Pandas: Rename all and only some of the column names
Write a Pandas program to rename all and only some of the column names from world alcohol consumption dataset.
Test Data:
Year WHO region Country Beverage Types Display Value 0 1986 Western Pacific Viet Nam Wine 0.00 1 1986 Americas Uruguay Other 0.50 2 1985 Africa Cte d'Ivoire Wine 1.62 3 1986 Americas Colombia Beer 4.27 4 1987 Americas Saint Kitts and Nevis Beer 1.98
Sample Solution:
Python Code :
import pandas as pd
# World alcohol consumption data
w_a_con = pd.read_csv('world_alcohol.csv')
new_w_a_con = pd.read_csv('world_alcohol.csv')
print("World alcohol consumption sample data:")
print(w_a_con.head())
print("\nRename all the column names:")
w_a_con.columns = ['year','who_region','country','beverage_types','display_values']
print(w_a_con.head())
print("\nRenaming only some of the column names:")
new_w_a_con.rename(columns = {"WHO region":"WHO_region","Display Value":"Display_Value" },inplace = True)
print(new_w_a_con.head())
Sample Output:
World alcohol consumption sample data: Year WHO region ... Beverage Types Display Value 0 1986 Western Pacific ... Wine 0.00 1 1986 Americas ... Other 0.50 2 1985 Africa ... Wine 1.62 3 1986 Americas ... Beer 4.27 4 1987 Americas ... Beer 1.98 [5 rows x 5 columns] Rename all the column names: year who_region ... beverage_types display_values 0 1986 Western Pacific ... Wine 0.00 1 1986 Americas ... Other 0.50 2 1985 Africa ... Wine 1.62 3 1986 Americas ... Beer 4.27 4 1987 Americas ... Beer 1.98 [5 rows x 5 columns] Renaming only some of the column names: Year WHO_region ... Beverage Types Display_Value 0 1986 Western Pacific ... Wine 0.00 1 1986 Americas ... Other 0.50 2 1985 Africa ... Wine 1.62 3 1986 Americas ... Beer 4.27 4 1987 Americas ... Beer 1.98 [5 rows x 5 columns]
Click to download world_alcohol.csv
Python Code Editor:
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Previous: Write a Pandas program to select consecutive columns and also select rows with Index label 0 to 9 with some columns from world alcohol consumption dataset.
Next: Write a Pandas program to find which years have all non-zero values and which years have any non-zero values from world alcohol consumption dataset.
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