<jupyter_start><jupyter_text>Load data from Github<jupyter_code>def str_to_float(s):
    try:
        # Parse strings with decimal (,) separators
        return float(s.replace(',','.')) if type(s) == str else s
    except ValueError:
        return None

def str_to_date(s):
    try:
        # Parse dates
        return datetime.strptime(s,'%Y-%m-%d') if type(s) == str else s
    except ValueError:
        return None
url = 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv'

covid_data_df = pd.read_csv(
    url,
    converters={'total_deaths_per_million': str_to_float,
                'date': str_to_date
               },
    low_memory=False
)<jupyter_output><empty_output><jupyter_text>Select countries<jupyter_code>countries = [
'Finland',
'France',
'Germany',
'Italy',
'Portugal',
'Spain',
'Sweden',
'Switzerland',
'Israel',
'United Kingdom'
]

countries = [
'Finland',
'France',
'Portugal',
'Spain',
'Sweden',
'Israel',
'United Kingdom'
]

covid_data_df = covid_data_df[covid_data_df['location'].isin(countries)]
def plot_serie(column_name):
    plt.rcParams['figure.figsize'] = [12, 10]
    plt.rcParams['figure.dpi'] = 200

    # fig = plt.figure()
    # fig.suptitle('Death per million', fontsize=15)

    for key, grp in covid_data_df.groupby(['location']): 
        plt.plot(grp['date'], grp[column_name], label = key)

    plt.ylabel(column_name, fontsize=18)
    plt.xlabel('Date', fontsize=16)
    plt.legend(loc="upper left")
    plt.show()
column_name = 'total_vaccinations_per_hundred'
plot_serie(column_name)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15255292652552926, 'fraction_numerical': 0.00933997509339975, 'mean_word_length': 4.037617554858934, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '208044', 'license': 'MIT', 'n_tokens_mistral': 621, 'n_tokens_neox': 591, 'n_words': 104, 'path': 'COVID Vaccine.ipynb', 'repo_name': 'simonmoreau/covid-data', 'size': '1606'}