dataframe-js
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Immutable and functional data structure for datascientists and developpers
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<script src="../dist/dataframe-min.js"></script>
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// In this example we will use dataframe-js to analyse a simple data set.
// The aim of this snippet is (not really to explore data but) to play with the library in order to do simple things.
// You will find the code, explanations and results as comments.
// Here we import the lib.
// You can also: import { DataFrame } from 'dataframe-js';
const DataFrame = dfjs.DataFrame;
// Here we load the titanic data set from the well known Rdatasets (http://vincentarelbundock.github.io/Rdatasets/datasets.html).
// We get the result via a Promise, as a new DataFrame. We rename it 'df'.
DataFrame.fromCSV(
"http://vincentarelbundock.github.io/Rdatasets/csv/COUNT/titanic.csv"
)
.then(df => {
// Let's go to display quicly our table.
df.show();
// It looks like that, with one passenger by line.
// | | class | age | sex | survived |
// ------------------------------------------------------------
// | 1 | 1st class | adults | man | yes |
// | 2 | 1st class | adults | man | yes |
// | 3 | 1st class | adults | man | yes |
// | 4 | 1st class | adults | man | yes |
// | 5 | 1st class | adults | man | yes |
// | 6 | 1st class | adults | man | yes |
// | 7 | 1st class | adults | man | yes |
// | 8 | 1st class | adults | man | yes |
// | 9 | 1st class | adults | man | yes |
// | 10 | 1st class | adults | man | yes |
// Ok, in the csv, the first column was row index named as ''. We will rename this column.
const cleanDF = df.rename("", "id");
// If we look at columnNames, the row index is replaced by the 'id' column name.
console.log(cleanDF.listColumns());
// [ 'id', 'Class', 'Sex', 'Age', 'Survived', 'Freq' ]
// Now, our DataFrame is 'clean' with. Let's go to a quick analysis.
console.log("Total passengers:", cleanDF.count()); // We have 1316 passengers in the Titanic.
console.log(
"Survivors:",
cleanDF.filter({ survived: "yes" }).count()
); // We have 499 survivors.
console.log(
"Died:",
cleanDF
.filter(row => row.get("survived") === "no")
.count()
); // and 817 died passengers.
// Ok now we will count the number of passengers by class + age + sex + survived by using groupBy and aggregation.
const countByGroup = cleanDF
.groupBy("class", "age", "sex", "survived")
.aggregate(group => group.count());
// Ok, now we have the repartition of passengers by class + age + sex + survived.
// But it could be easier to read if we rename the aggregation and sort rows by passengers.
const cleanCountByGroup = countByGroup
.rename("aggregation", "passengers")
.sortBy("passengers", true);
// And now show the result
cleanCountByGroup.show(300);
// | class | age | sex | survived | passengers |
// ------------------------------------------------------------
// | 3rd class | adults | man | no | 387 |
// | 2nd class | adults | man | no | 154 |
// | 1st class | adults | women | yes | 140 |
// | 1st class | adults | man | no | 118 |
// | 3rd class | adults | women | no | 89 |
// | 2nd class | adults | women | yes | 80 |
// | 3rd class | adults | women | yes | 76 |
// | 3rd class | adults | man | yes | 75 |
// | 1st class | adults | man | yes | 57 |
// | 3rd class | child | man | no | 35 |
// OK, if we just look at this table, we can see that rich people (1s Class), and more specifically women have the largest number of survivors.
// To resume this fact, it could be interesting to compute the % of survival for each group of passengers.
// We can do this by this way:
// First we compute the total number of passengers by class + age + sex.
const passengersByGroup = cleanDF
.groupBy("class", "age", "sex")
.aggregate(group => group.count())
.rename("aggregation", "totalPassengers");
// Then we have to join with the cleanCountByGroup table.
// And we compute a new Column, survival, to expose the percentage of survivors.
