gistfile1.txt

In [1]: %matplotlib qt

In [2]: import numpy as np

In [3]: import pandas

In [4]: x = np.random.rand(1, 100) * 100

In [5]: x
Out[5]: 
array([[ 31.52994306,  27.0837805 ,  33.90521734,   7.81624911,
         94.16987949,  28.39108981,  53.55282751,   2.76310603,
         88.06353126,  32.23307981,  63.00215162,  27.10032206,
         37.15109043,  91.68872297,  55.03006991,  43.61004024,
         29.94035752,  69.1287253 ,  94.99335713,  40.29024672,
         77.18706521,  26.64955824,  79.13713719,  88.67493152,
          1.68759453,  48.15853548,  99.05450679,  74.4452181 ,
         17.00973416,  66.050491  ,  56.79314643,  51.68895139,
         20.20797535,  11.75945424,  84.24547559,  32.00888191,
         92.29912045,  24.22421894,  20.37503263,  65.54743954,
         44.07153752,  65.6113049 ,  78.47281165,  44.87353296,
         84.62428066,  10.96547979,  59.13830519,  42.10934064,
          8.46759015,  37.63909805,  60.70344327,  67.49294925,
         12.11031029,  13.85780179,  71.03470453,   6.18474855,
         66.38387979,  19.76043282,  45.08657856,  28.62964671,
         39.8930534 ,  65.76321125,  73.85521824,  94.89151588,
         37.56637575,  37.15314425,  64.33288323,  10.96763029,
         79.89277552,   6.25780789,  92.81232913,  95.26988272,
         91.2067481 ,  56.44313349,  11.11227766,  42.82539788,
         98.91529372,  44.09645277,  89.60637178,  64.70873332,
          2.39855946,  68.18908929,  86.05233306,  10.37694636,
         49.02441349,  29.1022    ,  57.50062669,  23.53124511,
         66.33491069,  50.9733553 ,  66.74909567,  26.04383624,
         16.02023859,  79.16229866,  89.42715443,  33.68157573,
         85.54034076,  14.36538464,  10.13151483,  96.13846944]])

In [6]: df = pandas.DataFrame(x)

In [7]: df
Out[7]: 
          0          1          2         3          4         5          6   \
0  31.529943  27.083781  33.905217  7.816249  94.169879  28.39109  53.552828   

         7          8         9     ...             90         91         92  \
0  2.763106  88.063531  32.23308    ...      66.749096  26.043836  16.020239   

          93         94         95         96         97         98         99  
0  79.162299  89.427154  33.681576  85.540341  14.365385  10.131515  96.138469  

[1 rows x 100 columns]

In [8]: df.plot()
/usr/local/python/python-2.7/std/lib/python2.7/site-packages/matplotlib/axes/_base.py:2767: UserWarning: Attempting to set identical left==right results
in singular transformations; automatically expanding.
left=0.0, right=0.0
  'left=%s, right=%s') % (left, right))
Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2e9bc746d0>

In [9]: df
Out[9]: 
          0          1          2         3          4         5          6   \
0  31.529943  27.083781  33.905217  7.816249  94.169879  28.39109  53.552828   

         7          8         9     ...             90         91         92  \
0  2.763106  88.063531  32.23308    ...      66.749096  26.043836  16.020239   

          93         94         95         96         97         98         99  
0  79.162299  89.427154  33.681576  85.540341  14.365385  10.131515  96.138469  

[1 rows x 100 columns]

In [10]: df.cols()
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-10-6dbd35a0d9bf> in <module>()
----> 1 df.cols()

/usr/local/python/python-2.7/std/lib/python2.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
   2358                 return self[name]
   2359             raise AttributeError("'%s' object has no attribute '%s'" %
-> 2360                                  (type(self).__name__, name))
   2361 
   2362     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'cols'

In [11]: df.T
Out[11]: 
            0
0   31.529943
1   27.083781
2   33.905217
3    7.816249
4   94.169879
5   28.391090
6   53.552828
7    2.763106
8   88.063531
9   32.233080
10  63.002152
11  27.100322
12  37.151090
13  91.688723
14  55.030070
15  43.610040
16  29.940358
17  69.128725
18  94.993357
19  40.290247
20  77.187065
21  26.649558
22  79.137137
23  88.674932
24   1.687595
25  48.158535
26  99.054507
27  74.445218
28  17.009734
29  66.050491
..        ...
70  92.812329
71  95.269883
72  91.206748
73  56.443133
74  11.112278
75  42.825398
76  98.915294
77  44.096453
78  89.606372
79  64.708733
80   2.398559
81  68.189089
82  86.052333
83  10.376946
84  49.024413
85  29.102200
86  57.500627
87  23.531245
88  66.334911
89  50.973355
90  66.749096
91  26.043836
92  16.020239
93  79.162299
94  89.427154
95  33.681576
96  85.540341
97  14.365385
98  10.131515
99  96.138469

[100 rows x 1 columns]

In [12]: df.T.plot()
Out[12]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2e8ee3d790>

In [13]: df.T.plot(kind="bar")
Out[13]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2e8ed50b10>