Are there any specific rules for column names in great expectations? In particular, if you have a column like a.age ? would it have to be renamed to a_age in order to run an expectation on it?
The application of an expectation expects that it uses the name that the column assumes in the starting dataset. However you can put in the comments section a reference to what you mean. For example, in the case you mentioned:
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {
"column": "a.age"
},
"meta": {
"notes": {
"content": "a_age",
"format": "markdown"
}
}
}
and this you will find in the json file of the results. To answer your question: changing the name of the column by altering the dataset, with great_expectations, is not possible because one of the fundamental objectives is to apply expectations by making as few alterations as possible on the data.
Related
I have a Companies table in DynamoDB that looks like this:
company: {
id: "11",
name: "test",
jobs: [
{
"name": "painter",
"id": 3
},
{
"name": "gardner"
"id": 2
}
]
}
And I want to make a scan query that get all the companies with the "painter" job inside their jobs array
I am using python and boto3
I tried something like this but it didn't work
jobs = ["painter"]
response = self.table.scan(
FilterExpression=Attr('jobs.name').is_in(jobs)
)
Please help.
Thanks.
It looks like this may not be doable in general, however it's possible that the method applied in that link may still be useful. If you know the maximum length of the jobs array over all of your data, you could create an expression for each index chained with ORs. Notably I could not find documentation for handling map and list scan expressions, so I can't really say whether you'd also need to check that you're not going out of bounds.
As the question explained above, I faced the difficulty of reading json file to collect attribute values from the company database in Python, and would like to store the values into numpy.ndarray form. What I wanna do is to read through
all the companies numbering and select its own values.
For example:
"0000059745": {
"Income": 5928375,
"Assets": 958273479,
}
"0000212498": "Empty dictionary.",
"0000310826": {
"Income": 1928474,
"Assets": 2938479,
}
However, the relevant questions I checked on other sources were simply basic instruction of teaching people how to read json file with the same name for a single company, but did not explicitly have the similar problem as I had.
For instance:
"comp1": {
"Income": 5928375,
"Assets": 958273479,
}
"comp1": "Empty dictionary.",
"comp1": {
"Income": 1928474,
"Assets": 2938479,
}
Hence, what I would like to do it is something like below:
with open("input/company_data.json") as f:
for comp in companies["number"]:
for var in company_variables["Assets"]["Income"]:
// Storing both Assets and Income attribute values into dataNdArr as numpy.ndaarray type
dataNdArr = var
I hope someone could help me with further improving this deeper level of reading json file problem.
Thank you.
I have a json called thefile.json which looks like this:
{
"domain": "Something",
"domain": "Thingie",
"name": "Another",
"description": "Thing"
}
I am trying to write a python script which would made a set of the values in domain. In this example it would return
{'Something', 'Thingie'}
Here is what I tried:
import json
with open("thefile.json") as my_file:
data = json.load(my_file)
ids = set(item["domain"] for item in data.values())
print(ids)
I get the error message
unique_ids.add(item["domain"])
TypeError: string indices must be integers
Having looked up answers on stack exchange, I'm stumped. Why can't I have a string as an index, seeing as I am using a json whose data type is a dictionary (I think!)? How do I get it so that I can get the values for "domain"?
So, to start, you can read more about JSON formats here: https://www.w3schools.com/python/python_json.asp
Second, dictionaries must have unique keys. Therefore, having two keys named domain is incorrect. You can read more about python dictionaries here: https://www.w3schools.com/python/python_dictionaries.asp
Now, I recommend the following two designs that should do what you need:
Multiple Names, Multiple Domains: In this design, you can access websites and check the domain of each of its values like ids = set(item["domain"] for item in data["websites"])
{
"websites": [
{
"domain": "Something.com",
"name": "Something",
"description": "A thing!"
},
{
"domain": "Thingie.com",
"name": "Thingie",
"description": "A thingie!"
},
]
}
One Name, Multiple Domains: In this design, each website has multiple domains that can be accessed using JVM_Domains = set(data["domains"])
{
"domains": ["Something.com","Thingie.com","Stuff.com"]
"name": "Me Domains",
"description": "A list of domains belonging to Me"
}
I hope this helps. Let me know if I missed any details.
