### Installation

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

Install the BanxicoPy library using pip.

```bash
pip install BanxicoPy
```

--------------------------------

### BANXICOpy Usage Example

Source: https://github.com/andreslomeliv/datosmex/blob/master/README.md

Example of how to use the Banxico class from the BANXICOpy library to fetch economic series.

```python
from BANXICOpy import Banxico

# Se requiere un token proporcionado por el INEGI
token = 'foobar'
indicador = Banxico(token)

indicador.obtener_df(indicadores = ['SF46405','SF46410'], nombres = ['USD','EURO'], inicio = '2020-01-01', fin = '2021-09-14')
```

--------------------------------

### Route Calculation Examples

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Demonstrates equivalent ways to call the CalculateRoute function and display the result.

```python
#ruteo.CalcularRuta(linea_inicial = linea_inicial, destino_final = destino_final, tipo_vehiculo = 1, ruta = 'optima')
#ruteo.CalcularRuta(destino_inicial = destino_inicial, linea_final = linea_final, tipo_vehiculo = 1, ruta = 'optima')
#ruteo.CalcularRuta(destino_inicial = destino_inicial, destino_final = destino_final, tipo_vehiculo = 1, ruta = 'optima')

display(ruta_optima)
```

--------------------------------

### Detailed Route Information

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Example of how to get detailed route information.

```python
detalle_ruta = ruteo.DetalleRuta(linea_inicial = linea_inicial, linea_final = linea_final, tipo_vehiculo = 1, ruta = 'optima')
display(detalle_ruta)
```

--------------------------------

### INEGIpy Usage Example

Source: https://github.com/andreslomeliv/datosmex/blob/master/README.md

Example of how to use the Indicadores class from the INEGIpy library to fetch economic indicators.

```python
from INEGIpy import Indicadores

# Se requiere un token proporcionado por el INEGI
token = 'foobar'
indicador = Indicadores(token) 

indicador.obtener_df(indicadores = '628229', bancos = 'BIE', nombres = 'Inflación General', inicio = '2018-06', fin = '2021-12')
```

--------------------------------

### Example Usage of LocalidadesAmanzanadas

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Demonstrates how to use the LocalidadesAmanzanadas function with concatenated keys and displays the tail of the resulting DataFrame.

```python
localidades = marco.LocalidadesAmanzanadas(claves_concatenadas = ['01','09002','190310357'], as_geodf = False)
display(localidades.tail())
```

--------------------------------

### BuscarLinea Example

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Example of using the BuscarLinea function from the routing module to find a line based on latitude and longitude.

```python
linea_i =  ruteo.BuscarLinea(lat, lng)
display(linea_i)
```

--------------------------------

### BuscarEntidad Example

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of using BuscarEntidad to search for 'papeleria' in entity '09' with a final registration of 5, without returning a GeoDataFrame.

```python
df = denue.BuscarEntidad('papeleria', entidad='09', registro_final = 5, as_geodf=False)
display(df)
```

--------------------------------

### Fetching Localities Data

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Example of how to fetch locality data for specific municipalities within an entity and display it.

```python
localidades = marco.LocalidadesAmanzanadas(entidades = '09', municipios = ['002','003','004','005'], as_geodf = False)
display(localidades)
```

--------------------------------

### Calculate the optimal route

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of using the CalcularRuta function to find an optimal route between two points.

```python
ruta_optima = ruteo.CalcularRuta(linea_inicial = linea_inicial, linea_final = linea_final, tipo_vehiculo = 1, ruta = 'optima')
```

--------------------------------

### Cuantificar() Function Call

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Example of how to call the Cuantificar() function with default parameters.

```python
DENUE.Cuantificar(clave_area = '0', 
                  clave_actividad = '0', 
                  estrato = '0')
```

--------------------------------

### Quantify data by activity class and stratum

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of using the Cuantificar function to count businesses by activity code and stratum.

```python
df = denue.Cuantificar(clave_area = '09003', clave_actividad = ['464111', '464112'], estrato= '1') #farmacias con y sin minisuper en Coyoacán
display(df)
```

