def bb():
try:
# script to update the status of the blue bikes
r = requests.get('https://gbfs.bluebikes.com/gbfs/en/station_status.json')
r = r.json()['data']['stations']
df = pd.DataFrame.from_dict(json_normalize(r), orient='columns')
df = df.rename(columns={'last_reported':'last_reported','station_id':'description', 'num_bikes_available':'bikes_available',
'num_docks_available':'docks_available'})
bb_table = df[['last_reported','description', 'bikes_available', 'docks_available']]
bb_features = gis.content.get(fc_id).layers[7]
bb_features
bb_fset = SpatialDataFrame.from_layer(bb_features)
bb_fset = bb_features.query(where="category_subtype = 'Blue Bikes'") #querying without any conditions returns all the features
overlap_rows = pd.merge(left = bb_fset.sdf, right = bb_table, how='inner', on = 'description')
#print (overlap_rows.head())
features_for_update = [] #list containing corrected features
all_features = bb_fset.features
# inspect one of the features
#print (all_features[2])
# update all features that were joined
for root_id in overlap_rows['description']:
# get the feature to be updated
original_feature = [f for f in all_features if f.attributes['description'] == root_id][0]
feature_to_be_updated = deepcopy(original_feature)
# get the matching row from csv
matching_row = bb_table.where(bb_table.description == root_id).dropna()
#print('snr' + tablenumber + '', 'snr' + tablenumber + 'average', float(matching_row['snrval'].values))
timestamp = matching_row['last_reported'].values[0]
#print (str(datetime.fromtimestamp(timestamp)))
feature_to_be_updated.attributes['use_type'] = matching_row['bikes_available'].values[0]
feature_to_be_updated.attributes['source_name'] = matching_row['docks_available'].values[0]
#feature_to_be_updated.attributes['source_type'] = str(datetime.fromtimestamp(matching_row['last_reported'].values[0]))
feature_to_be_updated.attributes['source_type'] = str(datetime.fromtimestamp(matching_row['last_reported'].values[0]).strftime("%I:%M %p"))
features_for_update.append(feature_to_be_updated)
#print (features_for_update)
bb_features.edit_features(updates= features_for_update)
except:
print("Something went wrong")
os._exit(0)
from arcgis.gis import GIS
from arcgis.features import SpatialDataFrame
from IPython.display import display
import passwords
gis = GIS("https://www.arcgis.com", passwords.user_name, passwords.password)
search_results = gis.content.search(
query='title: "Park*" AND type: "Feature Service"')
display(search_results)
# In[2]:
feature_service_item = search_results[0]
feature_layer = feature_service_item.layers[0]
display(feature_layer)
# In[3]:
# Search for an item and construct a Spatial Data Frame
# In[4]:
sdf = SpatialDataFrame.from_layer(feature_layer)
sdf.head()
# In[ ]:
# sdf.to_featureclass(out_location = 'path/to/save/data', out_name = 'file.shp')
gis = GIS("URLofESRIPortal", UserID, Password)
print("Connected")
siteMessages = []
for key, value in sites.items():
#print(value)
item = gis.content.get(value)
#print(item)
flayer = item.layers[0]
#print(flayer)
#Query produces a Feature Set, which is needed to call .features
fSet = flayer.query()
#Stores all features so that ones needing update can be copied
all_features = fSet.features
#Pulls data into Data Frame for manipulation with Pandas
ESRIdf = SpatialDataFrame.from_layer(flayer)
#print(ESRIdf.head())
features_for_update = []
#Iterate over ESRI Data Frame
print("Starting Query")
for index, row in ESRIdf.iterrows():
# skip rows that already have a gateway IP or don't contain a machine
if pd.notnull(row['gatewayip']) or row['followup'] != 'No':
#print("Skipped Row")
continue
# Skip rows without an asset tag
elif pd.isnull(row['asset']):
#print("Skipped " + str(row['objectid']))
continue
else: