Python: multiple line plot with pandas and matplotlib || 09

In this video we use a multiple line plot to plot the Debt to GDP ratio of Eurozone countries, Japan, United Kingdom, United States by using pandas and matplotlib.

The data are taken from Dabrowski M., The Economic and Monetary Union: its past, present, and future, European Parliament, Monetary Dialogue January 2019.

## Eurozone countries plus Japan, UK, USA
country = ["Austria", "Belgium", "Cyprus", "Estonia", "Finland", "France", "Germany", "Greece", "Ireland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Malta", "Netherlands", "Portugal", "Slovakia", "Slovenia", "Spain", "Japan", "UK", "USA"]

## Debt to GDP from 1999 to 2018
yr_1999 = [61.1, 114.4, 55.7, 6.0, 44.0, 60.5, 60.0, 98.9, 46.6, 109.7, 11.8, 28.1, 7.1, 69.5, 57.9, 51.0, 47.1, 22.0, 62.5, 131.1, 39.8, 53.2]
yr_2000 = [65.7, 108.8, 56.0, 5.1, 42.5, 58.9, 58.9, 104.9, 36.1, 105.1, 12.1, 23.5, 6.5, 64.2, 50.9, 50.3, 49.6, 29.0, 58.0, 137.9, 37.0, 53.2]
yr_2001 = [66.4, 107.6, 57.5, 4.8, 40.9, 58.3, 57.7, 107.1, 33.2, 104.7, 13.9, 22.9, 6.9, 70.1, 48.2, 53.4, 48.3, 28.5, 54.2, 146.8, 34.3, 53.2]
yr_2002 = [67.0, 104.7, 61.0, 5.7, 40.2, 60.3, 59.4, 104.9, 30.6, 101.9, 13.1, 22.1,6.8, 64.9, 47.5, 56.2, 42.9, 28.4, 51.3, 156.8, 34.4, 55.6]
yr_2003 = [64.9, 101.1, 63.0, 5.6, 42.7, 64.4, 63.1, 101.5, 29.9, 100.5, 13.9, 20.4, 6.8, 68.7, 48.7, 58.7, 41.6, 27.0, 47.6, 162.7, 35.6, 58.7]
yr_2004 = [64.8, 96.5, 64.7, 5.1, 42.6, 65.9, 64.8, 102.9, 28.2, 100.1, 13.8, 18.7, 7.3, 71.1, 49.1, 62.0, 40.6, 26.8, 45.3, 171.7, 38.6, 66.2]
yr_2005 = [68.3, 94.7, 64.0, 4.5, 39.9, 67.4, 67.0, 107.4, 26.1, 101.9, 11.2, 17.6, 7.4, 70.0, 48.5, 67.4, 34.1, 26.3, 42.3, 176.8, 39.8, 65.6]
yr_2006 = [67.0, 91.1, 59.0, 4.4, 38.1, 64.6, 66.5, 103.6, 23.6, 102.6, 9.2, 17.2, 7.8, 64.5, 44.1, 69.2, 31.0, 26.0, 38.9, 176.4, 40.7, 64.3]
yr_2007 = [64.7, 87.0, 53.1, 3.7, 34.0, 64.5, 63.7, 103.1, 23.9, 99.8, 7.2, 15.9,
7.7, 62.3, 42.0, 68.4, 30.1, 22.7, 35.5, 175.4, 41.7, 64.8]
yr_2008 = [68.4, 92.5, 44.1, 4.5, 32.7, 68.8, 65.2, 109.4, 42.4, 102.4, 16.2, 14.6,14.9, 62.6, 53.8, 71.7, 28.5, 21.6, 39.4, 183.4, 49.7, 73.8]
yr_2009 = [79.6, 99.5, 52.8, 7.0, 41.7, 83.0, 72.6, 126.7, 61.5, 112.5, 32.5, 29.0, 15.7, 67.6, 55.8, 83.6, 36.3, 34.5, 52.7, 201.0, 63.7, 86.9]
yr_2010 = [82.4, 99.7, 55.8, 6.6, 47.1, 85.3, 80.9, 146.3, 86.0, 115.4, 40.3, 36.2, 19.8, 67.5, 58.6, 90.5, 41.2, 38.2, 60.1, 207.9, 75.2, 95.5]
yr_2011 = [82.2, 102.6, 65.2, 6.1, 48.5, 87.8, 78.6, 180.6, 110.9, 116.5, 37.5, 37.2,18.7, 70.1, 60.8, 111.4, 43.7, 46.4, 69.5, 222.1, 80.8, 99.9]
yr_2012 = [81.7, 104.3, 79.2, 9.7, 53.9, 90.6, 79.8, 159.6, 119.9, 123.4, 36.7, 39.8, 21.7, 67.7, 65.5, 126.2, 52.2, 53.8, 85.7, 229.0, 84.1, 103.3]
yr_2013 = [81.0, 105.5, 102.1, 10.2, 56.5, 93.4, 77.5, 177.9, 119.8, 129.0, 35.8, 38.8, 23.7, 68.4, 67.0, 129.0, 54.7, 70.4, 95.5, 232.5, 85.2, 104.9]
yr_2014 = [83.8, 107.0, 107.5, 10.7, 60.2, 94.9, 74.6, 180.2, 104.3, 131.8, 38.5, 40.5, 22.7, 63.7, 67.1, 130.6, 53.5, 80.3, 100.4, 236.1, 87.0, 104.6]
yr_2015 = [84.3, 106.1, 107.5, 10.0, 63.5, 95.6, 70.9, 178.8, 76.9, 131.5, 34.9, 42.6,22.0, 58.6, 64.0, 128.8, 52.3, 82.6, 99.4, 231.3, 87.9, 104.8]
yr_2016 = [83.6, 106.0, 106.6, 9.4, 62.9, 96.6, 67.9, 183.5, 73.6, 132.0, 37.4, 40.1,20.8, 56.3, 61.3, 129.9, 51.8, 78.6, 99.0, 235.6, 87.9, 106.8]
yr_2017 = [78.6, 103.4, 97.5, 9.0, 61.3, 96.8, 63.9, 181.8, 68.6, 131.8, 36.3, 39.7, 23.0, 50.7, 56.5, 125.7, 50.9, 73.6, 98.4, 237.6, 87.5, 105.2]
yr_2018 = [74.2, 101.2, 112.3, 8.8, 60.5, 96.7, 59.8, 188.1, 66.6, 130.3, 35.0, 37.0, 22.8, 45.1, 53.1, 120.8, 49.2, 69.7, 97.2, 238.2, 87.4, 106.1]