The following Python script must validate the number of decimal places in the records. In the schema, I am trying to define that it has 3 decimal places, using "multipleOf": 0.001.
I have a record with 5 decimal places: "scores": [1.12345]
It should report an error but it is returning:
<code>Validation ok
scores
0 1.12345
</code>
<code>Validation ok
scores
0 1.12345
</code>
Validation ok
scores
0 1.12345
How can I fix this?
<code>import jsonschema
import pandas as pd
schema = {
"type": "array",
"properties": {"scores": {"type": "number", "multipleOf": 0.001}},
}
df = pd.DataFrame(
{
"scores": [1.12345],
}
)
validator = jsonschema.Draft202012Validator(schema)
try:
validator.validate(instance=df.to_dict("records"))
print("Validation ok")
except jsonschema.ValidationError as e:
print(f"Validation error: {e.message}")
print(df)
</code>
<code>import jsonschema
import pandas as pd
schema = {
"type": "array",
"properties": {"scores": {"type": "number", "multipleOf": 0.001}},
}
df = pd.DataFrame(
{
"scores": [1.12345],
}
)
validator = jsonschema.Draft202012Validator(schema)
try:
validator.validate(instance=df.to_dict("records"))
print("Validation ok")
except jsonschema.ValidationError as e:
print(f"Validation error: {e.message}")
print(df)
</code>
import jsonschema
import pandas as pd
schema = {
"type": "array",
"properties": {"scores": {"type": "number", "multipleOf": 0.001}},
}
df = pd.DataFrame(
{
"scores": [1.12345],
}
)
validator = jsonschema.Draft202012Validator(schema)
try:
validator.validate(instance=df.to_dict("records"))
print("Validation ok")
except jsonschema.ValidationError as e:
print(f"Validation error: {e.message}")
print(df)
2
Your validation is not successful because the type:array is defined incorrectly. Your scores array instance is never validated because the schema only knows about "scores": 1.000 OR [] at the root.
EDIT:
I didn’t see you are using dataframes which are an array instance
Try this schema
<code>schema = {
"type": "array",
"items": {
"type": "object",
"properties": { "scores": {
"type": "array", "items": {"type": "number", "multipleOf": 0.001 }}}}},
}
</code>
<code>schema = {
"type": "array",
"items": {
"type": "object",
"properties": { "scores": {
"type": "array", "items": {"type": "number", "multipleOf": 0.001 }}}}},
}
</code>
schema = {
"type": "array",
"items": {
"type": "object",
"properties": { "scores": {
"type": "array", "items": {"type": "number", "multipleOf": 0.001 }}}}},
}
This would be equivalent to
<code>[{"scores": [1.000, 1.235, 2.333]}]
</code>
<code>[{"scores": [1.000, 1.235, 2.333]}]
</code>
[{"scores": [1.000, 1.235, 2.333]}]
5
It worked with Jeremy’s suggestion
<code>import jsonschema
import pandas as pd
schema = {
"type": "array",
"items": {
"type": "object",
"properties": {"scores": {"type": "array", "items": {"type": "number", "multipleOf": 0.001}}},
},
}
df = pd.DataFrame(
[
{
"scores": [1.1234],
}
]
)
validator = jsonschema.Draft202012Validator(schema)
try:
validator.validate(instance=df.to_dict("records"))
print("Validation ok")
except jsonschema.ValidationError as e:
print(f"Validation error: {e.message}")
print(df)
</code>
<code>import jsonschema
import pandas as pd
schema = {
"type": "array",
"items": {
"type": "object",
"properties": {"scores": {"type": "array", "items": {"type": "number", "multipleOf": 0.001}}},
},
}
df = pd.DataFrame(
[
{
"scores": [1.1234],
}
]
)
validator = jsonschema.Draft202012Validator(schema)
try:
validator.validate(instance=df.to_dict("records"))
print("Validation ok")
except jsonschema.ValidationError as e:
print(f"Validation error: {e.message}")
print(df)
</code>
import jsonschema
import pandas as pd
schema = {
"type": "array",
"items": {
"type": "object",
"properties": {"scores": {"type": "array", "items": {"type": "number", "multipleOf": 0.001}}},
},
}
df = pd.DataFrame(
[
{
"scores": [1.1234],
}
]
)
validator = jsonschema.Draft202012Validator(schema)
try:
validator.validate(instance=df.to_dict("records"))
print("Validation ok")
except jsonschema.ValidationError as e:
print(f"Validation error: {e.message}")
print(df)
Thank you Jeremy!