How to split a column of tuples into separate columns in a Pandas dataframe?

How to Extract Tuples from Pandas Dataframe Columns

Problem:

In a Pandas dataframe, it is common to have columns containing tuples. However, working with these tuples can be cumbersome. To facilitate analysis, it is often desirable to split these columns into multiple columns containing the individual tuple elements.

Solution:

To convert a column of tuples into separate columns, follow these steps:

1. Convert the column to a list of tuples using the tolist() method:

   <code class="python">column_list = column.tolist()</code>

2. Create a new dataframe from the list of tuples:

   <code class="python">new_df = pd.DataFrame(column_list, index=dataframe.index)</code>

3. Assign the new dataframe as new columns to the original dataframe:

   <code class="python">dataframe[['column_a', 'column_b']] = new_df[['0', '1']]</code>

Example:

Consider the following dataframe:

<code class="python">>>> d1
   y norm test  y norm train  len(y_train)  len(y_test)  \
0    64.904368    116.151232          1645          549
1    70.852681    112.639876          1645          549

                                    SVR RBF  \
0   (35.652207342877873, 22.95533537448393)
1  (39.563683797747622, 27.382483096332511)

                                        LCV  \
0  (19.365430594452338, 13.880062435173587)
1  (19.099614489458364, 14.018867136617146)

                                   RIDGE CV  \
0  (4.2907610988480362, 12.416745648065584)
1    (4.18864306788194, 12.980833914392477)

                                         RF  \
0   (9.9484841581029428, 16.46902345373697)
1  (10.139848213735391, 16.282141345406522)

                                           GB  \
0  (0.012816232716538605, 15.950164822266007)
1  (0.012814519804493328, 15.305745202851712)

                                             ET DATA
0  (0.00034337162272515505, 16.284800366214057)  j2m
1  (0.00024811554516431878, 15.556506191784194)  j2m
>>></code>

To split the LCV column into individual columns LCV-a and LCV-b, you can use the following code:

<code class="python">df[['LCV-a', 'LCV-b']] = pd.DataFrame(df['LCV'].tolist(), index=df.index)</code>

The resulting dataframe will be:

<code class="python">>>> df
   y norm test  y norm train  len(y_train)  len(y_test)  \
0    64.904368    116.151232          1645          549
1    70.852681    112.639876          1645          549

                                    SVR RBF  \
0   (35.652207342877873, 22.95533537448393)
1  (39.563683797747622, 27.382483096332511)

                                        LCV-a  LCV-b  \
0  19.365430594452338  13.880062435173587
1  19.099614489458364  14.018867136617146

                                   RIDGE CV  \
0  (4.2907610988480362, 12.416745648065584)
1    (4.18864306788194, 12.980833914392477)

                                         RF  \
0   (9.9484841581029428, 16.46902345373697)
1  (10.139848213735391, 16.282141345406522)

                                           GB  \
0  (0.012816232716538605, 15.950164822266007)
1  (0.012814519804493328, 15.305745202851712)

                                             ET DATA
0  (0.00034337162272515505, 16.284800366214057)  j2m
1  (0.00024811554516431878, 15.556506191784194)  j2m</code>

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