def main():
print('carga de archivo')
print('................')
print('\n')
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, '../data/FormatoDeAlmacenamiento1.csv')
df = pd.read_csv(filename, sep=';', header=None, na_values=" NaN")
#print (df)
hora = df[0].str.extract('((?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d)')
#print (hora)
#remplazo los na por cero
df2 = pd.DataFrame()
df2 = df2.fillna(0)
df2[0] = df[0].str.extract('((?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d)')
df2[1] = df[1]
#print(df2)
print("se agrupa por minutos")
df2[0] = pd.DatetimeIndex(df2[0])
df2.set_index(keys=0, inplace=True)
ini = datetime.time(00, 18, 0)
fin = datetime.time(23, 59, 0)
df3 = df2[[1]].between_time(ini, fin)
df3 = df3.groupby([1])[[1]].count()
print(df3)
def get_heatmap_temp_min(self):
filtros_temperaturas = self.data_access.get_filtros_clima()
def hora_hora(x):
return int(x[0:2])
tem = self.data_access.get_df_temperatura()
tem['Fecha'] = pd.to_datetime(tem['Fecha'])
tem['Day_week'] = tem['Fecha'].dt.day_name()
tem["Hora_single"] = tem["Hora"].apply(hora_hora)
df_tem_min_hora = tem.groupby(['Fecha', 'Day_week',
'Hora_single']).min()
estacion = filtros_temperaturas['estacion']
filtro = filtros_temperaturas['rango_clima']
df_tem_min_hora_finca = df_tem_min_hora.filter([estacion])
dist = df_tem_min_hora_finca.reset_index(level=[0, 1, 2])
dist['Month_number'] = dist['Fecha'].dt.month
dist['Month'] = dist['Fecha'].dt.strftime('%B')
dist['year'] = pd.DatetimeIndex(dist['Fecha']).year
dist['day'] = dist['Fecha'].dt.day
dist = dist.groupby([filtro, 'Hora_single'])[estacion].mean()
df = dist.to_frame()
dist_df = df.reset_index(level=[0, 1])
dist_df['x'] = dist_df[filtro]
dist_df['z'] = dist_df[estacion]
return dist_df
def historicalData(self, idx: int, b: BarData):
self.cacheEnrichTimeout = time.time() + 60
# super().historicalData(idx, b)
s = Series(data=[b.average],
index=pandas.DatetimeIndex([b.date],
dtype='datetime64[ns]',
freq='D'))
self.id2hist[idx]['data'] = self.id2hist[idx]['data'].append(s)
def contractDetails(self, idx: int, cd: ContractDetails):
c = cd.contract
sid, sym = c.conId, c.symbol
s = Series(data=[],
dtype=float,
index=pandas.DatetimeIndex([],
dtype='datetime64[ns]',
freq='D'))
self.id2hist[sid] = {'symbol': sym, 'data': s}
self.reqHistoricalData(sid, c, self.timeStr, self.duration,
self.barSize, self.type, 0, 1, False, [])
from data import data
from pandas import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
#This is a program used to query incoming stock data.
# get the data from the excel file
df = pd.read_excel('companyData.xlsx')
# Change the data frame to be indexed by date
df = df.set_index(pd.DatetimeIndex(df['date'].values))
# make a graph
plt.figure(figsize=(14.0, 8.0))
plt.plot(df['4. close'], label='close')
plt.title('Daily close price')
plt.xticks(rotation=45)
plt.xlabel('Date')
plt.ylabel('Price $')
# plt.show()
# Calculate MACD and signal line indicators
# Calculate the short term exponential moving average (EMA)
short_EMA = df['4. close'].ewm(span=12, adjust=False).mean()
# Calculate long term exponential moving average
long_EMA = df['4. close'].ewm(span=26, adjust=False).mean()
# calculate MACD line
MACD = short_EMA - long_EMA
# create signal line
signal = MACD.ewm(span=9, adjust=False).mean()
plt.figure(figsize=(12.5, 5.0))
return '-'
from pandas import pandas as pd
# from datetime import datetime as dt
# from random import random as rand
# from numpy.random import randn
print '1/8 - Loading data'
df = pd.read_csv('dataset.csv',
header=None,
parse_dates=[1],
names=['UserID', 'DateTime', 'AntennaID'],
infer_datetime_format=True)
temp = pd.DatetimeIndex(df['DateTime'])
df['Date'] = temp.date
df['Time'] = temp.time
"""
# Filtering User
df_user_1 = df.groupby(['UserID'], sort=False).agg({"Date": lambda x: x.nunique()})
df_user_2 = df_user_1[df_user_1['Date'] > 2]
df_user_3 = df_user_2['Date']
df_user_3.to_csv('output_df_user.csv')
df_user_list = pd.read_csv('output_df_user.csv',
header=None,
names=['UserID', 'DaySeen'])
df['ActiveUser'] = df['UserID'].isin(df_user_list['UserID'])
"""
df['DayData'] = df['DateTime'].apply(classify_weekday)