def SeriesBC_IMACEC(bc2):
replace_dot = [True for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
columnas_objetivo = [
name_col for name_col in bc2.columns if "Imacec" in name_col
]
for n, col_name in enumerate(columnas_objetivo):
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 4, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 20, 10)
plot_distribution(bc2, col_name, "IMACEC (%)",
"BancoCentral_distribuciones_" + col_name)
fig, ax = plt.subplots(figsize=(8, 8))
sns.boxplot(data=bc2[columnas_objetivo], orient="h", ax=ax, whis=[0, 100])
fig.savefig("Output/Plot/BancoCentral_boxplot_MACEC")
return bc2
def lambda_handler(event, context):
''' Creates JSON file containing tweet info for the the maximum and minimum
temperature in context'''
# Getting historical data
today = date.today()
kind = event['kind']
historical_data = get_historical_data(S3_BUCKET, HISTORICAL_OBS_LOC)
historical_today = historical_data[
(historical_data['Month'] == today.month)
& (historical_data['Day'] == today.day)].dropna()
# Get text for tweet
summary = get_temp_summary(historical_today, kind=kind, today=today)
# If no text, then no data
if summary is None:
return {
'statusCode':
404,
'body':
json.dumps(
f'No data for today {today.year}-{today.month}-{today.day}')
}
# Create plots
ax_timeseries, timeseries_alt = plot_time_series(historical_today, kind,
today)
timeseries_loc = os.path.join(
TWEET_MEDIA_PATH, today.strftime(f'%Y-%m-%d-{kind}-timeseries.png'))
plt_to_s3(ax_timeseries, S3_BUCKET, timeseries_loc)
ax_distplot, distplot_alt = plot_distribution(historical_today, kind,
today)
distplot_loc = os.path.join(
TWEET_MEDIA_PATH, today.strftime(f'%Y-%m-%d-{kind}-distplot.png'))
plt_to_s3(ax_distplot, S3_BUCKET, distplot_loc)
# Collate data and save to s3
tweet_data = json.dumps({
'body': summary,
'media': [timeseries_loc, distplot_loc],
'alt_text': [timeseries_alt, distplot_alt]
})
json_loc = os.path.join(TWEET_PATH,
today.strftime(f'%Y-%m-%d-{kind}.json'))
json_to_s3(tweet_data, S3_BUCKET, json_loc)
return {
'statusCode': 200,
'body':
json.dumps(f'Created tweet {str(tweet_data)} stored at {json_loc}')
}
def SeriesBC_PIB(bc2):
replace_dot = [True for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
#PIB, IVA y derechos de importación
columnas_objetivo = [
name_col for name_col in bc2.columns if "PIB" in name_col
]
# 'PIB_Agropecuario_silvicola'
n = 0
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Pesca'
n = 1
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 20, 10)
# 'PIB_Mineria'
n = 2
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 3, 4,
5] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Mineria_del_cobre'
n = 3
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 250, 10)
# 'PIB_Otras_actividades_mineras'
n = 4
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 20, 10)
# 'PIB_Industria_Manufacturera'
n = 5
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 2, 0, 0, 4,
5] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Alimentos'
n = 6
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 20, 10)
# 'PIB_Bebidas_y_tabaco'
n = 7
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Textil'
n = 8
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Maderas_y_muebles'
n = 9
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Celulosa'
n = 10
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 3, 0, 4, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 50, 10)
# 'PIB_Refinacion_de_petroleo'
n = 11
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 15, 10)
# 'PIB_Quimica'
n = 12
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Minerales_no_metalicos_y_metalica_basica'
n = 13
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Productos_metalicos'
n = 14
