Pandas: Read GDP per Capita From World Bank

What will we cover in this tutorial?

Introduction to the Pandas-Datareader, which is an invaluable way to get data from many sources, including the World Bank.

In this tutorial we will cover how to get the data from GDP per capita yearly data from countries and plot them.

Step 1: Get to know World Bank as a data source

The World Bank was founded in 1944 to make loans to low-income countries, with the purpose to decrease poverty in the world (see wikipedia.org for further history).

What you might not know, World Bank has an amazing sets of data that you can either browse on their webpage or get access to directly in Python using the Pandas-Datareader.

We will take a look at how to extract the NY.GDP.PCAP.KD indicator.

The what?

I know. The GDP per capita (constant 2010 US$), as it states on the webpage.

On that page you can get the GDP per capita for each country in the world back to 1960.

That is what we are going to do.

Step 2: Get the data

Reading the Pandas-datareaders World Bank documentation you fall over the following function.

pandas_datareader.wb.download(country=None, indicator=None, start=2003, end=2005, freq=None, errors=’warn’, **kwargs)

Where you can set the country (or countries) and indicator your want:

• country (string or list of strings.) – all downloads data for all countries 2 or 3 character ISO country codes select individual countries (e.g.“US“,“CA“) or (e.g.“USA“,“CAN“). The codes can be mixed.The two ISO lists of countries, provided by wikipedia, are hardcoded into pandas as of 11/10/2014.
• indicator (string or list of strings) – taken from the id field in WDIsearch()

Luckily we already have our indicator from Step 1 (NY.GDP.PCAP.KD). Then we just need to find some countries of interest.

Let’s take United States, France, Great Britain, Denmark and Norway.

from pandas_datareader import wb

dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'FR', 'GB', 'DK', 'NO'], start=1960, end=2019)
print(dat)

Resulting in the following output.

                    NY.GDP.PCAP.KD
country       year                
Denmark       2019    65147.427182
              2018    63915.468361
              2017    62733.019808
              2016    61877.976481
              2015    60402.129248
...                            ...
United States 1964    19824.587845
              1963    18999.888387
              1962    18462.935998
              1961    17671.150187
              1960    17562.592084
[300 rows x 1 columns]

Step 3: Visualize the data on a graph

We need to restructure the data in order to make a nice graph.

This can be done with unstack.

from pandas_datareader import wb
import matplotlib.pyplot as plt

dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'FR', 'GB', 'DK', 'NO'], start=1960, end=2019)
print(dat.unstack())

Which result in this output.

               NY.GDP.PCAP.KD                ...                            
year                     1960          1961  ...          2018          2019
country                                      ...                            
Denmark          20537.549556  21695.609308  ...  63915.468361  65147.427182
France           12743.925100  13203.320855  ...  43720.026351  44317.392315
Norway           23167.441740  24426.011426  ...  92119.522964  92556.321645
United Kingdom   13934.029831  14198.673562  ...  43324.049759  43688.437455
United States    17562.592084  17671.150187  ...  54795.450086  55809.007792

If we transpose this and remove the double index frames that will come, then it should be good to make a plot with.

from pandas_datareader import wb
import matplotlib.pyplot as plt

dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'FR', 'GB', 'DK', 'NO'], start=1960, end=2019)
print(dat.unstack().T.reset_index(0))
dat.unstack().T.reset_index(0).plot()
plt.title('GDP per capita')
plt.show()

Giving this output, where you can see what the Transpose (T) does.

country         level_0       Denmark  ...  United Kingdom  United States
year                                   ...                               
1960     NY.GDP.PCAP.KD  20537.549556  ...    13934.029831   17562.592084
1961     NY.GDP.PCAP.KD  21695.609308  ...    14198.673562   17671.150187
1962     NY.GDP.PCAP.KD  22747.292463  ...    14233.959944   18462.935998
1963     NY.GDP.PCAP.KD  22712.577808  ...    14816.480305   18999.888387
1964     NY.GDP.PCAP.KD  24620.461432  ...    15535.026991   19824.587845
1965     NY.GDP.PCAP.KD  25542.173921  ...    15766.195724   20831.299767
1966     NY.GDP.PCAP.KD  26032.378816  ...    15926.169851   21930.591173
1967     NY.GDP.PCAP.KD  27256.322071  ...    16282.026160   22235.415708

Giving the following output.

Continue the exploration in the following tutorial.

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