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raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

 import numpy as np

 import matplotlib.pyplot as plt

 from sklearn.ensemble import IsolationForest

 import pandas as pd



np.random.RandomState(1234)

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)

for column in df.columns:

if df[column].dtype == type(object):

    le = LabelEncoder()

    df[column] = le.fit_transform(df[column])

    np.random.get_state()



X_train, X_test = train_test_split(df, test_size=0.3)

print(X_test)

print(X_train)

X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))

clf = IsolationForest(behaviour='new', max_samples=100,

                      random_state=df, contamination='auto')

clf.fit(X_train)

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

 import numpy as np

 import matplotlib.pyplot as plt

 from sklearn.ensemble import IsolationForest

 import pandas as pd



np.random.RandomState(1234)

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)

for column in df.columns:

if df[column].dtype == type(object):

    le = LabelEncoder()

    df[column] = le.fit_transform(df[column])

    np.random.get_state()



X_train, X_test = train_test_split(df, test_size=0.3)

print(X_test)

print(X_train)

X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))

clf = IsolationForest(behaviour='new', max_samples=100,

                      random_state=df, contamination='auto')

clf.fit(X_train)

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

 import numpy as np

 import matplotlib.pyplot as plt

 from sklearn.ensemble import IsolationForest

 import pandas as pd



np.random.RandomState(1234)

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)

for column in df.columns:

if df[column].dtype == type(object):

    le = LabelEncoder()

    df[column] = le.fit_transform(df[column])

    np.random.get_state()



X_train, X_test = train_test_split(df, test_size=0.3)

print(X_test)

print(X_train)

X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))

clf = IsolationForest(behaviour='new', max_samples=100,

                      random_state=df, contamination='auto')

clf.fit(X_train)

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

 import numpy as np

 import matplotlib.pyplot as plt

 from sklearn.ensemble import IsolationForest

 import pandas as pd



np.random.RandomState(1234)

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('E://Market_dat.csv',names=['EVENT_DT', 'MARKET_NAME', 'Duration', 'TOTAL_COUNTS'],skiprows=1,index_col=0)

for column in df.columns:

if df[column].dtype == type(object):

    le = LabelEncoder()

    df[column] = le.fit_transform(df[column])

    np.random.get_state()



X_train, X_test = train_test_split(df, test_size=0.3)

print(X_test)

print(X_train)

X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))

clf = IsolationForest(behaviour='new', max_samples=100,

                      random_state=df, contamination='auto')

clf.fit(X_train)

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