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Electricidad'
n = 15
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Construccion'
n = 16
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 200, 10)
# 'PIB_Comercio'
n = 17
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 3, 0, 5, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 500, 10)
# 'PIB_Restaurantes_y_hoteles'
n = 18
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 4, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 500, 10)
# 'PIB_Transporte'
n = 19
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 3, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Comunicaciones'
n = 20
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Servicios_financieros'
n = 21
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Servicios_empresariales'
n = 22
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 4,
5] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Servicios_de_vivienda'
n = 23
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# 'PIB_Servicios_personales'
n = 24
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 500, 10)
# 'PIB_Administracion_publica'
n = 25
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 500, 10)
# 'PIB_a_costo_de_factores'
n = 26
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 2, 3, 0, 4,
5] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 5000, 10)
# 'PIB'
n = 27
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 4,
5] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 5000, 10)
# 'Impuesto_al_valor_agregado'
n = 28
col_name = bc2.columns[36]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 500, 10)
plot_distribution(bc2, col_name, "IVA (MM$)",
"BancoCentral_distribuciones_" + col_name)
# 'Derechos_de_Importacion'
n = 29
col_name = bc2.columns[37]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
plot_distribution(bc2, col_name, col_name,
"BancoCentral_distribuciones_" + col_name)
for col_name in columnas_objetivo:
plot_distribution(bc2, col_name, "PIB (MM$)",
"BancoCentral_distribuciones_" + col_name)
columnas_objetivo.append(bc2.columns[36])
columnas_objetivo.append(bc2.columns[37])
fig, (ax, ay) = plt.subplots(ncols=2, figsize=(16, 8))
sns.boxplot(data=bc2[[
i for i in columnas_objetivo
if (i not in ["PIB_a_costo_de_factores", "PIB"])
]],
orient="h",
ax=ax,
whis=[0, 100])
sns.boxplot(data=bc2[["PIB_a_costo_de_factores", "PIB"]],
orient="v",
ax=ay,
whis=[0, 100])
fig.savefig("Output/Plot/BancoCentral_boxplot_PIB")
return bc2
def SeriesBc_Ventas(bc2):
columnas_objetivo = [
name_col for name_col in bc2.columns
if ("Indice_de_ventas_comercio_real" in name_col)
]
# Indice_de_ventas_comercio_real_IVCM [ 9 10 11]
n = 0
col_name = columnas_objetivo[n]
fechasproblemas = [
'2014-01-01', '2014-02-01', '2014-04-01', '2014-05-01', '2014-06-01',
'2014-07-01', '2014-08-01', '2014-09-01', '2014-10-01', '2014-11-01',
'2015-01-01', '2015-02-01', '2015-04-01', '2015-06-01', '2015-07-01',
'2015-09-01', '2016-02-01', '2016-06-01', '2020-04-01', '2020-05-01',
'2020-06-01', '2020-07-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10**-1)
# Indice_de_ventas_comercio_real_no_durables_IVCM [ 7 10 11]
n = 1
col_name = columnas_objetivo[n]
fechasproblemas = [
'2014-01-01', '2014-02-01', '2014-04-01', '2014-05-01', '2014-06-01',
'2014-07-01', '2014-08-01', '2014-09-01', '2014-11-01', '2015-01-01',
'2015-02-01', '2015-04-01', '2015-06-01', '2015-07-01', '2015-09-01',
'2016-06-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01',
'2015-08-01', '2016-08-01', '2017-02-01', '2019-10-01', '2019-11-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 3, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [7, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10**-1)
# Indice_de_ventas_comercio_real_durables_IVCM [10 11]
n = 2
