melt()
melt turns wide data (many columns) into long data (one row per measurement).
In this page:
Syntax
long_df = pd.melt(df, id_vars=["id_column"], value_vars=["column1", "column2"],
var_name="variable", value_name="value")melt()
melt(id_vars, value_vars, var_name, value_name) unpivots columns into rows. id_vars stay as identifiers, while the rest become a variable column and a value column. Long format is what many plotting and grouping tools prefer.
Note:
If you leave out value_vars, every non-id column is melted.
Example: melt()
import pandas as pd
wide = pd.DataFrame({"name": ["Ann", "Bob"], "math": [90, 80], "art": [70, 85]})
print(wide.melt(id_vars="name", var_name="subject", value_name="score"))
# Output:
# name subject score
# 0 Ann math 90
# 1 Bob math 80
# 2 Ann art 70
# 3 Bob art 85Related Topics
Common Mistakes
- Forgetting id_vars so identifiers get melted
- Not naming the new columns
- Confusing melt with pivot
Chapter Summary
- melt goes from wide to long
- id_vars stay as identifiers
- var_name and value_name label the new columns
- Opposite of pivot
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