--------------------------------

### Localidades by Entidades and Municipios

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of how to retrieve locality data by specifying entity and municipality codes. This method limits the combinations of areas that can be queried.

```python
# definir las claves en su forma concatenada en vez de separadas por nivel de agregación permite realizar dioferentes combinaciones entre áreas:
# si se definen de manera separada una vez que se da una lista de áreas ya no se pueden definir los niveles siguiente. Es decir, si se pasa una lista de estados 
# no se puede definir un municipio, de manera que las posibilidades se reducen a una lista de estados, un estado con una lista de municipios, 
# un estado y un municipio con una lsita de localidades, y así sucesivamente. 

localidades = marco.LocalidadesAmanzanadas(entidades = '09', municipios = ['002','003','004','005'], as_geodf = False)
display(localidades)
```

```
Result:
      cvegeo cve_agee cve_agem cve_loc                               nom_loc  \
0  090020001       09      002    0001                          Azcapotzalco   
1  090030001       09      003    0001                              Coyoacán   
2  090040063       09      004    0063                            Santa Rosa   
3  090040010       09      004    0010                           Cruz Blanca   
4  090040050       09      004    0050                              La Venta   
5  090040073       09      004    0073                         Los Aguacates   
6  090040054       09      004    0054  Puerto las Cruces (Monte las Cruces)   
7  090040020       09      004    0020                  San Lorenzo Acopilco   
8  090040001       09      004    0001                 Cuajimalpa de Morelos   
9  090050001       09      005    0001                     Gustavo A. Madero   

   ambito     latitud     longitud altitud      pob     viv cve_carta  \
0  URBANO  19.4841028  -99.1843606    2244   432205  134204    E14A39   
1  URBANO  19.3502139  -99.1621456    2247   614447  191646    E14A39   
2   RURAL  19.3233383  -99.2949614    2857      818     204    E14A39   
3   RURAL  19.3177850  -99.3240103    2982      728     192    E14A39   
4   RURAL  19.3343756  -99.3102128    2862      486     124    E14A39   
5   RURAL  19.3699019  -99.3107319    2725      514     129    E14A39   
6   RURAL  19.2948611  -99.3476211    3200     1233     323    E14A38   
7  URBANO  19.3310047  -99.3256817    2936    26042    6627    E14A39   
8  URBANO  19.3573503  -99.2997922    2780   186693   52530    E14A39   
9  URBANO  19.4829453  -99.1134708    2230  1173351  340301    E14A39   

   estatus     periodo  
0        1  2015-06-01  
1        1  2015-06-01  
2        1  2015-06-01  
3        1  2015-06-01  
4        1  2015-06-01  
5        1  2017-02-28  
6        1  2015-06-01  
7        1  2015-06-01  
8        1  2015-06-01  
9        1  2015-06-01  
```
```

--------------------------------

### Querying Multiple Series by List

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

Fetch data for multiple time series using a list of indicators and their names, with an optional start date.

```python
# varias series dentro de una lista
indicadores = ["SF61745", "SP68257", "SF43718"]
nombres = ["Tasa Objetivo", "UDIS", "Tipo de Cambio"]
df = bmx.obtener_series(indicadores, nombres, inicio = '2017-05-13')
df.head()
```

--------------------------------

### Search for a destination by name

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of using the BuscarDestino function to find a location by its name.

```python
from INEGIpy import Ruteo
token = "TuToken"
ruteo = Ruteo(token)

destino_inicial = ruteo.BuscarDestino(busqueda = 'palacio de bellas artes, ciudad de m', cantidad = 1)
destino_final = ruteo.BuscarDestino(busqueda = ' zócalo, ciudad de m', cantidad = 1)
display(destino_inicial)
display(destino_final)
```

--------------------------------

### Get Entities and Plot

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Retrieves entity data for specified names and plots the result.

