Remove duplicates from two Pandas DataFrame

During my research on Indeed for classifying the job types, I found an issue regarding multiple labels for the same job i.e. temporary and part-time are assigned to the same jobs. I used the following code to remove common descriptions of jobs with different labels.

import pandas as pd
part_time = pd.read_csv("part_time.csv", index_col=0)
temporary = pd.read_csv("temporary.csv", index_col=0)
# find common jobs using description column with isin() function.
# A intersection B
A = part_time[part_time.description.isin(temporary.description)]
# remove common elements from both part_time and temporary jobs.
# temporary - A
# part_time - A
temporary = temporary[~temporary.description.isin(A)]
part_time = part_time[~part_time.description.isin(A)]
# now concat these two data frames and save.
total = pd.concat([temporary, part_time])
total.to_csv("indeed_jobs.csv", index=False)

Hoping it will help those who have the same issue.

Comments

Text Summarization for Urdu: Part 1

Text Summarization is an important task for large documents to get the idea of the document. There are two main summarization techniques used in NLP for text summarization. Extractive Text Summarization : This approach's name is self-explanatory. Most important sentences or phrases are extracted from the original text and a short summary provided with these important sentences. See the figure for the explanation. Abstractive Text Summarization : This approach uses more advanced deep learning techniques to generate new sentences by learning from the original text. It is a complex task and requires heavy computing power such as GPU. Let's dive into the code for generating the text summary. I'm using Arabic as a parameter because the contributor did an excellent job of handling a lot of things like stemming, Urdu characters support, etc. from summa.summarizer import summarize text = """ اسلام آباد : صدر مملکت ڈاکٹر عارف علوی بھی کورونا وائرس کا شکار ہوگئے۔ سما

Urdu Tokenization using SpaCy

SpaCy is an NLP library which supports many languages. It’s fast and has DNNs build in for performing many NLP tasks such as POS and NER. It has extensive support and good documentation. It is fast and provides GPU support and can be integrated with Tensorflow, PyTorch, Scikit-Learn, etc. SpaCy provides the easiest way to add any language support. A new language can be added by simply following Adding Languages article. I’ve added the Urdu language with dictionary-based lemmatization, lexical support and stop words( Urdu ). Here is how you can use the tokenizer for the Urdu language. First, install SpaCy . $ pip install spacy Now import spacy and create a blank object with support of Urdu language. I’m using blank because there is no proper model available for Urdu yet, but tokenization support available. import spacy nlp = spacy.blank('ur') doc = nlp(" کچھ ممالک ایسے بھی ہیں جہاں اس برس روزے کا دورانیہ 20 گھنٹے تک ہے۔") print("Urdu Tokeniza

How to build Urdu language model in SpaCy

Urdu alphabets SpaCy is the most commonly used NLP library for building NLP and chatbot apps. The Urdu language does not have resources for building chatbot and NLP apps. Most of the tools are proprietary or data is licensed. After adding the support for the Urdu language, I'm going to show you how to build an Urdu model which can be used for multiple applications such as word and sentence similarity, chatbots, knowledgebase, etc. Follow the steps to build the model. Step 1: Build word frequencies for Urdu. I've created a script that can be used to build word frequencies. There are multiple resources available for building word frequencies, you can choose whatever you want but the format should be like this. frequency document_id word Here is the script I'm using to build word frequencies for SpaCy. from __future__ import unicode_literals import string import codecs import glob from collections import Counter import re import plac from multiprocessing

UrduNLP

Search This Blog