from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

!pip install gdown

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import gdown

# ID del archivo en Google Drive
file_id = '1pBsKrMhPTpQFQSOYLCroiHwAqwqNaEyW'

# URL para descargar el archivo
url = f'https://drive.google.com/uc?id={file_id}'

# Descargar el archivo CSV
output = 'Centrolenidae.csv'
gdown.download(url, output, quiet=False)

Downloading...
From: https://drive.google.com/uc?id=1pBsKrMhPTpQFQSOYLCroiHwAqwqNaEyW
To: /content/Centrolenidae.csv
100%|██████████| 935k/935k [00:00<00:00, 12.1MB/s]

'Centrolenidae.csv'

import pandas as pd

# Leer el archivo CSV descargado
df = pd.read_csv('Centrolenidae.csv')

# Información general del DataFrame
print(df.info())

# Ver las primeras filas para asegurar que los datos se cargaron correctamente
print(df.head())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2566 entries, 0 to 2565
Data columns (total 29 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   id                             2566 non-null   int64  
 1   observed_on_string             2566 non-null   object 
 2   time_zone                      2566 non-null   object 
 3   user_id                        2566 non-null   int64  
 4   user_login                     2566 non-null   object 
 5   created_at                     2566 non-null   object 
 6   updated_at                     2566 non-null   object 
 7   quality_grade                  2566 non-null   object 
 8   url                            2566 non-null   object 
 9   tag_list                       51 non-null     object 
 10  description                    238 non-null    object 
 11  num_identification_agreements  2566 non-null   int64  
 12  captive_cultivated             2566 non-null   bool   
 13  oauth_application_id           1559 non-null   float64
 14  place_guess                    2556 non-null   object 
 15  latitude                       2566 non-null   float64
 16  longitude                      2566 non-null   float64
 17  positional_accuracy            2169 non-null   float64
 18  private_place_guess            0 non-null      float64
 19  public_positional_accuracy     2209 non-null   float64
 20  geoprivacy                     147 non-null    object 
 21  taxon_geoprivacy               2142 non-null   object 
 22  coordinates_obscured           2566 non-null   bool   
 23  positioning_method             756 non-null    object 
 24  species_guess                  2337 non-null   object 
 25  scientific_name                2566 non-null   object 
 26  common_name                    2516 non-null   object 
 27  iconic_taxon_name              2566 non-null   object 
 28  taxon_id                       2566 non-null   int64  
dtypes: bool(2), float64(6), int64(4), object(17)
memory usage: 546.4+ KB
None
      id observed_on_string                   time_zone  user_id  \
0  18212         2008-05-22  Eastern Time (US & Canada)     1325   
1  18776         2009-12-30             Central America      486   
2  19507         2011-05-28             Central America     1236   
3  22238         2011-05-28             Central America     1236   
4  25203             3/4/11  Pacific Time (US & Canada)     2246   

           user_login               created_at               updated_at  \
0             jplarry  2011-05-25 18:32:07 UTC  2019-09-27 02:32:38 UTC   
1              nito72  2011-05-29 21:29:44 UTC  2020-01-22 16:52:47 UTC   
2  victoracostachaves  2011-05-31 16:30:01 UTC  2024-03-01 13:20:15 UTC   
3  victoracostachaves  2011-06-19 21:04:42 UTC  2021-09-23 21:14:14 UTC   
4             redpine  2011-07-17 05:06:16 UTC  2021-01-23 22:37:31 UTC   

  quality_grade                                            url  \
0      research  http://www.inaturalist.org/observations/18212   
1      research  http://www.inaturalist.org/observations/18776   
2      research  http://www.inaturalist.org/observations/19507   
3      research  http://www.inaturalist.org/observations/22238   
4      research  http://www.inaturalist.org/observations/25203   

                       tag_list  ... public_positional_accuracy  geoprivacy  \
0                           NaN  ...                        NaN         NaN   
1                           NaN  ...                        NaN         NaN   
2    Hyalinobatrachium valerioi  ...                    31218.0    obscured   
3  Hyalinobatrachium talamancae  ...                    10000.0    obscured   
4                           NaN  ...                        NaN         NaN   

   taxon_geoprivacy  coordinates_obscured positioning_method  \
0              open                 False                NaN   
1              open                 False                NaN   
2              open                  True                NaN   
3               NaN                  True                NaN   
4              open                 False                NaN   

              species_guess               scientific_name  \
0  rana de vidrio esmeralda        Espadarana prosoblepon   
1    Reticulated Glass Frog    Hyalinobatrachium valerioi   
2    Reticulated Glass Frog    Hyalinobatrachium valerioi   
3  Green-striped Glass Frog  Hyalinobatrachium talamancae   
4  rana de vidrio esmeralda        Espadarana prosoblepon   

                common_name  iconic_taxon_name  taxon_id  
0  rana de vidrio esmeralda           Amphibia    134933  
1          Ranita de Vidrio           Amphibia     21323  
2          Ranita de Vidrio           Amphibia     21323  
3                       NaN           Amphibia     21357  
4  rana de vidrio esmeralda           Amphibia    134933  

