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In [89]:



    


import pandas as pd
import sys
import matplotlib



    











In [90]:



    


print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
print('Matplotlib version ' + matplotlib.__version__)



    


















    






Python version 2.7.12 |Anaconda 4.2.0 (x86_64)| (default, Jul  2 2016, 17:43:17) 
[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00)]
Pandas version 0.18.1
Matplotlib version 1.5.3

















In [91]:



    


names = ['Bob','Jessica','Mary','John','Mel']
births = [968, 155, 77, 578, 973]



    











In [92]:



    


zip(names, births)



    


















    
Out[92]:








[('Bob', 968), ('Jessica', 155), ('Mary', 77), ('John', 578), ('Mel', 973)]

















In [93]:



    


BabyDataSet = list (zip(names, births))
df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])



    











In [94]:



    


df



    


















    
Out[94]:










  
    

      
      Names
      Births
    

  
  
    

      0
      Bob
      968
    

    

      1
      Jessica
      155
    

    

      2
      Mary
      77
    

    

      3
      John
      578
    

    

      4
      Mel
      973
    

  




















In [95]:



    


water23 = pd.read_csv("../data/waterlevel/Water23.csv", index_col='date')



    











In [96]:



    


water23



    


















    
Out[96]:










  
    

      
      upperlevel
      downlevel
    

    

      date
      
      
    

  
  
    

      2015-01-16 15:50:00
      35.51
      19.60
    

    

      2015-01-16 16:00:00
      35.51
      19.61
    

    

      2015-01-16 16:10:00
      35.50
      19.61
    

    

      2015-01-16 17:00:00
      35.55
      19.67
    

    

      2015-01-16 18:00:00
      35.54
      19.72
    

    

      2015-01-16 19:00:00
      35.53
      19.78
    

    

      2015-01-16 20:00:00
      35.53
      19.83
    

    

      2015-01-16 21:00:00
      35.52
      19.87
    

    

      2015-01-16 22:00:00
      35.51
      19.92
    

    

      2015-01-16 23:00:00
      35.50
      19.96
    

    

      2015-01-17 0:00:00
      35.50
      20.00
    

    

      2015-01-17 1:00:00
      35.50
      20.04
    

    

      2015-01-17 2:00:00
      35.50
      20.08
    

    

      2015-01-17 3:00:00
      35.49
      20.11
    

    

      2015-01-17 4:00:00
      35.49
      20.14
    

    

      2015-01-17 5:00:00
      35.48
      20.17
    

    

      2015-01-17 6:00:00
      35.48
      20.20
    

    

      2015-01-17 7:00:00
      35.48
      20.23
    

    

      2015-01-17 8:00:00
      35.47
      20.26
    

    

      2015-01-17 9:00:00
      35.46
      20.29
    

    

      2015-01-17 10:00:00
      35.46
      20.31
    

    

      2015-01-17 11:00:00
      35.45
      20.34
    

    

      2015-01-17 12:00:00
      35.45
      20.35
    

    

      2015-01-17 13:00:00
      35.45
      20.35
    

    

      2015-01-17 14:00:00
      35.45
      20.36
    

    

      2015-01-17 15:00:00
      35.45
      20.37
    

    

      2015-01-17 16:00:00
      35.46
      20.38
    

    

      2015-01-17 17:00:00
      35.46
      20.40
    

    

      2015-01-17 18:00:00
      35.46
      20.41
    

    

      2015-01-17 19:00:00
      35.45
      20.43
    

    

      ...
      ...
      ...
    

    

      2015-10-13 11:40:00
      29.75
      20.41
    

    

      2015-10-13 11:50:00
      29.75
      20.41
    

    

      2015-10-13 12:00:00
      29.75
      20.41
    

    

      2015-10-13 12:10:00
      29.74
      20.41
    

    

      2015-10-13 12:20:00
      29.74
      20.41
    

    

      2015-10-13 12:30:00
      29.74
      20.41
    

    

      2015-10-13 12:40:00
      29.74
      20.41
    

    

      2015-10-13 12:50:00
      29.74
      20.41
    

    

      2015-10-13 13:00:00
      29.74
      20.40
    

    

      2015-10-13 13:10:00
      29.74
      20.40
    

    

      2015-10-13 13:20:00
      29.74
      20.40
    

    

      2015-10-13 13:30:00
      29.75
      20.40
    

    

      2015-10-13 13:40:00
      29.74
      20.40
    

    

      2015-10-13 13:50:00
      29.75
      20.40
    

    

      2015-10-13 14:00:00
      29.75
      20.40
    

    

      2015-10-13 14:10:00
      29.75
      20.40
    

    

      2015-10-13 14:20:00
      29.75
      20.40
    

    

      2015-10-13 14:30:00
      29.74
      20.40
    

    

