In [307]:

    

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 [308]:

    

meliferopolis = pd.read_csv('../meliferopolis.csv', parse_dates=['timestamp'], dayfirst=True, date_parser=dateparse, index_col='timestamp')
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 [309]:

    

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


    

In [310]:

    

lamine = lamine.resample('1H', how='mean')
meteobdx = meteobdx.resample('1H', how='mean')


    

In [313]:

    

all = pd.merge(meteobdx, lamine,  right_index=True, left_index=True)
all.head()


    

    
Out[313]:






  

    

      

      
temp
      
hum
      
precipm
      
pressurem
      
wspdm
      
wdird
      
vism
      
fog
      
rain
      
snow
      
hail
      
thunder
      
tornado
      
global_weight
    
    

      
timestamp
      

      

      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
2014-05-27 22:00:00+00:00
      
 10.5
      
 80.5
      
-9999
      
 1017.5
      
 0.00
      
   0
      
 10.0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 41.193846
    
    

      
2014-05-27 23:00:00+00:00
      
 10.0
      
 86.0
      
-9999
      
 1017.5
      
 0.95
      
   5
      
 10.0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 41.184615
    
    

      
2014-05-28 00:00:00+00:00
      
  9.0
      
 92.5
      
-9999
      
 1017.0
      
 1.85
      
 230
      
 10.0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 41.403077
    
    

      
2014-05-28 01:00:00+00:00
      
  8.0
      
 92.0
      
-9999
      
 1016.5
      
 2.80
      
 240
      
 10.0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 41.646154
    
    

      
2014-05-28 02:00:00+00:00
      
  8.0
      
 94.0
      
-9999
      
 1016.0
      
 0.00
      
   0
      
 22.5
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 41.178462
    
  

In [314]:

    

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["hour-minute"] = all.apply(lambda x: x['time-copy'].hour * 60  + x['time-copy'].minute, axis=1)


    

In [315]:

    

all['2014-06-19':'2014-07-03']["corrected_weight"].plot(figsize=(15, 3))
all['2014-06-19':'2014-07-03']["hum"].plot(figsize=(15, 3))


    

    
Out[315]:





<matplotlib.axes.AxesSubplot at 0x114720950>






    

In [316]:

    

# from pandas.tools.plotting import scatter_matrix
# scatter_matrix(all, alpha=0.8, figsize=(10, 10), diagonal='kde')
plt.scatter(all['hour-minute'], all['corrected_weight'], s=120)


    

    
Out[316]:





<matplotlib.collections.PathCollection at 0x114b15850>






    

In [278]:

    

plt.figure()
plt.scatter(all['2014-06-01':'2014-07-04']['hum'],all['2014-06-01':'2014-07-04']['corrected_weight'], s=30)


    

    
Out[278]:





<matplotlib.collections.PathCollection at 0x1148db590>






    

In [271]:

    

df = pd.DataFrame(all['corrected_weight'], index=all.index)
df['global_weight'] = all['global_weight']
df['hum'] = all['hum']
df['temp'] = all['temp']


    

In [282]:

    




    

In [273]:

    

all['2014-06-19':'2014-07-03']["corrected_weight"].plot(figsize=(15, 3))
all['2014-06-19':'2014-07-03']["hum"].plot(figsize=(15, 3))


    

    
Out[273]:





<matplotlib.axes.AxesSubplot at 0x114cbc8d0>






    

In [279]:

    

from mpl_toolkits.mplot3d import Axes3D


    

In [302]:

    

import matplotlib.pyplot as plt
import numpy as np


    

In [306]:

    

import time as time
xpos = [0]
ypos = all.index
zpos = all['corrected_weight']

dx = 1
dy = 1
dz = [2, 3]

# Move each (x, y) coordinate to center it on the tick

xpos = map(lambda x: x - 0.5, xpos)
ypos = map(lambda y: y.value - 0.5, ypos)

fig = plt.figure()
ax = plt.axes(projection='3d')
ax.bar3d(xpos, ypos, zpos, dx, dy, dz)

# Do not print years in exponential notation

y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax.yaxis.set_major_formatter(y_formatter)

plt.show()


    

    




/Library/Python/2.7/site-packages/mpl_toolkits/mplot3d/axes3d.py:2307: UserWarning: x, y, and z must be the same length.
  warnings.warn('x, y, and z must be the same length.')






    

In [304]:

    

t = all.index[0]


    

In [305]:

    

t.value


    

    
Out[305]:





1401228000000000000

In [ ]: