df.head()

"""
Explanation: Auto MPG Data
End of explanation
"""


hm = HeatMap(df, x=bins('mpg'), y=bins('displ'))
show(hm)

"""
Explanation: 2D Binning
Default behavior for 2d binning is to bin the dimensions provided, then count the rows that fall into each bin. This is visualizing how the source data represents all possible combinations of mpg and displacement.
End of explanation
"""


hm = HeatMap(df, x=bins('mpg'), y=bins('displ'), values='cyl', stat='mean')
show(hm)

"""
Explanation: Binning and Aggregating Values
For each x and y bin, the stat is used on values. After the binning operation, the aggregated values are then binned a second time so that a discrete color can be mapped to the aggregated value.
End of explanation
"""


hm = HeatMap(df, x=bins('mpg'), y=bins('displ', bins=15), 
             values='cyl', stat='mean')
show(hm)

"""
Explanation: Specifying the Number of Bins
End of explanation
"""


hm = HeatMap(df, x=bins('mpg'), y='cyl', values='displ', stat='mean')
show(hm)

"""
Explanation: Mixing binning and non-binned data
End of explanation
"""


hm = HeatMap(df, y=bins('displ'), x=bins('mpg'), values='cyl', stat='mean',
             spacing_ratio=0.9)

show(hm)

"""
Explanation: The Size of the Glyph Can be Altered
End of explanation
"""


hm = HeatMap(df, x=bins('mpg'), y=bins('displ'), stat='mean', values='cyl',
             palette=RdYlGn6)
show(hm)

"""
Explanation: Applying a Custom Palette
The number of discrete colors in the palette determines the number of bins used for applying colors.
Example with 6 Colors
End of explanation
"""


hm = HeatMap(df, x=bins('mpg'), y=bins('displ'), stat='mean', values='cyl',
             palette=RdYlGn9)
show(hm)

"""
Explanation: Example with 9 Colors
End of explanation
"""


hm = HeatMap(df, x=bins('mpg'), y=bins('displ'), values='cyl',
             stat='mean', legend='top_right')
show(hm)

"""
Explanation: Viewing Color Bins in Legend
End of explanation
"""


fruits = {'fruit': ['apples', 'apples', 'apples', 'apples', 'apples',
                    'pears', 'pears', 'pears', 'pears', 'pears',
                    'bananas', 'bananas', 'bananas', 'bananas', 'bananas'],
          'fruit_count': [4, 5, 8, 12, 4, 6, 5, 4, 8, 7, 1, 2, 4, 8, 12],
          'year': [2009, 2010, 2011, 2012, 2013, 2009, 2010, 2011, 2012, 2013, 2009, 2010,
                   2011, 2012, 2013]}
fruits['year'] = [str(yr) for yr in fruits['year']]
fruits_df = pd.DataFrame(fruits)

fruits_df.head()

"""
Explanation: Build Fruit Data
End of explanation
"""


hm = HeatMap(fruits, y='year', x='fruit', values='fruit_count', stat=None)
show(hm)

"""
Explanation: Without Dimension Binning
Each x and y value are treated as the coordinates of a bin. The values column is then binned and assigned to the color attribute, so discrete colors can be assigned.
End of explanation
"""


unempl_data = data.copy()
unempl_data.head()

# Remove the annual column if we don't want to show the total
del unempl_data['Annual']

# Convert numerical year to strings
unempl_data['Year'] = unempl_data['Year'].astype(str)

# de-pivot all columns, except for Year, into two columns.
# One column will have the values and the second will have the labels
unempl = pd.melt(unempl_data, var_name='Month', value_name='Unemployment', id_vars=['Year'])

"""
Explanation: Unemployment Data
Prepare the Data
Current data is in a pivoted form, which is difficult to use when defining plots by specifying columns. We will use the pandas melt function to de-pivot the columns with numerical data we want to use, while keeping the categorical data.
End of explanation
"""


unempl.head()

hm = HeatMap(unempl, x='Year', y='Month', values='Unemployment', stat=None,
             sort_dim={'x': False}, height=200, responsive=True)
show(hm)

"""
Explanation: De-pivoted Data
We now have the 3 dimensions that we want to map to attributes of the plot.
End of explanation
"""