col_name = columnas_objetivo[n]
fechasproblemas = [
'2014-01-01', '2014-02-01', '2014-04-01', '2014-05-01', '2014-06-01',
'2014-07-01', '2014-09-01', '2014-10-01', '2014-11-01', '2015-01-01',
'2015-02-01', '2015-03-01', '2015-04-01', '2015-05-01', '2015-06-01',
'2015-07-01', '2015-10-01', '2016-01-01', '2016-02-01', '2020-04-01',
'2020-05-01', '2020-06-01', '2020-07-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10**-1)
# Ventas_autos_nuevos [4 5]
n = 3
col_name = bc2.columns[-1]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [False for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
plot_distribution(bc2, col_name, "Ventas de autos nuevos (unidades)",
"BancoCentral_distribuciones_" + col_name)
for col_name in columnas_objetivo:
plot_distribution(bc2, col_name, "Índice_de_ventas (%)",
"BancoCentral_distribuciones_" + col_name)
fig, (ax, ay) = plt.subplots(ncols=2, figsize=(16, 8))
sns.boxplot(data=bc2[columnas_objetivo], orient="h", ax=ax, whis=[0, 100])
sns.boxplot(data=bc2[bc2.columns[-1]], orient="v", ax=ay, whis=[0, 100])
ay.set_xticklabels("")
ay.set_xlabel(col_name)
fig.savefig("Output/Plot/BancoCentral_boxplot_Ventas")
return bc2
def SeriesBC_Produccion(bc2):
columnas_objetivo = [
name_col for name_col in bc2.columns
if ("Indice_de_produccion" in name_col)
]
# Indice_de_produccion_industrial
n = 0
col_name = columnas_objetivo[n]
fechasproblemas = [
'2011-12-01', '2012-03-01', '2012-10-01', '2012-11-01', '2012-12-01',
'2013-03-01', '2013-07-01', '2013-08-01', '2013-10-01', '2013-11-01',
'2013-12-01', '2014-03-01', '2014-04-01', '2014-05-01', '2014-10-01',
'2014-11-01', '2014-12-01', '2015-01-01', '2015-03-01', '2015-05-01',
'2015-06-01', '2015-10-01', '2015-11-01', '2015-12-01', '2016-03-01',
'2016-05-01', '2016-11-01', '2016-12-01', '2017-05-01', '2017-08-01',
'2017-10-01', '2017-11-01', '2017-12-01', '2018-03-01', '2018-05-01',
'2018-06-01', '2018-08-01', '2018-10-01', '2018-11-01', '2018-12-01',
'2019-03-01', '2019-05-01', '2019-07-01', '2019-08-01', '2019-10-01',
'2019-11-01', '2019-12-01', '2020-01-01', '2020-03-01', '2020-10-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10)
# Indice_de_produccion_industrial__mineria
n = 1
col_name = columnas_objetivo[n]
fechasproblemas = [
'2004-12-01', '2005-12-01', '2006-12-01', '2007-03-01', '2007-12-01',
'2011-12-01', '2012-12-01', '2013-08-01', '2013-10-01', '2013-11-01',
'2013-12-01', '2014-05-01', '2014-06-01', '2014-10-01', '2014-11-01',
'2014-12-01', '2015-01-01', '2015-05-01', '2015-06-01', '2015-09-01',
'2015-10-01', '2015-12-01', '2016-03-01', '2016-05-01', '2016-11-01',
'2016-12-01', '2017-08-01', '2017-09-01', '2017-10-01', '2017-11-01',
'2017-12-01', '2018-03-01', '2018-07-01', '2018-09-01', '2018-10-01',
'2018-11-01', '2018-12-01', '2019-08-01', '2019-10-01', '2019-11-01',
'2019-12-01', '2020-05-01', '2020-08-01', '2020-10-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 4, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [7, 9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10)
# Indice_de_produccion_industrial_electricidad__gas_y_agua
n = 2
col_name = columnas_objetivo[n]
fechasproblemas = [
'2014-02-01', '2014-04-01', '2014-06-01', '2014-08-01', '2014-09-01',
'2014-10-01', '2014-11-01', '2015-02-01', '2015-09-01', '2015-11-01',
'2016-09-01', '2020-09-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10**-1)
# Indice_de_produccion_industrial__manufacturera
n = 3
col_name = columnas_objetivo[n]
fechasproblemas = [
'2007-03-01', '2008-03-01', '2008-04-01', '2011-03-01', '2011-11-01',
'2011-12-01', '2012-03-01', '2012-04-01', '2012-05-01', '2012-08-01',
'2012-10-01', '2012-11-01', '2012-12-01', '2013-01-01', '2013-03-01',
'2013-04-01', '2013-07-01', '2013-08-01', '2013-10-01', '2013-11-01',