```python
edos = marco.Entidades(nombres = ['ciudad de méxico','méxico','querétaro','san luis','nuevo león'])
edos.plot()
```

--------------------------------

### Localidades by Concatenated Keys

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of how to retrieve locality data using concatenated keys, which allows for more flexible combinations of areas across different aggregation levels.

```python
# en cambio, las claves concatenadas permiten diferentes municipios de diferentes entidades o incluso hacer combinaciones entre los niveles de agregación 

localidades = marco.LocalidadesAmanzanadas(claves_concatenadas = ['01','09002','190310357'], as_geodf = False)
display(localidades.tail())
```

```
Result:
        cvegeo cve_agee cve_agem cve_loc       nom_loc  ambito     latitud  \
392  010050106       01      005    0106   La Tomatina   RURAL  21.9014614   
393  010050007       01      005    0007  Los Arquitos   RURAL  21.9234458   
394  010050019       01      005    0019   Cieneguitas   RURAL  21.8954869   
395  090020001       09      002    0001  Azcapotzalco  URBANO  19.4841028   
396  190310357       19      031    0357       Anzures  URBANO  25.6905483   

         longitud altitud     pob     viv cve_carta  estatus     periodo  
392  -102.4151539    1962    1076     249    F13D18        1  2015-06-01  
393  -102.3857450    1908    1214     252    F13D18        1  2015-06-01  
394  -102.4265219    1990     208      54    F13D18        1  2015-06-01  
395   -99.1843606    2244  432205  134204    E14A39        1  2015-06-01  
396  -100.1183050    0397    5222    1521    G14C26        1  2015-06-01  
```
```

--------------------------------

### Find the nearest routing line by coordinates

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of using the BuscarLinea function to find the closest routing line to given coordinates.

```python
linea_inicial = ruteo.BuscarLinea(lat = 19.435237353, lng = -99.141374223) # equvalente a buscar bellas artes pero con las coordenadas
linea_final = ruteo.BuscarLinea(lat = 19.4326290000001, lng = -99.133203) # equivalente a buscar zócalo con las coordenadas
display(linea_inicial)
display(linea_final)
```

--------------------------------

### Fetch and display street data for Monterrey

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This snippet shows how to use the `marco.Vialidades` function to get street data for a specific municipality (Monterrey, '039') within an entity ('19') and then displays the first few rows of the resulting DataFrame.

```python
mty_vialidades = marco.Vialidades(entidades='19',municipios='039')
display(mty_vialidades.head())
```

--------------------------------

### Fetching Latest Opportunistic Data

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

Get the latest available data for a specific indicator, with options for increment type.

```python
bmx.dato_oportuno("SF61745", 'tasa_objetivo', incremento='PorcAcumAnual')
```

--------------------------------

### Fetch GDP data by state

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Fetch real GDP indicators for each state for the years 2019 and 2020. This involves defining the indicator IDs, getting the list of states, and then calling the `obtener_df` method.

```python
indicadores = [str(i) for i in range(472080,472112)] # indicadores del PIB real por entidad federativa
entidades = marco.Entidades()
nombres = entidades.nom_agee.tolist() # nombres de las entidades
pib_edos = inegi.obtener_df(indicadores = indicadores, nombres = nombres, inicio = '2019')
display(pib_edos)
```

--------------------------------

### Import and Initialization

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

Import the Banxico class and initialize it with your token.

```python
# uso actual

from BANXICOpy import Banxico
```

```python
token = '274066f5ed9caabbbbe6417dae8d4359f06ac5c853619436d9c82d55ed58fe83'
bmx = Banxico(token)
```

--------------------------------

### Initialize Banxico with token

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.md

Initializing the Banxico object with an API token.

```python
token = '274066f5ed9caabbbbe6417dae8d4359f06ac5c853619436d9c82d55ed58fe83'
bmx = Banxico(token)
```

