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chapter_5_productfragmentation-baci92.py
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chapter_5_productfragmentation-baci92.py
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"""
Chapter 5: Product Fragmentation
================================
Compute Product Fragmentation Dataset with Final and Parts and Components Indicators
Dataset
-------
BACI HS1992 Data
STATA Files
-----------
chapter_5_productfragmentation-baci92.do [Builds Dataset]
"""
from __future__ import division
import os
import gc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyeconlab import WDI
from dataset_info import SOURCE_DIR
WDI_DIR = SOURCE_DIR["wdi"]
wdi = WDI(source_dir=WDI_DIR)
from pyeconlab import DynamicProductLevelExportSystem
from dataset_info import CHAPTER_RESULTS
SOURCE_DIR = CHAPTER_RESULTS[5] #-Data is made using stata-#
RESULTS_DIR = CHAPTER_RESULTS[5]
#-Classifications-#
R3toR2 = pd.read_stata(SOURCE_DIR+"un-sitcr3-sitcr2.dta").set_index("sitcr3l5")
pc = pd.read_stata(SOURCE_DIR+"athukorala-pc-sitcr3l5.dta").set_index("sitcr3l5")
lall = pd.read_stata(SOURCE_DIR+"lall-techclassification.dta").set_index("sitcr2l3") #Interface <lall,sitc3,sitcdescription>. Note: This is SITC rev 2 but at the 3 digit level!
cntryreg = pd.read_stata(SOURCE_DIR+"un-countryinfo-regions-development.dta")
pscomm = pd.read_stata(SOURCE_DIR+"productspace-communityname-concord.dta").set_index("pscommunity")
psnodes = pd.read_stata(RESULTS_DIR+"productspace-sitcr2-node-data.dta")
#-----------------------------------------------#
#-Construct H5 Files of SITC R3 BACI TRADE DATA-#
#-----------------------------------------------#
#-Yearly Raw SITCR2 Conversion Files for merging within Python-#
store = pd.HDFStore(RESULTS_DIR+"baci92-sitcr3/"+"baci92-sitcr3-trade-1995-2013-yearly.h5", complevel=9, complib="zlib")
for year in xrange(1995,2013+1,1):
print "Processing year %s ..."%year
data = pd.read_stata(SOURCE_DIR+"baci92-sitcr3/"+"baci92-sitcr3-trade-%s.dta"%(year))
data = data.rename(columns={'sitcr3' : 'productcode'})
#-Country Region Information-#
data = data.merge(cntryreg[["iso3c","regionname","areaname"]], left_on=["eiso3c"], right_on=["iso3c"], how="inner") #-how="left"
data = data.rename(columns={'regionname':'eregionname', 'areaname':'eareaname'})
del data["iso3c"]
data = data.merge(cntryreg[["iso3c","regionname","areaname"]], left_on=["iiso3c"], right_on=["iso3c"], how="inner") #-how="left"
data = data.rename(columns={'regionname':'iregionname', 'areaname':'iareaname'})
del data["iso3c"]
store.put("Y%s"%year, data, format="table")
del data
gc.collect()
store.close()
#-H5 Dataset of STATA Compiled Trade DATASET-#
#-Includes: Parts and Components, Manufactured Goods, Lall and Leamer Product Classifications and ProductSpace Communities-#
store = pd.HDFStore(RESULTS_DIR+"baci92-sitcr3-dataset/"+"baci92-sitcr3-trade-1995-2013-yearly.h5", complevel=9, complib="zlib")
for year in xrange(1995,2013+1,1):
print "Processing year %s ..."%year
data = pd.read_stata(SOURCE_DIR+"baci92-sitcr3-dataset/"+"baci92-sitcr3-trade-%s-dataset.dta"%(year))
store.put("Y%s"%year, data, format="table")
del data
gc.collect()
store.close()
#-------------------------------------------------------------------------------------------------------------#
# ---- ANALYSIS ---- #
#-Load Data-#
#-Work with BACI EXPORT Data-#
for year in xrange(1995,2013+1,1):
df = pd.read_stata(SOURCE_DIR+"baci92-sitcr3-dataset/"+"baci92-sitcr3-export-%s-dataset.dta"%(year))
if year == 1995:
data = df
else:
data = data.append(df)
data.reset_index(inplace=True, drop=True)
data["export"] = data["export"]*1000 #Scale Exports by 1000
#-Locate Manufactured Trade-#
manuf = data.loc[data.sitccat == 'M'].reset_index(drop=True)
#----------#
#-Analysis-#
#----------#
#-----------------#
#-Within Analysis-#
#-----------------#
#-Total P&C Exports in Total World Export-#
world = data.groupby(["year","pc"]).sum()["export"]
plotdata = world.unstack()
plotdata = plotdata.rename(columns={0:'Other Exports', 1:'Parts and Components'})
plotdata.columns.name = ""
