Tag Archives: Pandas

Calculating distance from Lat & Lon coordinates using Python & Pandas

Given a GPS log file structured as follows (column separators omitted for clarity): Speed Latitud Longitud Time 2019-01-07 06:15:27 0 59.649582 17.721365 2019-01-07 06:16:28 0 59.649583 17.721372 2019-01-07 06:17:28 0 59.649583 17.721370 2019-01-07 06:18:28 0 59.649583 17.721372 2019-01-07 06:19:29 0 … Continue reading

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Vasaloppet 2018 – race time analysis

An analysis of race times for the ~11000 men and ~2000 women that participated in 2018 Vasaloppet. For explanations of the graphs, see earlier posts on Marcialonga or Tour de Ski. [Btw, the weird looking vertical orange/blue “spike” in the … Continue reading

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Marcialonga Ski 2018 – some Analytics

Now, with the power grid finally – after 62 hours! – back in business, I’m able to continue my stats/analytics exploration of the past Marcialonga ski race. First, some basic stats about the race: Total number of participants: 5558, of … Continue reading

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Bayesian Linear Regression with PYMC

Python, Pandas & PYMC example on Bayesian Linear Regression, adopted from Richard McElreath’s “Statistical Rethinking” class, where he uses R as modeling language instead of PYMC. Data in a csv-file describe various attributes such as weight, height, age, gender etc … Continue reading

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Python & Pandas to map gps coordinates to known locations

Assume you have a gps log file, with time and position (Lat,Lon in columns 9,10) info, like: 2018.12.12 00:41:20;0;0;0;0;0;1;0;25.8;59.348978;17.969643;0;0; 2018.12.12 01:41:21;0;0;0;0;0;1;0;25.7;59.348962;17.969627;0;0; 2018.12.12 02:41:21;0;0;0;0;0;1;0;25.7;59.349;17.969688;0;0; 2018.12.12 03:41:21;0;0;0;0;0;1;0;25.7;59.349;17.96966;0;0; 2018.12.12 04:41:22;0;0;0;0;0;1;0;25.6;59.349007;17.969618;0;0; 2018.12.12 04:48:50;0;0;0;0;1;1;1;25.2;59.349007;17.969635;0;0; 2018.12.12 04:49:51;0;0.001;0;0;1;1;1;28.3;59.349;17.969642;0;0; Assume further that you’d like to map each of … Continue reading

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Using Bayesian Inference to predict and bet on Italian Serie A Fotball

As my old timer readers know, I’be been using Bayesian Inference to predict and bet on various sporting events, such as FIFA World Cup, and IIHF World Championships. With some success. When the Italian premier division started for about a … Continue reading

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Bayesian Multi-predictor Regression – Valet2018

[Continuing my exploration of the Swedish election results, but I thought this might be of interest also for those of you not very interested in the Swedish elections, simply because the potential MatStat’s  insights – thus, the text is in … Continue reading

Posted in Bayes, Big Data, Data Analytics, Data Driven Management, Numpy, Politik, Probability, PYMC, Python, Research, Society, Statistics, Sverige | Tagged , , , , , , , , , , | 1 Comment

Val2018 – samband mellan röstning och inkomst/utbildningsnivå

Det börjar bli många olika analyser av valresultatet på den här bloggen, så här kommer ytterligare en: En regressionsanalys över valdatat (från Valmyndigheten) och befolkningsdatat (från SCB): Bayesian Linear Regression över sambanden Röstandelar per parti vs andelen högutbildade (minst 3 … Continue reading

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Val2018 Bayesian Inference – sammanfattning

Nu är ju inte den slutgiltiga rösträkningen klar, men resultatet ur ett statistiskt / matematiskt perspektiv är ändå så stabilt att jag väljer att summera mina resultat redan nu. I graferna nedan har jag använt mig av samtliga opinionsinstituts prognoser … Continue reading

Posted in Bayes, Data Analytics, Functional Stupidity, Politik, Probability, PYMC, Society, Statistics, Sverige | Tagged , , , , , , , , , , , , | Leave a comment

Val2018 – Bayesian Linear Regression, senaste opinionsmätningarna

Med endast en dag kvar till valet kan det vara intressant att titta på trenden på de olika opinionsinstitutens mätningar från de senaste två månaderna, fram till och med 2018/09/06. Inom “vanlig” statistik, så är en trendlinje just det, *en* … Continue reading

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