#!/usr/bin/env python
# coding: utf-8
# ### This is sample of using c3py library (https://yurz@bitbucket.org/yurz/c3py.git)
# #### Only Line, Area and Bar (Unstacked and Stacked) charts are supported at the momen. c3py can be used for displaying charts inside Jupyter Notebooks or rendering self-contained HTML files.
# In[1]:
from c3py import C3Chart as c3
# In[2]:
import sys
import IPython
# In[3]:
print(sys.version)
print(IPython.__version__)
# #### For displaying inside Jupyter - Load required Javascript libraries:
# In[4]:
c3.ipy_load()
# #### Download some GDP data from World Bank and prepare Pandas DataFrame object:
# In[12]:
from pandas_datareader import data, wb
# In[17]:
df = wb.download(indicator='NY.GDP.PCAP.PP.KD', country="all", start=2010, end=2015).unstack()
# #### This is how raw df looks:
# In[18]:
df.head()
# #### Cleanup and create df_selected with top 5 and bottom 5 countries based on GDP Per Capita Growth between 2010 and 2015:
# In[19]:
df.columns = df.columns.levels[1]
df = df.dropna(how="any")
df = df.astype(int)
df["5yr_change"] = (df["2014"]-df["2010"])/df["2010"]
df = df.sort_values("5yr_change")
df_selected = df.head().append(df.tail())
df_selected
# In[20]:
ch1 = c3(data=df_selected[['2010', '2011', '2012', '2013', '2014', '2015']].T,
width=1020, height=380, kind="line", title="GDP Per Capita 2010-2015 Growth - Top 5 and Bottom 5 Countries", id="ch1")
ch1()
# #### Now lat's create a copy of the above chart presented as an area chart
# In[21]:
ch2 = ch1.copy()
# In[22]:
ch2.id = "ch2"
ch2.kind = "area"
ch2()
# #### Render a HTML file with both files:
# In[23]:
with open("sample.html", "w") as f:
f.write("" + c3.head + ch1.to_html() + "
" + ch2.to_html() + "")
# [Yuri Zhylyuk](http://yznotes.com) 2016-10-23