You have a problem in your JSON, duplicate keys. I am not sure if it is forbiden, but I am sure it is bad formatted.
Besides that, of course it is gonna bring you lot of problems.
A dictionary can not have duplicate keys, what would be the return of a duplicate key?.
So, fix your JSON, something like this,
{
"domain": ["Something", "Thingie"],
"name": "Another",
"description": "Thing"
}
Guess what, good format almost solve your problem (you can have duplicates in the list) :)
I am trying to update a value in the nested array but can't get it to work.
My object is like this
{
"_id": {
"$oid": "1"
},
"array1": [
{
"_id": "12",
"array2": [
{
"_id": "123",
"answeredBy": [], // need to push "success"
},
{
"_id": "124",
"answeredBy": [],
}
],
}
]
}
I need to push a value to "answeredBy" array.
In the below example, I tried pushing "success" string to the "answeredBy" array of the "123 _id" object but it does not work.
callback = function(err,value){
if(err){
res.send(err);
}else{
res.send(value);
}
};
conditions = {
"_id": 1,
"array1._id": 12,
"array2._id": 123
};
updates = {
$push: {
"array2.$.answeredBy": "success"
}
};
options = {
upsert: true
};
Model.update(conditions, updates, options, callback);
I found this link, but its answer only says I should use object like structure instead of array's. This cannot be applied in my situation. I really need my object to be nested in arrays
It would be great if you can help me out here. I've been spending hours to figure this out.
Thank you in advance!
General Scope and Explanation
There are a few things wrong with what you are doing here. Firstly your query conditions. You are referring to several _id values where you should not need to, and at least one of which is not on the top level.
In order to get into a "nested" value and also presuming that _id value is unique and would not appear in any other document, you query form should be like this:
Model.update(
{ "array1.array2._id": "123" },
{ "$push": { "array1.0.array2.$.answeredBy": "success" } },
function(err,numAffected) {
// something with the result in here
}
);
Now that would actually work, but really it is only a fluke that it does as there are very good reasons why it should not work for you.
The important reading is in the official documentation for the positional $ operator under the subject of "Nested Arrays". What this says is:
The positional $ operator cannot be used for queries which traverse more than one array, such as queries that traverse arrays nested within other arrays, because the replacement for the $ placeholder is a single value
Specifically what that means is the element that will be matched and returned in the positional placeholder is the value of the index from the first matching array. This means in your case the matching index on the "top" level array.
So if you look at the query notation as shown, we have "hardcoded" the first ( or 0 index ) position in the top level array, and it just so happens that the matching element within "array2" is also the zero index entry.
To demonstrate this you can change the matching _id value to "124" and the result will $push an new entry onto the element with _id "123" as they are both in the zero index entry of "array1" and that is the value returned to the placeholder.
So that is the general problem with nesting arrays. You could remove one of the levels and you would still be able to $push to the correct element in your "top" array, but there would still be multiple levels.
Try to avoid nesting arrays as you will run into update problems as is shown.
The general case is to "flatten" the things you "think" are "levels" and actually make theses "attributes" on the final detail items. For example, the "flattened" form of the structure in the question should be something like:
{
"answers": [
{ "by": "success", "type2": "123", "type1": "12" }
]
}
Or even when accepting the inner array is $push only, and never updated:
{
"array": [
{ "type1": "12", "type2": "123", "answeredBy": ["success"] },
{ "type1": "12", "type2": "124", "answeredBy": [] }
]
}
Which both lend themselves to atomic updates within the scope of the positional $ operator
MongoDB 3.6 and Above
From MongoDB 3.6 there are new features available to work with nested arrays. This uses the positional filtered $[<identifier>] syntax in order to match the specific elements and apply different conditions through arrayFilters in the update statement:
Model.update(
{
"_id": 1,
"array1": {
"$elemMatch": {
"_id": "12","array2._id": "123"
}
}
},
{
"$push": { "array1.$[outer].array2.$[inner].answeredBy": "success" }
},
{
"arrayFilters": [{ "outer._id": "12" },{ "inner._id": "123" }]
}
)
The "arrayFilters" as passed to the options for .update() or even
.updateOne(), .updateMany(), .findOneAndUpdate() or .bulkWrite() method specifies the conditions to match on the identifier given in the update statement. Any elements that match the condition given will be updated.
