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Reshaping a DataFrame from wide to long format in Pandas requires using the melt() method. The melt() method takes a DataFrame and reshapes it from wide to long format by pivoting the columns into rows and unifying the column headers and values into a single column. The melt() method also requires the specification of the id_vars parameter, which defines which columns should be kept as is and which columns should be melted.
You can use the following basic syntax to convert a pandas DataFrame from a wide format to a long format:
df = pd.melt(df, id_vars='col1', value_vars=['col2', 'col3', ...])
In this scenario, col1 is the column we use as an identifier and col2, col3, etc. are the columns we unpivot.
The following example shows how to use this syntax in practice.
Example: Reshape Pandas DataFrame from Wide to Long
Suppose we have the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'B', 'C', 'D'], 'points': [88, 91, 99, 94], 'assists': [12, 17, 24, 28], 'rebounds': [22, 28, 30, 31]}) #view DataFrame df team points assists rebounds 0 A 88 12 22 1 B 91 17 28 2 C 99 24 30 3 D 94 28 31
We can use the following syntax to reshape this DataFrame from a wide format to a long format:
#reshape DataFrame from wide format to long format
df = pd.melt(df, id_vars='team', value_vars=['points', 'assists', 'rebounds'])
#view updated DataFrame
df
team variable value
0 A points 88
1 B points 91
2 C points 99
3 D points 94
4 A assists 12
5 B assists 17
6 C assists 24
7 D assists 28
8 A rebounds 22
9 B rebounds 28
10 C rebounds 30
11 D rebounds 31
The DataFrame is now in a long format.
We used the ‘team’ column as the identifier column and we unpivoted the ‘points’, ‘assists’, and ‘rebounds’ columns.
Note that we can also use the var_name and value_name arguments to specify the names of the columns in the new long DataFrame:
#reshape DataFrame from wide format to long format
df = pd.melt(df, id_vars='team', value_vars=['points', 'assists', 'rebounds'],
var_name='metric', value_name='amount')
#view updated DataFrame
df
team metric amount
0 A points 88
1 B points 91
2 C points 99
3 D points 94
4 A assists 12
5 B assists 17
6 C assists 24
7 D assists 28
8 A rebounds 22
9 B rebounds 28
10 C rebounds 30
11 D rebounds 31
Note: You can find the complete documentation for the pandas melt() function .
The following tutorials explain how to perform other common operations in Python: