Melting in Pandas is the process of converting a DataFrame from a wide format to a long format. In the wide format, data is spread across multiple columns. In simpler terms, it “unpivots” the DataFrame columns into rows, and it is useful for visualizing and performing statistical analysis on datasets.
Pandas provides two primary methods for melting DataFrames −
In this tutorial, we will learn about the melt() and wide_to_long() functions in Pandas and how these two methods can be used to transform a DataFrame from a wide format to a long format.
The melt() function in Pandas converts a wide DataFrame into a long format. Which is nothing but “unpivots” the DataFrame.
The following example demonstrates melting a simple DataFrame using the pandas.melt() function.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
'B': {0: 1, 1: 3, 2: 5},
'C': {0: 2, 1: 4, 2: 6}})
# Display the input DataFrame
print('Input DataFrame:\n', df)
# Melt the DataFrame
melted_df = pd.melt(df, id_vars=['A'], value_vars=['B'])
print('Output melted DataFrame:\n', melted_df)Input DataFrame:
A B C
0 a 1 2
1 b 3 4
2 c 5 6
Output melted DataFrame:
A variable value
0 a B 1
1 b B 3
2 c B 5This example demonstrates how to handle the missing values while melting the DataFrame using the pandas.melt() function.
import pandas as pd
# Create a DataFrame
index = pd.MultiIndex.from_tuples([("person", "A"), ("person", "B")])
df = pd.DataFrame({
"first": ["John", "Mary"],
"last": ["Doe", "Bo"],
"height": [5.5, 6.0],
"weight": [130, 150]}, index=index)
# Display the input DataFrame
print('Input DataFrame:\n', df)
# Melt the DataFrame
melted_df = pd.melt(df, id_vars=["first", "last"], ignore_index=False)
print('Output melted DataFrame:\n', melted_df)Input DataFrame:
first last height weight
person A John Doe 5.5 130
B Mary Bo 6.0 150
Output melted DataFrame:
first last variable value
person A John Doe height 5.5
B Mary Bo height 6.0
A John Doe weight 130.0
B Mary Bo weight 150.0The pandas.wide_to_long() function provides more control over the transformation. It’s useful when your columns have a structured naming pattern that includes a suffix.
This example uses the wide_to_long() function for performing the advanced melting transformations.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]})
# Display the input DataFrame
print('Input DataFrame:\n', df)
# Melt the DataFrame using wide_to_long()
long_df = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
print('Output Long Melted DataFrame:\n', long_df)Input DataFrame:
famid birth ht1 ht2
0 1 1 2.8 3.4
1 1 2 2.9 3.8
2 1 3 2.2 2.9
3 2 1 2.0 3.2
4 2 2 1.8 2.8
5 2 3 1.9 2.4
6 3 1 2.2 3.3
7 3 2 2.3 3.4
8 3 3 2.1 2.9
Output Long Melted DataFrame:
ht
famid birth age
1 1 1 2.8
2 3.4
2 1 2.9
2 3.8
3 1 2.2
2 2.9
2 1 1 2.0
2 3.2
2 1 1.8
2 2.8
3 1 1.9
2 2.4
3 1 1 2.2
2 3.3
2 1 2.3
2 3.4
3 1 2.1
2 2.9