fuzzywuzzy | 易学教程

python fuzzywuzzy's process.extract(): how does it work?

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问题 I am trying to understand how the python module fuzzywuzzy's function process.extract() work? I mainly read about the fuzzywuzzy package here: http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/, which is a great post explanining different scenarios when trying to do fuzzy matching. They discussed several scenarios for Partial String Similarity: 1) Out Of Order 2) Token Sort 3) Token Set And then, from this post: https://pathindependence.wordpress.com/2015/10/31/tutorial

少有人知的python数据科学库

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Python是门很神奇的语言，历经时间和实践检验，受到开发者和数据科学家一致好评，目前已经是全世界发展最好的编程语言之一。简单易用，完整而庞大的第三方库生态圈，使得Python成为编程小白和高级工程师的首选。在本文中，我们会分享不同于市面上的python数据科学库（如numpy、padnas、scikit-learn、matplotlib等），尽管这些库很棒，但是其他还有一些不为人知，但同样优秀的库需要我们去探索去学习。 1. Wget 从网络上获取数据被认为是数据科学家的必备基本技能，而Wget是一套非交互的基于命令行的文件下载库。ta支持HTTP、HTTPS和FTP协议，也支持使用IP代理。因为ta是非交互的，即使用户未登录，ta也可以在后台运行。所以下次如果你想从网络上下载一个页面，Wget可以帮到你哦。安装 pip isntall wget 用例 import wget url = 'http://www.futurecrew.com/skaven/song_files/mp3/razorback.mp3' filename = wget.download(url) Run and output 100% [................................................] 3841532 / 3841532 filename

Unable to detect gibberish names using Python

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问题 I am trying to build Python model that could classify account names as either legitimate or gibberish. Capitalization is not important in this particular case as some legitimate account names could be comprised of all upper-case or all lower-case letters. Disclaimer: this is just a internal research/experiment and no real action will be taken on the classifier outcome. In my particular, there are 2 possible characteristics that can reveal an account name as suspicious, gibberish or both:

Using Process.extract in fuzzywuzzy and the all max similar choices

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问题 I have the following input- query = 'Total replenishment lead time (in workdays)' choices = ['PLANNING_TIME_FENCE_CODE', 'BUILD_IN_WIP_FLAG','Lead_time_planning', 'Total replenishment lead time 1', 'Total replenishment lead time 2'] print(process.extract(query, choices)) I get the following output- [('Total replenishment lead time 1', 92), ('Total replenishment lead time 2', 92), ('Lead_time_planning', 50), ('PLANNING_TIME_FENCE_CODE', 36), ('BUILD_IN_WIP_FLAG', 26)] But I just want all the

Python 中有哪些让人眼前一亮的工具?

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作为最流行的编程语言之一，Python 拥有大量优秀的库，如Pandas、Numpy、Matplotlib、SciPy 等，它们极大的提升了开发速度。在这篇文章中，我给大家分享一些让人眼前一亮的库，这些库不仅有趣，而且非常实用，同时也展示 Python 社区的蓬勃发展。 1、Bashplotlib 老实说，当我第一次看到这个库时，我质疑为什么人们可能需要这个呢？Bashplotlib 是一个 Python 库，使我们能够在命令行粗旷的环境中绘制数据。很快我意识到，如果我们没有可用的GUI时，它可能会很有用。这种情况可能不会那么频繁，但它却是一个非常有趣的Python库。安装 pip install bashplotlib 让我们看看一些例子此外，还可以从文本文件的散点图中绘制数据 2、PrettyTable 我刚刚介绍的 Bashplotlib 用于在命令行环境中绘制数据，而 PrettyTable 则用于漂亮的格式输出表。安装 pip install prettytable 让我们看个例子 from prettytable import PrettyTable table = PrettyTable() table.field_names = [ 'Name' , 'Age' , 'City' ] table.add_row([ "Alice" , 20 ,

Group by fuzzy string matches with fuzzywuzzy and groupby

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来源： https://stackoverflow.com/questions/62025733/group-by-fuzzy-string-matches-with-fuzzywuzzy-and-groupby

Group by fuzzy string matches with fuzzywuzzy and groupby

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来源： https://stackoverflow.com/questions/62025733/group-by-fuzzy-string-matches-with-fuzzywuzzy-and-groupby

Fuzzy Wuzzy String Matching on 2 Large Data Sets Based on a Condition - python

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来源： https://stackoverflow.com/questions/42847396/fuzzy-wuzzy-string-matching-on-2-large-data-sets-based-on-a-condition-python

Fuzzy Wuzzy String Matching on 2 Large Data Sets Based on a Condition - python

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来源： https://stackoverflow.com/questions/42847396/fuzzy-wuzzy-string-matching-on-2-large-data-sets-based-on-a-condition-python

How to merge two CSV files by value in column using pandas PYTHON

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问题 I have 2 csv files price and performance. Here is the data layout of each Price: Performance: I import them into python using: import pandas as pd price = pd.read_csv("cpu.csv") performance = pd.read_csv("geekbench.csv") This works as intended, however I am unsure on how to create a new csv file with matches between Price[brand + model] and Performance[name] I want to take: Cores, tdp and price from Price Score, multicore_score and name from Performance Create a new csv file using these