title: Pandas: join dataframes and merge values of identical columns
body: <p>I got nine different dataframes that I want to join (or merge, or update) into one single dataframe. Each of those original dataframes consists of two columns only, an in seconds and value for that observation. The data looks like this:</p>
  
  <pre><code>   Filter_type         Time
  0          0.0  6333.137168
  
  
     Filter_type         Time
  0          0.0  6347.422576
  
  
     Filter_type         Time
  0          0.0  7002.406185
  
  
     Filter_type         Time
  0          0.0  7015.845717
  
  
     Sign_pos_X         Time
  0        11.5  6333.137168
  1        25.0  6347.422576
  2        25.5  7002.406185
  3        38.0  7015.845717
  
  
     Sign_pos_Y         Time
  0        -3.0  6333.137168
  1         8.0  6347.422576
  2        -7.5  7002.406185
  3        -0.5  7015.845717
  
  
     Sign_pos_Z         Time
  0         1.0  6333.137168
  1         1.0  6347.422576
  2         1.0  7002.406185
  3         7.5  7015.845717
  
  
     Supplementary_sign_type         Time
  0                      0.0  6333.137168
  1                      0.0  6347.422576
  2                      0.0  7002.406185
  3                      0.0  7015.845717
  
  
            Time  vision_only_sign_type
  0  6333.137168                    7.0
  1  6347.422576                    9.0
  2  7002.406185                    9.0
  3  7015.845717                   35.0
  </code></pre>
  
  <p>Since I want to join all of them into one single dataframe, I tried the following:</p>
  
  <pre><code>df2 = None
  
  for cell in df['Frames']:
      if not isinstance(cell, list):
          continue
  
      df_ = pd.DataFrame(cell)
      if df2 is None:
          # first iteration
          df2 = df_
          continue
  
      df2 = df2.merge(df_, on='Offset', how='outer') 
      #df2 = df2.join(df_)
      #df2.update(df_, join='outer')
  
  df2
  </code></pre>
  
  <p>The problem is, that the first four dataframes have the same name of the value column while the others don't. Therefore the result has three columns with the prefix 'Filter_type':</p>
  
  <pre><code>+----+-----------------+----------+-----------------+-----------------+-----------------+--------------+--------------+--------------+---------------------------+-------------------------+
  |    |   Filter_type_x |   Offset |   Filter_type_y |   Filter_type_x |   Filter_type_y |   Sign_pos_X |   Sign_pos_Y |   Sign_pos_Z |   Supplementary_sign_type |   vision_only_sign_type |
  |----+-----------------+----------+-----------------+-----------------+-----------------+--------------+--------------+--------------+---------------------------+-------------------------|
  |  0 |               0 |  6333.14 |             nan |             nan |             nan |         11.5 |         -3   |          1   |                         0 |                       7 |
  |  1 |             nan |  6347.42 |               0 |             nan |             nan |         25   |          8   |          1   |                         0 |                       9 |
  |  2 |             nan |  7002.41 |             nan |               0 |             nan |         25.5 |         -7.5 |          1   |                         0 |                       9 |
  |  3 |             nan |  7015.85 |             nan |             nan |               0 |         38   |         -0.5 |          7.5 |                         0 |                      35 |
  +----+-----------------+----------+-----------------+-----------------+-----------------+--------------+--------------+--------------+---------------------------+-------------------------+
  </code></pre>
  
  <p><strong>My question is</strong>: How can I force the merge/join to join all the 'Filter_type' columns into one. You can see that each row has only one value in all of such columns while the others are NaN. <strong>Result</strong> should look like this (having only one merged column 'Filter_type'):</p>
  
  <pre><code>+----+----------+--------------+--------------+--------------+---------------------------+-------------------------+---------------+
  |    |   Offset |   Sign_pos_X |   Sign_pos_Y |   Sign_pos_Z |   Supplementary_sign_type |   vision_only_sign_type |   Filter_type |
  |----+----------+--------------+--------------+--------------+---------------------------+-------------------------+---------------|
  |  0 |  6333.14 |         11.5 |         -3   |          1   |                         0 |                       7 |             0 |
  |  1 |  6347.42 |         25   |          8   |          1   |                         0 |                       9 |             0 |
  |  2 |  7002.41 |         25.5 |         -7.5 |          1   |                         0 |                       9 |             0 |
  |  3 |  7015.85 |         38   |         -0.5 |          7.5 |                         0 |                      35 |             0 |
  +----+----------+--------------+--------------+--------------+---------------------------+-------------------------+---------------+
  </code></pre>