I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
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Karthik Ravi kaddynator
☕
tsiege
/ The Technical Interview Cheat Sheet.md
Last active
July 20, 2024 16:44
This is my technical interview cheat sheet. Feel free to fork it or do whatever you want with it. PLEASE let me know if there are any errors or if anything crucial is missing. I will add more links soon.
mommi84
/ awesome-kge.md
Last active
July 4, 2024 09:07
Awesome Knowledge Graph Embedding Approaches
This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.
- 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
- Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding
sagarnanduunc
/ Making to_csv and read_csv consistent for pandas
Created
March 28, 2018 03:37
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| # This works in MACs | |
| # Dataframe can also be used in R | |
| df.to_csv("PATH WHERE YOU WANT TO SAVE YOUR FILE/filename.csv",quoting=csv.QUOTE_NONNUMERIC, date_format='%Y-%m-%d %H:%M:%S', encoding='utf-8',line_terminator = '\n') | |
| df = pd.read_csv("PATH WHERE YOUR FILE IS SAVED/filename.csv", encoding='utf-8',lineterminator = '\n',index_col=0) |
sagarnanduunc
/ Creating timeline of pandas data frame based on time granularity using Time Grouper
Last active
June 19, 2018 04:21
Grouping data in pandas based on date based column. Grouping can be done based on [seconds, hours, minutes, days, months and even years]. Results in a Time vs count data frame
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| # "field" is just a custom name that you want to give to the count of record column in the timeline dataframe | |
| def createTimeLine(df,field,granularity): | |
| # Since I did it on Twitter data, I used 'postedTime' but that can be generalized as well | |
| # Here I use Timegrouper which is group by based on time granularity (secs, mins, days, hours, months ....) | |
| timegrp = df.set_index('postedTime').groupby(pd.TimeGrouper(freq=granularity)) # Grouping data based on Granularity | |
| timeCount = {"day":[],field:[]} # Creating a dictionary having keys as "day" and field to convert into dataframe later | |
| # users = len(df.groupby("actorId")) | |
| for time_unit in timegrp: # Parsing through all the formed groups | |
| #print(time_unit[0].strftime('%Y-%m-%d'),": ",len(time_unit[1])) | |
| timeCount["day"].append(time_unit[0].strftime('%Y-%m-%d')) # adding the group |
kaddynator
/ w2v_visualizer.py
Created
November 3, 2019 14:44
— forked from BrikerMan/w2v_visualizer-v2.py
Convert gensim word2vec to tensorboard visualized model, detail: https://eliyar.biz/using-pre-trained-gensim-word2vector-in-a-keras-model-and-visualizing/
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| # encoding: utf-8 | |
| """ | |
| @author: BrikerMan | |
| @contact: eliyar917@gmail.com | |
| @blog: https://eliyar.biz | |
| @version: 1.0 | |
| @license: Apache Licence | |
| @file: w2v_visualizer.py | |
| @time: 2017/7/30 上午9:37 | |
| """ |