Scraping and analysis of leetcode-compensations page.

Overview

Leetcode compensations report

Scraping and analysis of leetcode-compensations page.

Salary Distribution Salary

Report

INDIA : 5th Jan 2019 - 5th Aug 2021 / fixed salary

INDIA : 5th Jan 2019 - 5th Aug 2021 / fixed salary, dark mode

INDIA : 5th Jan 2019 - 5th Aug 2021 / total salary

INDIA : 5th Jan 2019 - 5th Aug 2021 / total salary, dark mode

Directory structure

  • data
    • imgs - images for reports
    • logs - scraping logs
    • mappings - standardized company, location and title mappings as well as unmapped entities
    • meta - meta information for the posts like post_id, date, title, href.
    • out - data from info.all_info.get_clean_records_for_india()
    • posts - text from the post
    • reports - salary analysis by companies, titles and experience
  • info - functions to posts data(along with the standardized entities) in a tabular format
  • leetcode - scraper
  • utils - constants and helper methods

Setup

  1. Clone the repo.
  2. Put the chromedriver in the utils directory.
  3. Setup virual enviroment python -m venv leetcode.
  4. Install necessary packages pip install -r requirements.txt.
  5. To create the reports npm install vega-lite vega-cli canvas(needed to save altair plots).

Scraping

$ export PTYHONPATH=<project_directory>
$ python leetcode/posts_meta.py --till_date 2021/08/03

# sample output
2021-08-03 19:36:07.474 | INFO     | __main__:<module>:48 - page no: 1 | # posts: 15
$ python leetcode/posts.py

# sample output
2021-08-03 19:36:25.997 | INFO     | __main__:<module>:45 - post_id: 1380805 done!
2021-08-03 19:36:28.995 | INFO     | __main__:<module>:45 - post_id: 1380646 done!
2021-08-03 19:36:31.631 | INFO     | __main__:<module>:45 - post_id: 1380542 done!
2021-08-03 19:36:34.727 | INFO     | __main__:<module>:45 - post_id: 1380068 done!
2021-08-03 19:36:37.280 | INFO     | __main__:<module>:45 - post_id: 1379990 done!
2021-08-03 19:36:40.509 | INFO     | __main__:<module>:45 - post_id: 1379903 done!
2021-08-03 19:36:41.096 | WARNING  | __main__:<module>:34 - sleeping extra for post_id: 1379487
2021-08-03 19:36:44.530 | INFO     | __main__:<module>:45 - post_id: 1379487 done!
2021-08-03 19:36:47.115 | INFO     | __main__:<module>:45 - post_id: 1379208 done!
2021-08-03 19:36:49.660 | INFO     | __main__:<module>:45 - post_id: 1378689 done!
2021-08-03 19:36:50.470 | WARNING  | __main__:<module>:34 - sleeping extra for post_id: 1378620
2021-08-03 19:36:53.866 | INFO     | __main__:<module>:45 - post_id: 1378620 done!
2021-08-03 19:36:57.203 | INFO     | __main__:<module>:45 - post_id: 1378334 done!
2021-08-03 19:37:00.570 | INFO     | __main__:<module>:45 - post_id: 1378288 done!
2021-08-03 19:37:03.226 | INFO     | __main__:<module>:45 - post_id: 1378181 done!
2021-08-03 19:37:05.895 | INFO     | __main__:<module>:45 - post_id: 1378113 done!

Report DataFrame

$ ipython

In [1]: from info.all_info import get_clean_records_for_india                                                               
In [2]: df = get_clean_records_for_india()                                                                                  
2021-08-04 15:47:11.615 | INFO     | info.all_info:get_raw_records:95 - n records: 4134
2021-08-04 15:47:11.616 | WARNING  | info.all_info:get_raw_records:97 - missing post_ids: ['1347044', '1193859', '1208031', '1352074', '1308645', '1206533', '1309603', '1308672', '1271172', '214751', '1317751', '1342147', '1308728', '1138584']
2021-08-04 15:47:11.696 | WARNING  | info.all_info:_save_unmapped_labels:54 - 35 unmapped company saved
2021-08-04 15:47:11.705 | WARNING  | info.all_info:_save_unmapped_labels:54 - 353 unmapped title saved
2021-08-04 15:47:11.708 | WARNING  | info.all_info:get_clean_records_for_india:122 - 1779 rows dropped(location!=india)
2021-08-04 15:47:11.709 | WARNING  | info.all_info:get_clean_records_for_india:128 - 385 rows dropped(incomplete info)
2021-08-04 15:47:11.710 | WARNING  | info.all_info:get_clean_records_for_india:134 - 7 rows dropped(internships)
In [3]: df.shape                                                                                                            
Out[3]: (1963, 14)

