Bubble Plots using Python | Data Science | Machine Learning | Python

Bubble Plots using Python | Data Science | Machine Learning | Python

Bubble plots are better versions of the scatter plots, replacing the dots with bubbles. Most often, a bubble plot displays the values ​​of three numeric variables, with the data for each observation represented by a circle (“bubble”), while the horizontal and vertical positions of the bubble indicate the values ​​of two other variables.

Bubble plots are better versions of the scatter plots, replacing the dots with bubbles. Most often, a bubble plot displays the values ​​of three numeric variables, with the data for each observation represented by a circle (“bubble”), while the horizontal and vertical positions of the bubble indicate the values ​​of two other variables.

Data Preparation for Bubble Plots

For this task, I will be using the dataset which describes the information on Canadian immigration. It contains data from 1980 to 2013 and includes the number of immigrants from 195 countries. Now let’s import the necessary packages and dataset to get started with the task:

Now, let’s have a look at the columns of the dataset:

df.columns
Index([    'Type', 'Coverage',   'OdName',     'AREA', 'AreaName',      'REG',
        'RegName',      'DEV',  'DevName',       1980,       1981,       1982,
             1983,       1984,       1985,       1986,       1987,       1988,
             1989,       1990,       1991,       1992,       1993,       1994,
             1995,       1996,       1997,       1998,       1999,       2000,
             2001,       2002,       2003,       2004,       2005,       2006,
             2007,       2008,       2009,       2010,       2011,       2012,
             2013],
      dtype='object')

I will not use all the columns as I will only use the data which is required to understand the features that are needed to create the bubble plots:

Data Normalization for Bubble Plots

I will choose the data of India and Brazil for this task:

India = df.loc['India']
Brazil = df.loc['Brazil']

Normalization is done the data to bring the data into a similar range. Immigration data for India and Brazil have different ranges. I needed to bring them to a range of 0 to 1.

I will simply divide the India data by the maximum value of the India data series. I will do the same with the Brazil data series, then I will now plot the Indian and Bazil data against years. It will be helpful to have the years

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.