def lookup_and_transform(ts_kv_table):
"""The table has the following structure:
+---------------------------------+---------------+---------------+--------+
| entity_id | key | ts | value |
+=================================+===============+===============+========+
| 1ea47494dc14d40bd76a73c738b665f | Temperature | 1583010011665 | -1.8 |
+---------------------------------+---------------+---------------+--------+
| 1ea47494dc14d40bd76a73c738b665f | WindDirection | 1583010000692 | 227 |
+---------------------------------+---------------+---------------+--------+
The output is a dictionary {device_id:table} of tables like that:
+--------------+--------------+---------------+
| ts | Temperature | WindDirection |
+--------------+--------------+---------------+
|1583010011665 | -1.8 | 230 |
+--------------+--------------+---------------+
|1583010000692 | -2.5 | 227 |
+--------------+--------------+---------------+
"""
lkp = petl.lookup(ts_kv_table, 'entity_id', value=('key', 'ts', 'value'))
for entity_id in lkp:
tbl = [('key', 'ts', 'value')] + lkp[entity_id]
tbl = petl.recast(tbl, variablefield='key', valuefield='value')
cut_keys = KEYS_TO_REMOVE & set(petl.fieldnames(tbl))
tbl = petl.cutout(tbl, *cut_keys)
tbl = petl.transform.headers.sortheader(tbl)
tbl = petl.transform.basics.movefield(tbl, 'ts', 0)
lkp[entity_id] = petl.sort(tbl, 'ts')
return lkp
def test_fieldnames():
table = (('foo', 'bar'), ('a', 1), ('b', 2))
actual = fieldnames(table)
expect = ['foo', 'bar']
eq_(expect, actual)
class CustomField(object):
def __init__(self, key, description):
self.key = key
self.description = description
def __str__(self):
return self.key
def __repr__(self):
return 'CustomField(%r, %r)' % (self.key, self.description)
table = ((CustomField('foo', 'Get some foo.'), CustomField('bar', 'A lot of bar.')),
('a', 1),
('b', 2))
actual = fieldnames(table)
expect = ['foo', 'bar']
eq_(expect, actual)
def test_fieldnames():
table = (('foo', 'bar'), ('a', 1), ('b', 2))
actual = fieldnames(table)
expect = ['foo', 'bar']
eq_(expect, actual)
class CustomField(object):
def __init__(self, key, description):
self.key = key
self.description = description
def __str__(self):
return self.key
def __repr__(self):
return 'CustomField(%r, %r)' % (self.key, self.description)
table = ((CustomField('foo', 'Get some foo.'), CustomField('bar', 'A lot of bar.')),
('a', 1),
('b', 2))
actual = fieldnames(table)
expect = ['foo', 'bar']
eq_(expect, actual)
def test_fieldnames():
table = (("foo", "bar"), ("a", 1), ("b", 2))
actual = fieldnames(table)
expect = ["foo", "bar"]
eq_(expect, actual)
class CustomField(object):
def __init__(self, key, description):
self.key = key
self.description = description
def __str__(self):
return self.key
def __repr__(self):
return "CustomField(%r, %r)" % (self.key, self.description)
table = ((CustomField("foo", "Get some foo."), CustomField("bar", "A lot of bar.")), ("a", 1), ("b", 2))
actual = fieldnames(table)
expect = ["foo", "bar"]
eq_(expect, actual)
def test_fieldnames():
"""Test the fieldnames function."""
table = (('foo', 'bar'), ('a', 1), ('b', 2))
actual = fieldnames(table)
expect = ['foo', 'bar']
assertequal(expect, actual)
class CustomField(object):
def __init__(self, id, description):
self.id = id
self.description = description
def __str__(self):
return self.id
def __repr__(self):
return 'CustomField(%r, %r)' % (self.id, self.description)
table = ((CustomField('foo', 'Get some foo.'), CustomField('bar', 'A lot of bar.')),
('a', 1),
('b', 2))
actual = fieldnames(table)
expect = ['foo', 'bar']
assertequal(expect, actual)
def split_dataset(dataset, p_train_data, split_mode):
fields = list(fieldnames(dataset))
size_dataset = len(values(dataset, fields[0]))
size_train_data = int(round(size_dataset * p_train_data))
size_test_data = abs(size_train_data - size_dataset)
if split_mode == 'normal' :
train_data = head(dataset, size_train_data - 1)
if size_test_data == 0:
test_data = []
else:
test_data = tail(dataset, size_test_data - 1)
#################### Falta incluir Shuffle mode ###############
return train_data, test_data
foobaz list(foobaz) # header() ########## import petl as etl table = [['foo', 'bar'], ['a', 1], ['b', 2]] etl.header(table) # fieldnames() ############## import petl as etl table = [['foo', 'bar'], ['a', 1], ['b', 2]] etl.fieldnames(table) etl.header(table) # data() ######## import petl as etl table = [['foo', 'bar'], ['a', 1], ['b', 2]] d = etl.data(table) list(d) # dicts() ######### import petl as etl table = [['foo', 'bar'], ['a', 1], ['b', 2]]
# Fuente de los datos que vamos a leer
uri_confirmed = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
