Package: ds11mltoolkit
Welcome to ds11mltoolkit, we are delighted to see you here!Thank you for your interest, and we hope this library can help you in your daily life as aData ScientistTable of contentsWhat is ds11mltoolkit?How to install ds11mltoolkitDependenciesFunctions and methodsData AnalysisData visualization and explorationData processingMachine LearningGithub frameworkContributorsWhat is ds11mltoolKit?It is a Python package that will help you in your first steps as a Data Scientist."Faster, cleaner, easier"From simple databasis to complex neural networks, this library will accelerate your work processes in all stages of the machine learning cycle.How to install ds11mltoolkit?Install as you would normally install a Pypi library.pip install ds11mltoolkitWe suggest to import ds11mltoolkit as mlt, to make it easier to deploy by the usersimport ds11mltoolkit as mltDependenciesds11mltoolkit requires these libraries to work properly:beautifulsoup4==4.11.1imblearn==0.0keras==2.11.0matplotlib==0.1.6nltk==3.8.1opencv-python-headless==4.7.0.68pandas==1.3.5Pillow==9.3.0plotly==5.11.0requests==2.28.1scikit-image==1.0.2scikit-learn==0.19.3scipy==1.7.3seaborn==0.12.1selenium==4.7.2tensorflow==2.11.0wordcloud==1.7.0Functions and methodsIn the current version, ds11mltoolkit will provide users around 40 functions, divided in 4 groups:Data Analysisread_urlread_csv_zipchi_squared_testData visualisation and explorationheatmapsunburstcorrel_map_maxplot_mapplot_ngramwordcloudvizplot_cumulative_variance_ratioplot_roc_cruveplot_multiclass_prediction_imageData processinglist_categorical_columnslast_columnsuniq_valueload_imgsclass ImageDataGen(ImageDataGenerator) 3-in-1 functionsclean_textprocessing_model_classificationreplace_convert_numericlog_transform_numericadd_previous_exponential_smoothNan treatmentconvert_to_numericauto_dtype_converterwinner_loserlstm_modelMachine Learningexport_modelimport_modelworst_paramsload_model_zipquickregressionpolynomial_features_non_binarybalance_binary_targetimage_scrapcreate_multiclass_prediction_dfshow_scoringpredict_model_classificationUnsupervised KMeansUnsupervisedPCAQuick exampledf = pd.DataFrame(data= {'Cities': ['Madrid', 'Barcelona'], 
                            'Teams': ['Team 1', 'Team 2'],
                            'Players': ['Vinicius', 'Pedri'],
                            'Goals': [10, 9]})


def list_categorical_columns(df):
    '''
    Function that returns a list with the names of the categorical columns of a dataframe.

    Parameters
    ----------
    df : dataframe
    
    Return
    ----------
        features: list of names

    '''
    features = []

    for c in df.columns:
        t = str(df[c].dtype)
        if "object"  in t:
            features.append(c)
    return features

list_categorical_columns(df)

output: ['Cities', 'Teams', 'Players']Github frameworkContributorsMiguel de FrutosPedro VergaraBogdan RadacinaSean StevensonJosé NevadoCelia CabelloJared RivasNicolás EyzaguirreEnrique MoyaJavi LópezKyung Min OhnLeandro SalvadoRamón FernándezLicenseds11mltoolkit uses an “Interface-Protection Clause” on top of the MIT license. This library is free for personal use. Therefore, it can be used for both commercial and non-commercial purpose.See licensePlease don't hesitate to contact us if you have any questions or comments. Thank you for using our library!