def create_map(self, path='map.html', plugin_data_out=True, template=None):
'''Write Map output to HTML and data output to JSON if available
Parameters:
-----------
path: string, default 'map.html'
Path for HTML output for map
plugin_data_out: boolean, default True
If using plugins such as awesome markers, write all plugin
data such as JS/CSS/images to path
template: string, default None
Custom template to render
'''
self.map_path = path
self._build_map(template)
with codecs.open(path, 'w', 'utf8') as f:
f.write(self.HTML)
if self.json_data:
for path, data in iteritems(self.json_data):
with open(path, 'w') as g:
json.dump(data, g)
if self.plugins and plugin_data_out:
for name, plugin in iteritems(self.plugins):
with open(name, 'w') as f:
if isinstance(plugin, binary_type):
plugin = text_type(plugin, 'utf8')
f.write(plugin)
def create_map(self, path='map.html', plugin_data_out=True, template=None):
"""Write Map output to HTML and data output to JSON if available.
Parameters:
-----------
path: string, default 'map.html'
Path for HTML output for map
plugin_data_out: boolean, default True
If using plugins such as awesome markers, write all plugin
data such as JS/CSS/images to path
template: string, default None
Custom template to render
"""
self.map_path = path
self._build_map(template)
with codecs.open(path, 'w', 'utf8') as f:
f.write(self.HTML)
if self.json_data:
for path, data in iteritems(self.json_data):
with open(path, 'w') as g:
json.dump(data, g)
if self.plugins and plugin_data_out:
for name, plugin in iteritems(self.plugins):
with open(name, 'w') as f:
if isinstance(plugin, binary_type):
plugin = text_type(plugin, 'utf8')
f.write(plugin)
def transform_data(data):
"""
Transform Pandas DataFrame into JSON format.
Parameters
----------
data: DataFrame or Series
Pandas DataFrame or Series
Returns
-------
JSON compatible dict
Example
-------
>>> transform_data(df)
"""
if pd is None:
raise ImportError("The Pandas package is required"
" for this functionality")
if np is None:
raise ImportError("The NumPy package is required"
" for this functionality")
def type_check(value):
"""
Type check values for JSON serialization. Native Python JSON
serialization will not recognize some Numpy data types properly,
so they must be explicitly converted.
"""
if pd.isnull(value):
return None
elif (isinstance(value, pd.tslib.Timestamp)
or isinstance(value, pd.Period)):
return time.mktime(value.timetuple())
elif isinstance(value, (int, np.integer)):
return int(value)
elif isinstance(value, (float, np.float_)):
return float(value)
elif isinstance(value, str):
return str(value)
else:
return value
if isinstance(data, pd.Series):
json_data = [{
type_check(x): type_check(y)
for x, y in iteritems(data)
}]
elif isinstance(data, pd.DataFrame):
json_data = [{
type_check(y): type_check(z)
for x, y, z in data.itertuples()
}]
return json_data
def transform_data(data):
"""
Transform Pandas DataFrame into JSON format.
Parameters
----------
data: DataFrame or Series
Pandas DataFrame or Series
Returns
-------
JSON compatible dict
Example
-------
>>> transform_data(df)
"""
if pd is None:
raise ImportError("The Pandas package is required"
" for this functionality")
if np is None:
raise ImportError("The NumPy package is required"
" for this functionality")
def type_check(value):
"""
Type check values for JSON serialization. Native Python JSON
serialization will not recognize some Numpy data types properly,
so they must be explicitly converted.
"""
if pd.isnull(value):
return None
elif (isinstance(value, pd.tslib.Timestamp) or
isinstance(value, pd.Period)):
return time.mktime(value.timetuple())
elif isinstance(value, (int, np.integer)):
return int(value)
elif isinstance(value, (float, np.float_)):
return float(value)
elif isinstance(value, str):
return str(value)
else:
return value
if isinstance(data, pd.Series):
json_data = [{type_check(x): type_check(y) for
x, y in iteritems(data)}]
elif isinstance(data, pd.DataFrame):
json_data = [{type_check(y): type_check(z) for
x, y, z in data.itertuples()}]
return json_data