Report

$ python reports/plots.py # generate fixed comp. plots
$ python reports/report.py # fixed comp.
$ python reports/report_dark.py # fixed comp., dark mode

$ python reports/plots_tc.py # generate total comp. plots
$ python reports/report_tc.py # total comp.
$ python reports/report_dark.py # total comp., dark mode

Samples

title : Flipkart | Software Development Engineer-1 | Bangalore
url : https://leetcode.com/discuss/compensation/834212/Flipkart-or-Software-Development-Engineer-1-or-Bangalore
company : flipkart
title : sde 1
yoe : 0.0 years
salary : ₹ 1800000.0
location : bangalore
post Education: B.Tech from NIT (2021 passout) Years of Experience: 0 Prior Experience: Fresher Date of the Offer: Aug 2020 Company: Flipkart Title/Level: Software Development Engineer-1 Location: Bangalore Salary: INR 18,00,000 Performance Incentive: INR 1,80,000 (10% of base pay) ESOPs: 48 units => INR 5,07,734 (vested over 4 years. 25% each year) Relocation Reimbursement: INR 40,000 Telephone Reimbursement: INR 12,000 Home Broadband Reimbursement: INR 12,000 Gratuity: INR 38,961 Insurance: INR 27,000 Other Benefits: INR 40,000 (15 days accomodation + travel) (this is different from the relocation reimbursement) Total comp (Salary + Bonus + Stock): Total CTC: INR 26,57,695; First year: INR 22,76,895 Other details: Standard Offer for On-Campus Hire Allowed Branches: B.Tech CSE/IT (6.0 CGPA & above) Process consisted of Coding test & 3 rounds of interviews. I don't remember questions exactly. But they vary from topics such as Graph(Topological Sort, Bi-Partite Graph), Trie based questions, DP based questions both recursive and dp approach, trees, Backtracking.

title : Cloudera | SSE | Bangalore | 2019
url : https://leetcode.com/discuss/compensation/388432/Cloudera-or-SSE-or-Bangalore-or-2019
company : cloudera
title : sde 2
yoe : 2.5 years
salary : ₹ 2800000.0
location : bangalore
post Education: MTech from Tier 1 College Years of Experience: 2.5 Prior Experience: SDE at Flipkart Date of the Offer: Sept 10, 2019 Company: Cloudera Title/Level: Senior Software Engineer (SSE) Location: Bangalore, India Salary: Rs 28,00,000 Bonus: Rs 2,80,000 (10 % of base) PF & Gratuity: Rs 1,88,272 Stock bonus: 5000 units over 4 years ($9 per unit) Other Benefits: Rs 4,00,000 (Health, Term Life and Personal Accident Insurance, Annual Medical Health Checkup, Transportation, Education Reimbursement) Total comp (Salary + Bonus + Stock): Rs 4070572

title : Amadeus Labs | MTS | Bengaluru
url : https://leetcode.com/discuss/compensation/1109046/Amadeus-Labs-or-MTS-or-Bengaluru
company : amadeus labs
title : mts 1
yoe : 7.0 years
salary : ₹ 1700000.0
location : bangalore
post Education: B.Tech. in ECE Years of Experience: 7 Prior Experience: Worked at few MNCs Date of the Offer: Jan 2021 Company: Amadeus Labs Title/Level: Member of Technical Staff Location: Bengaluru, India Salary: ₹ 1,700,000 Signing Bonus: ₹ 50,000 Stock bonus: None Bonus: 137,000 Total comp (Salary + Bonus + Stock): ~₹1,887,000 Benefits: Employee and family Insurance

Owner
utsav
Lead MLE @ freshworks
utsav
A Python package for modular causal inference analysis and model evaluations

Causal Inference 360 A Python package for inferring causal effects from observational data. Description Causal inference analysis enables estimating t

International Business Machines 506 Dec 19, 2022
Driver Analysis with Factors and Forests: An Automated Data Science Tool using Python

Driver Analysis with Factors and Forests: An Automated Data Science Tool using Python 📊

Thomas 2 May 26, 2022
Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.