fig = plotdata.plot()
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_total_exports.png", dpi=400)
plt.clf()
#-Percent P&C-#
plotdata["%"] = plotdata["Parts and Components"] / (plotdata["Parts and Components"] + plotdata["Other Exports"])
fig = plotdata["%"].plot(ylim=0, title="Percent (P&C) in Total World Exports")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_total_exports.png", dpi=400)
plt.clf()
#-Total P&C Exports in Total Manufacturing Exports-#
plotdata = manuf.groupby(["year","pc"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'Final Products (Manufacturing)', 1:'Parts and Components'})
plotdata.columns.name = ""
fig = plotdata.plot()
fig.set_ylabel("US$ Trillion")
fig.set_xlabel("Year")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_total_manuf_exports.png", dpi=400)
plt.clf()
#-Percent P&C-#
plotdata["%"] = plotdata["Parts and Components"] / (plotdata["Parts and Components"] + plotdata["Final Products (Manufacturing)"])
fig2 = plotdata["%"].plot(ylim=0, title="Percent (P&C) in Total Manufacturing Exports")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_total_manuf_exports.png", dpi=400)
plt.clf()
#-Combined Plot-#
plotdata = manuf.groupby(["year","pc"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'FM', 1:'PC'})
plotdata.columns.name = ""
plotdata["%PC"] = (plotdata["PC"] / (plotdata["PC"] + plotdata["FM"]))*100
fig = plotdata.plot(secondary_y=["%PC"], xticks=xrange(1995,2013+1,2))
fig.set_ylabel("US$ Trillion")
fig.set_xlabel("Year")
fig.right_ax.set_ylabel("Percent PC (of Manufactures)")
fig.right_ax.set_ylim((0,100))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-pc_total_manuf_exports_withpercent-thesis.png", dpi=400)
#-Parts and Components in Developing Country Share-#
devmanuf = manuf.loc[manuf.lessdev == 1]
#-Combined Plot-#
plotdata = devmanuf.groupby(["year","np"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'OM', 1:'NP'})
plotdata.columns.name = ""
plotdata["%NP"] = (plotdata["NP"] / (plotdata["NP"] + plotdata["OM"]))*100
fig = plotdata.plot(secondary_y=["%NP"], xticks=xrange(1995,2013+1,2))
fig.set_ylabel("US$ Trillion")
fig.set_xlabel("Year")
fig.right_ax.set_ylabel("Percent NP (of Manufactures)")
fig.right_ax.set_ylim((0,100))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-pc_total_manuf_exports_withpercent-thesis.png", dpi=400)
#-Area Analysis-#
#-Total P&C Exports in Manufacturing Exports (by Area)-#
plotdata = manuf.groupby(["areaname","year","pc"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'Final Products', 1:'Parts and Components'})
plotdata.columns.name = ""
fig = plotdata.ix["Asia"].plot(title="Asia")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_total_manuf_exports-Asia.png", dpi=400)
plt.clf()
#-Percent of P&C Exports in Manufacturing Export (by Area)-#
plotdata = manuf.groupby(["areaname","year","pc"]).sum()
plotdata["totx"] = plotdata.groupby(level=["areaname", "year"])["export"].transform(np.sum)
plotdata["%x"] = plotdata["export"] / plotdata["totx"]
plotdata = plotdata["%x"].unstack()
plotdata = plotdata.rename(columns={0:'Final Products', 1:'Parts and Components'})
plotdata.columns.name = ""
fig = plotdata.ix["Asia"].plot(title="Asia")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_manuf_exports-Asia.png", dpi=400)
plt.clf()
#-Area %share in Manufacturing Trade (P&C)-#
fig2 = plotdata["Parts and Components"].unstack(level="areaname").plot(title="Percent P&C in Total Manufacturing Trade (by Area)")
fig2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig2.set_ylabel("Percent")
fig2.set_ylim(bottom=0)
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_manuf_exports-UNArea.png", dpi=400, bbox_inches="tight")
plt.clf()
#-Plot For Thesis-#
plotdata = plotdata["Parts and Components"].apply(lambda x: x*100)
plotdata = plotdata.unstack(level="areaname")
plotdata = plotdata.rename(columns={"Latin America and the Caribbean":"South America","Northern America":"North America"})
fig = plotdata["Africa"].plot(style='c--')
fig = plotdata["Asia"].plot(style='g^-')
fig = plotdata["Europe"].plot(style='r-.')
