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resid = pd.DataFrame({ | |
'Predicted weight (kg)': predicted, 'Percent error': resids}) | |
px.scatter(resid, x='Predicted weight (kg)', y='Percent error', | |
width=350, height=250) |
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px.histogram( | |
wiki, x='postproductivity', nbins=50, | |
labels={'postproductivity': 'Number of actions in 90 days post award'}, | |
width=350, height=250) |
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px.line(co2, x='DecDate', y='Avg', width=350, height=250, | |
labels={'DecDate':'Date', 'Avg':'Average monthly CO₂'}) |
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fig = px.histogram(X_train['baseurl'].value_counts(), width=450, height=250, | |
labels={"value": "Number of articles published at a URL"}) |
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px.scatter(crabs, y='inc', x= 'shell', width=350, height=250, | |
labels=dict(shell='Dungeness crab shell width (mm)', | |
inc='Growth (mm)')) |
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fig = px.scatter( | |
x=prediction_7var, y=error_7var, | |
labels=dict(x='Seven-variable prediction for AUM', y='Error'), | |
width=350, height=250) |
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def compute_ppsf(df): | |
return df.assign( | |
ppsf=df['price'] / df['bsqft'], | |
log_ppsf=lambda df: np.log10(df['ppsf'])) |
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/ | |
/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 |
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cd /home | |
find . -maxdepth 2 -name data8assets -group root | |
names= | |
for name in ${names[@]}; do cd $name ; chown -R $name:$name data8assets ; cd .. ; done |
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### 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 ;; |
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