Saiteja Kura kurasaiteja
kurasaiteja
/ final.py
Created
March 21, 2021 16:04
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| title = fill_title_and_subtitle("The Race Against Covid", "Visualizing Cumulative Statistics of Disease vs Vaccine") | |
| fig = go.Figure(data=[ | |
| go.Scatter( | |
| mode="lines+markers", | |
| name="Total Deaths", | |
| x=data['date'], | |
| y=data['cumulative_total_deaths'], | |
| marker_color="crimson", | |
| ), |
kurasaiteja
/ CumulativeBar.py
Last active
March 21, 2021 16:03
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| title = fill_title_and_subtitle("Vaccine vs Virus", "Comparing the total number of daily new cases and daily vaccinations globally") | |
| fig = go.Figure(data=[ | |
| go.Bar( | |
| name="New Cases", | |
| x=data['date'], | |
| y=data['daily_new_cases'], | |
| marker_color="crimson", | |
| ), | |
| go.Bar( | |
| name="Vaccinated", |
kurasaiteja
/ progress.py
Last active
March 21, 2021 15:07
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| countries = short.groupby('country')['total_vaccinations'].max().sort_values(ascending=False)[:10].index.tolist() | |
| title = get_multi_line_title("Vaccination Progress", "Rate of vaccinations for the top-10 vaccinated countries") | |
| line_plots = [] | |
| for c in countries: | |
| vacc_data = short[short.country == c] | |
| line_plots.append( | |
| go.Scatter( | |
| name = c, |
kurasaiteja
/ cumulative.py
Last active
March 21, 2021 15:53
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| from datetime import datetime | |
| vaccs = vaccine_df.copy() | |
| daily = daily_df.copy() | |
| daily.head() | |
| # standardise the dates | |
| vaccs.date =vaccs.date.apply(lambda x: datetime.strptime(x, "%Y-%m-%d")) | |
| daily.date =daily.date.apply(lambda x: datetime.strptime(x, "%Y-%m-%d")) | |
| # use only common countries and dates | |
| countries = vaccs.dropna(subset=['daily_vaccinations'])['country'].unique() |
kurasaiteja
/ clean_daily.py
Created
March 21, 2021 14:54
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| import math | |
| # unique dates | |
| dates = vaccine_df.date.unique().tolist() | |
| #For more smoother animations | |
| dates.extend(['2020-12-12', '2020-12-13']) | |
| # unique countries | |
| countries = vaccine_df.country.unique().tolist() | |
| # for easy processing |
kurasaiteja
/ death-rate.py
Last active
March 21, 2021 14:59
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| title = ("Death Rates", "Percentage of the confired cases have died from Covid-19") | |
| data = summary_df.copy() | |
| data['death_rate'] = (data['total_deaths']/data['total_confirmed'])*100 | |
| data = data.dropna(subset=['death_rate']) | |
| fig = px.scatter_geo(data, locations="country", color="continent", | |
| locationmode='country names', | |
| hover_name="country", size="death_rate", | |
| projection="natural earth") | |
| fig.update_layout(title=title, title_x=0.45) | |
| fig.show() |
kurasaiteja
/ helper.py
Last active
March 21, 2021 12:30
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| def fill_title_and_subtitle(title:str, subtitle:str): | |
| return f"{title}<br><sub>{subtitle}</sub>" | |
| def create_bar_chart(data: pd.DataFrame, xcolumn: str, ycolumn:str, title:str, colors:str, ylabel="Count", n=None): | |
| hovertemplate ='<br><b>%{x}</b>'+f'<br><b>{ylabel}: </b>'+'%{y}<br><extra></extra>' | |
| data = data.sort_values(ycolumn, ascending=False).dropna(subset=[ycolumn]) | |
| if n is not None: | |
| data = data.iloc[:n] | |
| else: |
kurasaiteja
/ combine.py
Last active
March 21, 2021 12:29
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| #We will create an aggregated dataset by aggreagting the required columns using previous function. | |
| summary_cols = ['people_vaccinated', | |
| 'people_vaccinated_per_hundred', | |
| 'people_fully_vaccinated', | |
| 'people_fully_vaccinated_per_hundred', | |
| 'total_vaccinations_per_hundred', | |
| 'total_vaccinations'] | |
| summary = summary_df.set_index("country") | |
| vaccines = vaccine_df[['country', 'vaccines']].drop_duplicates().set_index('country') |
kurasaiteja
/ clean.py
Created
March 17, 2021 13:53
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| vaccine_df.country = vaccine_df.country.replace().replace({ | |
| "Czechia": "Czech Republic", | |
| "United States": "USA", | |
| "United Kingdom": "UK", | |
| "Isle of Man": "Isle Of Man", | |
| "Republic of Ireland": "Ireland", | |
| "Northern Cyprus" : "Cyprus" | |
| }) | |
| #These 4 countries are a part of UK |
kurasaiteja
/ agg.py
Created
March 13, 2021 15:25
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| def aggregate(df: pd.Series, agg_col: str) -> pd.DataFrame: | |
| data = df.groupby("country")[agg_col].max() | |
| data = pd.DataFrame(data) | |
| return data |
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