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Update README.md
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saeedesmaili authored Dec 19, 2024
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Find the Max value for a certain column in certain aggregated columns
```python
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"john","age":"19"}]
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"John","age":"19"}]
from leopards import Max
count = Max(l,"age",['name'],dtype=int)
```
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* If you don't pass the aggregation columns, the maximum will be found across dataset.
* You can pass the datatype of the column to convert it on the fly while evaluating
```python
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"john","age":"19"}]
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"John","age":"19"}]
from leopards import Max
m = Max(l,"age",dtype=int)
```
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Find the Max value for a certain column in certain aggregated columns
```python
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"john","age":"19"}]
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"John","age":"19"}]
from leopards import Min
m = Min(l,"age",['name'])
```
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