pyquirrinds 0.0.1

Librería para limpieza y visualización de datos, y Machine learning

LIBRERÍA DE LA CLASE DE LA CLASE DE DATA SCIENCE DE THE BRIDGE, GRUPO DE SEPTIEMBRE 2022 MODALIDAD FULL TIME

Esta librería incluye funciones de limpieza, visualización y machine learning

Limpieza

read_it(url) google_img(path_api, urls, directory_names, directory_path) dif_encoder(x, y) splityear(x) simbolcleaner(x) too_many_nans(df, threshold=0, clean=True) num_processor(df, chars1=','', chars2='@'€%"$') mueve_imagenes(carpeta_fuente, carpeta_train, carpeta_test, n_max=500, split=0.2) read_data(path, im_size, class_names) edad(df, columna) igualar_strings(df, columna, string_deseado) outliers(df, columna) porcentaje(columna) trimestre(df, string_columna) deteccion_outliers(data, features) lista_de_listas(lista) ratio_nulos(data, features)

Visualización

visualize_data(x, y) s_temporal(df, a, y) comparacion_stemporal(train, test, prediction, lower_series, upper_series) candle_plot(df) grafica_creator(df) grid_creator(data, x, y, hue) Line_Line_bar_party(x, y, y1, label_x="x", label_y="y", label_y1="y1", plotsize=(20, 12), barcolor="grey", linecolor_y="green" linecolor_y1="b") balanced_target(X_train, y_train) feature_importances_visualization(best_estimator, X_train, plotsize=(20, 10)) matrices_comparadas(y, x_test_scaled, y_test, nombre_modelo, y_2, x_test_scaled_2, y_test_2, nombre_modelo_2, size) plot_matriz_confusion(y, x_test_scaled, y_test, nombre_modelo, size) piechart_etiquetado(data, size) test_transformers(df, cols) report_plot(tree_entrenado, X_test, y_test, columnas_X) bar_plot(df, columna) mapa_folium(df, geojson, key, coord, legend="Mapa") vis_line(df, ejex, ejey, group="", type=0) matrix_sca (df, dimensiones, agrupar, titulo="Scatter Matrix") pca_visualization(df)

Machine Learning

my_pca(n_components, df) my_kmeans(n_clusters, df) anomalias_var(feature) FeatureImportance_rf(X, y, n) FeatureSelection_var(X, min_var) Impute_Tree_Regressor(df: pd.core.frame.DataFrame, n_max_depth: int, random_state: int) Impute_Tree_classifier(df: pd.core.frame.DataFrame, categorical_variable: str, n_max_depth: int, random_state: int) relative_absolute_error(y_train: pd.core.series.Series, y_test: pd.core.series.Series, y_predicted: pd.core.series.Series, type_metric='error') specificity(y_true, y_pred) classifier_cat(dataf) cat_to_num(dataf) ver_balance(target) under(X, y) over(X, y) sampling(X, y) gradBoosting(X_train, X_test, y_train, y_test) cal_cols(df, column, n=0) bi_ray(n, bi=[[1], [0]], num_loop=1) frame_maker(df, columns, up_array, num_loops=0, num_col=0, dict_decod={}, full_frame=pd.DataFrame([])) bi_hot_encoding(df, columns=None) get_clusters(X_train, cluster_fd=KNeighborsClassifier(), cluster_mk=DBSCAN()) model_dic(df_model, n=0, dic={}) class cluster_ensemble Dec_tree_clf(X, y) LogisticReg(X, y) RandomForest(X, y) pickleizer(nombre, modelo=None) DPRegressor(X: pd.DataFrame, y: pd.Series)