“Process finished with exit code 0” but desired output is not shown

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I am new to python and have created this tiny class "myclass" which is inside a module called linear_regression_example.py. It prints out a regression summary and a density plot:

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()

I also have another file, called test.py:

import linear_regression_example as lre



test = lre.myclass()

test.myregression()

Running test.py in pycharm results in the output "Process finished with exit code 0" but no summary or plot is shown. Maybe someone here knows where the problem lies.

Best regards

Dominik

asked Jan 3 at 10:06

Dominik

I just tried this and it works fine!

– DirtyBit
Jan 3 at 10:10

add a comment |

I am new to python and have created this tiny class "myclass" which is inside a module called linear_regression_example.py. It prints out a regression summary and a density plot:

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()

I also have another file, called test.py:

import linear_regression_example as lre



test = lre.myclass()

test.myregression()

Running test.py in pycharm results in the output "Process finished with exit code 0" but no summary or plot is shown. Maybe someone here knows where the problem lies.

Best regards

Dominik

asked Jan 3 at 10:06

Dominik

I just tried this and it works fine!

– DirtyBit
Jan 3 at 10:10

add a comment |

I am new to python and have created this tiny class "myclass" which is inside a module called linear_regression_example.py. It prints out a regression summary and a density plot:

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()

I also have another file, called test.py:

import linear_regression_example as lre



test = lre.myclass()

test.myregression()

Running test.py in pycharm results in the output "Process finished with exit code 0" but no summary or plot is shown. Maybe someone here knows where the problem lies.

Best regards

Dominik

asked Jan 3 at 10:06

Dominik

I am new to python and have created this tiny class "myclass" which is inside a module called linear_regression_example.py. It prints out a regression summary and a density plot:

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()

I also have another file, called test.py:

import linear_regression_example as lre



test = lre.myclass()

test.myregression()

Running test.py in pycharm results in the output "Process finished with exit code 0" but no summary or plot is shown. Maybe someone here knows where the problem lies.

Best regards

Dominik

python matplotlib

asked Jan 3 at 10:06

Dominik

asked Jan 3 at 10:06

Dominik

asked Jan 3 at 10:06

Dominik

asked Jan 3 at 10:06

Dominik

asked Jan 3 at 10:06

Dominik

I just tried this and it works fine!

– DirtyBit
Jan 3 at 10:10

add a comment |

I just tried this and it works fine!

– DirtyBit
Jan 3 at 10:10

I just tried this and it works fine!

– DirtyBit
Jan 3 at 10:10

add a comment |

1 Answer
1

active

oldest

votes

linear_regression_example.py

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()



if __name__ == '__main__':

    test = myclass()

    test.myregression()

test.py

import linear_regression_example  as lre



test = lre.myclass()

test.myregression()

OUTPUT (from test.py)

test.py

answered Jan 3 at 10:13

DirtyBit

13.1k41943

1

Thank you very much!

– Dominik
Jan 3 at 10:27

But I have to confess that I do not fully understand the structure here. What would I have to to if I wanted to change the parameter "result" to 0 when calling myclass from test.py?

– Dominik
Jan 3 at 10:44

add a comment |

Your Answer

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1 Answer
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1 Answer
1

active

oldest

votes

linear_regression_example.py

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()



if __name__ == '__main__':

    test = myclass()

    test.myregression()

test.py

import linear_regression_example  as lre



test = lre.myclass()

test.myregression()

OUTPUT (from test.py)

test.py

answered Jan 3 at 10:13

DirtyBit

13.1k41943

1

Thank you very much!

– Dominik
Jan 3 at 10:27

But I have to confess that I do not fully understand the structure here. What would I have to to if I wanted to change the parameter "result" to 0 when calling myclass from test.py?

– Dominik
Jan 3 at 10:44

add a comment |

linear_regression_example.py

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()



if __name__ == '__main__':

    test = myclass()

    test.myregression()

test.py

import linear_regression_example  as lre



test = lre.myclass()

test.myregression()

OUTPUT (from test.py)

test.py

answered Jan 3 at 10:13

DirtyBit

13.1k41943

1

Thank you very much!

– Dominik
Jan 3 at 10:27

But I have to confess that I do not fully understand the structure here. What would I have to to if I wanted to change the parameter "result" to 0 when calling myclass from test.py?

– Dominik
Jan 3 at 10:44

add a comment |

linear_regression_example.py

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()



if __name__ == '__main__':

    test = myclass()

    test.myregression()

test.py

import linear_regression_example  as lre



test = lre.myclass()

test.myregression()

OUTPUT (from test.py)

test.py

answered Jan 3 at 10:13

DirtyBit

13.1k41943

linear_regression_example.py

import statsmodels.api as sm

import sklearn.datasets as skld

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



class myclass:

    def __init__(self, result=1):

        self.result = result



    def myregression(self):

        y_X = skld.load_boston()

        y = y_X['target']

        X = y_X['data']

        n = y_X['feature_names']

        y = pd.DataFrame(y)

        X = pd.DataFrame(X, columns=n)

        X = sm.add_constant(X)

        mod = sm.OLS(y, X)

        result = mod.fit()

        if self.result == 1:

            print(result.summary())

        pred = mod.predict(result.params)

        pred = pd.DataFrame(pred)

        errors = y - pred

        sns.distplot(errors)

        plt.show()



if __name__ == '__main__':

    test = myclass()

    test.myregression()

test.py

import linear_regression_example  as lre



test = lre.myclass()

test.myregression()

OUTPUT (from test.py)

test.py

answered Jan 3 at 10:13

DirtyBit

13.1k41943

answered Jan 3 at 10:13

DirtyBit

13.1k41943

answered Jan 3 at 10:13

DirtyBit

13.1k41943

answered Jan 3 at 10:13

DirtyBit

13.1k41943

1

Thank you very much!

– Dominik
Jan 3 at 10:27

But I have to confess that I do not fully understand the structure here. What would I have to to if I wanted to change the parameter "result" to 0 when calling myclass from test.py?

– Dominik
Jan 3 at 10:44

add a comment |

1

Thank you very much!

– Dominik
Jan 3 at 10:27

But I have to confess that I do not fully understand the structure here. What would I have to to if I wanted to change the parameter "result" to 0 when calling myclass from test.py?

– Dominik
Jan 3 at 10:44

Thank you very much!

– Dominik
Jan 3 at 10:27

But I have to confess that I do not fully understand the structure here. What would I have to to if I wanted to change the parameter "result" to 0 when calling myclass from test.py?

– Dominik
Jan 3 at 10:44

add a comment |

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