--------------------------------

### Initialize clients

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Initialize the Indicadores client with an API token and the MarcoGeoestadistico client.

```python
token = 'TuToken'
inegi = Indicadores(token)
marco = MarcoGeoestadistico()
```

--------------------------------

### Initialize INEGIpy clients

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Initializes the DENUE, MarcoGeoestadistico, and Ruteo clients with API tokens.

```python
token = 'TuToken'
denue = DENUE(token)
marco = MarcoGeoestadistico()
token_ruteo = 'TuOtroToken'
ruteo = Ruteo(token_ruteo)
```

--------------------------------

### BuscarAreaAct Example

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Example of using BuscarAreaAct to search for 'soriana' within a specific area code '09003' and activity code '46', without returning a GeoDataFrame.

```python
df = denue.BuscarAreaAct('soriana', clave_area = '09003', clave_actividad = '46', as_geodf = False)
display(df)
```

--------------------------------

### Fetching Base Monetaria with Annual Percentage Growth

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.md

This snippet demonstrates how to fetch the 'Base Monetaria' series with an annual percentage growth increment and display the first few rows.

```python
base = bmx.obtener_series('SF44043', 'Base Monetaria', inicio = '2020', incremento='PorcAnual')
base.head()
```

--------------------------------

### Instalación

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Instalación de la librería INEGIpy usando pip.

```bash
pip install INEGIpy
```

--------------------------------

### Import and Initialize Ruteo

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

This snippet shows how to import the Ruteo class and initialize it with an API token.

```python
from INEGIpy import Ruteo
token = 'TuToken'
ruteo = Ruteo(token)
```

--------------------------------

### Initialize Indicators object

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

This snippet shows how to import the Indicators class and initialize an object with an API token.

```python
from INEGIpy import Indicadores
token = 'TuToken'
inegi = Indicadores(token)
```

--------------------------------

### Import BANXICOpy

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.md

Importing the Banxico class from the BANXICOpy library.

```python
from BANXICOpy import Banxico
```

--------------------------------

### Search for businesses by name and location, then plot results

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This snippet demonstrates how to search for businesses (e.g., 'papeleria') within a specified radius around a given latitude and longitude using the DENUE client, and then plots the results.

```python
df = denue.Buscar('papeleria', latitud = 19.32593, longitud = -99.17253, distancia = 3_000)
df.plot()
```

--------------------------------

### Get Indicator Catalog

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Retrieves a catalog of indicators, filtering by 'BIE', and displays the head of the resulting DataFrame.

```python
indicadores = inegi.catalogo_indicadores('BIE')

display(indicadores.head())
```

--------------------------------

### Initialize DENUE client

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This code initializes the DENUE client from the INEGIpy library, requiring an API token.

```python
from INEGIpy import DENUE
token = "TuToken"
denue = DENUE(token)
```

--------------------------------

### Fetching Metadata

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

Retrieve metadata for specified indicators.

```python
metadatos = bmx.metadatos(indicadores)
metadatos
```

--------------------------------

### Get Indicator Data with Metadata

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Retrieves indicator data along with metadata, displaying both the DataFrame head and the metadata.

```python
df, metadatos = inegi.obtener_df(indicadores = "6200093954", 
                                 nombres = 'poblacion_ocupada_aguascalientes',
                                 clave_area = '01',
                                 inicio = '2000', 
                                 fin = '2010',
                                 metadatos = True)

display(df.head())
display(metadatos)
```

--------------------------------

### Get Indicator Data

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Retrieves indicator data for a specified period and displays the head and tail of the resulting DataFrame.

```python
df = inegi.obtener_df(indicadores = ["289242","289242"], 
                      nombres = ['Indicador Coincidente', 'Indicador Adelantado'], 
                      inicio = '2000', 
                      fin = '2010')

display(df.head())
display(df.tail())
```

--------------------------------

### Initialize MarcoGeoestadistico

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Initializes the MarcoGeoestadistico class.

```python
from INEGIpy import MarcoGeoestadistico

marco = MarcoGeoestadistico()
```