uri_death = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
uri_recovered = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'
# Procesamos primero casos confirmados
t_confirmed = etl.fromcsv(uri_confirmed)
# Cambiamos el nombre a los encabezados
t_confirmed = etl.rename(t_confirmed, {'Country/Region': 'Country'})
# Ajustamos los tipos de datos
# A partir de la columna 5, el tipo de dato es integer, que es el número de personas/casos
# Adicionalmente aprovechamos para cambiar el formato de la fecha de 1/23/20 a 2020-01-23 en el header
headers = etl.fieldnames(t_confirmed)
i = 0
for header in headers:
if i >= 4:
t_confirmed = etl.convert(t_confirmed, header,
int) # corregimos el tipo de dato
fecha = datetime.datetime.strptime(
header, '%m/%d/%y') # calculamos la fecha en formato correcto
t_confirmed = etl.rename(t_confirmed, header,
fecha.strftime('%Y-%m-%d'))
i = i + 1
# Eliminamos las columnas de Province/State, Lat y Lon que no vamos a utilizar
t_confirmed = etl.cutout(t_confirmed, 0, 2, 3)
# Ajustamos algunos nombres de países para luego asignarles una región/continente
########## import petl as etl table = [["foo", "bar"], ["a", 1], ["b", 2]] etl.header(table) # fieldnames() ############## import petl as etl table = [["foo", "bar"], ["a", 1], ["b", 2]] etl.fieldnames(table) etl.header(table) # data() ######## import petl as etl table = [["foo", "bar"], ["a", 1], ["b", 2]] d = etl.data(table) list(d) # dicts() #########
def procesar_fuente(path, nombre):
try:
# Procesamos primero casos confirmados
tabla = etl.fromcsv(path)
# Cambiamos el nombre a los encabezados
tabla = etl.rename(tabla, {'Country/Region': 'Country'})
# Ajustamos los tipos de datos
# A partir de la columna 5, el tipo de dato es integer, que es el número de personas/casos
# Adicionalmente aprovechamos para cambiar el formato de la fecha de 1/23/20 a 2020-01-23 en el header
headers = etl.fieldnames(tabla)
i=0
for header in headers:
if i>=4:
tabla = etl.convert(tabla, header, int) # corregimos el tipo de dato
fecha = datetime.datetime.strptime(header, '%m/%d/%y') # calculamos la fecha en formato correcto
tabla = etl.rename(tabla, header, fecha.strftime('%Y-%m-%d'))
i = i + 1
# Eliminamos las columnas de Province/State, Lat y Lon que no vamos a utilizar
tabla = etl.cutout(tabla, 0, 2, 3)
# Ajustamos algunos nombres de países para luego asignarles una región/continente
tabla = etl.convert(tabla, 'Country', 'replace', 'Congo (Brazzaville)', 'Congo')
tabla = etl.convert(tabla, 'Country', 'replace', 'Congo (Kinshasa)', 'Democratic Republic of the Congo')
tabla = etl.convert(tabla, 'Country', 'replace', 'Cote d\'Ivoire', 'Ivory Coast')
tabla = etl.convert(tabla, 'Country', 'replace', 'Korea, South', 'South Korea')
tabla = etl.convert(tabla, 'Country', 'replace', 'West Bank and Gaza', 'Palestine')
tabla = etl.convert(tabla, 'Country', 'replace', 'Burma', 'Myanmar')
tabla = etl.convert(tabla, 'Country', 'replace', 'US', 'USA')
tabla = etl.convert(tabla, 'Country', 'replace', 'Taiwan*', 'Taiwan')
# Luego procedemos a agrupar y acumular los resultados por el país
df_confirmed = etl.todataframe(tabla)
df = df_confirmed.groupby(['Country']).sum()
tabla = etl.fromdataframe(df, include_index=True)
# Renombramos el campo de Country nuevamente
tabla = etl.rename(tabla, {'index': 'Country'})
# Luego agregamos las columnas de fecha como datos y renombramos las nuevas columnas
tabla = etl.melt(tabla, 'Country')
tabla = etl.rename(tabla, {'variable': 'Date'})
tabla = etl.rename(tabla, {'value': 'Cases'})
# Luego agregamos el continente para agrupar
tabla = etl.addfield(tabla, 'Continent', lambda rec: get_continent_code(rec['Country']))
# Y nuevamente nos aseguramos que sean del tipo de dato que deben ser.
tabla = etl.convert(tabla, 'Cases', int)
tabla = etl.convert(tabla, 'Date', lambda v: datetime.datetime.strptime(v, '%Y-%m-%d') )
#Finalmente, subimos el archivo al repositorio de datos
conn = pymysql.connect(password='******', database='covid', user='******')
conn.cursor().execute('SET SQL_MODE=ANSI_QUOTES')
etl.todb(tabla, conn, nombre, create=True, drop=True)
conn.close()
except:
print('Se ha presentado un error! ', sys.exc_info()[0])
raise
def extract_values(dataset, field_position):
fields = list(fieldnames(dataset))
field_values = values(dataset, field_position)
return field_values
def add_fields(fields, table):
for field in fields:
if field not in etl.fieldnames(table):
table = etl.addfield(table, field, None)
return table
def remove_fields(fields, table):
for field in fields:
if field in etl.fieldnames(table):
table = etl.cutout(table, field)
return table