[5 rows x 29 columns]

print(df.columns.tolist())

['id', 'observed_on_string', 'time_zone', 'user_id', 'user_login', 'created_at', 'updated_at', 'quality_grade', 'url', 'tag_list', 'description', 'num_identification_agreements', 'captive_cultivated', 'oauth_application_id', 'place_guess', 'latitude', 'longitude', 'positional_accuracy', 'private_place_guess', 'public_positional_accuracy', 'geoprivacy', 'taxon_geoprivacy', 'coordinates_obscured', 'positioning_method', 'species_guess', 'scientific_name', 'common_name', 'iconic_taxon_name', 'taxon_id']

import folium
import pandas as pd

# Asegurar de que las coordenadas (latitud, longitud) sean válidas y no nulas
df_clean = df.dropna(subset=['latitude', 'longitude'])
df_clean = df_clean[(df_clean['latitude'] >= -90) & (df_clean['latitude'] <= 90)]
df_clean = df_clean[(df_clean['longitude'] >= -180) & (df_clean['longitude'] <= 180)]

# Crear el mapa centrado en Costa Rica
m = folium.Map(location=[9.19, -83.55], zoom_start=7)

# Agregar los puntos de observación al mapa
for index, row in df_clean.iterrows():
    folium.CircleMarker(
        location=[row['latitude'], row['longitude']],
        radius=5,
        color='blue',
        fill=True,
        fill_color='blue',
        fill_opacity=0.6
    ).add_to(m)

# Mostrar el mapa
m

import pandas as pd

# Datos de ejemplo con coordenadas
data = {
    'latitud': [9.9383, 9.9271, 9.9523],
    'longitud': [-84.0833, -84.0420, -84.0755]
}

# Crear un DataFrame
ranita_df = pd.DataFrame(data)

# Guardar el DataFrame como un archivo CSV
ranita_df.to_csv('/content/ranita_ubicaciones.csv', index=False)

# Ver el archivo creado
print(ranita_df)

   latitud  longitud
0   9.9383  -84.0833
1   9.9271  -84.0420
2   9.9523  -84.0755

import pandas as pd
import rasterio
from rasterio import sample

# Cargar las coordenadas de la ranita desde el archivo CSV
ranita_df = pd.read_csv('/content/ranita_ubicaciones.csv')

# Abrir el archivo raster de precipitaciones
with rasterio.open('/content/precip_cr.tif') as src:
    # Extraer valores de precipitaciones para las ubicaciones de la ranita
    latitudes = ranita_df['latitud'].values
    longitudes = ranita_df['longitud'].values

    # Crear una lista de las coordenadas (longitud, latitud)
    coords = [(lon, lat) for lon, lat in zip(longitudes, latitudes)]

    # Extraer valores de precipitaciones
    precip_values = [v[0] for v in src.sample(coords)]

# Agregar los valores de precipitaciones al DataFrame de las ubicaciones de la ranita
ranita_df['precipitacion'] = precip_values

# Mostrar los primeros registros
print(ranita_df.head())

   latitud  longitud  precipitacion
0   9.9383  -84.0833            329
1   9.9271  -84.0420            327
2   9.9523  -84.0755            339

import folium
import pandas as pd
import rasterio
from rasterio import sample

# Cargar las coordenadas de la ranita desde el archivo CSV
ranita_df = pd.read_csv('/content/ranita_ubicaciones.csv')

# Abrir el archivo raster de precipitaciones
with rasterio.open('/content/precip_cr.tif') as src:
    # Extraer valores de precipitaciones para las ubicaciones de la ranita
    latitudes = ranita_df['latitud'].values
    longitudes = ranita_df['longitud'].values

    # Crear una lista de las coordenadas (longitud, latitud)
    coords = [(lon, lat) for lon, lat in zip(longitudes, latitudes)]

    # Extraer valores de precipitaciones
    precip_values = [v[0] for v in src.sample(coords)]

# Agregar los valores de precipitaciones al DataFrame de las ubicaciones de la ranita
ranita_df['precipitacion'] = precip_values

# Crear un mapa centrado en Costa Rica (aproximadamente)
mapa = folium.Map(location=[9.7489, -83.7534], zoom_start=7)

# Agregar las ubicaciones de las ranitas con sus valores de precipitación al mapa
for idx, row in ranita_df.iterrows():
    folium.Marker(
        location=[row['latitud'], row['longitud']],
        popup=f"Precipitación: {row['precipitacion']} mm",
        icon=folium.Icon(color="green", icon="cloud")
    ).add_to(mapa)

# Guardar el mapa en un archivo HTML
mapa.save('/content/mapa_ranitas.html')

# Mostrar el mapa en el notebook
mapa

Análisis de la Distribución Geoespacial de las Especies de la Familia Centrolenidae en Costa Rica: Relación con Factores Ambientales¶