'2013-12-01', '2014-01-01', '2014-03-01', '2014-04-01', '2014-05-01',
'2014-10-01', '2014-12-01', '2015-03-01', '2015-04-01', '2015-05-01',
'2015-07-01', '2015-10-01', '2015-11-01', '2015-12-01', '2016-03-01',
'2016-04-01', '2016-05-01', '2016-08-01', '2016-11-01', '2016-12-01',
'2017-03-01', '2017-05-01', '2017-08-01', '2017-11-01', '2017-12-01',
'2018-03-01', '2018-04-01', '2018-05-01', '2018-06-01', '2018-08-01',
'2018-10-01', '2018-11-01', '2018-12-01', '2019-01-01', '2019-03-01',
'2019-04-01', '2019-05-01', '2019-07-01', '2019-08-01', '2019-10-01',
'2019-11-01', '2019-12-01', '2020-01-01', '2020-02-01', '2020-03-01',
'2020-10-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 4, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [7, 9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10)
# "Generación eléctrica"
col_name = bc2.columns[79]
fechasproblemas = [
'2015-12-01', '2016-12-01', '2019-01-01', '2019-02-01', '2019-03-01',
'2019-04-01', '2019-05-01', '2019-06-01', '2019-07-01', '2019-10-01',
'2019-12-01', '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
'2020-05-01', '2020-06-01', '2020-07-01', '2020-09-01', '2020-10-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 2, 0, 0, 3, 4,
5] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [4, 6, 9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10)
plot_distribution(bc2, col_name, "Generación eléctrica (GWh)",
"BancoCentral_distribuciones_" + col_name)
#Resumen descriptivo
for col_name in columnas_objetivo:
plot_distribution(bc2, col_name, "Índice de producción (%)",
"BancoCentral_distribuciones_" + col_name)
fig, (ax, ay) = plt.subplots(ncols=2, figsize=(16, 8))
sns.boxplot(data=bc2[columnas_objetivo], orient="h", ax=ax, whis=[0, 100])
sns.boxplot(data=bc2[bc2.columns[79]], orient="v", ax=ay, whis=[0, 100])
ay.set_xticklabels("")
ay.set_xlabel(col_name)
fig.savefig("Output/Plot/BancoCentral_boxplot_Produccion")
return bc2
def SeriesBC_TCMTCR(bc2):
columnas_objetivo = [
name_col for name_col in bc2.columns
if ("ipo_de_cambio" in name_col) and ("dolar" not in name_col)
]
# Tipo_de_cambio_nominal_multilateral___TCM
n = 0
col_name = columnas_objetivo[n]
fechasproblemas = [
'1997-04-01', '1997-07-01', '1997-09-01', '2005-12-01', '2006-04-01',
'2008-02-01', '2008-04-01', '2010-11-01', '2011-01-01', '2011-03-01',
'2011-05-01', '2011-07-01', '2011-08-01', '2011-09-01', '2012-02-01',
'2012-03-01', '2012-05-01', '2012-06-01', '2012-08-01', '2012-09-01',
'2012-10-01', '2012-12-01', '2013-01-01', '2013-04-01', '2013-05-01',
'2013-07-01', '2013-08-01', '2013-09-01', '2013-10-01', '2010-12-01',
'2011-06-01', '1997-03-01', '2012-04-01', '2013-02-01', '2013-06-01',
'1997-08-01', '2008-03-01', '2010-09-01', '2010-10-01', '2011-02-01',
'2011-04-01', '2012-07-01', '2012-11-01', '2013-03-01', '1997-10-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 1, 2, 3, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [5, 6, 7, 9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10**-1)
plot_distribution(bc2, col_name, "TCM (-)",
"BancoCentral_distribuciones_" + col_name)
# Indice_de_tipo_de_cambio_real___TCR_promedio_1986_100
n = 1
col_name = columnas_objetivo[n]
fechasproblemas = [
'1986-02-01', '1986-03-01', '1986-08-01', '1986-09-01', '1986-10-01',
'1986-11-01', '1986-12-01', '1987-01-01', '1987-02-01', '1987-03-01',
'1987-04-01', '1987-05-01', '1987-06-01', '1987-07-01', '1987-08-01',
'1987-09-01', '1987-10-01', '1987-11-01', '1988-01-01', '1988-02-01',
'1988-03-01', '1988-04-01', '1988-06-01', '1988-07-01', '1988-09-01',
'1988-10-01', '1988-11-01', '1988-12-01', '1989-01-01', '1989-02-01',
'1989-03-01', '1989-04-01', '1989-05-01', '1989-06-01', '1989-07-01',
'1989-08-01', '1989-09-01', '1989-10-01', '1989-11-01', '1989-12-01',
'1990-01-01', '1990-02-01', '1990-03-01', '1990-04-01', '1990-05-01',