fig = plotdata["South America"].plot(style='b*-')
fig = plotdata["North America"].plot(style='kx-')
fig = plotdata["Oceania"].plot(style='m-')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent PC (in Manufactures)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_manuf_exports-UNArea-thesis.png", dpi=400, bbox_inches="tight")
#~~Region Analysis~~#
#-Total P&C Exports in Manufacturing Exports (by Region)-#
plotdata = manuf.groupby(["regionname","year","pc"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'Final Products', 1:'Parts and Components'})
plotdata.columns.name = ""
fig = plotdata.ix["Eastern Asia"].plot(title="Total P&C and Manufacturing Export (Eastern Asia)")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_tota_manuf_exports-EAsia.png", dpi=400)
plt.clf()
fig = plotdata.ix["South-Eastern Asia"].plot(title="Total P&C and Manufacturing Export (South-Eastern Asia)")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_tota_manuf_exports-SEAsia.png", dpi=400)
plt.clf()
#-Region %share in Manufacturing Exports-#
plotdata["%"] = plotdata["Parts and Components"] / (plotdata["Parts and Components"] + plotdata["Final Products"])
fig = plotdata["%"].unstack(level="regionname")[["Eastern Asia", "South-Eastern Asia"]].plot(title="PC % share in Manufacturing Exports (by Region)", ylim=(0,0.5))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_manuf_exports-EAsia+SEAsia.png", dpi=400)
plt.clf()
#-Table-#
table = plotdata["%"].unstack(level="regionname")
table.to_excel(RESULTS_DIR+"tables/"+"baci92-percent_pc_in_manuf_exports-UNRegions.xlsx")
#-Thesis Plots-#
plotdata = plotdata["%"].unstack(level="regionname") * 100
plotdata.columns.name = ""
plotdata = plotdata.rename(columns={'Eastern Asia':'East Asia', 'South-Eastern Asia':'South-East Asia', 'Southern Asia':'South Asia'})
fig = plotdata["East Asia"].plot(style='g-', legend=True)
fig = plotdata["South-East Asia"].plot(style='g:', legend=True)
fig = plotdata["South Asia"].plot(style='g*--', legend=True)
fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=3)
fig.set_ylabel("Percent PC (in Manufactures)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_tota_manuf_exports-East+SoutEast+SouthAsia-thesis.png", dpi=400, bbox_inches="tight")
#~~Country Analysis~~#
#-Total Exports-#
#-Total P&C in Total Exports (By Country)-#
plotdata = data.groupby(["country","year","pc"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'Final Products', 1:'Parts and Components'})
plotdata.columns.name = ""
fig = plotdata.ix["JPN"].plot(title="Total P&C and Total Export (Japan)")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_and_total_exports-JPN.png", dpi=400)
plt.clf()
fig = plotdata.ix["CHN"].plot(title="Total P&C and Total Export (China)")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_and_total_exports-CHN.png", dpi=400)
plt.clf()
#-Country %share in Total Exports (By Country)-#
plotdata["%"] = plotdata["Parts and Components"] / (plotdata["Parts and Components"] + plotdata["Final Products"])
fig = plotdata["%"].unstack(level="country")[["CHN", "JPN", "TWN", "KOR"]].plot(title="Total P&C %share of Total Export (Countries)")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_total_exports-CHN+JPN+TWN+KOR.png", dpi=400, bbox_inches="tight")
plt.clf()
fig = plotdata["%"].unstack(level="country")[["IDN", "MYS", "PHL", "SGP", "THA", "VNM"]].plot(title="Total P&C %share of Total Export (Countries)")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_total_exports-IDN+MYS+PHL+SGP+THA+VNM.png", dpi=400, bbox_inches="tight")
plt.clf()
#-Table-#
table = plotdata["%"].unstack(level="country").T
table.to_excel(RESULTS_DIR+"tables/"+"baci92-percent_pc_in_total_exports-Countries.xlsx")
SELECTION = ["AUS","USA","CHN","JPN","IDN","IND","THA","MYS","PHL","VNM"]
table.ix[SELECTION].to_excel(RESULTS_DIR+"tables/"+"baci92-percent_pc_in_total_exports-SelectionCntry.xlsx")
#-Manufacturing Exports Only-#
#-Total P&C Exports in Manufacturing Exports (by Country)-#
plotdata = manuf.groupby(["country","year","pc"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'Final Products', 1:'Parts and Components'})
plotdata.columns.name = ""
fig = plotdata.ix["JPN"].plot(title="Total P&C and Manufacturing Export (Japan)")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_and_manuf_exports-JPN.png", dpi=400)
plt.clf()
fig = plotdata.ix["CHN"].plot(title="Total P&C and Manufacturing Export (China)")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_pc_and_manuf_exports-CHN.png", dpi=400)
plt.clf()
#-Country %share in Manufacturing Export-#
plotdata["%"] = plotdata["Parts and Components"] / (plotdata["Parts and Components"] + plotdata["Final Products"])
fig = plotdata["%"].unstack(level="country")[["CHN", "JPN", "TWN", "KOR"]].plot(title="Total P&C %share of Manufacturing Export (Countries)")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_manuf_exports-CHN+JPN+TWN+KOR.png", dpi=400, bbox_inches="tight")
plt.clf()
fig = plotdata["%"].unstack(level="country")[["IDN", "MYS", "PHL", "SGP", "THA", "VNM"]].plot(title="Total P&C %share of Manufacturing Export (Countries)")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_in_manuf_exports-IDN+MYS+PHL+SGP+THA+VNM.png", dpi=400, bbox_inches="tight")
plt.clf()
#-Table-#
table = plotdata["%"].unstack(level="country").T
table.to_excel(RESULTS_DIR+"tables/"+"baci92-percent_pc_in_manuf_exports-Countries.xlsx")
SELECTION = ["AUS","USA","CHN","JPN","IDN","IND","THA","MYS","PHL","VNM"]
table.ix[SELECTION].to_excel(RESULTS_DIR+"tables/"+"baci92-percent_pc_in_manuf_exports-SelectionCntry.xlsx")
#-Chart for Thesis-#
plotdata = plotdata["%"].unstack(level="country")*100
fig = plotdata["CHN"].plot(style='r-')
fig = plotdata["JPN"].plot(style='g--')
fig = plotdata["TWN"].plot(style='b*-')
fig = plotdata["KOR"].plot(style='c^-')
fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=4)
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.set_ylabel("Percent PC (in Manufactures)")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_and_manuf_exports-CHN+JPN+TWN+KOR-thesis.png", dpi=400, bbox_inches="tight")
plt.clf()
fig = plotdata["IDN"].plot(style='rs-')
fig = plotdata["MYS"].plot(style='gx--')
fig = plotdata["PHL"].plot(style='b*-')
fig = plotdata["SGP"].plot(style='c^-')
fig = plotdata["THA"].plot(style='m+:')
fig = plotdata["VNM"].plot(style='yo-')
fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=6)
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.set_ylabel("Percent PC (in Manufactures)")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_and_manuf_exports-IDN+MYS+PHL+SGP+THA+VNM-thesis.png", dpi=400, bbox_inches="tight")
#-Product Decompositions-#
#-Percent PC within Lall Categories-#
plotdata = data.groupby(["year","lall","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata["%"] = (plotdata["PC"]/(plotdata["PC"]+plotdata["OP"]))*100
plotdata = plotdata["%"].fillna(0).unstack(level="lall")
plotdata.columns.name = ""
plotdata = plotdata[["PP","RB1","RB2","LT1","LT2","MT1","MT2","MT3","HT1","HT2"]]
plotdata["RB"] = plotdata[["RB1","RB2"]].mean(axis=1)
plotdata["LT"] = plotdata[["LT1","LT2"]].mean(axis=1)
fig = plotdata["HT2"].plot(style="ro--")
fig = plotdata["HT1"].plot(style="r^-")
fig = plotdata["MT3"].plot(style="mo--")
fig = plotdata["MT2"].plot(style="m^-.")
