##############################################################################
#
# An example of converting a Pandas dataframe to an xlsx file with a grouped
# column chart using Pandas and XlsxWriter.
#
# Copyright 2013-2019, John McNamara, jmcnamara@cpan.org
#

import pandas as pd
from vincent.colors import brews

# Some sample data to plot.
farm_1 = {'Apples': 10, 'Berries': 32, 'Squash': 21, 'Melons': 13, 'Corn': 18}
farm_2 = {'Apples': 15, 'Berries': 43, 'Squash': 17, 'Melons': 10, 'Corn': 22}
farm_3 = {'Apples': 6,  'Berries': 24, 'Squash': 22, 'Melons': 16, 'Corn': 30}
farm_4 = {'Apples': 12, 'Berries': 30, 'Squash': 15, 'Melons': 9,  'Corn': 15}

data  = [farm_1, farm_2, farm_3, farm_4]
index = ['Farm 1', 'Farm 2', 'Farm 3', 'Farm 4']

# Create a Pandas dataframe from the data.
df = pd.DataFrame(data, index=index)

# Create a Pandas Excel writer using XlsxWriter as the engine.
sheet_name = 'Sheet1'
writer     = pd.ExcelWriter('pandas_chart_columns.xlsx', engine='xlsxwriter')
df.to_excel(writer, sheet_name=sheet_name)

# Access the XlsxWriter workbook and worksheet objects from the dataframe.
workbook  = writer.book
worksheet = writer.sheets[sheet_name]

# Create a chart object.
chart = workbook.add_chart({'type': 'column'})

# Some alternative colors for the chart.
colors = ['#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#FF7F00']

# Configure the series of the chart from the dataframe data.
for col_num in range(1, len(farm_1) + 1):
    chart.add_series({
        'name':       ['Sheet1', 0, col_num],
        'categories': ['Sheet1', 1, 0, 4, 0],
        'values':     ['Sheet1', 1, col_num, 4, col_num],
        'fill':       {'color':  colors[col_num - 1]},
        'overlap':    -10,
    })

# Configure the chart axes.
chart.set_x_axis({'name': 'Total Produce'})
chart.set_y_axis({'name': 'Farms', 'major_gridlines': {'visible': False}})

# Insert the chart into the worksheet.
worksheet.insert_chart('H2', chart)

# Close the Pandas Excel writer and output the Excel file.
writer.save()