      2015-10-13 14:40:00
      29.75
      20.40
    

    

      2015-10-13 14:50:00
      29.75
      20.40
    

    

      2015-10-13 15:00:00
      29.75
      20.39
    

    

      2015-10-13 15:10:00
      29.75
      20.40
    

    

      2015-10-13 15:20:00
      29.75
      20.39
    

    

      2015-10-13 15:30:00
      29.75
      20.39
    

    

      2015-10-13 15:40:00
      29.75
      20.39
    

    

      2015-10-13 15:50:00
      29.75
      20.39
    

    

      2015-10-13 16:00:00
      29.75
      20.39
    

    

      2015-10-13 16:10:00
      29.75
      20.39
    

    

      2015-10-13 16:20:00
      29.75
      20.39
    

    

      2015-10-13 16:30:00
      29.75
      20.39
    

  



34824 rows × 2 columns



















In [97]:



    


ClimateWater = pd.read_csv("../data/waterlevel/ClimateWater.csv", index_col='date')



    











In [98]:



    


ClimateWater



    


















    
Out[98]:










  
    

      
      Temp(C)
      Rainfall(mm)
      Moisture(%)
      SurfaceTemp(surC)
      WaterH1
    

    

      date
      
      
      
      
      
    

  
  
    

      2015-01-01
      -3.9
      0.2
      62.9
      -1.8
      39.30
    

    

      2015-01-02
      -4.3
      2.4
      74.8
      -0.8
      39.34
    

    

      2015-01-03
      -1.1
      NaN
      69.4
      0.3
      39.34
    

    

      2015-01-04
      3.1
      NaN
      87.6
      2.6
      39.34
    

    

      2015-01-05
      5.5
      13.5
      77.4
      3.1
      39.42
    

    

      2015-01-06
      0.0
      2.5
      66.3
      1.9
      39.47
    

    

      2015-01-07
      -3.0
      NaN
      53.9
      -3.0
      39.53
    

    

      2015-01-08
      -2.3
      NaN
      73.0
      -1.9
      39.56
    

    

      2015-01-09
      0.4
      0.0
      80.9
      0.1
      39.59
    

    

      2015-01-10
      0.9
      NaN
      71.4
      0.1
      39.59
    

    

      2015-01-11
      2.5
      0.0
      61.0
      1.5
      39.59
    

    

      2015-01-12
      -1.8
      NaN
      52.8
      -1.1
      39.68
    

    

      2015-01-13
      0.8
      NaN
      57.4
      -0.3
      39.70
    

    

      2015-01-14
      3.3
      NaN
      61.3
      1.7
      39.72
    

    

      2015-01-15
      3.1
      NaN
      62.3
      2.8
      39.74
    

    

      2015-01-16
      3.3
      0.0
      74.0
      2.3
      39.76
    

    

      2015-01-17
      -1.3
      NaN
      60.5
      1.0
      39.76
    

    

      2015-01-18
      0.6
      2.0
      77.3
      -0.2
      39.76
    

    

      2015-01-19
      2.7
      0.3
      68.0
      2.4
      39.82
    

    

      2015-01-20
      0.4
      NaN
      71.3
      1.8
      39.84
    

    

      2015-01-21
      3.5
      5.0
      77.6
      2.3
      39.86
    

    

      2015-01-22
      2.5
      1.0
      89.1
      4.2
      39.89
    

    

      2015-01-23
      1.7
      NaN
      81.3
      2.2
      39.92
    

    

      2015-01-24
      3.5
      NaN
      78.6
      3.8
      39.92
    

    

      2015-01-25
      7.1
      2.5
      73.5
      4.1
      39.92
    

    

      2015-01-26
      5.6
      0.5
      94.5
      5.9
      39.97
    

    

      2015-01-27
      -1.0
      NaN
      65.4
      1.5
      40.00
    

    

      2015-01-28
      -2.8
      NaN
      58.9
      -1.6
      40.02
    

    

      2015-01-29
      0.5
      NaN
      65.4
      0.7
      40.04
    

    

      2015-01-30
      -0.8
      NaN
      63.9
      1.6
      40.06
    

    

      ...
      ...
      ...
      ...
      ...
      ...
    

