data-visualization

问题 Using the Highstock (Highcharts) library, I'm wondering, how do I combine A) adding flags as in here (or see fig.1 ) with B) dynamically updating data, using the addPoint function (ex: series.addPoint([x, y], true, true) see here). There doesn't seem to be a facility for it in the addPoint function. series : [{ name : 'USD to EUR', data : data, id : 'dataseries' }, { type : 'flags', data : [{ x : Date.UTC(2011, 3, 25), title : 'H', text : 'Euro Contained by Channel Resistance' }], onSeries :

问题 I have a data set in excel. There are total 9 columns: col1 - member_id col2 - A_timespent_in_hrs col3 - B_timespent_in_hrs col4 - total_timespent_in_hrs (col2 + col3) col5 - A_pv (not to be considered in the problem) col6 - B_pv (not to be considered in the problem) col7 - total_pv (col5 + col6)(not to be considered in the problem) col8 - A_timespent_in_hrs % wrt to total_timespent_in_hrs col9 - B__timespent_in_hrs % wrt to total_timespent_in_hrs I need to plot a graph in R (line graph)

问题 I need some raw data to visualize it with google charts and some other APIs. Problem is that i some raw data that includes timestamps too. For example visitors visiting a website i.e. from which device (mobile/computer etc) they accessed website, at what time (hours:minutes:second:miliseconds) and what which links they visited etc. Please help me to know if someone knows about such kind of dummy raw data on web. 回答1: You can build your own dataset using Google Spreadsheets. For example,

问题 This question already has answers here : Saving grid.arrange() plot to file (6 answers) Closed 5 years ago . This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 5 years ago . I have quite a specific problem with producing 300ppi plots in tiff format for publication. I have found ggsave to work beautifully with a single plot, which can then be exported to GIMP to compress the resulting large tiff file. However, it seems to run into trouble

问题 I have 3 main values (longitude, latitude and speed) in csv. Using Folium library i can map the location with lon and lat degree.my current code is: import pandas as pd from geopy.geocoders import Nominatim import folium from pandas import * import numpy as np from matplotlib import cm import folium df = DataFrame({'lon': [9.899193, 9.899236, 9.899292, 9.899331, 9.899372, 9.899411, 9.89946, 9.899518, 9.899393, 9.899409, 9.899456, 9.899498, 9.89952, 9.899558, 9.8996, 9.899625, 9.899646, 9

问题 I'm trying to imitate the following effects: Orginal version V3: https://bl.ocks.org/mbostock/7881887 Converted to V4: https://bl.ocks.org/lydiawawa/1fe3c80d35e046c1636663442f34680b/86d1bda1dabb7f3a6d11cb1a16053564078ed964 An example used dataset: https://jsfiddle.net/hf998do7/1/ This is what I have so far : https://blockbuilder.org/lydiawawa/0899a02cc86f2274f52e27064bc86500 I want to make a bubble graph that shows clusters of Race, the size of the bubbles are assigned by BMI. The dots will

问题 This is an improved question based on my previous post: How to cluster bubble chart with many groups I worked on my code and has updated the block( based on Amber Thomas' script): https://blockbuilder.org/lydiawawa/1fe3c80d35e046c1636663442f34680b The animation toggles between mixed clusters and split clusters. It has too many jitters and the cluster with more dots seem to have none stop colliding effect. I'm hoping to achieve the following effect (v4 updated): https://bl.ocks.org/lydiawawa

问题 Let's say I'm trying to predict an apartment price. So, I have a lot of labeled data, where on each apartment I have features that could affect the price like: city street floor year built socioeconomic status square feet etc. And I train a model, let's say XGBOOST. Now, I want to predict the price of a new apartment. Is there a good way to show what is "good" in this apartment, and what is bad, and by how much (scaled 0-1)? For example: The floor number is a "strong" feature (i.e. - in this

问题 Want to build a 3D Bar Chart using Mayavi (on my Asus Laptop Intel CoreTM i7-4510U CPU @ 2.00 GHz with 8 GBs de RAM, Windows 10) using a Jupyter Notebook (on a Python virtualenv) but I'm getting a grey screen. Once the data was imported, I clicked in New > Python 3 and wrote Used pandas' fast CSV parser, pandas.read_csv(), and once I ran line 4, I could see the memory usage increase to 88% of the capable using CleanMem Mini Monitor and got results in less than 1 minute. Then, to build the bar

问题 I have a data.csv file done this way: CODE,YEAR,MODALITY,VALUE AB,2000,first,15 AB,2000,second, AB,2000,third,33 AB,2001,first,20 AB,2001,second,25 AB,2001,third,87 AB,2002,first,6 AB,2002,second, AB,2002,third,16 AB,2003,first,50 AB,2003,second,50 AB,2003,third,10 AB,2004,first,20 AB,2004,second,55 AB,2004,third,8 AC,2000,first, AC,2000,second,97 AC,2000,third,77 AC,2001,first,42 AC,2001,second,5 AC,2001,third,81 AC,2002,first, AC,2002,second,63 AC,2002,third,14 AC,2003,first,5 AC,2003