thesis_pranathiiyer

replication-materials-pranathiiyer created by GitHub Classroom

https://github.com/macs30200-s22/thesis_pranathiiyer

Science Score: 44.0%

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  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
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  • Scientific vocabulary similarity
    Low similarity (9.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

replication-materials-pranathiiyer created by GitHub Classroom

Basic Info
  • Host: GitHub
  • Owner: macs30200-s22
  • Language: Python
  • Default Branch: main
  • Size: 23.3 MB
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  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 4 years ago · Last pushed about 2 years ago
Metadata Files
Readme Citation

README.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be12919b",
   "metadata": {},
   "source": [
    "The code and data in this repository are a preliminary step towards the final objective of understanding the structure, trends, relevance,and success of matrimonial advertisements in the Indian context, throught this project. While this repository currently contains advertisements for prospective brides and grooms from the year 2001 to 2009, and 2014, the final project will also have data for 2010, 2011, 2012, and 2013.These years are slightly difficult to scrape owing to changes in the website. The data for grooms can be found under ```grooms_data``` in the folder ```data```, and the data for brides can be found under ```brides_data``` in the folder ```data```.\n",
    "\n",
    "The code is written in Python 3.9.10 and all of its dependencies can be installed by running the following in the terminal (with the requirements.txt file included in this repository):\n",
    "\n",
    "                         pip install -r requirements.txt\n",
    "                         \n",
    "Then, you can import the analysis and plot_num_ads module located in this repository to reproduce the analysis in the project that this code supplements (in a Jupyter Notebook, like README.ipynb in this repository, or in any other Python script):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fcca8297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plot_num_ads"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d19e02",
   "metadata": {},
   "source": [
    "This module primarily plots the number of advertisements for brides and grooms across all years. This can help us understand how these ads and their volumnes have changed with respect to brides and grooms in the context of newspapers. Once you import the above module, you can use its plot function to reproduce the figure comparing number of advertisements across years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0138ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_num_ads.plot_fig()" ] }, { "cell_type": "markdown", "id": "0d431b09", "metadata": {}, "source": [ "### Findings:\n", "This shows that the number of advertisements for grooms and brides move in the same direction (this could also be a function of problems in scraping). Moreover, it shows that the number of advertisements for grooms peaked heavily after 2006. While the time would have to be examined (as to why this is observed only after 2006 and not before), there is anecdotal evidence that there has traditionally been more societal pressure for women to get married, and this finding supports this at a very rudimentary level. Of course, a more thorough qualitative analysis is required to make any claim." ] }, { "cell_type": "markdown", "id": "f29acbc5", "metadata": {}, "source": [ "You can then import the analysis module to delve deeper into some of the actual analysis of the project. This module produces analysis for a single data file. Hence, you could input any data file from the ```data``` folder using the commands below. The below functions are performed on the advertisements seeking brides in the year 2008. A similar analysis can be repreduced for all years." ] }, { "cell_type": "code", "execution_count": 1, "id": "b59f69a4", "metadata": {}, "outputs": [], "source": [ "import analysis" ] }, { "cell_type": "code", "execution_count": 12, "id": "d5459c14", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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wordcount
0nurse/doctor1
1mba2546
2ma255
3kashyap300
4rajput1161
.........
9140processing1
914100301
9142accoutancy1
914311:501
9144jagdeepkumar1
\n", "

9145 rows × 2 columns

\n", "
" ], "text/plain": [ " word count\n", "0 nurse/doctor 1\n", "1 mba 2546\n", "2 ma 255\n", "3 kashyap 300\n", "4 rajput 1161\n", "... ... ...\n", "9140 processing 1\n", "9141 0030 1\n", "9142 accoutancy 1\n", "9143 11:50 1\n", "9144 jagdeepkumar 1\n", "\n", "[9145 rows x 2 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = analysis.word_to_count('data/brides_data/brides-wanted_2008.csv')\n", "analysis.counts_data(data)\n" ] }, { "cell_type": "markdown", "id": "084ee6ed", "metadata": {}, "source": [ "### Findings\n", "While a lot of junk words appear, upon sorting this table we observe that words such as mba, ma, kashyap (a community), rajput (a community) appear several times. This finding is an indication of how these advertisements can be explained along dimensions of occupation, caste, and gender." ] }, { "cell_type": "markdown", "id": "535cde07", "metadata": {}, "source": [ "You can also run the below commands to reproduce the other plots in the project.\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "9b37e7c4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "analysis.final_func('data/brides_data/brides-wanted_2008.csv')" ] }, { "cell_type": "markdown", "id": "7e902f45", "metadata": {}, "source": [ "The above commands can easily be run for any of the files from the data folder to reproduce other analyses.\n" ] }, { "cell_type": "markdown", "id": "fb3d1606", "metadata": {}, "source": [ "### Findings\n", "These plots are very informative. They tell us that Sikh, which is a religious community in northern india, is the most frequently used proper noun, and mba ia the most used three letter word. This gives another strong signal towards the heavy use of caste and occupation in these ads. Moreover, it would be interesting to explore why 'usa' is accuring almost as frequently as india." ] }, { "cell_type": "code", "execution_count": null, "id": "dfd7412f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }

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Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
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cff-version: 1.2.0
title: Deciphering Indian matrimonial advertisements
message: >-
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type: software
authors:
  - given-names: Pranathi
    family-names: Iyer
    email: 'pranathiiyer@uchicago,.edu'
    affiliation: Student

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Dependencies

requirements.txt pypi
  • gensim ==4.1.2
  • matplotlib ==3.5.1
  • nltk ==3.6.1
  • numpy ==1.20.1
  • pandas ==1.2.4
  • seaborn ==0.11.2
  • sklearn =1.0.2
  • spacy ==3.2.1