'1990-07-01', '1990-08-01', '1990-09-01', '1990-10-01', '1990-11-01',
'1990-12-01', '1991-01-01', '1991-02-01', '1991-03-01', '1991-04-01',
'1991-05-01', '1991-06-01', '1991-07-01', '1991-08-01', '1991-10-01',
'1991-11-01', '1991-12-01', '1992-01-01', '1992-08-01', '1992-09-01',
'1993-04-01', '1993-05-01', '2001-08-01', '2001-09-01', '2001-10-01',
'2001-11-01', '2002-09-01', '2002-10-01', '2002-12-01', '2003-01-01',
'2003-02-01', '2003-03-01', '2003-04-01', '2003-05-01', '2003-06-01',
'2003-07-01', '2003-08-01', '2003-09-01', '2003-10-01', '2004-05-01',
'2004-06-01', '2004-07-01', '2004-08-01', '2004-09-01', '2004-10-01',
'2004-11-01', '2008-10-01', '2008-12-01', '2009-01-01', '2014-08-01',
'2014-09-01', '2019-11-01', '2019-12-01', '2020-01-01', '2020-02-01',
'2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01',
'2020-09-01', '2020-10-01', '1987-12-01', '1988-08-01', '1991-09-01',
'2005-03-01', '2008-11-01', '2015-08-01', '2020-08-01', '1988-05-01',
'1990-06-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10)
plot_distribution(bc2, col_name, "TCR (-)",
"BancoCentral_distribuciones_" + col_name)
#Resumen descriptivo
fig, ax = plt.subplots(figsize=(8, 8))
sns.boxplot(data=bc2[columnas_objetivo], orient="h", ax=ax, whis=[0, 100])
fig.savefig("Output/Plot/BancoCentral_boxplot_TCMTCR")
return bc2
def SeriesBC_Ocupacion(bc2):
columnas_objetivo = [
name_col for name_col in bc2.columns if "Ocupa" in name_col
]
columnas_objetivo.append(bc2.columns[72])
lista_A = [
columnas_objetivo[i]
for i in [1, 2, 3, 12, 11, 10, 9, 8, 6, 5, 15, 16, 17, 19, 20]
]
lista_B = [columnas_objetivo[i] for i in [0, 13]]
lista_C = [columnas_objetivo[i] for i in [4, 14, 7]]
lista_D = columnas_objetivo[18]
lista_E = columnas_objetivo[21]
lista_F = columnas_objetivo[22]
for col_name in lista_A:
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 4, 5,
6] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
for col_name in lista_B:
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 3, 4,
5] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
for col_name in lista_C:
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
col_name = lista_D
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 40, 10)
col_name = lista_E
fechasproblemas = [
"2013-05-01", "2013-06-01", "2016-08-01", "2016-10-01", "2016-11-01",
"2018-09-01", "2018-10-01", "2018-11-01", "2016-09-01", "2014-04-01",
"2014-05-01"
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 4, 0, 0, 0, 7,
8] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [6, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10**-1)
col_name = lista_F
fechasproblemas = ["2020-06-01", "2020-05-01"]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "", fechasproblemas,
10**-1)
#Resumen descriptivo
for col_name in columnas_objetivo:
plot_distribution(bc2, col_name, "Ocupados (miles de personas)",
"BancoCentral_distribuciones_" + col_name)
fig, (ax, ay) = plt.subplots(ncols=2, figsize=(16, 8))
sns.boxplot(data=bc2[columnas_objetivo[1:]],
orient="h",
ax=ax,
whis=[0, 100])
sns.boxplot(data=bc2[columnas_objetivo[0]],
orient="v",
ax=ay,
whis=[0, 100])
ay.set_xticklabels("")
ay.set_xlabel(columnas_objetivo[0])
fig.savefig("Output/Plot/BancoCentral_boxplot_Ocupación")
return bc2
def SeriesBC_Precio(bc2):
# Precios y Tasa de cambio
columnas_objetivo = [
name_col for name_col in bc2.columns if "Precio" in name_col
]
# Precio_de_la_gasolina_en_EEUU_dolaresm3
n = 0
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [1, 1, 2, 2, 2, 3, 3, 4, 5,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [True for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 28, 10, ["1979-06-01"],
10)
# Precio_de_la_onza_troy_de_oro_dolaresoz
n = 1