fig = plotdata["MT1"].plot(style="m*-")
fig = plotdata["LT"].plot(style="y^-")
fig = plotdata["RB"].plot(style="go-")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent PC within Lall Category")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_and_manuf_exports-lallcat-thesis.png", dpi=400, bbox_inches="tight")
plt.clf()
#-Country Concentration within Lall Type of Parts and Components Vs Other Goods-#
plotdata = data.groupby(["country","year","lall","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata["%"] = (plotdata["PC"]/(plotdata["PC"]+plotdata["OP"]))*100
plotdata["pctot"] = plotdata["PC"].groupby(level=["country","year"]).transform(np.sum)
plotdata["%pctot"] = (plotdata["PC"]/plotdata["pctot"])*100
#-Japan-#
jpn = plotdata.ix["JPN"]["%pctot"].unstack(level="lall")
jpn.plot()
#-Lall Category Share (Composition) of Parts and Components Exports-#
plotdata = data.groupby(["year","lall","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata = plotdata["PC"].dropna().to_frame()
plotdata = plotdata["PC"].groupby(level="year").apply(lambda x: 100*x/float(x.sum())) #-Percent Composition-#
plotdata = plotdata.unstack(level="lall")
plotdata = plotdata[["RB1","RB2","LT1","LT2","MT1","MT2","MT3","HT1","HT2"]]
plotdata["RB"] = plotdata[["RB1","RB2"]].mean(axis=1)
plotdata["LT"] = plotdata[["LT1","LT2"]].mean(axis=1)
fig = plotdata["HT2"].plot(style="ro--")
fig = plotdata["HT1"].plot(style="r^-")
fig = plotdata["MT3"].plot(style="mo--")
fig = plotdata["MT2"].plot(style="m^-.")
fig = plotdata["MT1"].plot(style="m*-")
fig = plotdata["LT"].plot(style="y^-")
fig = plotdata["RB"].plot(style="go-")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Composition of PC Export (by Value) in Lall Categories")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc-lallcat-thesis.png", dpi=400, bbox_inches="tight")
plt.clf()
#-Lall Category Share (Composition) of Parts and Components Exports [By Country]-#
plotdata = data.groupby(["country","year","lall","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata = plotdata["PC"].dropna().to_frame()
plotdata = plotdata["PC"].groupby(level=["country","year"]).apply(lambda x: 100*x/float(x.sum())) #-Percent Composition-#
plotdata = plotdata.unstack(level="lall")
plotdata = plotdata[["RB1","RB2","LT1","LT2","MT1","MT2","MT3","HT1","HT2"]]
plotdata["RB"] = plotdata[["RB1","RB2"]].mean(axis=1)
plotdata["LT"] = plotdata[["LT1","LT2"]].mean(axis=1)
cntry = "JPN"
cdata = plotdata.ix[cntry]
fig = cdata["HT2"].plot(style="ro--")
fig = cdata["HT1"].plot(style="r^-")
fig = cdata["MT3"].plot(style="mo--")
fig = cdata["MT2"].plot(style="m^-.")