// Then, we drop totalPassengers column which is now useless.
const informationsByGroup = cleanCountByGroup
.innerJoin(passengersByGroup, ["class", "age", "sex"])
.withColumn(
"survival",
row =>
row.get("passengers") /
row.get("totalPassengers")
)
.drop("totalPassengers");
informationsByGroup.show(100);
// | class | age | sex | survived | passen... | survival |
// ------------------------------------------------------------------------
// | 3rd class | adults | man | no | 387 | 0.8376... |
// | 3rd class | adults | man | yes | 75 | 0.1623... |
// | 3rd class | adults | women | no | 89 | 0.5393... |
// | 3rd class | adults | women | yes | 76 | 0.4606... |
// | 3rd class | child | man | no | 35 | 0.7291... |
// | 3rd class | child | man | yes | 13 | 0.2708... |
// | 3rd class | child | women | no | 17 | 0.5483... |
// | 3rd class | child | women | yes | 14 | 0.4516... |
// | 2nd class | adults | man | no | 154 | 0.9166... |
// | 2nd class | adults | man | yes | 14 | 0.0833... |
// | 2nd class | adults | women | yes | 80 | 0.8602... |
// | 2nd class | adults | women | no | 13 | 0.1397... |
// | 2nd class | child | man | yes | 11 | 1 |
// | 2nd class | child | women | yes | 13 | 1 |
// | 1st class | adults | man | no | 118 | 0.6742... |
// | 1st class | adults | man | yes | 57 | 0.3257... |
// | 1st class | adults | women | yes | 140 | 0.9722... |
// | 1st class | adults | women | no | 4 | 0.0277... |
// | 1st class | child | man | yes | 5 | 1 |
// | 1st class | child | women | yes | 1 | 1 |
// If we want to have an overview of the gender effects on survival we can use the DataFrame.stat module:
informationsByGroup
.groupBy("sex")
.aggregate(group => group.stat.mean("survival"))
.rename("aggregation", "mean")
.show();
informationsByGroup
.groupBy("sex")
.aggregate(group => group.stat.sd("survival"))
.rename("aggregation", "standard_deviation")
.show();
// | sex | mean |
// ------------------------
// | man | 0.6 |
// | women | 0.6 |
// | sex | standa... |
// ------------------------
// | man | 0.3560... |
// | women | 0.3517... |
// Gender effects seem not obvious. What about the age effects on survival ?
const survivalMeanByAge = informationsByGroup
.groupBy("age")
.aggregate(group => group.stat.mean("survival"))
.rename("aggregation", "mean");
const survivalSDByAge = informationsByGroup
.groupBy("age")
.aggregate(group => group.stat.sd("survival"))
.rename("aggregation", "standard_deviation");
survivalMeanByAge.show();
survivalSDByAge.show();
// | age | mean |
// ------------------------
// | adults | 0.5 |
// | child | 0.75 |
// | age | standa... |
// ------------------------
// | adults | 0.3496... |
// | child | 0.2951... |
// Ok that's better.
// Now, our boss wants a csv export of the age effects, in an exotic format (excel, damn it):
// | | adults | child |
// -------------------------------------
// | mean | 0.5 | 0.75 |
// | sd | 0.3496... | 0.2951... |
// First we join our results.
const ageEffect = survivalMeanByAge.innerJoin(
survivalSDByAge,
"age"
);
ageEffect.show();
// We now remove age column (you will understand why in few lines) and transpose the table (with columnNames);
const transposedAgeEffect = ageEffect
.drop("age")
.transpose(true);
// It's magical, and it looks like that:
transposedAgeEffect.show();
// Now we will use the previously removed age column as columnNames.
// Then we reorganize columns order.
const transposedAgeEffectWithColumnNames = transposedAgeEffect
.renameAll([...ageEffect.toArray("age"), ""])
.restructure(["", "adults", "child"]); // you can also .select('', 'adults', 'child');
// Which gives the good table:
transposedAgeEffectWithColumnNames.show();
// Now you have just to export it as a csv:
transposedAgeEffectWithColumnNames.toCSV(
true,
"yourReport.csv"
);
})
.catch(err => {
console.log(err);
});
</script>
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