20. Ocean Freight Infra

https://ec.europa.eu/eurostat/databrowser/view/mar_go_aa/default/table?lang=en

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns;sns.set()

df = pd.read_csv("assets/ocean-freight-infra/mar_go_aa.tsv", sep="\t", na_values=": ")

df.columns

Index(['direct,unit,rep_mar\time', '2019 ', '2018 ', '2017 ', '2016 ', '2015 ',
       '2014 ', '2013 ', '2012 ', '2011 ', '2010 ', '2009 ', '2008 ', '2007 ',
       '2006 ', '2005 ', '2004 ', '2003 ', '2002 ', '2001 ', '2000 ', '1999 ',
       '1998 ', '1997 '],
      dtype='object')

df["port"] = df["direct,unit,rep_mar\\time"].apply(lambda x: x.split(",")[-1])

df[["port", "2019 "]].loc[
    (
        df.port.apply(lambda x: len(x) > 2)
    ) & (
        df["2019 "] >0
    )
].dropna()

	port	2019
4	BE_0BEANR	108697.0
6	BE_0BEGNE	25665.0
9	BE_0BEOST	1422.0
10	BE_0BEZEE	16083.0
12	BG_0BGBOJ	10622.0
...	...	...
5066	UK_1GBWIC	29.0
5067	UK_1GBWIS	106.0
5068	UK_1GBWOR	319.0
5069	UK_1GBWPT	3321.0
5071	UK_1GBWTS	63.0

2397 rows × 2 columns