fig = cdata["MT1"].plot(style="m*-")
fig = cdata["LT"].plot(style="y^-")
fig = cdata["RB"].plot(style="go-")
fig.set_title(cntry)
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Composition of PC Export (by Value) in Lall Categories")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc-lallcat-country(%s)-thesis.png"%cntry, dpi=400, bbox_inches="tight")
plt.clf()
#-Decomposition within Leamer Categories-#
plotdata = data.groupby(["year","leamer","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata["%"] = (plotdata["PC"]/(plotdata["PC"]+plotdata["OP"]))*100
plotdata = plotdata["%"].fillna(0).unstack(level="leamer")
plotdata.columns.name = ""
plotdata = plotdata[["1","2","3","4","5","6","7","8","9","10"]]
plotdata = plotdata.rename(columns={'7':'Labour Intensive','8':'Capital Intensive','9':'Machinery','10':'Chemical'})
fig = plotdata["Labour Intensive"].plot(style="bo-")
fig = plotdata["Capital Intensive"].plot(style="m^-")
fig = plotdata["Machinery"].plot(style="r*-")
fig = plotdata["Chemical"].plot(style="g^-")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent PC (in Leamer Category)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_and_manuf_exports-leamercat-thesis.png", dpi=400, bbox_inches="tight")
#-Decomposition within Product Space Categories-#
plotdata = data.groupby(["year","pscommunity","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata["%"] = (plotdata["PC"]/(plotdata["PC"]+plotdata["OP"]))*100
plotdata = plotdata["%"].fillna(0).unstack(level="pscommunity")
plotdata.columns.name = ""
plotdata = plotdata.rename(columns=pscomm.to_dict()["description"])
plotdata = plotdata.rename(columns={'Construction Materials & Equipment':'Construction Equipment'})
fig = plotdata["Aircraft"].plot(style="ro-")
fig = plotdata["Electronics"].plot(style="b+-")
fig = plotdata["Machinery"].plot(style="g^-")
fig = plotdata["Construction Equipment"].plot(style="kx-")
fig = plotdata["Petrochemicals"].plot(style="m.-")
fig = plotdata["Textile & Fabrics"].plot(style="cs-")
fig = plotdata["Garments"].plot(style="yp-")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent PC (in Product Space Community)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_pc_and_manuf_exports-selectpscommunities-thesis.png", dpi=400, bbox_inches="tight")
#-Product Space Share of Parts and Components Trade-#
plotdata = data.groupby(["year","pscommunity","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata = plotdata["PC"].dropna().to_frame()
plotdata = plotdata["PC"].groupby(level="year").apply(lambda x: 100*x/float(x.sum())) #-Percent Composition-#
plotdata = plotdata.unstack(level="pscommunity")
plotdata = plotdata.rename(columns=pscomm.to_dict()["description"])
plotdata = plotdata.rename(columns={'Construction Materials & Equipment':'Construction Equipment'})
fig = plotdata["Machinery"].plot()
fig = plotdata["Electronics"].plot()
fig = plotdata["Aircraft"].plot()
fig = plotdata["Construction Equipment"].plot()
fig = plotdata["Chemicals and Health"].plot()
fig = plotdata["Boilers"].plot()
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Share (% Export Value) by Product Space Community")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc-selectpscommunities-thesis.png", dpi=400, bbox_inches="tight")
pllt.clf()
#-Composition of P&C within SITC Chapter Level 2-#
data["sitc2"] = data["productcode"].apply(lambda x: x[0:2])
plotdata = data.groupby(["country","sitc2","year","pc"]).sum()["export"].unstack(level="pc")
plotdata.columns = ["OP","PC"]
plotdata.columns.name = ""
plotdata["pctot"] = plotdata["PC"].groupby(level=["country","year"]).transform(np.sum)
plotdata["%"] = (plotdata["PC"]/(plotdata["pctot"]))*100
plotdata = plotdata["%"].unstack(level="sitc2").fillna(0)
#-Comparable Across Countries-#
# fig = cdata["78"].plot(style="r+-")
# fig = cdata["77"].plot(style="g.--")
# fig = cdata["76"].plot(style="bo:")
# fig = cdata["75"].plot(style="c*-.")
# fig = cdata["74"].plot(style="yp-")
# fig = cdata["73"].plot(style="ms--")
# fig = cdata["72"].plot(style="rx:")
# fig = cdata["71"].plot(style="gD-.")
#-Japan-#
cdata = plotdata.ix["JPN"]
s = cdata.max()
s.sort(ascending=False) #Top 5 ITems
print s[0:5].index
fig = cdata["77"].plot(style="g.--")
fig = cdata["75"].plot(style="c*-.")
fig = cdata["78"].plot(style="r+-")
fig = cdata["71"].plot(style="gD-.")
fig = cdata["72"].plot(style="rx:")
fig.set_title("JPN")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent Share of PC Exports")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc_in_exports-JPN-thesis.png", dpi=400, bbox_inches="tight")
plt.cla()
#-China-#
cdata = plotdata.ix["CHN"]
s = cdata.max()
s.sort(ascending=False) #Top 5 ITems
print s[0:5].index
fig = cdata["75"].plot(style="c*-.")
fig = cdata["76"].plot(style="bo:")
fig = cdata["77"].plot(style="g.--")
fig = cdata["71"].plot(style="gD-.")
fig = cdata["74"].plot(style="yp-")
fig.set_title("CHN")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent Share of PC Exports")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc_in_exports-CHN-thesis.png", dpi=400, bbox_inches="tight")
plt.cla()
#-Taiwan-#
cdata = plotdata.ix["TWN"]
s = cdata.max()
s.sort(ascending=False) #Top 5 ITems
print s[0:5].index
fig = cdata["75"].plot(style="c*-.")
fig = cdata["77"].plot(style="g.--")
fig = cdata["76"].plot(style="bo:")
fig = cdata["78"].plot(style="r+-")
fig = cdata["74"].plot(style="yp-")
fig.set_title("TWN")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent Share of PC Exports")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc_in_exports-TWN-thesis.png", dpi=400, bbox_inches="tight")
plt.cla()
#-THA-#
cdata = plotdata.ix["THA"]
s = cdata.max()
s.sort(ascending=False) #Top 5 ITems
print s[0:5].index
fig = cdata["75"].plot(style="c*-.")
