# Today's goal is to reproduce this (without the headlines, I'll leave that for later):
from IPython.core.display import HTML
HTML('<iframe width="500" height="330" src="//www.google.com/trends/fetchComponent?hl\75en-US\46q\75cat,+dog,+fish\46date\0751/2012+12m\46cmpt\75q\46content\0751\46cid\75TIMESERIES_GRAPH_AVERAGES_CHART\46export\0755\46w\075500\46h\075330" style="border: none;"></iframe>')

# Ready, set, go!

# imports, from the docs: "The pyplot interface is generally preferred for
# non-interactive plotting (i.e., scripting). The pylab interface is convenient
# for interactive calculations and plotting"
import matplotlib.pyplot as plt
%matplotlib inline
# Seems that need some space
fig = plt.figure(figsize=(5.21, 2.6), dpi=120) # got the (in x in)dpi equivalent of 500px x 300px

# The image is divided in two and the first part is roughly 1/6 of the image.
# To understand the subplots I went to http://matplotlib.org/users/gridspec.html
bars = plt.subplot2grid((1,3), (0,0), colspan=1)
plot = plt.subplot2grid((1,3), (0,1), colspan=2, axisbelow=True)

# This has the underlying structure

# Cleaning and removing lines and ticks
bars.set_yticks([])
bars.set_xlabel("Average")
bars.set_xticks([])
bars.axes.set_ylim((0, 100))
bars.spines['top'].set_visible(False)
bars.spines['left'].set_visible(False)
bars.spines['right'].set_visible(False)

plot.set_xticklabels(["Apr 2012", "Jul 2012", "Oct 2012"])
plot.set_xticks([4*4, 4*7, 4*10]) # setting the months where they begin (in weeks)
plot.set_yticklabels([])
plot.set_ylim((0, 120))
plot.spines['top'].set_visible(False)
plot.spines['left'].set_visible(False)
plot.spines['right'].set_visible(False)

# I don't know yet how many point I will have, so I will set the xticks later.
# and the same for the bars in the other subplot.

import pandas as pd
report = pd.read_csv('0_data.csv', na_values=' ') # na_values tells pandas, in the data, what is a null value.

# Now using the attributes 'cat', 'dog' and 'fish' I can access each column of the CSV file
report.fish.tolist()

# plot each column with their color. 
plot.plot(report.cat.tolist(), color='blue', label='cat')
plot.plot(report.dog.tolist(),  color='red', label='dog')
plot.plot(report.fish.tolist(), color='orange', label='fish')

# and the same for the bars in the other subplot.
bars.axes.set_xlim((0, 5))
bars.bar(1, report.cat.mean(), color='blue', edgecolor='none', width=0.9, label='cat')
bars.bar(2, report.dog.mean(), color='red', edgecolor='none', width=0.92, label='dog')
bars.bar(3, report.fish.mean(), color='orange', edgecolor='none', width=0.92, label='fish')

# The legend
plot.legend(frameon=False, loc=2, fontsize='xx-small', ncol=3, mode='none')

# Almost there...
# A few adjustments to fonts and colors
plot.xaxis.set_ticks_position('none')
plot.yaxis.set_ticks_position('none')

for tk in plot.axes.get_xmajorticklabels():
    plt.setp(tk, color='gray', fontsize='xx-small')

plt.setp(bars.xaxis.label, color='gray', fontsize='xx-small')

plot.grid(which='major', axis='y', linewidth=.8, linestyle='-', color='lightgray')
plot.spines['bottom'].set_color('gray')
bars.spines['bottom'].set_color('gray')

plt.subplots_adjust(wspace=-0.1)

# The entire code

# imports, from the docs: "The pyplot interface is generally preferred for
# non-interactive plotting (i.e., scripting). The pylab interface is convenient
# for interactive calculations and plotting"
import matplotlib.pyplot as plt

# Seems that need some space
fig = plt.figure(figsize=(5.21, 2.6), dpi=120) # got the (in x in)dpi equivalent of 500px x 300px

# The image is divided in two and the first part is roughly 1/6 of the image.
# To understand the subplots I went to http://matplotlib.org/users/gridspec.html
bars = plt.subplot2grid((1,3), (0,0), colspan=1)
plot = plt.subplot2grid((1,3), (0,1), colspan=2, axisbelow=True)

# This has the underlying structure
# Cleaning and removing lines and ticks
bars.set_yticks([])
bars.set_xlabel("Average")
bars.set_xticks([])
bars.axes.set_ylim((0, 100))
bars.spines['top'].set_visible(False)
bars.spines['left'].set_visible(False)
bars.spines['right'].set_visible(False)

plot.set_xticklabels(["Apr 2012", "Jul 2012", "Oct 2012"])
plot.set_xticks([4*4, 4*7, 4*10]) # setting the months where they begin (in weeks)
plot.set_yticklabels([])
plot.set_ylim((0, 120))
plot.spines['top'].set_visible(False)
plot.spines['left'].set_visible(False)
plot.spines['right'].set_visible(False)

# I don't know yet how many point I will have, so I will set the xticks later.
# and the same for the bars in the other subplot.
import pandas as pd
report = pd.read_csv('0_data.csv', na_values=' ')

# plot each column with their color. 
plot.plot(report.cat.tolist(), color='blue', label='cat')
plot.plot(report.dog.tolist(),  color='red', label='dog')
plot.plot(report.fish.tolist(), color='orange', label='fish')
# and the same for the bars in the other subplot.
bars.axes.set_xlim((0, 5))
bars.bar(1, report.cat.mean(), color='blue', edgecolor='none', width=0.9, label='cat')
bars.bar(2, report.dog.mean(), color='red', edgecolor='none', width=0.92, label='dog')
bars.bar(3, report.fish.mean(), color='orange', edgecolor='none', width=0.92, label='fish')

# The legend
plot.legend(frameon=False, loc=2, fontsize='xx-small', ncol=3, mode='none')

# A few adjustments to fonts and colors
plot.xaxis.set_ticks_position('none')
plot.yaxis.set_ticks_position('none')

for tk in plot.axes.get_xmajorticklabels():
    plt.setp(tk, color='gray', fontsize='xx-small')

plt.setp(bars.xaxis.label, color='gray', fontsize='xx-small')

plot.grid(which='major', axis='y', linewidth=.8, linestyle='-', color='lightgray')
plot.spines['bottom'].set_color('gray')
bars.spines['bottom'].set_color('gray')

plt.subplots_adjust(wspace=-0.1)