gboeing / data-visualization Goto Github PK

When I was trying "pip install keys"

It was showing
ERROR: Could not find a version that satisfies the requirement keys (from versions: none)
ERROR: No matching distribution found for keys

The graphs on Wikipedia are out of date. https://en.wikipedia.org/wiki/Projections_of_population_growth
I need an up-to-date version of the "estimates of population levels in different continents" graph for a new book (log scale). I would be very grateful if you could update this graph https://en.wikipedia.org/wiki/Projections_of_population_growth#/media/File:World_population_(UN).svg

I think this particular piece of code causes error
num_clusters = len(set(cluster_labels))

clusters = pd.Series([coords[cluster_labels==n] for n in range(num_clusters)])

since when min_samples>1, there are points that are considered 'noisy' and get a label '-1'
that label should be discarded,

cluster_set = set(cluster_labels)

if -1 in cluster_set: cluster_set.remove(-1)

num_clusters = len(cluster_set)

If you get this error
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED]
Then change
response = requests.get(request_url).json()
to
response = requests.get(request_url,verify=False).json()
wherever you see requests.get

Where is this path specifically? I use Gmail but there is 0 messages found

I am using same code as
https://github.com/gboeing/data-visualization/blob/master/location-history/google-location-history-cluster.ipynb
, with my own dataset which have over 500K points of lat and long.
It is causing out of memory ( 48 GB of RAM) and causing OS to kill the process.
I tried reducing down to 150K , still same problem.
I am using scikit-learn 0.18.1 on Linux with Anaconda + Python2.7

Great set of Jupyter notebooks!
I have a sticking point at the end of the visualize-location-history.ipynb notebook, where running the last cell just displays the zoom in/out for the apparent map frame. I can see it working on your website, as referenced at the end of the doc. However, looking at the console, the leaflet css and javascript throw errors. The .css throws an error about unsupported MIME type, but more importantly, the leaflet.js javascript and the location-dataset.js JSON data both throw 403 forbidden errors.

I don't know enough about Jupyter's security model to get why this is failing, but I'd love to see the results of the data chomping and visualization!

Thanks!

This is in Google Chrome 75.0.3770.100 on MacOS 10.13.6
Python version is 3.7.3, IPython 7.5.0, and Notebook Server 5.7.8.