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Deal with NAN values when creating models with python


Deal with NAN values when creating models with python

By : Daniel Nguyễn
Date : October 24 2020, 08:10 AM
I hope this helps . To answer this question I think you need to answer the more fundamental question "is Y=NaN a label?"
If you want the trained model to predict one of three labels (Yes, No, NaN) then fill the NaNs with a label (e.g. "Missing"). If you don't (Yes or No) then I don't see how you can train on observations with no target. Drop those and train on the rest.
code :


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How to deal with time values over 24 hours in python?

How to deal with time values over 24 hours in python?


By : Kongu Cse
Date : March 29 2020, 07:55 AM
Any of those help The %H parameter can only parse values in the range 0-23. You'll have to manually deal with those specific time stamps:
code :
try:
     time = datetime.datetime.strptime(time, " %H:%M:%S.%f")
except ValueError:
     time = time.replace(' 24', ' 23')
     time = datetime.datetime.strptime(time, " %H:%M:%S.%f")
     time += datetime.timedelta(hours=1)
Creating a context object with values from several related models

Creating a context object with values from several related models


By : Shaddahellup
Date : March 29 2020, 07:55 AM
How to deal with NaN values in data in Python?

How to deal with NaN values in data in Python?


By : user3380356
Date : March 29 2020, 07:55 AM
I hope this helps . I have a large data set containing many NaN values in multiple columns. , You need to write it as,
code :
df = df.dropna(subset=["Deviation from Partisanship"])
df.dropna(subset=["Deviation from Partisanship"], inplace=True)
How can I deal with duplicate values when creating a dictionary in Python?

How can I deal with duplicate values when creating a dictionary in Python?


By : Rafael Medeiros
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further What you ask is not possible with a Python dict, since that goes against the definition of a dict--keys must be unique. The comments explain why that is the case.
However, there are multiple other ways that may be useful to you that can achieve almost the same affect. One simple, yet not terribly useful, way is:
code :
almost_dictionary = list(zip(list1, list2))
[(1, 2), (2, 3), (3, 4), (4, 5), (1, 6)]
dictionary = defaultdict(list)
for k, v in zip(list1, list2):
    dictionary[k].append(v)
print(dictionary)
defaultdict(<class 'list'>, {1: [2, 6], 2: [3], 3: [4], 4: [5]})
How to deal with Hex values in YAML using both Go and Python?

How to deal with Hex values in YAML using both Go and Python?


By : Mr.King
Date : March 29 2020, 07:55 AM
To fix the issue you can do You can load and dump that output in Python while preserving the hex value using ruamel.yaml (disclaimer: I am the author of that Python package):
code :
import sys
import ruamel.yaml

yaml_str = """\
someHexValue: 0xDef9C64256DeE61ebf5B212238df11C7E532e3B7
someString: Hello World
someStringWithQuotes: '"Hello World"'
"""

yaml = ruamel.yaml.YAML()
data = yaml.load(yaml_str)
yaml.dump(data, sys.stdout)
someHexValue: 0xDEF9C64256DEE61EBF5B212238DF11C7E532E3B7
someString: Hello World
someStringWithQuotes: '"Hello World"'
import sys
import ruamel.yaml

data = dict(someHexValue="0xDef9C64256DeE61ebf5B212238df11C7E532e3B7")

yaml = ruamel.yaml.YAML()
yaml.dump(data, sys.stdout)
someHexValue: '0xDef9C64256DeE61ebf5B212238df11C7E532e3B7'
        intv, err := strconv.ParseInt(plain, 0, 64)
        if err == nil {
            if intv == int64(int(intv)) {
                return yaml_INT_TAG, int(intv)
            } else {
                return yaml_INT_TAG, intv
            }
        }
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