need using Pyplot in ISL2

assets/literate/ISL-lab-2.md

@@ -165,6 +165,8 @@ There are multiple libraries that can be used to  plot things in Julia:
 In these tutorials we use `PyPlot` but you could use another package of course.
 
 ```julia:ex21
+using PyPlot
+
 figure(figsize=(8,6))
 plot(mpg)
 

assets/literate/ISL-lab-2_script.jl

@@ -58,6 +58,8 @@ mpg |> mean
 @show nrow(auto)
 @show ncol(auto)
 
+using PyPlot
+
 figure(figsize=(8,6))
 plot(mpg)
 

assets/pages/isl/lab-2/code/ex21.jl

@@ -1,4 +1,6 @@
 # This file was generated, do not modify it. # hide
+using PyPlot
+
 figure(figsize=(8,6))
 plot(mpg)
 

assets/pages/isl/lab-2/code/output/ex10.out

@@ -1 +1 @@
-(μ, σ) = (-0.003429405251489602, 1.0011574237294552)
+(μ, σ) = (0.028927854346407975, 1.0322439340512704)

assets/pages/isl/lab-2/code/output/ex10.res

@@ -1 +1 @@
-(-0.003429405251489602, 1.0011574237294552)
+(0.028927854346407975, 1.0322439340512704)

assets/pages/isl/lab-2/code/output/ex16.res

@@ -1,7 +1 @@
-3×9 DataFrame
-│ Row │ MPG     │ Cylinders │ Displacement │ Horsepower │ Weight  │ Acceleration │ Year    │ Origin  │ Name                      │
-│     │ Float64 │ Float64   │ Float64      │ Float64    │ Float64 │ Float64      │ Float64 │ Float64 │ String                    │
-├─────┼─────────┼───────────┼──────────────┼────────────┼─────────┼──────────────┼─────────┼─────────┼───────────────────────────┤
-│ 1   │ 18.0    │ 8.0       │ 307.0        │ 130.0      │ 3504.0  │ 12.0         │ 70.0    │ 1.0     │ chevrolet chevelle malibu │
-│ 2   │ 15.0    │ 8.0       │ 350.0        │ 165.0      │ 3693.0  │ 11.5         │ 70.0    │ 1.0     │ buick skylark 320         │
-│ 3   │ 18.0    │ 8.0       │ 318.0        │ 150.0      │ 3436.0  │ 11.0         │ 70.0    │ 1.0     │ plymouth satellite        │
+DataFrames.DataFrame(AbstractArray{T,1} where T[[18.0, 15.0, 18.0], [8.0, 8.0, 8.0], [307.0, 350.0, 318.0], [130.0, 165.0, 150.0], [3504.0, 3693.0, 3436.0], [12.0, 11.5, 11.0], [70.0, 70.0, 70.0], [1.0, 1.0, 1.0], ["chevrolet chevelle malibu", "buick skylark 320", "plymouth satellite"]], DataFrames.Index(Dict(:Cylinders => 2,:Horsepower => 4,:MPG => 1,:Displacement => 3,:Origin => 8,:Year => 7,:Acceleration => 6,:Weight => 5,:Name => 9), [:MPG, :Cylinders, :Displacement, :Horsepower, :Weight, :Acceleration, :Year, :Origin, :Name]))

assets/pages/isl/lab-2/code/output/ex17.res

@@ -1,13 +1 @@
-9×4 DataFrame
-│ Row │ variable     │ mean    │ median │ std      │
-│     │ Symbol       │ Union…  │ Union… │ Union…   │
-├─────┼──────────────┼─────────┼────────┼──────────┤
-│ 1   │ MPG          │ 23.4459 │ 22.75  │ 7.80501  │
-│ 2   │ Cylinders    │ 5.47194 │ 4.0    │ 1.70578  │
-│ 3   │ Displacement │ 194.412 │ 151.0  │ 104.644  │
-│ 4   │ Horsepower   │ 104.469 │ 93.5   │ 38.4912  │
-│ 5   │ Weight       │ 2977.58 │ 2803.5 │ 849.403  │
-│ 6   │ Acceleration │ 15.5413 │ 15.5   │ 2.75886  │
-│ 7   │ Year         │ 75.9796 │ 76.0   │ 3.68374  │
-│ 8   │ Origin       │ 1.57653 │ 1.0    │ 0.805518 │
-│ 9   │ Name         │         │        │          │
