Animorphs Reading Journey

All Animorphs

I’ve loved this series.

Reading Animorphs as a kid and reading it as an adult, I’m surprised how well it holds up. Is it the perfect series? No. But still has some gold especially for those kids as they come of age thinking about bigger concepts of ‘right’ vs ‘wrong’ turning into a muddied gray.

Bluesky Comments

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%run ../../books/_help_reading.py
import pandas as pd
import matplotlib.dates as mdates
import seaborn as sns
import matplotlib.pyplot as plt

def one_title_data(data: pd.DataFrame, book_name: str | None = None) -> pd.DataFrame:
    if book_name is None:
        data_filtered = data.copy()
    else:
        data_filtered = data[data.title == book_name].copy()

    data_filtered.duration = pd.to_timedelta(data_filtered.duration)
    data_filtered.start = pd.to_datetime(
        data_filtered.start,
        format="%Y-%m-%d %H:%M:%S",
    )
    data_filtered.end = pd.to_datetime(
        data_filtered.end,
        format="%Y-%m-%d %H:%M:%S",
    )

    return data_filtered


def get_summary_by_day(
        data: pd.DataFrame,
        col: str = 'duration_frac'
) -> pd.DataFrame:
    ####
    temp_ = (
        data
        .groupby(
            by=data.start.dt.floor('d')
        )[col]
        .sum()
    )

    start_date = data.start.dt.floor('d').min()
    end_date = data.end.dt.floor('d').max()

    idx_ = pd.date_range(
        start=start_date,
        end=end_date,
    )

    s = temp_.reindex(idx_, fill_value=0)
    temp_df_ = pd.DataFrame(s).reset_index()
    return temp_df_

##### PLOTTING

def generate_plot(
        data: pd.DataFrame,
        book_name: str,
        x: str='index',
        y: str='duration_frac',
        figsize: tuple=(6,4),
):
    f, ax = plt.subplots(
        figsize=figsize,
    )

    sns.barplot(
        data=data,
        x='index',
        y='duration_frac',
        color='green',
        ax=ax,
    )

    # Axis formatting
    x_dates = [
        t.strftime('%D')
        for t in sorted(
            data['index'].unique().tolist()
        )
    ]
    ax.set_xticklabels(
        labels=x_dates,
    )

    #
    ax.set_title(
        f'Reading Journey of `{book_name}`'
        f'\n{data.duration_frac.sum():.2f}hrs'
        f' over {x_dates[0]} - {x_dates[-1]}'
    )
    ax.set_ylabel('Hours Read')
    ax.set_xlabel('Date')

    f.tight_layout()
    f.autofmt_xdate()
    return f,ax

def get_year_data(data, year=2024):
    df_yr = data[
        (data.start < f'{year+1}-01-01') &
        (data.start >= f'{year}-01-01') 
    ]

    # Add in 'dummy' weeks to fill up the gap
    # Create a date range for the entire year
    date_range = pd.date_range(start=f'{year}-01-01', end=f'{year}-12-31')
    # Create a DataFrame with all dates, weeks, and weekdays (0 activity)
    df_full = pd.DataFrame({'date': date_range})
    # isocalendar will range from 0 to 51
    df_full['week'] = df_full['date'].dt.isocalendar().week
    # first few days could loop to prev year's week 52; change to prev week num
    df_full.loc[
        ((df_full['date'].dt.month == 1) &
        (df_full['week'] == 52)),
        'week'
    ] = df_full['week'].min() - 1
    # Last few days could loop to next year's week 1; change to next week num
    df_full.loc[
        ((df_full['date'].dt.month == 12) &
        (df_full['week'] == 1)),
        'week'
    ] = df_full['week'].max() + 1
    df_full['weekday'] = df_full['date'].dt.weekday

    df_orig = pd.DataFrame(columns=['weekday', 'week', 'activity'])
    # Offset by one for display (starts at zero)
    df_orig['week'] = df_yr.start.dt.isocalendar().week
    df_orig.loc[
        ((df_yr.start.dt.month == 1) &
        (df_orig['week'] == 52)),
        'week'
    ] = df_orig['week'].min() - 1
    df_orig.loc[
        ((df_yr.start.dt.month == 12) &
        (df_orig['week'] == 1)),
        'week'
    ] = df_orig['week'].max() + 1
    # Monday is the first day of the week now (starts at zero)
    df_orig['weekday'] = (df_yr.start.dt.strftime('%w').astype(int) - 1) % 7
    df_orig['activity'] = df_yr.duration_frac
    df_new = pd.concat([
        df_full, 
        df_orig,
    ]).fillna(0)

    # Reshape the data and plot it
    df_pivot = df_new.pivot_table(
        columns="week",
        index="weekday",
        values="activity",
        aggfunc='sum',
        dropna=False,
        sort=True,
    )
    df_pivot.fillna(0, inplace=True)

    return df_pivot.sort_values('weekday', ascending=False)

df = pd.read_csv(
  'https://github.com/MrGeislinger/victorsothervector/raw/main/'
  'data/reading/all_reading-clean.csv'
)

all_titles = one_title_data(df, book_name=None)
all_titles = all_titles[all_titles.title.str.contains('Animorph')]
one_title_summary = get_summary_by_day(all_titles)
f,ax = generate_plot(
    data=one_title_summary,
    book_name='Full Animorphs Series',
    figsize=(8,4),
);

ax.xaxis.set_major_locator(mdates.MonthLocator())
plt.setp(ax.get_xticklabels(), rotation=30, ha='right')

Figure 1: Reading done for the full Animorphs series