Project 4: Exploring the UK's milk imports and exports¶

by Tony Hirst and Michel Wermelinger, 19 November 2015, 28 March 2016, 18 October 2017

This is the project notebook for Week 4 of The Open University's Learn to Code for Data Analysis course.

A country's economy depends, sometimes heavily, on its exports and imports. The United Nations Comtrade database provides data on global trade. It will be used to analyse the UK's imports and exports of milk and cream in 2015:

How much does the UK export and import and is the balance positive (more exports than imports)?
Which are the main trading partners, i.e. from/to which countries does the UK import/export the most?
Which are the regular customers, i.e. which countries buy milk from the UK every month?
Which countries does the UK both import from and export to?

import warnings
warnings.simplefilter('ignore', FutureWarning)

from pandas import *
%matplotlib inline

Getting and preparing the data¶

The data is obtained from the United Nations Comtrade website, by selecting the following configuration:

Type of Product: goods
Frequency: monthly
Periods: January to May of 2015
Reporter: United Kingdom
Partners: all
Flows: imports and exports
HS (as reported) commodity codes: 0401 (Milk and cream, neither concentrated nor sweetened) and 0402 (Milk and cream, concentrated or sweetened)

Clicking on 'Preview' results in a message that the data exceeds 500 rows. Data was downloaded using the Download CSV button and the download file renamed appropriately.

LOCATION = 'comtrade_milk_uk_jan_jul_15.csv'

The data can also be downloaded directly from Comtrade using the "View API Call" URL, modified in two ways:

max=500 is increased to max=5000 to make sure all data is loaded,
&fmt=csv is added at the end to obtain the data in CSV format.

# LOCATION = 'http://comtrade.un.org/api/get?max=5000&type=C&freq=M&px=HS&ps=201505%2C201504%2C201503%2C201502%2C201501&r=826&p=all&rg=1%2C2&cc=0401%2C0402&fmt=csv'

On reading in the data, the commodity code has to be read as a string, to not lose the leading zero.

milk = read_csv(LOCATION, dtype={'Commodity Code':str})
milk.tail(2)

The data only covers the first five months of 2015. Most columns are irrelevant for this analysis, or contain always the same value, like the year and reporter columns. The commodity code is transformed into a short but descriptive text and only the relevant columns are selected.

def milkType(code):
    if code == '0401': # neither concentrated nor sweetened
        return 'unprocessed'
    if code == '0402': # concentrated or sweetened
        return 'processed' 
    return 'unknown'

COMMODITY = 'Milk and cream'
milk[COMMODITY] = milk['Commodity Code'].apply(milkType)
MONTH = 'Period'
PARTNER = 'Partner'
FLOW = 'Trade Flow'
VALUE = 'Trade Value (US$)'
headings = [MONTH, PARTNER, FLOW, COMMODITY, VALUE]
milk = milk[headings]
milk.head()

The data contains the total imports and exports per month, under the 'World' partner. Those rows are removed to keep only the per-country data.

milk = milk[milk[PARTNER] != 'World']
milk.head()

Total trade flow¶

To answer the first question, 'how much does the UK export and import and is the balance positive (more exports than imports)?', the dataframe is split into two groups: exports from the UK and imports into the UK. The trade values within each group are summed up to get the total trading.

grouped = milk.groupby([FLOW])
grouped[VALUE].aggregate(sum)

Trade Flow
Exports    265029661
Imports    156483978
Name: Trade Value (US$), dtype: int64

This shows a trade surplus of over 100 million dollars.

Main trade partners¶

To address the second question, 'Which are the main trading partners, i.e. from/to which countries does the UK import/export the most?', the dataframe is split by country instead, and then each group aggregated for the total trade value. This is done separately for imports and exports. The result is sorted in descending order so that the main partners are at the top.

imports = milk[milk[FLOW] == 'Imports']
grouped = imports.groupby([PARTNER])
print('The UK imports from', len(grouped), 'countries.')
print('The 5 biggest exporters to the UK are:')
totalImports = grouped[VALUE].aggregate(sum).sort_values(inplace=False,ascending=False)
totalImports.head()

The UK imports from 21 countries.
The 5 biggest exporters to the UK are:

Partner
Ireland        46263897
France         28314091
Germany        21899123
Netherlands    17658912
Belgium        14325697
Name: Trade Value (US$), dtype: int64

The export values can be plotted as a bar chart, making differences between countries easier to see.

totalImports.head(10).plot(kind='barh')