fig = cdata["77"].plot(style="g.--")
fig = cdata["78"].plot(style="g^-")
fig = cdata["76"].plot(style="bo:")
fig = cdata["74"].plot(style="yp-")
fig.set_title("THA")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent Share of PC Exports")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc_in_exports-THA-thesis.png", dpi=400, bbox_inches="tight")
plt.cla()
#-IDN-#
cdata = plotdata.ix["IDN"]
s = cdata.max()
s.sort(ascending=False) #Top 5 ITems
print s[0:5].index
fig = cdata["77"].plot(style="g.--")
fig = cdata["75"].plot(style="c*-.")
fig = cdata["76"].plot(style="bo:")
fig = cdata["78"].plot(style="g^-")
fig = cdata["72"].plot(style="rx:")
fig.set_title("IDN")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent Share of PC Exports")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc_in_exports-IDN-thesis.png", dpi=400, bbox_inches="tight")
plt.cla()
#-MYS-#
cdata = plotdata.ix["MYS"]
s = cdata.max()
s.sort(ascending=False) #Top 5 ITems
print s[0:5].index
fig = cdata["75"].plot(style="c*-.")
fig = cdata["77"].plot(style="g.--")
fig = cdata["76"].plot(style="bo:")
fig = cdata["72"].plot(style="rx:")
fig = cdata["71"].plot(style="gD-.")
fig.set_title("MYS")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent Share of PC Exports")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc_in_exports-MYS-thesis.png", dpi=400, bbox_inches="tight")
plt.cla()
#-PHL-#
cdata = plotdata.ix["PHL"]
s = cdata.max()
s.sort(ascending=False) #Top 5 ITems
print s[0:5].index
fig = cdata["77"].plot(style="g.--")
fig = cdata["75"].plot(style="c*-.")
fig = cdata["76"].plot(style="bo:")
fig = cdata["78"].plot(style="g^-")
fig = cdata["71"].plot(style="gD-.")
fig.set_title("PHL")
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("Percent Share of PC Exports")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-composition_pc_in_exports-PHL-thesis.png", dpi=400, bbox_inches="tight")
plt.cla()
#------------------#
#-Network Products-#
#------------------#
#
#-Plots
#
#-Work with BACI EXPORT Data-#
for year in xrange(1995,2013+1,1):
df = pd.read_stata(SOURCE_DIR+"baci92-sitcr3-dataset/"+"baci92-sitcr3-export-%s-dataset.dta"%(year))
if year == 1995:
data = df
else:
data = data.append(df)
data.reset_index(inplace=True, drop=True)
data["export"] = data["export"]*1000 #Scale Exports by 1000
#-Locate Manufactured Trade-#
manuf = data.loc[data.sitccat == 'M'].reset_index(drop=True)
#-----------------#
#-Within Analysis-#
#-----------------#
#-Total Network Products in Total Manufacturing Exports-#
plotdata = manuf.groupby(["year","np"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'Final Products (Manufacturing)', 1:'Network Products'})
plotdata.columns.name = ""
fig = plotdata.plot()
fig.set_ylabel("US$ Trillion")
fig.set_xlabel("Year")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-total_np_total_manuf_exports.png", dpi=400)
plt.clf()
#-Percent Network Products-#
plotdata["%"] = plotdata["Network Products"] / (plotdata["Network Products"] + plotdata["Final Products (Manufacturing)"])
fig2 = plotdata["%"].plot(ylim=0, title="Percent (Network Products) in Total Manufacturing Exports")
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-percent_np_total_manuf_exports.png", dpi=400)
plt.clf()
#-Combined Plot-#
plotdata = manuf.groupby(["year","np"]).sum()["export"]
plotdata = plotdata.unstack()
plotdata = plotdata.rename(columns={0:'FM', 1:'NP'})
plotdata.columns.name = ""
plotdata["%NP"] = (plotdata["NP"] / (plotdata["NP"] + plotdata["FM"]))*100
fig = plotdata.plot(secondary_y=["%NP"], xticks=xrange(1995,2013+1,2))
fig.set_ylabel("US$ Trillion")
fig.set_xlabel("Year")
fig.right_ax.set_ylabel("Percent NP (of Manufactures)")
fig.right_ax.set_ylim((0,100))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-np_total_manuf_exports_withpercent-thesis.png", dpi=400)
#-Decomposition Network Trade (P&C and Final Assembly Products)-#
df = manuf.copy()
df["ptype"] = df["pc"].apply(lambda x: "pc" if x == 1 else "other")
df["ptype"] = df[["ptype","fap"]].apply(lambda row: "fap" if row["fap"] == 1 else row["ptype"], axis=1)
df = df.groupby(["year","ptype"])["export"].sum()
dfp = df.groupby("year").apply(lambda x: 100*x/float(x.sum()))
fig = dfp.unstack().plot()
#-Share of Parts and Components in Network Products-#
df = manuf.copy()
df1 = df.groupby(["year","country","np"]).sum()["export"].unstack()
df1.columns = ["np=0","np=1"]
df1 = df1["np=1"]
df2 = df.groupby(["year","country","pc"]).sum()["export"].unstack()
df2.columns = ["pc=0","pc=1"]
df2 = df2["pc=1"]
df3 = df2/df1
df3 = df3.reorder_levels([1,0])
df3.ix["JPN"].plot(ylim=0)
#------------------#
#-Between Analysis-#
#------------------#
#/Plots/pd.options.display.mpl_style = 'default'#
#-World Shares-#
#-Network Products-#
netp = data.loc[data.np == 1]
#-Areas (World Share) in Network Product Export-#
df = netp.groupby(["year","areaname"]).sum()["export"]
df = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
