I was part of a team working on a project that involved cleaning and standardising thousands of rows of data. This data was some of the "dirtiest" we had ever come across and the process of cleaning it usually took about a week for each team member to complete manually. It took a few attempts but, eventually, I was able to develop a usable version of Flookup... and its impact was so significant that our task times were reduced to an average of 30 minutes, with our error rate never exceeding 1% after that.
Flookup's answer:
Flookup's answer:
Flookup's answer:
Flookup features an intuitive set of functions and a finetuned fuzzy matching algorithm capable of tackling the most challenging and untidy datasets found online. It has excelled not only in Western projects but also in projects across Africa, South America and even those involving Asian languages like Chinese.
Flookup helps complete laborious fuzzy matching or lookup tasks quickly and efficiently. It is also the most affordable deduping solution online; useful for removing or highlighting duplicates from mailing lists, contacts and leads.
Based on our record, Pandas seems to be more popular. It has been mentiond 199 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
It's also possible for you to give a package an alias by using the as keyword. For instance, you could use the pandas package as pd like this:. - Source: dev.to / 27 days ago
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / about 2 months ago
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / about 1 month ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 3 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 6 months ago
NumPy - NumPy is the fundamental package for scientific computing with Python
FuzzyWuzzy - FuzzyWuzzy is a Fuzzy String Matching in Python that uses Levenshtein Distance to calculate the differences between sequences.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Google Sheets + MonkeyLearn - Power up your Google Sheets with text analysis
OpenCV - OpenCV is the world's biggest computer vision library
SheetHacks by Polymer Search - Discover the best tips & tricks for Google Sheets & Excel