    

      2015-12-02
      9.5
      11.6
      77.0
      7.5
      33.88
    

    

      2015-12-03
      4.0
      4.1
      66.0
      3.8
      33.99
    

    

      2015-12-04
      3.6
      5.5
      85.1
      3.4
      34.09
    

    

      2015-12-05
      6.6
      NaN
      67.9
      4.9
      34.18
    

    

      2015-12-06
      2.1
      NaN
      65.0
      3.7
      34.24
    

    

      2015-12-07
      2.5
      NaN
      65.5
      3.2
      34.29
    

    

      2015-12-08
      3.8
      NaN
      70.8
      3.7
      34.30
    

    

      2015-12-09
      7.6
      NaN
      71.8
      5.8
      34.40
    

    

      2015-12-10
      9.6
      9.7
      90.0
      8.1
      34.43
    

    

      2015-12-11
      7.7
      NaN
      79.0
      6.9
      34.50
    

    

      2015-12-12
      7.0
      NaN
      70.5
      5.5
      34.50
    

    

      2015-12-13
      8.6
      NaN
      64.9
      8.4
      34.57
    

    

      2015-12-14
      6.9
      6.9
      88.9
      6.8
      34.60
    

    

      2015-12-15
      7.0
      0.0
      78.4
      7.0
      34.60
    

    

      2015-12-16
      0.6
      19.5
      90.0
      1.7
      34.68
    

    

      2015-12-17
      -1.5
      0.0
      69.5
      2.3
      34.80
    

    

      2015-12-18
      0.1
      NaN
      86.8
      2.3
      34.80
    

    

      2015-12-19
      2.9
      NaN
      86.0
      3.6
      34.86
    

    

      2015-12-20
      3.0
      0.0
      80.5
      2.7
      34.90
    

    

      2015-12-21
      5.5
      0.5
      84.4
      6.4
      35.00
    

    

      2015-12-22
      3.6
      0.0
      86.3
      3.3
      35.00
    

    

      2015-12-23
      6.0
      0.0
      89.1
      6.9
      35.00
    

    

      2015-12-24
      3.8
      NaN
      76.3
      4.0
      35.10
    

    

      2015-12-25
      1.9
      0.0
      54.8
      2.5
      35.10
    

    

      2015-12-26
      6.4
      0.2
      68.9
      4.6
      35.10
    

    

      2015-12-27
      -2.1
      NaN
      62.9
      0.9
      35.20
    

    

      2015-12-28
      -1.4
      NaN
      67.1
      0.9
      35.20
    

    

      2015-12-29
      0.0
      NaN
      73.5
      0.4
      35.20
    

    

      2015-12-30
      3.0
      7.0
      78.3
      3.1
      35.30
    

    

      2015-12-31
      2.6
      NaN
      83.1
      2.9
      35.28
    

  



365 rows × 5 columns



















In [99]:



    


%pylab inline



    


















    






Populating the interactive namespace from numpy and matplotlib

















In [100]:



    


#water23['upperlevel'].plot()
fig = plt.figure(figsize=(12,4))
ax1 = plt.subplot(111)
water23['downlevel'].plot(ax=ax1)
ax1_1 = ax1.twinx()
water23['upperlevel'].plot(ax=ax1_1)
#climate["Rainfall(mm)"].plot.bar(figsize=(12,5))



    


















    
Out[100]:








<matplotlib.axes._subplots.AxesSubplot at 0x11a8dd190>










    
























In [101]:



    


climate = pd.read_csv("../data/waterlevel/ClimateWater.csv", index_col='date')



    











In [102]:



    


#climate["WaterH1"].hist(bins=100)
water23['upperlevel'].hist(bins=500)
#water23['downlevel'].hist(bins=500)



    


















    
Out[102]:








<matplotlib.axes._subplots.AxesSubplot at 0x11eeb2f90>










    
























In [103]:



    


pd=climate["WaterH1"]



    











In [104]:



    


pd



    


















    
Out[104]:








date
2015-01-01    39.30
2015-01-02    39.34
2015-01-03    39.34
2015-01-04    39.34
2015-01-05    39.42
2015-01-06    39.47
2015-01-07    39.53
2015-01-08    39.56
2015-01-09    39.59
2015-01-10    39.59
2015-01-11    39.59
2015-01-12    39.68
2015-01-13    39.70
2015-01-14    39.72
2015-01-15    39.74
2015-01-16    39.76
2015-01-17    39.76
2015-01-18    39.76
2015-01-19    39.82
2015-01-20    39.84
2015-01-21    39.86
2015-01-22    39.89
2015-01-23    39.92
2015-01-24    39.92
2015-01-25    39.92
2015-01-26    39.97
2015-01-27    40.00
2015-01-28    40.02
2015-01-29    40.04
2015-01-30    40.06
              ...  
2015-12-02    33.88
2015-12-03    33.99
2015-12-04    34.09
2015-12-05    34.18
2015-12-06    34.24
2015-12-07    34.29
2015-12-08    34.30
2015-12-09    34.40
2015-12-10    34.43
2015-12-11    34.50
2015-12-12    34.50
2015-12-13    34.57
2015-12-14    34.60
2015-12-15    34.60
2015-12-16    34.68
2015-12-17    34.80
2015-12-18    34.80
2015-12-19    34.86
2015-12-20    34.90
2015-12-21    35.00
2015-12-22    35.00
2015-12-23    35.00
2015-12-24    35.10
2015-12-25    35.10
2015-12-26    35.10
2015-12-27    35.20
2015-12-28    35.20
2015-12-29    35.20
2015-12-30    35.30
2015-12-31    35.28
Name: WaterH1, dtype: float64

















