def to_dataframe(self,
index=None,
exclude=None,
columns=None,
coerce_float=False):
"""
Outputs table as a Pandas Dataframe
`Args:`
index: str, list
Field of array to use as the index, alternately a specific set
of input labels to use
exclude: list
Columns or fields to exclude
columns: list
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the
columns in the result (any names not found in the data will
become all-NA columns)
`Returns:`
dataframe
Pandas DataFrame object
"""
return petl.todataframe(self.table,
index=index,
exclude=exclude,
columns=columns,
coerce_float=coerce_float)
def get_delta(source_table, target_table, key='id'):
source_table_headers = etl.header(source_table)
target_table_headers = etl.header(target_table)
if source_table_headers != target_table_headers:
raise Exception(
'Source table columns do not match target table columns')
source_ids = etl.cut(source_table, key)
target_ids = etl.cut(target_table, key)
added_ids_table, _ = etl.diff(source_ids, target_ids)
merged_table = etl.merge(source_table, target_table, key=key)
load_frame = etl.todataframe(
etl.selectin(target_table, key, etl.values(added_ids_table, key)))
print(load_frame)
for row in etl.data(merged_table):
for i, col in enumerate(row):
if isinstance(col, etl.transform.reductions.Conflict):
changes = tuple(col)
print('For car {}, {} changed from {} to {}'.format(
row[0], source_table_headers[i], changes[1], changes[0]))
row_dict = dict(zip(source_table_headers, list(row)))
row_dict[source_table_headers[i]] = changes[0]
row_dict = {key: [val] for (key, val) in row_dict.items()}
print(row_dict)
df = pd.DataFrame(row_dict)
load_frame = load_frame.append(df, ignore_index=True)
break
return etl.fromdataframe(load_frame)
# ('apples', 1, 2.5),
# ('oranges', 3, 4.4),
# ('pears', 7, .1)]
# a = etl.toarray(t, dtype='U4, i2, f4')
# print(a)
#
# array1 = np.array([('apples', 1, 2.5),
# ('oranges', 3, 4.4),
# ('pears', 7, .1)], dtype='U4, i2, f4')
# tb7 = etl.fromarray(array1)
# print(tb7)
# dataframe(pandas)
import pandas as pd
records = [('apple', 1, 2.5), ('orange', 3, 4.5), ('pears', 5, 6.5)]
df = pd.DataFrame.from_records(records, columns=('foo', 'bar', 'baz'))
tb8 = etl.fromdataframe(df)
print(tb8)
# load data from given table into dataframe
table = [('foo', 'bar', 'baz'), ('apple', 1, 2.5), ('orange', 3, 4.5),
('pears', 5, 6.5)]
df = etl.todataframe(table)
print(df)
# excel xls/xlsx
# HDFS
# oracle
conn_target = create_engine('postgresql://username:hostname:5432/password')
except:
logger.error(
"ERROR: Unexpected error: Could not connect to PostgreSQL instance.")
sys.exit()
logger.info("SUCCESS: Connection to RDS PostgreSQL instance succeeded")
#Source
table = etl.fromdb(
conn, """select
res_company.name,
sum(product_qty) as qty,
sum(price_total) as total
from report_pos_order
inner join res_company on res_company.id = report_pos_order.company_id
where date(report_pos_order.date AT TIME ZONE 'GMT +7') = current_date
group by res_company.name
order by sum(price_total) desc""")
#Transformation
#grouping with aggregation
aggregation = OrderedDict()
aggregation['qty'] = 'qty', sum
aggregation['total'] = 'total', sum
table1 = etl.aggregate(table, 'name', aggregation)
dfsum = etl.todataframe(table1)
#Target
dfsum.to_sql('GMV Warung', conn_target, if_exists='replace', index=None)
'Congo (Kinshasa)',
'Democratic Republic of the Congo')
t_confirmed = etl.convert(t_confirmed, 'Country', 'replace', 'Cote d\'Ivoire',
'Ivory Coast')
t_confirmed = etl.convert(t_confirmed, 'Country', 'replace', 'Korea, South',
'South Korea')
t_confirmed = etl.convert(t_confirmed, 'Country', 'replace',
'West Bank and Gaza', 'Palestine')
t_confirmed = etl.convert(t_confirmed, 'Country', 'replace', 'Burma',
'Myanmar')
t_confirmed = etl.convert(t_confirmed, 'Country', 'replace', 'US', 'USA')
t_confirmed = etl.convert(t_confirmed, 'Country', 'replace', 'Taiwan*',
'Taiwan')
# Luego procedemos a agrupar y acumular los resultados por el país
df_confirmed = etl.todataframe(t_confirmed)
df = df_confirmed.groupby(['Country']).sum()
t_confirmed = etl.fromdataframe(df, include_index=True)
# Renombramos el campo de Country nuevamente
t_confirmed = etl.rename(t_confirmed, {'index': 'Country'})
# Luego agregamos las columnas de fecha como datos y renombramos las nuevas columnas
t_confirmed = etl.melt(t_confirmed, 'Country')
t_confirmed = etl.rename(t_confirmed, {'variable': 'Date'})
t_confirmed = etl.rename(t_confirmed, {'value': 'Cases'})
# Luego agregamos el continente para agrupar
t_confirmed = etl.addfield(t_confirmed, 'Continent',
lambda rec: get_continent_code(rec['Country']))
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
from __future__ import division, print_function, absolute_import
# todataframe()
###############
import petl as etl
table = [("foo", "bar", "baz"), ("apples", 1, 2.5), ("oranges", 3, 4.4), ("pears", 7, 0.1)]
df = etl.todataframe(table)
df
# fromdataframe()
#################
import petl as etl
import pandas as pd
records = [("apples", 1, 2.5), ("oranges", 3, 4.4), ("pears", 7, 0.1)]
df = pd.DataFrame.from_records(records, columns=("foo", "bar", "baz"))
table = etl.fromdataframe(df)
table