SamLau95

resid = pd.DataFrame({
    'Predicted weight (kg)': predicted, 'Percent error': resids})
px.scatter(resid, x='Predicted weight (kg)', y='Percent error',
           width=350, height=250)

SamLau95

px.histogram(
    wiki, x='postproductivity', nbins=50,
    labels={'postproductivity': 'Number of actions in 90 days post award'},
    width=350, height=250)

SamLau95

px.line(co2, x='DecDate', y='Avg', width=350, height=250,
        labels={'DecDate':'Date', 'Avg':'Average monthly CO₂'})

SamLau95

fig = px.histogram(X_train['baseurl'].value_counts(), width=450, height=250,
                   labels={"value": "Number of articles published at a URL"})

SamLau95

px.scatter(crabs, y='inc', x= 'shell', width=350, height=250,
           labels=dict(shell='Dungeness crab shell width (mm)',
                       inc='Growth (mm)'))

SamLau95

fig = px.scatter(
    x=prediction_7var, y=error_7var,
    labels=dict(x='Seven-variable prediction for AUM', y='Error'),
    width=350, height=250)

SamLau95

def compute_ppsf(df):
    return df.assign(
        ppsf=df['price'] / df['bsqft'],
        log_ppsf=lambda df: np.log10(df['ppsf']))

SamLau95

/
/chapters/intro
/chapters/01/what-is-data-science
/chapters/01/1/intro
/chapters/01/1/1/computational-tools
/chapters/01/1/2/statistical-techniques
/chapters/01/2/why-data-science
/chapters/01/3/Plotting_the_Classics
/chapters/01/3/1/Literary_Characters
/chapters/01/3/2/Another_Kind_Of_Character

SamLau95

cd /home
find . -maxdepth 2 -name data8assets -group root

names=
for name in ${names[@]}; do cd $name ; chown -R $name:$name data8assets ; cd .. ; done

SamLau95

###   Handy Extract Program

ext () {
    if [ -f $1 ] ; then
        case $1 in
            *.tar.bz2)   tar xvjf $1        ;;
            *.tar.gz)    tar xvzf $1     ;;
            *.bz2)       bunzip2 $1       ;;
            *.rar)       unrar x $1     ;;
            *.gz)        gunzip $1     ;;