Question: On the pandas tag, I often see users asking questions about melting dataframes in pandas. I am going to attempt a canonical Q&A (self-answer) with this topic. I am is going to clarify:  What is melt?  How do I use melt?  When do I use melt?   I see some hotter questions about melt, like:  Convert columns into rows with Pandas: This one actually could be good, but some more explanation would be better.  Pandas Melt Function: A nice question, and the answer is good, but it's a bit too vague and doesn't have much explanation.  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 going to 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: Problem 1: How do I melt a dataframe so that the original dataframe becomes the following?  Name Age Subject Grade 0 Bob 13 English C 1 John 16 English B 2 Foo 16 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 16 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 their 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 their 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: df = 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  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 is going to 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.

Answer: Note for pandas versions < 0.20.0: I will be using df.melt(...) for my examples, but you will 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 documentation 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’. 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:  First we got the original dataframe.  Then the melt firstly merges the Math and English columns and makes the dataframe replicated (longer).  Then finally it 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: 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 16 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 16 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:  How would I unmelt a melted dataframe? Let's say I already melted this dataframe: df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')   This could be solved with pivot_table. We would have to specify the 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