--------------------------------

### Querying Series as Growth Rates

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

Fetch a series and specify the increment type, such as annual percentage growth.

```python
# series como tasas de crecimiento
base = bmx.obtener_series('SF44043', 'Base Monetaria', inicio = '2020', incremento='PorcAnual')
base.head()
```

--------------------------------

### Initialize DENUE

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Initializes the DENUE (Directorio Estadístico Nacional de Unidades Económicas) module with an API token.

```python
from INEGIpy import DENUE
token = 'TuToken'
denue = DENUE(token)
```

--------------------------------

### Initialize Indicadores with Token

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Initializes the Indicadores class with an API token.

```python
from INEGIpy import Indicadores
token = "92170321-528f-f1dd-5d59-f8613e072746"
inegi = Indicadores(token)
```

--------------------------------

### Get concatenated key for Monterrey and plot AGEBs

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This snippet demonstrates how to retrieve the concatenated geographical key for Monterrey and then use it to fetch and plot the AGEBs (Ageoestadisticas Basicas) for that area.

```python
cve_concatenada = nl_municipios[nl_municipios.nom_agem == 'Monterrey'].cvegeo.iloc[0]
print('Clave Concatenada de Monterrey: {}'.format(cve_concatenada))
mty_agebs = marco.AGEBs(claves_concatenadas=cve_concatenada)
mty_agebs.plot()
```

--------------------------------

### Plotting municipalities and establishments in CDMX

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This code uses the geostatistical framework to get the geographic area layer for plotting, specifically focusing on municipalities in CDMX and overlaying Zócalo and establishment data.

```python
# utilizamos el marco geostadistico para obtener la capa del area geografica sobre la que plotear
# Si no conoces la clave de alguna entidad, municipio o localidad la puedes buscar por nombre en el marco geoestadístico

# regresa todas las manzanas en CDMX
muns = marco.Municipios(entidades='09')

ax = muns.plot(alpha = 0.5)
zocalo.plot(ax=ax, color='yellow', zorder=2, markersize=20)
estabs.plot(ax=ax, color='red', alpha=0.4)
```

--------------------------------

### Inicializar clientes y obtener datos del PIB

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Ejemplo de cómo inicializar los clientes de Indicadores y MarcoGeoestadistico, y obtener el PIB real por entidad federativa.

```python
token = 'TuToken'
inegi = Indicadores(token)
marco = MarcoGeoestadistico()

indicadores = [str(i) for i in range(472080,472112)] # indicadores del PIB real por entidad federativa
entidades = marco.Entidades()
nombres = entidades.nom_agee.tolist() # nombres de las entidades
pib_edos = inegi.obtener_df(indicadores = indicadores, nombres = nombres, inicio = '2019')
display(pib_edos)
```

--------------------------------

### Inicializar clientes para DENUE, MarcoGeoestadistico y Ruteo

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Inicializa las instancias de las clases DENUE, MarcoGeoestadistico y Ruteo con los tokens correspondientes.

```python
from INEGIpy import DENUE, MarcoGeoestadistico, Ruteo
import geopandas as gpd
import pandas as pd
```

```python
token = 'TuToken'
denue = DENUE(token)
marco = MarcoGeoestadistico()
token_ruteo = 'TuOtroToken'
ruteo = Ruteo(token_ruteo)
```

--------------------------------

### consulta_metadatos Method

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Retrieves metadata descriptions for one or more series.

```python
Indicadores.consulta_metadatos(metadatos)
```

--------------------------------

### Obtener Serie Method

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.md

Method to obtain time series data for specified indicators.

```python
Banxico.obtener_serie(indicadores, 
                      nombres = None, 
                      inicio = None, 
                      fin = None,
                      decimales = True,
                      incremento = None)

```

--------------------------------

### Importar módulos

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Importación de los módulos necesarios para trabajar con indicadores y marco geoestadístico.

```python
from INEGIpy import Indicadores, MarcoGeoestadistico
```

