On the pandas tag, I often see users asking questions about melting dataframes in pandas. I am gonna attempt a cannonical Q&A (self-answer) with this topic.

I am gonna clarify:

1. What is melt?

2. How do I use melt?

3. When do I use melt?

I see some hotter questions about melt, like:

• pandas convert some columns into rows : This one actually could be good, but some more explanation would be better.

• Pandas Melt Function : Nice question answer is good, but it's a bit too vague, not much expanation.

• Melting a pandas dataframe : Also a nice answer! But it's only for that particular situation, which is pretty simple, only pd.melt(df)

• Pandas dataframe use columns as rows (melt) : Very neat! But the problem is that it's only for the specific question the OP asked, which is also required to use pivot_table as well.

So I am gonna attempt a canonical Q&A for this topic.

Dataset:

I will have all my answers on this dataset of random grades for random people with random ages (easier to explain for the answers :D):

import pandas as pd
df = pd.DataFrame({'Name': ['Bob', 'John', 'Foo', 'Bar', 'Alex', 'Tom'], 
                   'Math': ['A+', 'B', 'A', 'F', 'D', 'C'], 
                   'English': ['C', 'B', 'B', 'A+', 'F', 'A'],
                   'Age': [13, 16, 16, 15, 15, 13]})


>>> df
   Name Math English  Age
0   Bob   A+       C   13
1  John    B       B   16
2   Foo    A       B   16
3   Bar    F      A+   15
4  Alex    D       F   15
5   Tom    C       A   13
>>> 

Problems:

I am gonna have some problems and they will be solved in my self-answer below.

Problem 1:

How do I melt a dataframe so that the original dataframe becomes:

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

I want to transpose this so that one column would be each subject and the other columns would be the repeated names of the students and there age and score.

Problem 2:

This is similar to Problem 1, but this time I want to make the Problem 1 output Subject column only have Math, I want to filter out the English column:

   Name  Age Subject Grades
0   Bob   13    Math     A+
1  John   16    Math      B
2   Foo   16    Math      A
3   Bar   15    Math      F
4  Alex   15    Math      D
5   Tom   13    Math      C

I want the output to be like the above.

Problem 3:

If I was to group the melt and order the students by there scores, how would I be able to do that, to get the desired output like the below:

  value             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Tom, Bob           Math, English
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

I need it to be ordered and the names separated by comma and also the Subjects separated by comma in the same order respectively

Problem 4:

How would I unmelt a melted dataframe? Let's say I already melted this dataframe:

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

To become:

    Name  Age  Subject Grades
0    Bob   13     Math     A+
1   John   16     Math      B
2    Foo   16     Math      A
3    Bar   15     Math      F
4   Alex   15     Math      D
5    Tom   13     Math      C
6    Bob   13  English      C
7   John   16  English      B
8    Foo   16  English      B
9    Bar   15  English     A+
10  Alex   15  English      F
11   Tom   13  English      A

Then how would I translate this back to the original dataframe, the below:

   Name Math English  Age
0   Bob   A+       C   13
1  John    B       B   16
2   Foo    A       B   16
3   Bar    F      A+   15
4  Alex    D       F   15
5   Tom    C       A   13

How would I go about doing this?

Problem 5:

If I was to group by the names of the students and separate the subjects and grades by comma, how would I do it?

   Name        Subject Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

I want to have a dataframe like above.

Problem 6:

If I was gonna completely melt my dataframe, all columns as values, how would I do it?

     Column Value
0      Name   Bob
1      Name  John
2      Name   Foo
3      Name   Bar
4      Name  Alex
5      Name   Tom
6      Math    A+
7      Math     B
8      Math     A
9      Math     F
10     Math     D
11     Math     C
12  English     C
13  English     B
14  English     B
15  English    A+
16  English     F
17  English     A
18      Age    13
19      Age    16
20      Age    16
21      Age    15
22      Age    15
23      Age    13

I want to have a dataframe like above. All columns as values.

Please check my self-answer below :)

Note for users with pandas version under < 0.20.0, I will be using df.melt(...) for my examples, but your version would be too low for df.melt, you would need to use pd.melt(df, ...) instead.

Documentation references:

Most of the solutions here would be used with melt, so to know the method melt, see the documentaion explanation

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

And the parameters are:

Parameters

• id_vars : tuple, list, or ndarray, optional

  Column(s) to use as identifier variables.

• value_vars : tuple, list, or ndarray, optional

  Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

• var_name : scalar

  Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

• value_name : scalar, default ‘value’

  Name to use for the ‘value’ column.

• col_level : int or str, optional

  If columns are a MultiIndex then use this level to melt.

• ignore_index : bool, default True

  If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

  New in version 1.1.0.

Logic to melting:

Melting merges multiple columns and converts the dataframe from wide to long, for the solution to Problem 1 (see below), the steps are:

1. First we got the original dataframe.

2. Then the melt firstly merges the Math and English columns and makes the dataframe replicated (longer).

3. Then finally adds the column Subject which is the subject of the Grades columns value respectively.

This is the simple logic to what the melt function does.

Solutions:

I will solve my own questions.

Problem 1:

Problem 1 could be solve using pd.DataFrame.melt with the following code:

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

This code passes the id_vars argument to ['Name', 'Age'], then automatically the value_vars would be set to the other columns (['Math', 'English']), which is transposed into that format.

You could also solve Problem 1 using stack like the below:

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

This code sets the Name and Age columns as the index and stacks the rest of the columns Math and English, and resets the index and assigns Grade as the column name, then renames the other column level_2 to Subject and then sorts by the Subject column, then finally resets the index again.

Both of these solutions output:

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

Problem 2:

This is similar to my first question, but this one I only one to filter in the Math columns, this time the value_vars argument can come into use, like the below:

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

Or we can also use stack with column specification:

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

Both of these solutions give:

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

Problem 3:

Problem 3 could be solved with melt and groupby, using the agg function with ', '.join, like the below:

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

It melts the dataframe then groups by the grades and aggregates them and joins them by a comma.

stack could be also used to solve this problem, with stack and groupby like the below:

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

This stack function just transposes the dataframe in a way that is equivalent to melt, then resets the index, renames the columns and groups and aggregates.

Both solutions output:

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

Problem 4:

We first melt the dataframe for the input data:

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

Then now we can start solving this Problem 4.

Problem 4 could be solved with pivot_table, we would have to specify to the pivot_table arguments, values, index, columns and also aggfunc.

We could solve it with the below code:

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

Output:

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

The melted dataframe is converted back to the exact same format as the original dataframe.

We first pivot the melted dataframe and then reset the index and remove the column axis name.

Problem 5:

Problem 5 could be solved with melt and groupby like the following:

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

That melts and groups by Name.

Or you could stack:

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

Both codes output:

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

Problem 6:

Problem 6 could be solved with melt and no column needed to be specified, just specify the expected column names:

print(df.melt(var_name='Column', value_name='Value'))

That melts the whole dataframe

Or you could stack:

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

Both codes output:

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

Conclusion:

melt is a really handy function, often it's required, once you meet these types of problems, don't forget to try melt, it may well solve your problem.

Remember for users with pandas versions under < 0.20.0, you would have to use pd.melt(df, ...) instead of df.melt(...).