![]() I'm not going through all the functions provided here. The first one is "File", then "Edit", "View", "Run", "Kernel", "Tabs" and "Settings" and "Help". There are a couple of menus provided here. Now JupyterLab is working on Chrome browser because I made that choice before. It is correct chosen and then launch JupyterLab. Now confirm if your working environment is "ml_env". But in this Anaconda navigator, you can also check what libraries are available in your working environment. Surely, you can use Anaconda prompt as I explained it while we were installing Anaconda. This is a way of checking the libraries installed in your working environment. If you click "Environments", There are all packages installed, installed in "ml_env". And if you look at the left side, there are three choices. Make a choice, then "ml_env" will be choosen. But we are going to keep working on "ml_env" environment. Currently, the default choice is base environment. But before clicking "launch" JupyterLab, we need to first to make a choice for the working environment. With opened JupyterLab I will start coding immediately after starting this class. In the following video clips, I will not show you this process. A little bit wait, then Anaconda will be ready. So, always, in order to open JupyterLab you need to first to open Anaconda navigator. Click it then you have Anaconda navigator. In order to open JupyterLab, as I said before, first click "windows", and then click "all apps". In this video clip, I will explain how to use JupyterLab that we installed in the previous video clip. Figure ( data = data, layout = layout ) py. Surface ( x = x, y = y, z = z ) data = layout = go. cos ( tGrid ) # z = r*cos(t) surface = go. sin ( tGrid ) # y = r*sin(s)*sin(t) z = r * np. sin ( tGrid ) # x = r*cos(s)*sin(t) y = r * np. ![]() sin ( 7 * sGrid + 5 * tGrid ) # r = 2 + sin(7s+5t) x = r * np. Import chart_otly as py import aph_objects as go import numpy as np s = np. ![]() iplot ( fig, filename = 'jupyter-Nuclear Waste Sites on American Campuses' ) Layout ( title = 'Nuclear Waste Sites on Campus', autosize = True, hovermode = 'closest', showlegend = False, mapbox = dict ( accesstoken = mapbox_access_token, bearing = 0, center = dict ( lat = 38, lon =- 94 ), pitch = 0, zoom = 3, style = 'light' ), ) fig = dict ( data = data, layout = layout ) py. read_csv ( ' %20o n%20American%20Campuses.csv' ) site_lat = df. Import chart_otly as py import aph_objects as go import pandas as pd # mapbox_access_token = 'ADD YOUR TOKEN HERE' df = pd. See examples of statistic, scientific, 3D charts, and more here.
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