plotdata = df.rename(columns={'Latin America and the Caribbean':'Latin America'})
fig = plotdata["Africa"].plot(style='r+-', legend=True)
fig = plotdata["Asia"].plot(style='b.--', legend=True)
fig = plotdata["Europe"].plot(style='go:', legend=True)
fig = plotdata["Latin America"].plot(style='c*-.', legend=True)
fig = plotdata["Northern America"].plot(style='kp-', legend=True)
fig = plotdata["Oceania"].plot(style='ys--', legend=True)
fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=3)
fig.set_ylabel("World Share in Network \nProduct Exports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-np-world_export_shares-byarea-thesis.png", dpi=400, bbox_inches="tight")
#-Region (World Share) in Network Product Export-#
df = netp.groupby(["year","regionname"]).sum()["export"]
plotdata = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
plotdata = plotdata.rename(columns={'Eastern Asia':'East Asia', 'South-Eastern Asia':'South-East Asia',
'Western Europe':'West Europe', 'Northern America':'North America',
'Eastern Europe':'East Europe', 'Northern Europe':'North Europe',
'Southern Europe':'South Europe'})
# #-Area Plot-#
# fig = plotdata[["East Asia","South-East Asia","North America","Central America","South America","West Europe",
# "East Europe","South Europe"]].plot(kind='area', stacked=True)
# fig.legend(loc='center left', bbox_to_anchor=(1, 0.5)) #-Lines not large enough-Not Time to Fix-#
# fig.set_ylabel("World Share in Network \nProduct Exports (%)")
# fig.set_ylim(bottom=0)
# fig.set_xlabel("Year")
# fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
# plt.tight_layout()
#-Line Plot-#
fig = plotdata["East Asia"].plot(style='r+-')
fig = plotdata["South-East Asia"].plot(style='b.-')
fig = plotdata["North America"].plot(style='go-')
fig = plotdata["Central America"].plot(style='c*-')
fig = plotdata["South America"].plot(style='mp-')
fig = plotdata["West Europe"].plot(style='ks-')
fig = plotdata["East Europe"].plot(style='rx--')
fig = plotdata["South Europe"].plot(style='bD--')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("World Share in Network \nProduct Exports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-np-world_export_shares-byregion-thesis.png", dpi=400, bbox_inches="tight")
#-Country (World Share) in Network Product Export-#
df = netp.groupby(["year","country"]).sum()["export"]
plotdata = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
#-Line Plot-#
fig = plotdata["CHN"].plot(style='r+-')
fig = plotdata["DEU"].plot(style='b.-')
fig = plotdata["USA"].plot(style='go-')
fig = plotdata["JPN"].plot(style='c*-')
fig = plotdata["KOR"].plot(style='mp-')
fig = plotdata["TWN"].plot(style='ys-')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("World Share in Network \nProduct Exports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-np-world_export_shares-bycntry-top6-thesis.png", dpi=400, bbox_inches="tight")
#-Parts and Components-#
pc = data.loc[data.pc == 1]
#-Areas (World Share) in Parts and Component Exports-#
df = pc.groupby(["year","areaname"]).sum()["export"]
df = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
plotdata = df.rename(columns={'Latin America and the Caribbean':'Latin America'})
fig = plotdata["Africa"].plot(style='r+-', legend=True)
fig = plotdata["Asia"].plot(style='b.--', legend=True)
fig = plotdata["Europe"].plot(style='go:', legend=True)
fig = plotdata["Latin America"].plot(style='c*-.', legend=True)
fig = plotdata["Northern America"].plot(style='kp-', legend=True)
fig = plotdata["Oceania"].plot(style='ys--', legend=True)
fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=3)
fig.set_ylabel("World Share in Parts & \nComponents Exports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-pc-world_export_shares-byarea-thesis.png", dpi=400, bbox_inches="tight")
#-Region (World Share) in Parts and Components Export-#
df = pc.groupby(["year","regionname"]).sum()["export"]
plotdata = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
plotdata = plotdata.rename(columns={'Eastern Asia':'East Asia', 'South-Eastern Asia':'South-East Asia',
'Western Europe':'West Europe', 'Northern America':'North America',
'Eastern Europe':'East Europe', 'Northern Europe':'North Europe',
'Southern Europe':'South Europe'})
# #-Area Plot-#
# fig = plotdata[["East Asia","South-East Asia","North America","Central America","South America","West Europe",
# "East Europe","South Europe"]].plot(kind='area', stacked=True)
# fig.legend(loc='center left', bbox_to_anchor=(1, 0.5)) #-Lines not large enough-Not Time to Fix-#
# fig.set_ylabel("World Share in Parts & \nComponents Exports (%)")
# fig.set_ylim(bottom=0)
# fig.set_xlabel("Year")
# fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
# plt.tight_layout()
#-Line Plot-#
fig = plotdata["East Asia"].plot(style='r+-')
fig = plotdata["South-East Asia"].plot(style='b.-')
fig = plotdata["North America"].plot(style='go-')
fig = plotdata["Central America"].plot(style='c*-')