--------------------------------

### Querying Multiple Series by Range

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

Fetch data for a range of series using a string representation of the range.

```python
# varias series como un rango
df = bmx.obtener_series('SF1-SF5')
df.head()
```

--------------------------------

### Calculate Routes and Apply Delay

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Iterates through a list of lines, calculates the optimal route for each using the CalcularRuta function, and applies a 30-second delay every 100 requests to manage potential API rate limits or long processing times.

```python
import time

rutas = []
for j in range(len(lineas_f)):
    r = ruteo.CalcularRuta(linea_i, lineas_f[j])
    rutas.append(r)
    if j%100 == 0: time.sleep(30)
    
len(rutas)
```

--------------------------------

### Metadatos Method

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.md

Method to obtain metadata for specified indicators.

```python
Banxico.metadatos(indicadores)

```

--------------------------------

### Calculate Percentage Changes

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Calculates the percentage change for each state's GDP using the `pct_change()` method and displays the result.

```python
cambios_pcts = pib_edos.pct_change()
display(cambios_pcts)
```

--------------------------------

### catalogo_indicadores Method

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Retrieves a DataFrame with descriptions of indicators from a specified bank.

```python
Indicadores.catalogo_indicadores(banco,
                                 indicador = None)
```

--------------------------------

### Ruteo Class Initialization

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Initializes the Ruteo class with an INEGI token.

```python
class INEGIpy.Ruteo(token)
```

--------------------------------

### Obtener municipios de Nuevo León

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Este snippet muestra cómo obtener los municipios del estado de Nuevo León (entidad '19') y visualizar las primeras 10 filas del resultado.

```python
nl_municipios = marco.Municipios(entidades='19')
display(nl_municipios.head(10))
```

--------------------------------

### Fetch business details by ID and display

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This code fetches detailed information for a specific business using its unique key ('993591') via the `denue.Ficha` method and displays the resulting DataFrame without converting it to a GeoDataFrame.

```python
df = denue.Ficha(clave = '993591', as_geodf=False)
display(df)
```

--------------------------------

### Query Metadata

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Queries and displays the detailed description of the provided metadata.

```python
desc_metadatos = inegi.consulta_metadatos(metadatos)

display(desc_metadatos)
```

--------------------------------

### Import necessary libraries

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Imports the required modules from INEGIpy and other data science libraries.

```python
from INEGIpy import DENUE, MarcoGeoestadistico, Ruteo
import geopandas as gpd
import pandas as pd
```

--------------------------------

### Calculating routes to multiple destinations

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This snippet demonstrates how to find the nearest street lines for multiple destination establishments and notes a potential issue with scale in the routing system.

```python
# Para calcular una ruta se requiere la utilizar la función BuscarLinea (o BuscarDestino dependiendo de lo que se busca) con el fin de obtener la línea final de la ruta
# Como tenemos varios puntos finales más bien sería una lista de líneas

# Noté que en ocasiones el Sistema de Ruteo no encuentra información para una coordenada si la escala no está correcta por lo que aumenté el valor default de 
# la escala a 1,000,000 lo cual resolvió el problema para las coordenadas resultantes del DENUE sin embargo es importante tenerlo en cuenta para otras bases. 

lineas_f = [ruteo.BuscarLinea(estabs.Latitud.iloc[i], estabs.Longitud.iloc[i]) for i in range(estabs.shape[0])]
len(lineas_f)
```