Because the structure is "nested", we actually use "multiple filters" as is specified with an "array" of filter definitions as shown. The marked "identifier" is used in matching against the positional filtered $[<identifier>] syntax actually used in the update block of the statement. In this case inner and outer are the identifiers used for each condition as specified with the nested chain.
This new expansion makes the update of nested array content possible, but it does not really help with the practicality of "querying" such data, so the same caveats apply as explained earlier.
You typically really "mean" to express as "attributes", even if your brain initially thinks "nesting", it's just usually a reaction to how you believe the "previous relational parts" come together. In reality you really need more denormalization.
Also see How to Update Multiple Array Elements in mongodb, since these new update operators actually match and update "multiple array elements" rather than just the first, which has been the previous action of positional updates.
NOTE Somewhat ironically, since this is specified in the "options" argument for .update() and like methods, the syntax is generally compatible with all recent release driver versions.
However this is not true of the mongo shell, since the way the method is implemented there ( "ironically for backward compatibility" ) the arrayFilters argument is not recognized and removed by an internal method that parses the options in order to deliver "backward compatibility" with prior MongoDB server versions and a "legacy" .update() API call syntax.
So if you want to use the command in the mongo shell or other "shell based" products ( notably Robo 3T ) you need a latest version from either the development branch or production release as of 3.6 or greater.
See also positional all $[] which also updates "multiple array elements" but without applying to specified conditions and applies to all elements in the array where that is the desired action.
I know this is a very old question, but I just struggled with this problem myself, and found, what I believe to be, a better answer.
A way to solve this problem is to use Sub-Documents. This is done by nesting schemas within your schemas
MainSchema = new mongoose.Schema({
array1: [Array1Schema]
})
Array1Schema = new mongoose.Schema({
array2: [Array2Schema]
})
Array2Schema = new mongoose.Schema({
answeredBy": [...]
})
This way the object will look like the one you show, but now each array are filled with sub-documents. This makes it possible to dot your way into the sub-document you want. Instead of using a .update you then use a .find or .findOne to get the document you want to update.
Main.findOne((
{
_id: 1
}
)
.exec(
function(err, result){
result.array1.id(12).array2.id(123).answeredBy.push('success')
result.save(function(err){
console.log(result)
});
}
)
Haven't used the .push() function this way myself, so the syntax might not be right, but I have used both .set() and .remove(), and both works perfectly fine.
I have a python script which contains dictionaries and is used as input from another python script which performs calculations. I want to use the first script which is used as input, to create more scripts with the exact same structure in the dictionaries but different values for the keys.
Original Script: Car1.py
Owner = {
"Name": "Jim",
"Surname": "Johnson",
}
Car_Type = {
"Make": "Ford",
"Model": "Focus",
"Year": "2008"
}
Car_Info = {
"Fuel": "Gas",
"Consumption": 5,
"Max Speed": 190
}
I want to be able to create more input files with identical format but for different cases, e.g.
New Script: Car2.py
Owner = {
"Name": "Nick",
"Surname": "Perry",
}
Car_Type = {
"Make": "BMW",
"Model": "528",
"Year": "2015"
}
Car_Info = {
"Fuel": "Gas",
"Consumption": 10,
"Max Speed": 280
}
So far, i have only seen answers that print just the keys and the values in a new file but not the actual name of the dictionary as well. Can someone provide some help? Thanks in advance!
If you really want to do it that way (not recommended, because of the reasons statet in the comment by spectras and good alternatives) and import your input Python file:
This question has answers on how to read out the dictionaries names from the imported module. (using the dict() on the module while filtering for variables that do not start with "__")
Then get the new values for the dictionary entries and construct the new dicts.
Finally you need to write a exporter that takes care of storing the data in a python readable form, just like you would construct a normal text file.
I do not see any advantage over just storing it in a storage format.
read the file with something like
text=open('yourfile.py','r').read().split('\n')
and then interpret the list of strings you get... after that you can save it with something like
new_text = open('newfile.py','w')
[new_text.write(line) for line in text]
new_text.close()
as spectras said earlier, not ideal... but if that's what you want to do... go for it