How to Reshape a Pandas DataFrame Using the Melt Function and a Dictionary?

Pandas Melt Function: Reshaping Dataframes for Analysis

Question:

Consider a dataframe with multiple columns and a dictionary:

df = pd.DataFrame([[2, 4, 7, 8, 1, 3, 2013], [9, 2, 4, 5, 5, 6, 2014]], columns=['Amy', 'Bob', 'Carl', 'Chris', 'Ben', 'Other', 'Year'])<br>

d = {'A': ['Amy'], 'B': ['Bob', 'Ben'], 'C': ['Carl', 'Chris']}<br>

How do we reshape the dataframe to resemble the following structure, where columns are melted and grouped?

    Group   Name  Year  Value<br> 0      A    Amy  2013      2<br> 1      A    Amy  2014      9<br> 2      B    Bob  2013      4<br> 3      B    Bob  2014      2<br> 4      B    Ben  2013      1<br> 5      B    Ben  2014      5<br> 6      C   Carl  2013      7<br> 7      C   Carl  2014      4<br> 8      C  Chris  2013      8<br> 9      C  Chris  2014      5<br>10  Other         2013      3<br>11  Other         2014      6<br>

Answer:

To reshape the dataframe using the melt function, follow these steps:

1. Melt the dataframe: Melt the dataframe into a wide format using the melt function. This will convert the columns into rows, with the id_vars parameter used to specify the columns that should remain intact.

   m = pd.melt(df, id_vars=['Year'], var_name='Name')

2. Create a mapping dictionary: Reshape the dictionary d to create a mapping between column names and group names.

   d2 = {}
   for k, v in d.items():
       for item in v:
           d2[item] = k

3. Add 'Group': Map the newly created dictionary d2 to the 'Name' column to add the 'Group' column.

   m['Group'] = m['Name'].map(d2)

4. Move 'Other': Move 'Other' values from the 'Name' column to the 'Group' column.

   mask = m['Name'] == 'Other'
   m.loc[mask, 'Name'] = ''
   m.loc[mask, 'Group'] = 'Other'

The resulting dataframe will contain the desired flattened structure:

print(m)

    Year   Name  value Group
0   2013    Amy      2      A
1   2014    Amy      9      A
2   2013    Bob      4      B
3   2014    Bob      2      B
4   2013   Carl      7      C
...    ...    ...    ...    ...
7   2014  Chris      5      C
8   2013    Ben      1      B
9   2014    Ben      5      B
10  2013             3  Other
11  2014             6  Other

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