I'm trying to do PyMongo aggregate - $group averages of arrays, and I cannot find any examples that matches my problem.
{
Subject: "Dave",
Strength: [1,2,3,4]
},
{
Subject: "Dave",
Strength: [1,2,3,5]
},
{
Subject: "Dave",
Strength: [1,2,3,6]
},
{
Subject: "Stuart",
Strength: [4,5,6,7]
},
{
Subject: "Stuart",
Strength: [6,5,6,7]
},
{
Subject: "Kevin",
Strength: [1,2,3,4]
},
{
Subject: "Kevin",
Strength: [9,4,3,4]
}
{
Subject: "Dave",
mean_strength = [1,2,3,5]
},
{
Subject: "Stuart",
mean_strength = [5,5,6,7]
},
{
Subject: "Kevin",
mean_strength = [5,3,3,4]
}
I have tried this approach but MongoDB is interpreting the arrays as Null?
pipe = [{'$group': {'_id': 'Subject', 'mean_strength': {'$avg': '$Strength'}}}]
results = db.Walk.aggregate(pipeline=pipe)
Out: [{'_id': 'SubjectID', 'total': None}]
I've looked through the MongoDB documentation and I cannot find or understand if there is any way to do this?
You could use $unwind
with includeArrayIndex
. As the name suggests, includeArrayIndex
adds the array index to the output. This allows for grouping by Subject
and array position in Strength
. After calculating the average, the results need to be sorted to ensure the second $group
and $push
add the results back into the right order. Finally there is a $project
to include and rename the relevant columns.
db.test.aggregate([{
"$unwind": {
"path": "$Strength",
"includeArrayIndex": "rownum"
}
},
{
"$group": {
"_id": {
"Subject": "$Subject",
"rownum": "$rownum"
},
"mean_strength": {
"$avg": "$Strength"
}
}
},
{
"$sort": {
"_id.Subject": 1,
"_id.rownum": 1
}
},
{
"$group": {
"_id": "$_id.Subject",
"mean_strength": {
"$push": "$mean_strength"
}
}
},
{
"$project": {
"_id": 0,
"Subject": "$_id",
"mean_strength": 1
}
}
])
For your test input, this returns:
{ "mean_strength" : [ 5, 5, 6, 7 ], "Subject" : "Stuart" }
{ "mean_strength" : [ 5, 3, 3, 4 ], "Subject" : "Kevin" }
{ "mean_strength" : [ 1, 2, 3, 5 ], "Subject" : "Dave" }
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With