In [105]:



    


pd.iloc[5]



    


















    
Out[105]:








39.469999999999999

















In [106]:



    


climate["WaterH1"].plot(figsize=(20,5))
climate["Rainfall(mm)"].plot(figsize=(20,5))



    


















    
Out[106]:








<matplotlib.axes._subplots.AxesSubplot at 0x11c2fa450>










    
























In [107]:



    


climate["WaterH1"].plot(figsize=(12,5))



    


















    
Out[107]:








<matplotlib.axes._subplots.AxesSubplot at 0x11fa3c290>










    
























In [108]:



    


water23['upperlevel'].plot(figsize=(12,5))
water23['downlevel'].plot()
#climate["Rainfall(mm)"].plot.bar(figsize=(12,5))



    


















    
Out[108]:








<matplotlib.axes._subplots.AxesSubplot at 0x11caf3ed0>










    
























In [109]:



    


newindex = []
for ind in water23.index:
    newindex.append(ind.split()[0])



    











In [110]:



    


vals, inds = np.unique(newindex, return_inverse=True)



    











In [111]:



    


upperh = np.ones(vals.size)*np.nan
downh = np.ones(vals.size)*np.nan
for i in range (vals.size):
    active = inds==i
    upperh[i] = water23["upperlevel"].values[active].sum() / active.sum()
    downh[i] = water23["downlevel"].values[active].sum() / active.sum()



    











In [ ]:



    






    











In [ ]:



    






    











In [112]:



    


climate["WaterH1"].plot(figsize=(20,3))
grid(True)



    


















    
























In [113]:



    


water23['upperlevel'].plot(figsize=(20,3))
grid(True)



    


















    
























In [114]:



    


water23['downlevel'].plot(figsize=(20,3))
grid(True)



    


















    
























In [115]:



    


climate.keys()



    


















    
Out[115]:








Index([u'Temp(C)', u'Rainfall(mm)', u'Moisture(%)', u'SurfaceTemp(surC)',
       u'WaterH1'],
      dtype='object')

















In [116]:



    


climate["Moisture(%)"].plot(figsize=(20,3))



    


















    
Out[116]:








<matplotlib.axes._subplots.AxesSubplot at 0x1204e8290>










    
























In [117]:



    


climate["SurfaceTemp(\xa1\xc6C)"].plot(figsize=(20,3))
grid(True)



    


















    






---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-117-9981b0cd9aba> in <module>()
----> 1 climate["SurfaceTemp(\xa1\xc6C)"].plot(figsize=(20,3))
      2 grid(True)

/Users/sklim/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1995             return self._getitem_multilevel(key)
   1996         else:
-> 1997             return self._getitem_column(key)
   1998 
   1999     def _getitem_column(self, key):

/Users/sklim/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_column(self, key)
   2002         # get column
   2003         if self.columns.is_unique:
-> 2004             return self._get_item_cache(key)
   2005 
   2006         # duplicate columns & possible reduce dimensionality

/Users/sklim/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
   1348         res = cache.get(item)
   1349         if res is None:
-> 1350             values = self._data.get(item)
   1351             res = self._box_item_values(item, values)
   1352             cache[item] = res

/Users/sklim/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in get(self, item, fastpath)
   3288 
   3289             if not isnull(item):
-> 3290                 loc = self.items.get_loc(item)
   3291             else:
   3292                 indexer = np.arange(len(self.items))[isnull(self.items)]

/Users/sklim/anaconda/lib/python2.7/site-packages/pandas/indexes/base.pyc in get_loc(self, key, method, tolerance)
   1945                 return self._engine.get_loc(key)
   1946             except KeyError:
-> 1947                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   1948 
   1949         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4018)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)()

KeyError: 'SurfaceTemp(\xa1\xc6C)'
















In [ ]:



    


climate["Rainfall(mm)"].plot(figsize=(20,3))
grid(True)



    











In [ ]:



    


climate["Rainfall(mm)"].plot(figsize=(20,3)).bar



    











In [ ]:



    






    











In [ ]:



    


plt.plot(downh)



    











In [ ]:



    


waterdataset = list (zip(vals, upperh, downh))
#df = pd.DataFrame(data = waterdataset, columns=['vals', 'upperh', 'downh'])



    











In [ ]:



    


pd.DataFrame??



    











In [ ]:



    


df['upperH'].plot()



    











In [ ]:

Content source: sgkang/DamGeophysics

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