+DataFrames.DataFrame(AbstractArray{T,1} where T[[:MPG, :Cylinders, :Displacement, :Horsepower, :Weight, :Acceleration, :Year, :Origin, :Name], Union{Nothing, Float64}[23.44591836734694, 5.471938775510204, 194.41198979591837, 104.46938775510205, 2977.5841836734694, 15.541326530612247, 75.9795918367347, 1.5765306122448979, nothing], Union{Nothing, Float64}[22.75, 4.0, 151.0, 93.5, 2803.5, 15.5, 76.0, 1.0, nothing], Union{Nothing, Float64}[7.8050074865717995, 1.7057832474527845, 104.64400390890465, 38.49115993282848, 849.4025600429493, 2.758864119188082, 3.6837365435778295, 0.8055181834183056, nothing]], DataFrames.Index(Dict(:std => 4,:variable => 1,:mean => 2,:median => 3), [:variable, :mean, :median, :std]))

pub/isl/lab-2.html

@@ -27,7 +27,7 @@
 <pre><code class="julia hljs">x = randn(<span class=hljs-number >1_000</span>) <span class=hljs-comment ># 1_000 points iid from a N(0, 1)</span>
 μ = mean(x)
 σ = std(x)
-<span class=hljs-meta >@show</span> (μ, σ)</code></pre><div class=code_output ><pre><code class="plaintext hljs">(μ, σ) = (-0.003429405251489602, 1.0011574237294552)
+<span class=hljs-meta >@show</span> (μ, σ)</code></pre><div class=code_output ><pre><code class="plaintext hljs">(μ, σ) = (0.028927854346407975, 1.0322439340512704)
 </code></pre></div>
 <p>Indexing data starts at 1, use <code>:</code> to indicate the full range</p>
 <pre><code class="julia hljs">X = [<span class=hljs-number >1</span> <span class=hljs-number >2</span>; <span class=hljs-number >3</span> <span class=hljs-number >4</span>; <span class=hljs-number >5</span> <span class=hljs-number >6</span>]
@@ -50,27 +50,9 @@ <h2 id=loading_data ><a href="/MLJTutorials/pub/isl/lab-2.html#loading_data">Loa
 <pre><code class="julia hljs"><span class=hljs-keyword >using</span> DataFrames</code></pre>
 <p>Let&#39;s load some data from RDatasets &#40;the full list of datasets is available <a href="http://vincentarelbundock.github.io/Rdatasets/datasets.html">here</a>&#41;.</p>
 <pre><code class="julia hljs">auto = dataset(<span class=hljs-string >"ISLR"</span>, <span class=hljs-string >"Auto"</span>)
-first(auto, <span class=hljs-number >3</span>)</code></pre><div class=code_output ><pre><code class="plaintext hljs">3×9 DataFrame
-│ Row │ MPG     │ Cylinders │ Displacement │ Horsepower │ Weight  │ Acceleration │ Year    │ Origin  │ Name                      │
-│     │ Float64 │ Float64   │ Float64      │ Float64    │ Float64 │ Float64      │ Float64 │ Float64 │ String                    │
-├─────┼─────────┼───────────┼──────────────┼────────────┼─────────┼──────────────┼─────────┼─────────┼───────────────────────────┤
-│ 1   │ 18.0    │ 8.0       │ 307.0        │ 130.0      │ 3504.0  │ 12.0         │ 70.0    │ 1.0     │ chevrolet chevelle malibu │
-│ 2   │ 15.0    │ 8.0       │ 350.0        │ 165.0      │ 3693.0  │ 11.5         │ 70.0    │ 1.0     │ buick skylark 320         │
-│ 3   │ 18.0    │ 8.0       │ 318.0        │ 150.0      │ 3436.0  │ 11.0         │ 70.0    │ 1.0     │ plymouth satellite        │</code></pre></div>