fig = plotdata["South America"].plot(style='mp-')
fig = plotdata["West Europe"].plot(style='ks-')
fig = plotdata["East Europe"].plot(style='rx--')
fig = plotdata["South Europe"].plot(style='bD--')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("World Share in Parts & \nComponents Exports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-pc-world_export_shares-byregion-thesis.png", dpi=400, bbox_inches="tight")
#-Country (World Share) in Network Trade-#
df = pc.groupby(["year","country"]).sum()["export"]
plotdata = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
s = df.ix[2013]
s.sort(ascending=False)
print s[0:5]
#-Line Plot-#
fig = plotdata["CHN"].plot(style='r+-')
fig = plotdata["DEU"].plot(style='b.-')
fig = plotdata["USA"].plot(style='go-')
fig = plotdata["JPN"].plot(style='c*-')
fig = plotdata["KOR"].plot(style='mp-')
fig = plotdata["TWN"].plot(style='ys-')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("World Share in Parts & \nComponents Exports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-pc-world_export_shares-bycntry-top6-thesis.png", dpi=400, bbox_inches="tight")
#
#-Final Assembly Products-#
#
fap = data.loc[data.fap == 1]
#-Areas (World Share) in Final Assembly Exports-#
df = fap.groupby(["year","areaname"]).sum()["export"]
df = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
plotdata = df.rename(columns={'Latin America and the Caribbean':'Latin America'})
fig = plotdata["Africa"].plot(style='r+-', legend=True)
fig = plotdata["Asia"].plot(style='b.--', legend=True)
fig = plotdata["Europe"].plot(style='go:', legend=True)
fig = plotdata["Latin America"].plot(style='c*-.', legend=True)
fig = plotdata["Northern America"].plot(style='kp-', legend=True)
fig = plotdata["Oceania"].plot(style='ys--', legend=True)
fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=3)
fig.set_ylabel("World Share in Final Assembly \nExports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-fap-world_export_shares-byarea-thesis.png", dpi=400, bbox_inches="tight")
#-Region (World Share) in Final Assembly Export-#
df = fap.groupby(["year","regionname"]).sum()["export"]
plotdata = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
plotdata = plotdata.rename(columns={'Eastern Asia':'East Asia', 'South-Eastern Asia':'South-East Asia',
'Western Europe':'West Europe', 'Northern America':'North America',
'Eastern Europe':'East Europe', 'Northern Europe':'North Europe',
'Southern Europe':'South Europe'})
# #-Area Plot-#
# fig = plotdata[["East Asia","South-East Asia","North America","Central America","South America","West Europe",
# "East Europe","South Europe"]].plot(kind='area', stacked=True)
# fig.legend(loc='center left', bbox_to_anchor=(1, 0.5)) #-Lines not large enough-Not Time to Fix-#
# fig.set_ylabel("World Share in Final Assembly \nExports (%)")
# fig.set_ylim(bottom=0)
# fig.set_xlabel("Year")
# fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
# plt.tight_layout()
#-Line Plot-#
fig = plotdata["East Asia"].plot(style='r+-')
fig = plotdata["South-East Asia"].plot(style='b.-')
fig = plotdata["North America"].plot(style='go-')
fig = plotdata["Central America"].plot(style='c*-')
fig = plotdata["South America"].plot(style='mp-')
fig = plotdata["West Europe"].plot(style='ks-')
fig = plotdata["East Europe"].plot(style='rx--')
fig = plotdata["South Europe"].plot(style='bD--')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("World Share in Final Assembly \nExports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-fap-world_export_shares-byregion-thesis.png", dpi=400, bbox_inches="tight")
#-Country (World Share) in Final Assembly Exports-#
df = fap.groupby(["year","country"]).sum()["export"]
plotdata = df.groupby(level=["year"]).apply(lambda x: 100*x/float(x.sum())).unstack()
s = plotdata.ix[2013]
s.sort(ascending=False)
print s[0:5]
#-Line Plot-#
fig = plotdata["CHN"].plot(style='r+-')
fig = plotdata["DEU"].plot(style='b.-')
fig = plotdata["USA"].plot(style='go-')
fig = plotdata["JPN"].plot(style='c*-')
fig = plotdata["KOR"].plot(style='mp-')
fig = plotdata["TWN"].plot(style='ys-')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.set_ylabel("World Share in Final Assembly \nExports (%)")
fig.set_ylim(bottom=0)
fig.set_xlabel("Year")
fig.xaxis.axes.set_xticks(xrange(1995,2013+1,2))
plt.tight_layout()
plt.savefig(RESULTS_DIR+"plots/"+"baci92-fap-world_export_shares-bycntry-top6-thesis.png", dpi=400, bbox_inches="tight")
#
#-Composition Tables-#
#
#--------#
#-EXPORT-#
#--------#
#-BACI EXPORT DATA-#
for year in xrange(1995,2013+1,1):
df = pd.read_stata(SOURCE_DIR+"baci92-sitcr3-dataset/"+"baci92-sitcr3-export-%s-dataset.dta"%(year))
if year == 1995:
data = df
else:
data = data.append(df)
data.reset_index(inplace=True, drop=True)
data["export"] = data["export"]*1000 #Scale Exports by 1000
#-Manufactured Trade
manuf = data.loc[data.sitccat == 'M'].reset_index(drop=True)
#-Final Assembly Products
fap = data.loc[data.fap == 1]
#-Parts and Components