title: Reading csv file using pandas where columns are separated by varying amounts of whitespace and commas
body: <p>I want to read the csv file as a pandas dataframe. CSV file is here: <a href="https://www.dropbox.com/s/o3xc74f8v4winaj/aaaa.csv?dl=0" rel="nofollow">https://www.dropbox.com/s/o3xc74f8v4winaj/aaaa.csv?dl=0</a></p>
  
  <p>In particular,</p>
  
  <ol>
  <li><p>I want to skip the first row</p></li>
  <li><p>The column headers are in row 2. In this case, they are: 1, 1, 2 and TOT. I do not want to hardcode them though. It is ok if the only column that gets extracted is TOT</p></li>
  <li><p>I do not want to use a non-pandas approach if possible.</p></li>
  </ol>
  
  <p>Here is what I am doing:</p>
  
  <pre><code>df = pandas.read_csv('https://www.dropbox.com/s/o3xc74f8v4winaj/aaaa.csv?dl=0', skiprows=1, skipinitialspace=True, sep=' ')
  </code></pre>
  
  <p>But this gives the error:</p>
  
  <pre><code>*** CParserError: Error tokenizing data. C error: Expected 5 fields in line 4, saw 6
  </code></pre>
  
  <p>The output should look something like this:</p>
  
  <pre><code>                    1       1       2     TOT
  0   DEPTH(m)        0.01    1.24    1.52    
  1   BD  33kpa(t/m3) 1.6     1.6     1.6
  2   SAND(%)         42.1    42.1    65.1    
  3   SILT(%)         37.9    37.9    16.9    
  4   CLAY(%)         20      20      18  
  5   ROCK(%)         12      12      12  
  6   WLS(kg/ha)      0       5       0.1    5.1
  7   WLM(kg/ha)      0       5       0.1    5.1
  8   WLSL(kg/ha)     0       4       0.1    4.1
  9   WLSC(kg/ha)     0       2.1     0      2.1
  10  WLMC(kg/ha)     0       2.1     0      2.1
  11  WLSLC(kg/ha)    0       1.7     0      1.7
  12  WLSLNC(kg/ha)   0       0.4     0      0.4
  13  WBMC(kg/ha)     9       1102.1  250.9   1361.9
  14  WHSC(kg/ha)     69      8432    1920    10420
  15  WHPC(kg/ha)     146     18018   4102    22266
  16  WOC(kg/ha)      224     27556   6272    34
  17  WLSN(kg/ha)     0       0       0       0
  18  WLMN(kg/ha)     0       0.2     0       0.2
  19  WBMN(kg/ha)     0.9     110.2   25.1    136.2
  20  WHSN(kg/ha)     7       843     192     1042
  21  WHPN(kg/ha)     15      1802    410     2227
  22  WON(kg/ha)      22      2755    627     3405
  23  CFEM(kg/ha)     0           
  </code></pre>