--------------------------------

### Calcular y mostrar cambios porcentuales del PIB

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

Calcula la tasa de cambio porcentual del PIB por entidad federativa y muestra los resultados.

```python
cambios_pcts = pib_edos.pct_change()
display(cambios_pcts)
```

```python
cambios_pcts = cambios_pcts.stack().reset_index()
cambios_pcts.columns = ['fechas','nom_agee','cambio_pct_pib_real']
display(cambios_pcts.head())
```

```python
entidades = entidades.merge(cambios_pcts, how = 'left')
entidades.cambio_pct_pib_real = entidades.cambio_pct_pib_real*100
display(entidades.head())
```

```python
ax = entidades.plot(column = 'cambio_pct_pib_real', 
                    cmap = 'hot',
                    legend = True, 
                    legend_kwds={'label':'Cambio (%)', 'orientation':"horizontal"},
                    figsize = (8,8),
                    edgecolor = 'black',
                    linewidth = 0.5)
ax.set_title('Cambio Porcentual del PIB real por Entidad Federeativa \n 2019 - 2020')
ax.set_axis_off()
```

--------------------------------

### Search for establishments within a radius

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Uses the DENUE client to find establishments (filtered to 'oxxo') within a 5km radius of a given coordinate and plots them.

```python
# utilizamos el DENUE para obtener una capa con los establecimientos en un radio de 5 km 
# inicié queriendo ver todos los establecimientos pero son demasiados para hacer buenos visuales así que acoté a solo los Oxxos
lat, lng = zocalo.geometry.iloc[0].y, zocalo.geometry.iloc[0].x
estabs = denue.Buscar('oxxo',lat,lng,5000)

ax = zocalo.plot(color='yellow', zorder=2, markersize=50)
estabs.plot(ax=ax, alpha=0.3, color='red', zorder=1)
```

--------------------------------

### obtener_df Method

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Retrieves data for specified indicators as a pandas DataFrame.

```python
Indicadores.obtener_df(indicadores, 
                       nombres = None, 
                       inicio = None, 
                       fin = None,
                       banco = None,
                       metadatos = False)
```

--------------------------------

### Search for businesses by name and display results

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.ipynb

This snippet searches for businesses with the name 'oxxo', limiting the results to the first 5 entries, and displays the resulting DataFrame without converting it to a GeoDataFrame.

```python
df = denue.Nombre(nombre = 'oxxo', registro_final= 5, as_geodf=False)
display(df)
```

--------------------------------

### dato_oportuno() function signature

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.md

Signature of the dato_oportuno function, detailing its parameters.

```python
Banxico.dato_oportuno(indicadores,
                      nombres = None,
                      decimales = True
                      incremento = None)
```

--------------------------------

### Class Definition

Source: https://github.com/andreslomeliv/datosmex/blob/master/BANXICOpy/README.ipynb

The Banxico class requires a token provided by the Bank of Mexico.

```python
class Banxico(token)

```

--------------------------------

### DetalleRuta() Function Signature

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Shows the parameters for the DetalleRuta function, which calculates route details.

```python
Ruteo.DetalleRuta(linea_inicial = None, 
                  linea_final = None, 
                  destino_inicial = None, 
                  destino_final = None, 
                  tipo_vehiculo = 0, 
                  ruta = 'optima', 
                  ejes_excedentes = 0, 
                  saltar_lineas = None, 
                  proyeccion = 'GRS80')
```

--------------------------------

### Analyze and plot radial distances

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Calculates and prints the minimum, maximum, and mean radial distances of establishments. Then, it plots the municipalities, destination, and establishments, coloring them by radial distance.

```python
# podemos ver que la distancia mínima son 300 metros, la máxima es cercana a los 5 km y la media es de 3.1 km

estabs.distancia_radial.min(), estabs.distancia_radial.max(), estabs.distancia_radial.mean()

ax = muns.plot(alpha = 0.5, figsize=(8,8))
zocalo.plot(ax=ax, color='yellow', zorder=2, markersize=20)
estabs.plot(ax=ax, alpha=0.4, column='distancia_radial', legend = True, legend_kwds={'label':'Distancia (m)','orientation':"horizontal"})
```

--------------------------------

### Entidades Method

Source: https://github.com/andreslomeliv/datosmex/blob/master/INEGIpy/README.md

Retrieves state-level geoestatistical data.

```python
MarcoGeoestadistico.Entidades(entidades = None, 
                              nombres = None, 
                              as_geodf = True)
```