import pandas as pd import altair as alt

pd.to_datetime("today")

pd.to_datetime('2020-05-07 3pm')

pd.to_datetime('12.6.1929')

pd.to_datetime('12.6.1929', dayfirst=True)

sessions=["2.4.2020", "9.4.2020", "16.4.2020", "23.4.2020", "7.5.2020", "14.5.2020", "28.5.2020", "4.6.2020", "11.6.2020", "25.6.2020", "2.7.2020", "9.7.2020"] pd.to_datetime(sessions, dayfirst=True)

pd.to_datetime('2020-05-07', format="%Y-%m-%d")

# data downloaded from OPSD - see how to filter elements on this page: # https://data.open-power-system-data.org/time_series/2020-10-06 url = "https://data.open-power-system-data.org/index.php?package=time_series&version=2020-10-06&action=customDownload&resource=3&filter%5BRegion%5D%5B%5D=DE&filter%5BVariable%5D%5B%5D=load_actual_entsoe_transparency&filter%5BVariable%5D%5B%5D=solar_generation_actual&filter%5BVariable%5D%5B%5D=wind_generation_actual&downloadCSV=Download+CSV" opsd = pd.read_csv(url, parse_dates=['utc_timestamp']) # parse timestamp column

opsd = opsd.drop(columns=["cet_cest_timestamp"])

opsd = opsd.rename({ 'utc_timestamp': 'datetime', 'DE_load_actual_entsoe_transparency': 'load', 'DE_solar_generation_actual': 'solar', 'DE_wind_generation_actual': 'wind' }, axis=1)

opsd["datetime"] = opsd["datetime"].dt.tz_convert("Europe/Berlin") opsd

start_date = pd.to_datetime('2015-01-01').tz_localize('Europe/Berlin') end_date = pd.to_datetime('2019-12-31').tz_localize('Europe/Berlin') opsd = opsd[(opsd['datetime'] >= start_date) & (opsd['datetime'] <= end_date)] opsd

opsd.sample(10)

opsd.info()

opsd = opsd.set_index("datetime")

opsd.info()

opsd.index.year.unique()

opsd.loc["2017-06-21"]

opsd.loc["2017-06"]

opsd.loc["2017-10-04":"2017-10-06"]

sums = opsd.resample("Y").sum() sums

# first we create the sums per week weekly_sums = opsd.resample("W").sum() # then we generate the weekly means for each quarter quarterly_means = weekly_sums.resample("Q").mean() # for readability we'll revert the values back to integers quarterly_means.astype(int)

presidents = pd.read_csv("us_presidents.csv") presidents

presidents['start'] = pd.to_datetime(presidents['start'], format="%Y") presidents['end'] = pd.to_datetime(presidents['end'], format="%Y")

# we add height as a parameter to make the time spans appear less clunky bars = alt.Chart(presidents).mark_bar(height=5).encode( # we use two columns for the x-positions where the bar starts and ends x='start', x2='end', # we sort by start dates and hide the axis, as names will be added below y=alt.Y('name').sort('x').axis(None), color="party" ) # the labels are added to the chart using the mark_text command # no need to specify x and y as this is re-used from the bar chart labels = bars.mark_text(align='right', dx=-5).encode( text='name' ) # both charts integrated into a layered chart through the magic of a plus sign bars + labels

days = opsd.resample("D").sum()

opsd.reset_index().melt("datetime")

alt.data_transformers.disable_max_rows()

alt.Chart(days.reset_index().melt("datetime")).mark_circle().encode( x='datetime', y='value', color='variable', ).properties(width=700, height=400)

alt.Chart(days.reset_index().melt("datetime")).mark_line(strokeWidth=1).encode( x='datetime', y='value', color='variable', ).properties(width=700, height=400)

# we reduce the granularity to months and sum up the values months = opsd.resample("M").sum() # to make the line a bit smoother, we include an interpolate parameter alt.Chart(months.reset_index().melt("datetime")).mark_line(opacity=0.75, interpolate="basis").encode( x='datetime', y='value', color='variable', ).properties(width=700, height=400)

days = opsd.resample("D").sum() months = days.resample("M").mean()

chart1 = alt.Chart(days.reset_index().melt("datetime")).mark_line(strokeWidth=1, opacity=0.25).encode( x='datetime', y='value', color='variable', ).properties(width=700, height=400) # not necessary to repeat below (the 2nd chart uses this) chart2 = alt.Chart(months.reset_index().melt("datetime")).mark_line(interpolate="basis", opacity=1).encode( x='datetime', y='value', color='variable', ) chart1 + chart2

rolling = days.rolling(60, center=True, win_type="triang").mean() chart1 = alt.Chart(rolling.reset_index().melt("datetime")).mark_line(strokeWidth=1.5, opacity=1).encode( x='datetime', y='value', color='variable', ).properties(width=700, height=400) # same as the two charts in previous code cell, except more transparent chart2 = alt.Chart(days.reset_index().melt("datetime")).mark_line(strokeWidth=1, opacity=0.1).encode( x='datetime', y='value', color='variable', ) chart3 = alt.Chart(months.reset_index().melt("datetime")).mark_line(interpolate="basis", opacity=.25).encode( x='datetime', y='value', color='variable', ) chart1 + chart2 + chart3

burglind = opsd.loc["2018-01-01":"2018-01-07"] alt.Chart(burglind.reset_index().melt("datetime")).mark_line().encode( x='datetime', y='value', color='variable' ).properties(width=700, height=400)

years = opsd.resample("Y").sum()

years["renewable"] = years["solar"]+years["wind"]

renewable = years.drop(columns=["load", "solar", "wind"])

renewable["year"] = years.index.year renewable

renewable = renewable.reset_index(drop=True) renewable

alt.Chart(renewable).mark_bar(width=30, fill="lightgreen").encode( x='year:O', # O ~ treat the years as ordinal data y='renewable:Q' # Q ~ treat renewable as quantitative data ).properties(width=300, height=300)

# remove solar and wind columns weekdays = opsd.drop(columns=["solar", "wind"]) # add a column for weekdays weekdays["weekday"] = weekdays.index.weekday

weekdays = weekdays.groupby("weekday").mean() alt.Chart(weekdays.reset_index()).mark_bar(width=20).encode( x='weekday:O', # O ~ treat weekday as ordinal data y='load:Q' # Q ~ treat load as quantitative data ).properties(width=300, height=300)

weeks = opsd.resample("W").sum() days = opsd.resample("D").sum() brush = alt.selection_interval(encodings=['x']) upper = alt.Chart(weeks.reset_index().melt("datetime")).mark_area(interpolate="basis").encode( x = alt.X('datetime:T', axis=None), y = alt.Y('value:Q', axis=None), color='variable' ).properties(width=700, height=50).add_params(brush) lower = alt.Chart(days.reset_index().melt("datetime")).mark_line(strokeWidth=1).encode( x = alt.X('datetime:T', scale=alt.Scale(domain=brush)), y='value', color='variable', ).properties(width=700, height=300) upper & lower