notebook.community

Edit and run



In [432]:

    
import dateutil.parser
import pandas as pd
import matplotlib.pyplot as plt

pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier

dateparse = lambda x: dateutil.parser.parse(x)



In [433]:

    
lamine = pd.read_csv('../lamine.csv', parse_dates=['timestamp'], dayfirst=True, date_parser=dateparse, index_col='timestamp')
meteobdx =  pd.read_csv('../meteowunderbordeaux.csv', parse_dates=['timestamp'], dayfirst=True, date_parser=dateparse, index_col='timestamp')



In [434]:

    
del lamine['name']
lamine.rename(columns={'value':'global_weight'}, inplace=True)



In [435]:

    
# lamine = lamine['2014-06-29':'2014-07-03']
lamine = lamine.resample('1H', how='mean')

#meteobdx = meteobdx['2014-06-29':'2014-07-03']
meteobdx = meteobdx.resample('1H', how='mean')



In [436]:

    
#lamine.tail(24)



In [452]:

    
all = pd.merge(meteobdx, lamine,  right_index=True, left_index=True)
all["corrected_weight"] = all.apply(lambda x: x[13] - x[0], axis=1)
all['time-copy'] = all.index 
all["hour"] = all.apply(lambda x: x['time-copy'].hour, axis=1)
all.tail()









    Out[452]:






  
    
      
      temp
      hum
      precipm
      pressurem
      wspdm
      wdird
      vism
      fog
      rain
      snow
      hail
      thunder
      tornado
      global_weight
      corrected_weight
      time-copy
      hour
    
    
      timestamp
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      2014-07-04 10:00:00+00:00
       23.0
       64.5
      -9999
       1013.0
       15.75
       250
       25
       0
       0.5
       0
       0
       0.5
       0
       59.753590
       36.753590
      2014-07-04 10:00:00
       10
    
    
      2014-07-04 11:00:00+00:00
       21.5
       84.5
      -9999
       1013.5
        7.45
       295
       10
       0
       0.5
       0
       0
       0.0
       0
       60.387179
       38.887179
      2014-07-04 11:00:00
       11
    
    
      2014-07-04 12:00:00+00:00
       23.5
       70.5
      -9999
       1013.0
       14.80
       300
       10
       0
       0.5
       0
       0
       0.0
       0
       60.296923
       36.796923
      2014-07-04 12:00:00
       12
    
    
      2014-07-04 13:00:00+00:00
       23.5
       66.5
      -9999
       1013.0
       22.25
       260
       10
       0
       0.0
       0
       0
       0.0
       0
       60.596154
       37.096154
      2014-07-04 13:00:00
       13
    
    
      2014-07-04 14:00:00+00:00
       23.5
       67.5
      -9999
       1013.5
       23.15
       260
       10
       0
       0.5
       0
       0
       0.0
       0
       60.522308
       37.022308
      2014-07-04 14:00:00
       14



In [460]:

    
pivot = all.pivot(index='time-copy', columns='hour', values='corrected_weight')



In [462]:

    
pivot.resample('1D', how='mean').to_csv('../dataviz/3d/3d.csv')



In [397]:



In [ ]:

	temp	hum	precipm	pressurem	wspdm	wdird	vism	fog	rain	snow	hail	thunder	tornado	global_weight	corrected_weight	time-copy	hour
timestamp
2014-07-04 10:00:00+00:00	23.0	64.5	-9999	1013.0	15.75	250	25	0	0.5	0	0	0.5	0	59.753590	36.753590	2014-07-04 10:00:00	10
2014-07-04 11:00:00+00:00	21.5	84.5	-9999	1013.5	7.45	295	10	0	0.5	0	0	0.0	0	60.387179	38.887179	2014-07-04 11:00:00	11
2014-07-04 12:00:00+00:00	23.5	70.5	-9999	1013.0	14.80	300	10	0	0.5	0	0	0.0	0	60.296923	36.796923	2014-07-04 12:00:00	12
2014-07-04 13:00:00+00:00	23.5	66.5	-9999	1013.0	22.25	260	10	0	0.0	0	0	0.0	0	60.596154	37.096154	2014-07-04 13:00:00	13
2014-07-04 14:00:00+00:00	23.5	67.5	-9999	1013.5	23.15	260	10	0	0.5	0	0	0.0	0	60.522308	37.022308	2014-07-04 14:00:00	14