{"id":138407,"date":"2022-05-13T10:54:46","date_gmt":"2022-05-13T14:54:46","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=138407"},"modified":"2022-11-21T09:54:30","modified_gmt":"2022-11-21T14:54:30","slug":"data-manipulation-and-visualization-techniques-in-julia-part-iii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/data-manipulation-and-visualization-techniques-in-julia-part-iii\/","title":{"rendered":"Data Manipulation and Visualization Techniques in Julia \u2013 Part III"},"content":{"rendered":"\n

Learn how to create new dataframes with Part I<\/a> and how to perform basic mathematical operations in Part II<\/a>.<\/em><\/p>\n\n\n\n

Grouping data<\/h3>\n\n\n\n

Let\u2019s look at ways to group data, which comes in handy while summarising data.<\/p>\n\n\n\n

In-built datasets in Julia<\/strong><\/p>\n\n\n\n

The package RDatasets.jl in Julia helps you import all the in-build packages in R that can be used for testing purposes.<\/p>\n\n\n\n

Here\u2019s how you can find out the list of available datasets. It has 763 datasets.<\/p>\n\n\n\n

We\u2019ll work with one of the in-built datasets (\u201ciris\u201d) in this section. \u201ciris\u201d provides the data for multiple measurements of 3 plant species and 4 features for each of them. More details about this dataset can be found here<\/a>.<\/p>\n\n\n\n

The following snapshot shows the variables in the iris dataset.<\/p>\n\n\n\n

\"Iris<\/figure>\n\n\n\n

Source: https:\/\/ai.plainenglish.io\/iris-flower-classification-step-by-step-tutorial-c8728300dc9e<\/a><\/em><\/p>\n\n\n\n

SepalLength<\/th>SepalWidth<\/th>PetalLength<\/th>PetalWidth<\/th>Species<\/th><\/tr>
Float64<\/th>Float64<\/th>Float64<\/th>Float64<\/th>Cat\u2026<\/th><\/tr><\/thead>
5.1<\/td>3.5<\/td>1.4<\/td>0.2<\/td>setosa<\/td><\/tr>
4.9<\/td>3.0<\/td>1.4<\/td>0.2<\/td>setosa<\/td><\/tr>
4.7<\/td>3.2<\/td>1.3<\/td>0.2<\/td>setosa<\/td><\/tr>
4.6<\/td>3.1<\/td>1.5<\/td>0.2<\/td>setosa<\/td><\/tr>
5.0<\/td>3.6<\/td>1.4<\/td>0.2<\/td>setosa<\/td><\/tr>
5.4<\/td>3.9<\/td>1.7<\/td>0.4<\/td>setosa<\/td><\/tr>
4.6<\/td>3.4<\/td>1.4<\/td>0.3<\/td>setosa<\/td><\/tr>
5.0<\/td>3.4<\/td>1.5<\/td>0.2<\/td>setosa<\/td><\/tr>
4.4<\/td>2.9<\/td>1.4<\/td>0.2<\/td>setosa<\/td><\/tr>
4.9<\/td>3.1<\/td>1.5<\/td>0.1<\/td>setosa<\/td><\/tr>
5.4<\/td>3.7<\/td>1.5<\/td>0.2<\/td>setosa<\/td><\/tr>
4.8<\/td>3.4<\/td>1.6<\/td>0.2<\/td>setosa<\/td><\/tr>
4.8<\/td>3.0<\/td>1.4<\/td>0.1<\/td>setosa<\/td><\/tr>
4.3<\/td>3.0<\/td>1.1<\/td>0.1<\/td>setosa<\/td><\/tr>
5.8<\/td>4.0<\/td>1.2<\/td>0.2<\/td>setosa<\/td><\/tr>
5.7<\/td>4.4<\/td>1.5<\/td>0.4<\/td>setosa<\/td><\/tr>
5.4<\/td>3.9<\/td>1.3<\/td>0.4<\/td>setosa<\/td><\/tr>
5.1<\/td>3.5<\/td>1.4<\/td>0.3<\/td>setosa<\/td><\/tr>
5.7<\/td>3.8<\/td>1.7<\/td>0.3<\/td>setosa<\/td><\/tr>
5.1<\/td>3.8<\/td>1.5<\/td>0.3<\/td>setosa<\/td><\/tr>
5.4<\/td>3.4<\/td>1.7<\/td>0.2<\/td>setosa<\/td><\/tr>
5.1<\/td>3.7<\/td>1.5<\/td>0.4<\/td>setosa<\/td><\/tr>
4.6<\/td>3.6<\/td>1.0<\/td>0.2<\/td>setosa<\/td><\/tr>
5.1<\/td>3.3<\/td>1.7<\/td>0.5<\/td>setosa<\/td><\/tr>
4.8<\/td>3.4<\/td>1.9<\/td>0.2<\/td>setosa<\/td><\/tr>
5.0<\/td>3.0<\/td>1.6<\/td>0.2<\/td>setosa<\/td><\/tr>
5.0<\/td>3.4<\/td>1.6<\/td>0.4<\/td>setosa<\/td><\/tr>
5.2<\/td>3.5<\/td>1.5<\/td>0.2<\/td>setosa<\/td><\/tr>
5.2<\/td>3.4<\/td>1.4<\/td>0.2<\/td>setosa<\/td><\/tr>
4.7<\/td>3.2<\/td>1.6<\/td>0.2<\/td>setosa<\/td><\/tr>
\u22ee<\/td>\u22ee<\/td>\u22ee<\/td>\u22ee<\/td>\u22ee<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Here\u2019s the summary of this dataset.<\/p>\n\n\n\n

