This short tutorial shows you how you can use melt() funtion in Pandas. It is often used when we need to change the format of dataframe to fit into a certain statistical functions.
Example 1 of Using melt()
import pandas as pd
City1= [6,2,3,4,5]
City2= [2,1,3,4,5]
City3= [4,1,2,4,5]
city_data = pd.DataFrame(
{'City1': City1,
'City2': City2,
'City3': City3
})
print(city_data)City1 City2 City3 0 6 2 4 1 2 1 1 2 3 3 2 3 4 4 4 4 5 5 5
city_data=city_data.melt( var_name="Cities",
value_name="Household_size")
print(city_data)Cities Household_size 0 City1 6 1 City1 2 2 City1 3 3 City1 4 4 City1 5 5 City2 2 6 City2 1 7 City2 3 8 City2 4 9 City2 5 10 City3 4 11 City3 1 12 City3 2 13 City3 4 14 City3 5
Example 2 of Using melt()
import pandas as pd
City1= [6,2,3,4,5]
City2= [2,1,3,4,5]
City3= [4,1,2,4,5]
Housing_Type=["Apartment","Apartment","House","House","House"]
city_data = pd.DataFrame(
{'Housing_Type':Housing_Type,
'City1': City1,
'City2': City2,
'City3': City3
})
print(city_data)
city_data=city_data.melt(
id_vars=["Housing_Type",],
var_name="Cities",
value_name="Household_size")
print(city_data)Housing_Type City1 City2 City3 0 Apartment 6 2 4 1 Apartment 2 1 1 2 House 3 3 2 3 House 4 4 4 4 House 5 5 5 Housing_Type Cities Household_size 0 Apartment City1 6 1 Apartment City1 2 2 House City1 3 3 House City1 4 4 House City1 5 5 Apartment City2 2 6 Apartment City2 1 7 House City2 3 8 House City2 4 9 House City2 5 10 Apartment City3 4 11 Apartment City3 1 12 House City3 2 13 House City3 4 14 House City3 5