title: Standardize data with diffent number of dimensions in Python
body: <p>I have a dataframe different in the dimension of each row. As I acknowledge, I think that I should make the number of row dimensions equal to each other. So I performed sklearn.preprocessing.StandardScaler on the data before I can use any data analysis method.</p>
  
  <pre><code>0       1       2       3       4       5       6       7       8 
  0.00    5.00    16.00   13.00   18.00   34.00   33.00   24.00   19.00
  0.00    6.00    17.00   27.00   9.00    7.00    0.00    nan     nan
  0.00    5.00    16.00   12.00   14.00   15.00   0.00    nan     nan 
  0.00    8.00    0.00    nan     nan     nan     nan     nan     nan
  0.00    5.00    10.00   32.00   17.00   24.00   25.00   26.00   10.00
  0.00    5.00    14.00   16.00   17.00   19.00   10.00   6.00    0.00
  0.00    8.00    8.00    15.00   24.00   10.00   9.00    0.00    nan
  0.00    6.00    21.00   33.00   38.00   22.00   0.00    nan     nan
  0.00    9.00    8.00    9.00    14.00   13.00   12.00   13.00   9.00
  </code></pre>
  
  <p>I used this code:</p>
  
  <pre><code>from sklearn.preprocessing import StandardScaler
  sc_df = StandardScaler()
  df = sc_df.fit_transform(df)
  </code></pre>
  
  <p>But it has the error: Input contains NaN, infinity or a value too large for dtype('float64'), since my data contain NaN value.</p>
  
  <p>What should I do to perform Data Analysis on this data?</p>
  
  <p>Edit: the empty cell is not noise, they are the characteristic of each row.</p>