Stock Statistics/Indicators Calculation Helper VERSION: 0.3.2 Introduction Supply a wrapper StockDataFrame based on the pandas.DataFrame with inline s

Cedric Zhuang 1.1k Dec 28, 2022
Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which he recommends to buy. We will use this data to build a portfolio

Backtesting the "Cramer Effect" & Recommendations from Cramer Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which

Gábor Vecsei 12 Aug 30, 2022
Flenser is a simple, minimal, automated exploratory data analysis tool.

Flenser Have you ever been handed a dataset you've never seen before? Flenser is a simple, minimal, automated exploratory data analysis tool. It runs

John McCambridge 79 Sep 20, 2022
signac-flow - manage workflows with signac

signac-flow - manage workflows with signac The signac framework helps users manage and scale file-based workflows, facilitating data reuse, sharing, a

Glotzer Group 44 Oct 14, 2022
Fit models to your data in Python with Sherpa.

Table of Contents Sherpa License How To Install Sherpa Using Anaconda Using pip Building from source History Release History Sherpa Sherpa is a modeli

134 Jan 07, 2023
Single machine, multiple cards training; mix-precision training; DALI data loader.

Template Script Category Description Category script comparison script train.py, loader.py for single-machine-multiple-cards training train_DP.py, tra

2 Jun 27, 2022
An implementation of the largeVis algorithm for visualizing large, high-dimensional datasets, for R

largeVis This is an implementation of the largeVis algorithm described in (https://arxiv.org/abs/1602.00370). It also incorporates: A very fast algori

336 May 25, 2022
Additional tools for particle accelerator data analysis and machine information

PyLHC Tools This package is a collection of useful scripts and tools for the Optics Measurements and Corrections group (OMC) at CERN. Documentation Au

PyLHC 3 Apr 13, 2022
ped-crash-techvol: Texas Ped Crash Tech Volume Pack

ped-crash-techvol: Texas Ped Crash Tech Volume Pack In conjunction with the Final Report "Identifying Risk Factors that Lead to Increase in Fatal Pede

Network Modeling Center; Center for Transportation Research; The University of Texas at Austin 2 Sep 28, 2022
Methylation/modified base calling separated from basecalling.

Remora Methylation/modified base calling separated from basecalling. Remora primarily provides an API to call modified bases for basecaller programs s

Oxford Nanopore Technologies 72 Jan 05, 2023
An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks

qgrid Qgrid is a Jupyter notebook widget which uses SlickGrid to render pandas DataFrames within a Jupyter notebook. This allows you to explore your D

Quantopian, Inc. 2.9k Jan 08, 2023
InDels analysis of CRISPR lines by NGS amplicon sequencing technology for a multicopy gene family.

CRISPRanalysis InDels analysis of CRISPR lines by NGS amplicon sequencing technology for a multicopy gene family. In this work, we present a workflow

2 Jan 31, 2022
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang

Uber Open Source 1.6k Dec 29, 2022
Python for Data Analysis, 2nd Edition

Python for Data Analysis, 2nd Edition Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media Buy

Wes McKinney 18.6k Jan 08, 2023
Python utility to extract differences between two pandas dataframes.

Python utility to extract differences between two pandas dataframes.

Jaime Valero 8 Jan 07, 2023
A utility for functional piping in Python that allows you to access any function in any scope as a partial.

WithPartial Introduction WithPartial is a simple utility for functional piping in Python. The package exposes a context manager (used with with) calle

Michael Milton 1 Oct 26, 2021
BErt-like Neurophysiological Data Representation

BENDR BErt-like Neurophysiological Data Representation This repository contains the source code for reproducing, or extending the BERT-like self-super

114 Dec 23, 2022
Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities. This is aimed at those looking to get into the field of D

Joachim 1 Dec 26, 2021