<matplotlib.axes._subplots.AxesSubplot at 0x110cfa470>

exports = milk[milk[FLOW] == 'Exports']
grouped = exports.groupby([PARTNER])
print('The UK exports to', len(grouped), 'countries.')
print('The 5 biggest importers from the UK are:')
grouped[VALUE].aggregate(sum).sort_values(ascending=False,inplace=False).head()

The UK exports to 107 countries.
The 5 biggest importers from the UK are:

Partner
Ireland                 128155891
Netherlands              18018603
China                    14111163
Germany                  10828464
China, Hong Kong SAR      9482458
Name: Trade Value (US$), dtype: int64

Regular importers¶

Given that there are two commodities, the third question, 'Which are the regular customers, i.e. which countries buy milk from the UK every month?', is meant in the sense that a regular customer imports both commodities every month. This means that if the exports dataframe is grouped by country, each group has exactly ten rows (two commodities bought each of the five months). To see the countries, only the first month of one commodity has to be listed, as by definition it's the same countries every month and for the other commodity.

def buysEveryMonth(group):
    return len(group) == 10

grouped = exports.groupby([PARTNER])
regular = grouped.filter(buysEveryMonth)
regular[(regular[MONTH] == 201501) & (regular[COMMODITY] == 'processed')]

Just over 75% of the total UK exports are due to these regular customers.

regular[VALUE].sum() / exports[VALUE].sum()

0.7583526396315317

Bi-directional trade¶

To address the fourth question, 'Which countries does the UK both import from and export to?', a pivot table is used to list the total export and import value for each country.

countries = pivot_table(milk, index=[PARTNER], columns=[FLOW], 
                        values=VALUE, aggfunc=sum)
countries.head()

Removing the rows with a missing value will result in only those countries with bi-directional trade flow with the UK.

countries.dropna()

Conclusions¶

The milk and cream trade of the UK from January to May 2015 was analysed in terms of which countries the UK mostly depends on for income (exports) and goods (imports). Over the period, the UK had a trade surplus of over 100 million US dollars.

Ireland is the main partner, but it imported from the UK almost the triple in value than it exported to the UK.

The UK exported to over 100 countries during the period, but only imported from 21 countries, the main ones (top five by trade value) being geographically close. China and Hong Kong are the main importers that are not also main exporters.

The UK is heavily dependent on its regular customers, the 16 countries that buy all types of milk and cream every month. They contribute three quarters of the total export value.

The UK has bi-directional trade (i.e. both exports and imports) with 20 countries, although for some the trade value (in US dollars) is suspiciously low, which raises questions about the data's accuracy.

	Classification	Year	Period	Period Desc.	Aggregate Level	Is Leaf Code	Trade Flow Code	Trade Flow	Reporter Code	Reporter	...	Qty	Alt Qty Unit Code	Alt Qty Unit	Alt Qty	Netweight (kg)	Gross weight (kg)	Trade Value (US$)	CIF Trade Value (US$)	FOB Trade Value (US$)	Flag
635	HS	2015	201505	May 2015	4	0	2	Exports	826	United Kingdom	...	NaN	NaN	NaN	NaN	2213	NaN	37883	NaN	NaN	0
636	HS	2015	201505	May 2015	4	0	2	Exports	826	United Kingdom	...	NaN	NaN	NaN	NaN	1588	NaN	5676	NaN	NaN	0

Trade Flow	Exports	Imports
Partner
Algeria	8087885.0	NaN
Angola	2416129.0	NaN
Antigua and Barbuda	22218.0	NaN
Areas, nes	51205.0	NaN
Australia	81644.0	NaN

Trade Flow	Exports	Imports
Partner
Austria	56279.0	780.0
Belgium	7209121.0	14325697.0
Czech Rep.	2590.0	486889.0
Denmark	335068.0	13681759.0
France	9381762.0	28314091.0
Germany	10828464.0	21899123.0
Hungary	71378.0	4762.0
Ireland	128155891.0	46263897.0
Italy	173689.0	321635.0
Latvia	567.0	870.0
Lithuania	1172.0	318407.0
Netherlands	18018603.0	17658912.0
Poland	106659.0	4933917.0
Portugal	25855.0	308654.0
Romania	36.0	975996.0
Slovakia	65.0	39990.0
Spain	1229172.0	4910615.0
Sweden	34972.0	1962413.0
United Arab Emirates	1590593.0	27225.0
United States of America	548910.0	46044.0

	Period	Partner	Trade Flow	Milk and cream	Trade Value (US$)
0	201501	World	Imports	unprocessed	14104602
1	201501	World	Exports	unprocessed	26259792
2	201501	Australia	Exports	unprocessed	50331
3	201501	Austria	Exports	unprocessed	360
4	201501	Belgium	Imports	unprocessed	1424271