+first(auto, <span class=hljs-number >3</span>)</code></pre><div class=code_output ><pre><code class="plaintext hljs">DataFrames.DataFrame(AbstractArray{T,1} where T[[18.0, 15.0, 18.0], [8.0, 8.0, 8.0], [307.0, 350.0, 318.0], [130.0, 165.0, 150.0], [3504.0, 3693.0, 3436.0], [12.0, 11.5, 11.0], [70.0, 70.0, 70.0], [1.0, 1.0, 1.0], ["chevrolet chevelle malibu", "buick skylark 320", "plymouth satellite"]], DataFrames.Index(Dict(:Cylinders =&gt; 2,:Horsepower =&gt; 4,:MPG =&gt; 1,:Displacement =&gt; 3,:Origin =&gt; 8,:Year =&gt; 7,:Acceleration =&gt; 6,:Weight =&gt; 5,:Name =&gt; 9), [:MPG, :Cylinders, :Displacement, :Horsepower, :Weight, :Acceleration, :Year, :Origin, :Name]))</code></pre></div>
 <p>The <code>describe</code> function allows to get an idea for the data:</p>
-<pre><code class="julia hljs">describe(auto, :mean, :median, :std)</code></pre><div class=code_output ><pre><code class="plaintext hljs">9×4 DataFrame
-│ Row │ variable     │ mean    │ median │ std      │
-│     │ Symbol       │ Union…  │ Union… │ Union…   │
-├─────┼──────────────┼─────────┼────────┼──────────┤
-│ 1   │ MPG          │ 23.4459 │ 22.75  │ 7.80501  │
-│ 2   │ Cylinders    │ 5.47194 │ 4.0    │ 1.70578  │
-│ 3   │ Displacement │ 194.412 │ 151.0  │ 104.644  │
-│ 4   │ Horsepower   │ 104.469 │ 93.5   │ 38.4912  │
-│ 5   │ Weight       │ 2977.58 │ 2803.5 │ 849.403  │
-│ 6   │ Acceleration │ 15.5413 │ 15.5   │ 2.75886  │
-│ 7   │ Year         │ 75.9796 │ 76.0   │ 3.68374  │
-│ 8   │ Origin       │ 1.57653 │ 1.0    │ 0.805518 │
-│ 9   │ Name         │         │        │          │</code></pre></div>
+<pre><code class="julia hljs">describe(auto, :mean, :median, :std)</code></pre><div class=code_output ><pre><code class="plaintext hljs">DataFrames.DataFrame(AbstractArray{T,1} where T[[:MPG, :Cylinders, :Displacement, :Horsepower, :Weight, :Acceleration, :Year, :Origin, :Name], Union{Nothing, Float64}[23.44591836734694, 5.471938775510204, 194.41198979591837, 104.46938775510205, 2977.5841836734694, 15.541326530612247, 75.9795918367347, 1.5765306122448979, nothing], Union{Nothing, Float64}[22.75, 4.0, 151.0, 93.5, 2803.5, 15.5, 76.0, 1.0, nothing], Union{Nothing, Float64}[7.8050074865717995, 1.7057832474527845, 104.64400390890465, 38.49115993282848, 849.4025600429493, 2.758864119188082, 3.6837365435778295, 0.8055181834183056, nothing]], DataFrames.Index(Dict(:std =&gt; 4,:variable =&gt; 1,:mean =&gt; 2,:median =&gt; 3), [:variable, :mean, :median, :std]))</code></pre></div>
 <p>To retrieve column names, you can use <code>names</code>:</p>
 <pre><code class="julia hljs">names(auto)</code></pre><div class=code_output ><pre><code class="plaintext hljs">9-element Array{Symbol,1}:
  :MPG
@@ -118,7 +100,9 @@ <h2 id=plotting_data ><a href="/MLJTutorials/pub/isl/lab-2.html#plotting_data">P
 
 </ul>
 <p>In these tutorials we use <code>PyPlot</code> but you could use another package of course.</p>
-<pre><code class="julia hljs">figure(figsize=(<span class=hljs-number >8</span>,<span class=hljs-number >6</span>))
+<pre><code class="julia hljs"><span class=hljs-keyword >using</span> PyPlot
+
+figure(figsize=(<span class=hljs-number >8</span>,<span class=hljs-number >6</span>))
 plot(mpg)
 </code></pre>
 <p><img src="/MLJTutorials/assets/literate/ISL-lab-2-mpg.svg" alt="" />

scripts/ISL-lab-2.jl

@@ -124,6 +124,8 @@ mpg |> mean
 #
 # In these tutorials we use `PyPlot` but you could use another package of course.
 
+using PyPlot
+
 figure(figsize=(8,6))
 plot(mpg)