How Can Pandas Melt and Dictionary Manipulation Reshape a Wide Dataframe into a Tidy Format?

Pandas Melt Function: Reshaping Dataframes with Ease

The Pandas melt function is a powerful tool for reshaping dataframes, transforming wide data into a long, tidy format. This makes data easier to analyze and manipulate.

Problem: Reshaping a Dataframe

Consider the following dataframe and 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'])

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

The goal is to reshape the dataframe into a tidy format with additional columns:

    Group   Name  Year  Value
0      A    Amy  2013      2
1      A    Amy  2014      9
2      B    Bob  2013      4
...
10  Other         2013      3
11  Other         2014      6

Using Melt and Reshaping Dictionary

The melt function alone will not complete the transformation. To get the desired result, we need to manipulate the dictionary as well:

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

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

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

Handling 'Other' Values

Finally, we move 'Other' from the 'Name' column to the 'Group' column:

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

The resulting dataframe now matches the desired format. The melt function, combined with some additional manipulation, provides a flexible and efficient way to reshape dataframes.

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