semvec/pypi-packages huggingface dataset:
azqueuemanager-json 0.0.2
JSON for AZQueueManager
License: MIT
Requires Python: >=3.10
Keywords: azqueuemanager,extension,json

# Base AzQueueManager  Extension Template
teamplate for building extensions for AZ Queue Manager

## Getting Started
- Create a new repository from this template.
- Update the pyproject.toml file
  - add your requirements `project.dependencies`
a-pandas-ex-to-dict 0.10
Pandas DataFrame to nested dict
License: MIT
Keywords: pandas,crosstab,DataFrame,merge,concat,Series


# Pandas DataFrame to nested dict

```python

# from: https://stackoverflow.com/a/74810722/15096247

$pip install a-pandas-ex-to-dict

from a_pandas_ex_to_dict import pd_add_df_to_nested_dict
import pandas as pd
from pprint import pprint

pd_add_df_to_nested_dict()

df = pd.read_csv("https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")
df = df[:10]
dfdict2 = df.d_to_nested_dict(["Survived", "Pclass", "Embarked", "Fare", "Cabin"])
dfdict = df.d_to_nested_dict()
pprint(dfdict2)
pprint(dfdict)


{0: {1: {'S': {51.8625: {'E46': 1}}},
     3: {'Q': {8.4583: {}}, 'S': {7.25: {}, 8.05: {}, 21.075: {}}}},
 1: {1: {'C': {71.2833: {'C85': 1}}, 'S': {53.1: {'C123': 1}}},
     2: {'C': {30.0708: {}}},
     3: {'S': {7.925: {}, 11.1333: {}}}}}
{1: {0: {3: {'Braund, Mr. Owen Harris': {'male': {22.0: {1: {0: {'A/5 21171': {7.25: 1}}}}}}}}},
 2: {1: {1: {'Cumings, Mrs. John Bradley (Florence Briggs Thayer)': {'female': {38.0: {1: {0: {'PC 17599': {71.2833: 1}}}}}}}}},
 3: {1: {3: {'Heikkinen, Miss. Laina': {'female': {26.0: {0: {0: {'STON/O2. 3101282': {7.925: 1}}}}}}}}},
 4: {1: {1: {'Futrelle, Mrs. Jacques Heath (Lily May Peel)': {'female': {35.0: {1: {0: {'113803': {53.1: 1}}}}}}}}},
 5: {0: {3: {'Allen, Mr. William Henry': {'male': {35.0: {0: {0: {'373450': {8.05: 1}}}}}}}}},
 6: {0: {3: {'Moran, Mr. James': {'male': {}}}}},
 7: {0: {1: {'McCarthy, Mr. Timothy J': {'male': {54.0: {0: {0: {'17463': {51.8625: 1}}}}}}}}},
 8: {0: {3: {'Palsson, Master. Gosta Leonard': {'male': {2.0: {3: {1: {'349909': {21.075: 1}}}}}}}}},
 9: {1: {3: {'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)': {'female': {27.0: {0: {2: {'347742': {11.1333: 1}}}}}}}}},
 10: {1: {2: {'Nasser, Mrs. Nicholas (Adele Achem)': {'female': {14.0: {1: {0: {'237736': {30.0708: 1}}}}}}}}}}
```
azqueuemanager-rss 0.0.2
rss-parser for AZQueueManager
License: MIT
Requires Python: >=3.10
Keywords: azqueuemanager,extension,rss,azure storage queues

# AzQueueManager-RSS Extension
RSS Extension  for [AZ Queue Manager](https://pypi.org/project/azqueuemanager/)

|||
---|---
|Transform | RSSTransform | 
|transform_in | ✅ |
|transform_preview | 🚫 |
|transform_out | 🚫 |


to install:
```bash
pip install azqueuemanager-rss
```
datasciencetbpy 0.0.2
Librería para limpieza y visualización de datos, y Machine learning
License: MIT
Requires Python: >=3.7

# 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)