These operators work on Web, Mobile, Tweetdeck.
Adapted from TweetDeck Help, @lucahammer Guide, @eevee Twitter Manual, @pushshift and Twitter / Tweetdeck itself. Contributions / tests, examples welcome!
| Class | Operator | Finds Tweets… | Eg: |
|---|
Andy Clarke andy-clarke-uofg
👋
andy-clarke-uofg
/ twitter_query_language.md
Last active
February 8, 2023 14:34
Twitter Query Language
andy-clarke-uofg
/ Collecting-Tweets.ipynb
Last active
September 21, 2022 06:02
Collecting Tweets from the Twitter API using tweepy.
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andy-clarke-uofg
/ wpd-overview-dummy-data.csv
Created
May 12, 2022 16:01
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| lng | lat | |
|---|---|---|
| -74.1179903701844 | 4.75055095371542 | |
| -74.1179903701844 | 4.75055095371542 | |
| -74.1179903701844 | 4.75055095371542 | |
| -74.1179903701844 | 4.75055095371542 | |
| -74.1179903701844 | 4.75055095371542 | |
| -46.632005 | -23.519901 | |
| -46.725853 | -23.548752 | |
| -46.615347 | -23.650206 | |
| -46.508786 | -23.482543 |
andy-clarke-uofg
/ random-brazil-coordinates.csv
Created
April 14, 2022 12:50
Randomly Generated Coordinates in Brazil
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| lng,lat | |
| -53.64057227283197,-15.150617321621077 | |
| -42.99421760836617,-11.145332942844876 | |
| -44.41791507114796,-8.157790364362803 | |
| -43.66038973901276,-21.148947704964765 | |
| -50.294873612911196,-7.517881971758156 | |
| -39.23700952309843,-9.933767804515153 | |
| -56.39064428015981,-4.383264074730363 | |
| -45.240887220743545,-13.103282452658972 | |
| -42.645435327462764,-21.757497469260148 |
andy-clarke-uofg
/ notebook.ipynb
Last active
February 2, 2022 15:05
Reads a list of search queries as generated by Google Adwords Keyword Planner and performs a search for each query, returning the top 10 URLs for each query.
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andy-clarke-uofg
/ trustpilot-scraping.ipynb
Last active
January 11, 2022 10:20
Notebook for scraping reviews from Trustpilot
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andy-clarke-uofg
/ customer-segmentation.csv
Created
October 27, 2021 08:11
Customer Segmentation Example Dataset
We can make this file beautiful and searchable if this error is corrected: It looks like row 5 should actually have 29 columns, instead of 27 in line 4.
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| Recency,MntWines,MntFruits,MntMeatProducts,MntFishProducts,MntSweetProducts,MntGoldProds,NumDealsPurchases,NumWebPurchases,NumCatalogPurchases,NumStorePurchases,NumWebVisitsMonth,Year_Birth,Education,Marital_Status,Income,Kidhome,Teenhome,Dt_Customer,AcceptedCmp3,AcceptedCmp4,AcceptedCmp5,AcceptedCmp1,AcceptedCmp2,Complain,Response,umap_cluster,month_name,weekday_name | |
| 58,635,88,546,172,88,88,3,8,10,4,7,1957-01-01T00:00:00Z,Graduation,Single,58138,0,0,2012-09-04T00:00:00Z,false,false,false,false,false,false,true,Cluster 1,January,Tuesday | |
| 38,11,1,6,2,1,6,2,1,1,2,5,1954-01-01T00:00:00Z,Graduation,Single,46344,1,1,2014-03-08T00:00:00Z,false,false,false,false,false,false,false,Cluster 12,January,Friday | |
| 26,426,49,127,111,21,42,1,8,2,10,4,1965-01-01T00:00:00Z,Graduation,Together,71613,0,0,2013-08-21T00:00:00Z,false,false,false,false,false,false,false,Cluster 2,January,Friday | |
| 26,11,4,20,10,3,5,2,2,0,4,6,1984-01-01T00:00:00Z,Graduation,Together,26646,1,0,2014-02-10T00:00:00Z,false,false,false,false,false,false,false,C |
andy-clarke-uofg
/ 💉 VAERS 2021 | Cleaning & Joining.ipynb
Last active
June 9, 2021 08:45
A notebook documenting the way a team at Graphext cleaned and joined data from the 2021 VAERS wave.
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andy-clarke-uofg
/ graphext-vaccines-nlp-steps.txt
Created
June 8, 2021 17:58
NLP Steps Added to VAERS Study Project
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| # Configure English as language of text | |
| make_constant(ds["Symptom Description"], { | |
| "value": "en", | |
| "out_type": "category" | |
| }) -> (ds.lang) | |
| # Parse and extract ADJECTIVES from SYMPTOM DESCRIPTION column. | |
| extract_keywords(ds["Symptom Description"], ds.lang, { | |
| "keywords": { | |
| "pos_tags": [ |
andy-clarke-uofg
/ 5110-Stroke-Patients.csv
Created
May 4, 2021 09:34
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| id | gender | age | hypertension | heart_disease | ever_married | work_type | Residence_type | avg_glucose_level | bmi | smoking_status | stroke | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9046 | Male | 67 | 0 | 1 | Yes | Private | Urban | 228.69 | 36.6 | formerly smoked | 1 | |
| 51676 | Female | 61 | 0 | 0 | Yes | Self-employed | Rural | 202.21 | N/A | never smoked | 1 | |
| 31112 | Male | 80 | 0 | 1 | Yes | Private | Rural | 105.92 | 32.5 | never smoked | 1 | |
| 60182 | Female | 49 | 0 | 0 | Yes | Private | Urban | 171.23 | 34.4 | smokes | 1 | |
| 1665 | Female | 79 | 1 | 0 | Yes | Self-employed | Rural | 174.12 | 24 | never smoked | 1 | |
| 56669 | Male | 81 | 0 | 0 | Yes | Private | Urban | 186.21 | 29 | formerly smoked | 1 | |
| 53882 | Male | 74 | 1 | 1 | Yes | Private | Rural | 70.09 | 27.4 | never smoked | 1 | |
| 10434 | Female | 69 | 0 | 0 | No | Private | Urban | 94.39 | 22.8 | never smoked | 1 | |
| 27419 | Female | 59 | 0 | 0 | Yes | Private | Rural | 76.15 | N/A | Unknown | 1 |
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