Codes for information visualization using python lab report 2

choropleth_map.py

import plotly.plotly as py
import pandas as pd

df = pd.read_csv('2014_world_gdp_with_codes.csv')

data = [ dict(
        type = 'choropleth',
        locations = df['CODE'],
        z = df['GDP (BILLIONS)'],
        text = df['COUNTRY'],
        colorscale = [[0,"rgb(5, 10, 172)"],[0.35,"rgb(40, 60, 190)"],[0.5,"rgb(70, 100, 245)"],\
            [0.6,"rgb(90, 120, 245)"],[0.7,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"]],
        autocolorscale = False,
        reversescale = True,
        marker = dict(
            line = dict (
                color = 'rgb(180,180,180)',
                width = 0.5
            ) ),
        colorbar = dict(
            autotick = False,
            tickprefix = '$',
            title = 'GDP<br>Billions US$'),
      ) ]

layout = dict(
    title = '2014 Global GDP',
    geo = dict(
        showframe = False,
        showcoastlines = False,
        projection = dict(
            type = 'Mercator'
        )
    )
)


fig = dict( data=data, layout=layout )
py.iplot( fig, validate=False, filename='d3-world-map' )

dendrogram.py

import plotly.plotly as py
import plotly.figure_factory as ff
import numpy as np

X = np.random.rand(10, 10)
names = ['Jack', 'Oxana', 'John', 'Chelsea', 'Mark', 'Alice', 'Charlie', 'Rob', 'Lisa', 'Lily']
fig = ff.create_dendrogram(X, orientation='left', labels=names)
fig['layout'].update({'width':800, 'height':800})
py.iplot(fig, filename='dendrogram_with_labels')

heat_map.py

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Read the Pharma Sector data 
df = pd.read_csv("Pharma_Heatmap_data.csv")

# Create an array of stock symbols & their respective percentage price change
symbol = ((np.asarray(df['Symbol'])).reshape(6,5))
perchange = ((np.asarray(df['Change'])).reshape(6,5))

# Create a pivot table
result = df.pivot(index='Yrows',columns='Xcols',values='Change')

# Create an array to annotate the heatmap
labels = (np.asarray(["{0} \n {1:.2f}".format(symb,value)
                      for symb, value in zip(symbol.flatten(),
                                               perchange.flatten())])
         ).reshape(6,5)

# Define the plot
fig, ax = plt.subplots(figsize=(13,7))

# Add title to the Heat map
title = "Pharma Sector Heat Map"

# Set the font size and the distance of the title from the plot
plt.title(title,fontsize=18)
ttl = ax.title
ttl.set_position([0.5,1.05])

# Hide ticks for X & Y axis
ax.set_xticks([]) 
ax.set_yticks([]) 

# Remove the axes
ax.axis('off')

# Use the heatmap function from the seaborn package
sns.heatmap(result,annot=labels,fmt="",cmap='RdYlGn',linewidths=0.30,ax=ax)

# Display the Pharma Sector Heatmap
plt.show()

violin_plot.py

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set(style="white", color_codes=True)
iris = pd.read_csv("iris.csv") 

# We use violinplot to plot the data
sns.violinplot(x="species", y="petal_length", data=iris, size=6)