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [False for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# Precio_de_la_onza_troy_de_plata_dolaresoz
n = 2
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 4, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [7]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# Precio_del_cobre_refinado_BML_dolareslibra
n = 3
col_name = columnas_objetivo[n]
#display(bc2[col_name])
fechasconproblemas = [
'1984-12-01', '1986-10-01', '1986-12-01', '1987-12-01', '1988-09-01',
'1988-11-01', '1989-02-01', '1989-10-01', '1989-12-01', '1992-09-01',
'1995-07-01', '1996-07-01', '1997-01-01', '1997-03-01', '1997-12-01',
'1999-12-01', '2006-01-01', '2006-04-01', '2011-02-01', '2016-11-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 3, 2, 3, 3, 6, 0, 8, 8,
8] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [True for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 0.4, 10,
fechasconproblemas, 10)
# Precio_del_diesel_centavos_de_dolargalon
n = 4
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [False for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# Precio_del_gas_natural_dolaresmillon_de_unidades_termicas_britanicas
n = 5
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 4, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [7]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# Precio_del_petroleo_Brent_dolaresbarril
n = 6
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [False for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# Precio_del_kerosene_dolaresm3
n = 7
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 1, 0, 2, 3,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [True for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 10**5, 10**-2)
# Precio_del_petroleo_WTI_dolaresbarril
n = 8
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [False for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# Precio_del_propano_centavos_de_dolargalon_DTN
n = 9
col_name = columnas_objetivo[n]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 0, 0,
0] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [False for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, "", "")
# "Tipo_de_cambio_del_dolar_observado_diario"
col_name = "Tipo_de_cambio_del_dolar_observado_diario"
fechasproblemas = [
'1982-08-01', '1982-09-01', '1982-10-01', '1982-11-01', '1982-12-01',
'1983-01-01', '1983-02-01', '1983-03-01', '1983-04-01', '1983-05-01',
'1983-06-01', '1983-07-01', '1983-08-01', '1983-09-01', '1983-10-01',
'1983-11-01', '1983-12-01', '1984-01-01', '1984-02-01', '1984-03-01',
'1984-04-01', '1984-05-01', '1984-06-01', '1984-07-01', '1984-08-01'
]
if bc2[col_name].dtypes == object:
Ncorrecion = [0, 0, 0, 0, 0, 0, 0, 5, 6,
7] #[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
replace_dot = [
True if (n_bool in [9, 10, 11]) else False
for n_bool in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
]
bc2 = stringtofloat_BancoCentral(bc2, col_name, Ncorrecion,
replace_dot, 100, 10, fechasproblemas,
10**-1)
plot_distribution(bc2, col_name, "Precio ($/dolares)",
"BancoCentral_distribuciones_" + col_name)
#Resumen descriptivo
vlabel = {
0: "Precio (dolares/m3)",
1: "Precio (dolares/oz)",
2: "Precio (dolares/oz)",
3: "Precio (dolares/libra)",
4: "Precio (dolares/galon)",
5: "Precio (dolares/Munidades termicas)",
6: "Precio (dolares/barril)",
7: "Precio (dolares/m3)",
8: "Precio (dolares/barril)",
9: "Precio (dolares/galon)"
}
for n, col_name in enumerate(columnas_objetivo):
plot_distribution(bc2, col_name, vlabel[n],
"BancoCentral_distribuciones_" + col_name)
columnas_objetivo.append("Tipo_de_cambio_del_dolar_observado_diario")
fig, ax = plt.subplots(figsize=(8, 8))
sns.boxplot(data=bc2[columnas_objetivo], orient="h", ax=ax, whis=[0, 100])
fig.savefig("Output/Plot/BancoCentral_boxplot_Precios")
return bc2