variable<\/th>mean<\/th>min<\/th>median<\/th>max<\/th>nmissing<\/th>eltype<\/th><\/tr>
Symbol<\/th>Union\u2026<\/th>Any<\/th>Union\u2026<\/th>Any<\/th>Int64<\/th>DataType<\/th><\/tr><\/thead>
SepalLength<\/td>5.84333<\/td>4.3<\/td>5.8<\/td>7.9<\/td>0<\/td>Float64<\/td><\/tr>
SepalWidth<\/td>3.05733<\/td>2.0<\/td>3.0<\/td>4.4<\/td>0<\/td>Float64<\/td><\/tr>
PetalLength<\/td>3.758<\/td>1.0<\/td>4.35<\/td>6.9<\/td>0<\/td>Float64<\/td><\/tr>
PetalWidth<\/td>1.19933<\/td>0.1<\/td>1.3<\/td>2.5<\/td>0<\/td>Float64<\/td><\/tr>
Species<\/td> <\/td>setosa<\/td> <\/td>virginica<\/td>0<\/td>CategoricalValue{String, UInt8}<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Let\u2019s look at some of the questions you might want to answer using the iris dataset.<\/p>\n\n\n\n

We can perform arithmetic operations by grouping data based on various columns. Here\u2019s how we can get the answer to the following question –<\/p>\n\n\n\n

What\u2019s the mean value of the sepal length of each species?<\/em><\/p>\n\n\n\n

Species<\/th>mm<\/th><\/tr>
Category<\/th>Float64<\/th><\/tr><\/thead>
setosa<\/td>5.006<\/td><\/tr>
versicolor<\/td>5.936<\/td><\/tr>
virginica<\/td>6.588<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Another package that helps make the operations more intuitive is Pipe.jl. It lets you write operations as they are performed instead of the backward approach.<\/p>\n\n\n\n

Species<\/th>mm<\/th><\/tr>
Category<\/th>Float64<\/th><\/tr><\/thead>
setosa<\/td>5.006<\/td><\/tr>
versicolor<\/td>5.936<\/td><\/tr>
virginica<\/td>6.588<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Species<\/th>nrow<\/th><\/tr>
Category<\/th>Float64<\/th><\/tr><\/thead>
setosa<\/td>50<\/td><\/tr>
versicolor<\/td>50<\/td><\/tr>
virginica<\/td>50<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Stay tuned for the next installment, in which Anshul Tayal will demonstrate how to deal with missing data<\/em>.<\/p>\n\n\n\n

Visit QuantInsti to read the full article: https:\/\/blog.quantinsti.com\/data-manipulation-visualization-using-julia\/<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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