> ## Content Index > Fetch the complete content index at: https://datascientyst.com/llms.txt > Use this file to discover other available public pages before exploring further. # How to Melt Pandas DataFrame - pd.melt in Examples - URL: https://datascientyst.com/use-melt-pandas-dataframe-pd-melt-examples/ - Published: 2022-09-01T21:17:32.000Z - Updated: 2022-09-01T21:17:32.000Z - Author: John D K - Tags: melt() In this quick tutorial, we'll see how to use melt in Pandas. We'll first look into basic pd.melt usage, then `pd.melt()` parameters, and finally some advanced examples and alternatives of melt in Pandas and Python. In short we can do: **(1) pd.melt() in Pandas** ```python pd.melt(df, id_vars=['A'], value_vars=['B']) ``` **(2) pd.melt() and MultiIndex** ```python pd.melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')]) ``` ## Setup In this article we will use DataFrame which has information for population of countries: ```python import pandas as pd data = [{"Country":"China","1950":"562,580","1955":"607,047","1960":"651,340","1980":"987,822","continent":"Asia"}, {"Country":"India","1950":"369,881","1955":"404,268","1960":"445,394","1980":"684,888","continent":"Asia"}, {"Country":"United States","1950":"151,869","1955":"165,070","1960":"179,980","1980":"227,225","continent":"N. America"}, {"Country":"Indonesia","1950":"82,979","1955":"90,255","1960":"100,146","1980":"150,322","continent":"Asia"}, {"Country":"Russia","1950":"101,937","1955":"111,126","1960":"119,632","1980":"139,039","continent":"Europe"}, {"Country":"Brazil","1950":"53,444","1955":"61,652","1960":"71,412","1980":"121,064","continent":"S. America"}] pd.DataFrame(data) ``` Final data format is perfect example of where Pandas melt function is useful: | | Country | 1950 | 1955 | 1960 | 1980 | continent | | - | ------------- | ------- | ------- | ------- | ------- | ---------- | | 0 | China | 562,580 | 607,047 | 651,340 | 987,822 | Asia | | 1 | India | 369,881 | 404,268 | 445,394 | 684,888 | Asia | | 2 | United States | 151,869 | 165,070 | 179,980 | 227,225 | N. America | | 3 | Indonesia | 82,979 | 90,255 | 100,146 | 150,322 | Asia | | 4 | Russia | 101,937 | 111,126 | 119,632 | 139,039 | Europe | | 5 | Brazil | 53,444 | 61,652 | 71,412 | 121,064 | S. America | ## 1: What is melt in Pandas The picture below shows melt function in action ![](https://datascientyst.com/content/images/2022/09/use-melt-pandas-dataframe-pd-melt-examples-datascientyst.png) There are 2 important parameters of this method: - `id_vars` \- identifier variables - `value_vars` \- measured variables, which are "melt" or "unpivoted" to row axis (non-identifier columns) - `value` \- is the column values - `variable` \- the column names So the melt function will turn multiple columns - `value_vars` \- to rows. There will be two non-identifier columns. There are `id_vars` \- identifier variables which will be considered as identifier columns. **Info** In short pd.melt() unpivots data. It makes it easier to filter, compare, visualize and use in DB like style. ## 2: Why is melt useful Pandas melt function is useful when data is in form of: - pivot - pivot table - cross table And we need to convert the multiple columns to rows. Looking at the data above we might want to find the top 5 most populated pairs - year and country. Using `melt()` will help us to get this information much faster. The reverse operation of melt can be found on: [Opposite of Melt in Python and Pandas](https://datascientyst.com/opposite-of-melt-python-pandas/) ## 3: Pandas melt example Let's see how to use the melt function. First we will identify the parameters: - `id_vars=['Country', 'continent']` - `value_vars=['1950', '1955']` ```python pd.melt(df, id_vars=['Country', 'continent'], value_vars=['1950', '1955']) ``` This would result into: | | Country | continent | variable | value | | -- | ------------- | ---------- | -------- | ------- | | 0 | China | Asia | 1950 | 562,580 | | 1 | India | Asia | 1950 | 369,881 | | 2 | United States | N. America | 1950 | 151,869 | | 3 | Indonesia | Asia | 1950 | 82,979 | | 4 | Russia | Europe | 1950 | 101,937 | | 5 | Brazil | S. America | 1950 | 53,444 | | 6 | China | Asia | 1955 | 607,047 | | 7 | India | Asia | 1955 | 404,268 | | 8 | United States | N. America | 1955 | 165,070 | | 9 | Indonesia | Asia | 1955 | 90,255 | | 10 | Russia | Europe | 1955 | 111,126 | | 11 | Brazil | S. America | 1955 | 61,652 | So the output has 4 columns: - 2 identifier columns - Country - continent - 2 non identifier columns - variable - value If we add more `value_vars` columns the number of the columns will be the same. Only `id_vars` change the number of the output columns. ## 4: Pandas melt - change names If we like to change the non-identifier columns we can use 2 parameters: - `var_name` - `value_name` and set the new names for the result columns: ```python pd.melt(df, id_vars=['Country', 'continent'], value_vars=['1950', '1955'], var_name ='year', value_name ='value') ``` ## 5: Pandas melt - parameters There are several parameters of melt function. Most of them were mentioned in the article. Two remains to be described: - `col_level` \- specifies the level to melt (MultiIndex) - `ignore_index` \- ignore the original index ## 6: Pandas melt - MultiIndex Melt function can be used for MultiIndex DataFrame. To simulate melt function for MultiIndex we will turn the columns into MultiIndex by: ```python df = pd.concat({'year': df}, names=['Firstlevel'], axis=1) ``` More about: [How to Add a Level to Index in Pandas DataFrame](https://datascientyst.com/add-level-index-pandas-dataframe/) We can check the columns by: ```python df.columns ``` result: ``` MultiIndex([('year', 'Country'), ('year', '1950'), ('year', '1955'), ('year', '1960'), ('year', '1980'), ('year', 'continent')], names=['Firstlevel', None]) ``` Melt can be invoked on MultiIndex columns by: ```python pd.melt(df, id_vars=[('year', 'Country')], value_vars=[('year', '1950'), ('year', '1955')]) ``` The result is: | | (year, Country) | Firstlevel | None | value | | -- | --------------- | ---------- | ---- | ------- | | 0 | China | year | 1950 | 562,580 | | 1 | India | year | 1950 | 369,881 | | 2 | United States | year | 1950 | 151,869 | | 3 | Indonesia | year | 1950 | 82,979 | | 4 | Russia | year | 1950 | 101,937 | | 5 | Brazil | year | 1950 | 53,444 | | 6 | China | year | 1955 | 607,047 | | 7 | India | year | 1955 | 404,268 | | 8 | United States | year | 1955 | 165,070 | | 9 | Indonesia | year | 1955 | 90,255 | | 10 | Russia | year | 1955 | 111,126 | | 11 | Brazil | year | 1955 | 61,652 | ## Conclusion Pandas melt is a very useful function for reshaping DataFrames. It can multiple columns to rows. We saw how it works, why it's important and several examples. The function can help "unpivoting" data.