• Radar charts are a way to visualize multivariate data.
  • Giving an axis for each variable, and these axes are arranged radially around a central point and spaced equally.
  • The data from a single observation are plotted along each axis and connected to form a polygon. Multiple observations can be placed in a single chart by displaying multiple polygons, overlaying them, and reducing the opacity of each polygon.
  • Radar charts are a way to visualize multivariate data.
  • Giving an axis for each variable, and these axes are arranged radially around a central point and spaced equally.
  • The data from a single observation are plotted along each axis and connected to form a polygon. Multiple observations can be placed in a single chart by displaying multiple polygons, overlaying them and reducing the opacity of each polygon.
  • Showing the rise and fall of various data series over time.
  • Conveying total amounts over time as well as some sub-categorical breakdowns (but only to a point).
  • Emphasizing a part-to-whole relationship over time when one part is very large, or changes from being very large to very small.
  • Showing change over time.
  • Showing the rise and fall of various data series over time.
  • Conveying total amounts over time as well as some sub-categorical breakdowns (but only to a point).
  • Emphasizing a part-to-whole relationship over time when one part is very large, or changes from being very large to very small.
  • Showing change over time.
  • Showing the rise and fall of various data series over time.
  • Conveying total amounts over time as well as some sub-categorical breakdowns (but only to a point).
  • Emphasizing a part-to-whole relationship over time when one part is very large, or changes from being very large to very small.
  • Showing change over time.
  • provides a way to visualize values over a geographical area, which can show variation or patterns across the displayed location.
  • The data variable uses color progression to represent itself in each region of the map.
!pip install geopandas
!pip install geoplot
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import geopandas
import geoplot
# world data from geopandas
# https://geopandas.org/index.html
world_data = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world_data.head()
  • provides a way to visualize values over a geographical area, which can show variation or patterns across the displayed location.
  • The data variable uses color progression to represent itself in each region of the map.
  • Typically, this can be a blending from one color to another, a single hue progression, transparent to opaque, light to dark, or an entire color spectrum.

Elad Gvirtz

Data driven is more than a quote — drivenn.io

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