bidterew.blogg.se - Jupyter notebook tutorial scala

#JUPYTER NOTEBOOK TUTORIAL SCALA INSTALL#
#JUPYTER NOTEBOOK TUTORIAL SCALA CODE#

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.

Plotly: a graphing library for making interactive, publication-quality graphs.

SciPy: a Python-based ecosystem of packages for math, science, and engineering.

NumPy: a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data.

Pandas: import data via a url and create a dataframe to easily handle data for analysis and graphing.

Some useful packages that we'll use in this tutorial include: You can reload all changed modules before executing a new line. IPython comes with automatic reloading magic.

#JUPYTER NOTEBOOK TUTORIAL SCALA CODE#

You may want to reload submodules if you've edited the code in one.

#JUPYTER NOTEBOOK TUTORIAL SCALA INSTALL#

When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ! prefix, e.g.: !pip install packagename Skip down to the for more information on using IRkernel with Jupyter notebooks and graphing examples. You can also use Jupyter notebooks to execute R code. The bulk of this tutorial discusses executing python code in Jupyter notebooks.