Plot Data from MySQL in Python/v3

import plotly
plotly.__version__

import plotly.plotly as py
import plotly.graph_objs as go

import MySQLdb
import pandas as pd

conn = MySQLdb.connect(host="localhost", user="root", passwd="xxxx", db="world")
cursor = conn.cursor()
cursor.execute('select Name, Continent, Population, LifeExpectancy, GNP from Country');

rows = cursor.fetchall()
str(rows)[0:300]

df = pd.DataFrame( [[ij for ij in i] for i in rows] )
df.rename(columns={0: 'Name', 1: 'Continent', 2: 'Population', 3: 'LifeExpectancy', 4:'GNP'}, inplace=True);
df = df.sort_values(['LifeExpectancy'], ascending=[1]);

country_names = df['Name']
for i in range(len(country_names)):
    try:
        country_names[i] = str(country_names[i]).decode('utf-8')
    except:
        country_names[i] = 'Country name decode error'

trace1 = go.Scatter(
    x=df['GNP'],
    y=df['LifeExpectancy'],
    text=country_names,
    mode='markers'
)
layout = go.Layout(
    title='Life expectancy vs GNP from MySQL world database',
    xaxis=dict( type='log', title='GNP' ),
    yaxis=dict( title='Life expectancy' )
)
data = [trace1]
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='world GNP vs life expectancy')

# (!) Set 'size' values to be proportional to rendered area,
#     instead of diameter. This makes the range of bubble sizes smaller
sizemode='area'

# (!) Set a reference for 'size' values (i.e. a population-to-pixel scaling).
#     Here the max bubble area will be on the order of 100 pixels
sizeref=df['Population'].max()/1e2**2

colors = {
    'Asia':"rgb(255,65,54)",
    'Europe':"rgb(133,20,75)",
    'Africa':"rgb(0,116,217)",
    'North America':"rgb(255,133,27)",
    'South America':"rgb(23,190,207)",
    'Antarctica':"rgb(61,153,112)",
    'Oceania':"rgb(255,220,0)",
}

# Define a hover-text generating function (returns a list of strings)
def make_text(X):
    return 'Country: %s\
    <br>Life Expectancy: %s years\
    <br>Population: %s million'\
    % (X['Name'], X['LifeExpectancy'], X['Population']/1e6)

# Define a trace-generating function (returns a Scatter object)
def make_trace(X, continent, sizes, color):
    return go.Scatter(
        x=X['GNP'],  # GDP on the x-xaxis
        y=X['LifeExpectancy'],    # life Exp on th y-axis
        name=continent,    # label continent names on hover
        mode='markers',    # (!) point markers only on this plot
        text=X.apply(make_text, axis=1).tolist(),
        marker= dict(
            color=color,           # marker color
            size=sizes,            # (!) marker sizes (sizes is a list)
            sizeref=sizeref,       # link sizeref
            sizemode=sizemode,     # link sizemode
            opacity=0.6,           # (!) partly transparent markers
            line= dict(width=3,color="white")  # marker borders
        )
    )

# Initialize data object 
data = []

# Group data frame by continent sub-dataframe (named X), 
#   make one trace object per continent and append to data object
for continent, X in df.groupby('Continent'):

    sizes = X['Population']                 # get population array 
    color = colors[continent]               # get bubble color

    data.append(
        make_trace(X, continent, sizes, color)  # append trace to data object
    )

    # Set plot and axis titles
title = "Life expectancy vs GNP from MySQL world database (bubble chart)"
x_title = "Gross National Product"
y_title = "Life Expectancy [in years]"

# Define a dictionary of axis style options
axis_style = dict(
    type='log',
    zeroline=False,       # remove thick zero line
    gridcolor='#FFFFFF',  # white grid lines
    ticks='outside',      # draw ticks outside axes 
    ticklen=8,            # tick length
    tickwidth=1.5         #   and width
)

# Make layout object
layout = go.Layout(
    title=title,             # set plot title
    plot_bgcolor='#EFECEA',  # set plot color to grey
    hovermode="closest",
    xaxis=dict(
        axis_style,      # add axis style dictionary
        title=x_title,   # x-axis title
        range=[2.0,7.2], # log of min and max x limits
    ),
    yaxis=dict(
        axis_style,      # add axis style dictionary
        title=y_title,   # y-axis title
    )
)

# Make Figure object
fig = go.Figure(data=data, layout=layout)

# (@) Send to Plotly and show in notebook
py.iplot(fig, filename='s3_life-gdp')