Method: Using the + operator + list comprehension
In this task, we simply add a string to the back or front position using the + operator, and list comprehension is used to iterate over all elements.
# Python3 code to demonstrate working of
# Append suffix / prefix to strings in list
# Using list comprehension + "+" operator
# initializing list
test_list = [’a’, ’b’, ’c’, ’d’]
# printing list
print("The original list : " + str(test_list))
# initializing append_str
append_str = ’gfg’
# Append suffix / prefix to strings in list
pre_res = [append_str + sub for sub in test_list]
suf_res = [sub + append_str for sub in test_list]
# Printing result
print("list after prefix addition : " + str(pre_res))
print("list after suffix addition : " + str(suf_res))
Output:
The original list: [’a’, ’b’,’ c’, ’d’] list after prefix addition: [’ gfga’, ’gfgb’,’ gfgc’, ’gfgd’] list afterix addition: [’ agfg’, ’bgfg’,’ cgfg ’,’ dgfg’]
Python add_prefix
Python is a great language for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one such package and makes it much easier to import and analyze data.
Dataframe.add_prefix() can be used with both series and dataframes.
- For series, row labels are prefixed.
- For a DataFrame, the column labels are prefixed.
Syntax: DataFrame.add_prefix(prefix)
Parameters:
prefix : string
Returns: with_prefix: type of caller
Example #1: Prefix col_ in each columns in the dataframe
# importing pandas as pd
import pandas as pd
# Making data frame from the csv file
df = pd.read_csv("nba.csv")
# Printing the first 10 rows of the
# dataframe for visualization
df[:10]
# Using add_prefix() function # to add ’col_’ in each column label df = df.add_prefix(’col_’) # Print the dataframe df
Example #2: Using add_prefix() with Series in pandas
add_prefix() alters the row index labels in the case of series.
# importing pandas as pd import pandas as pd # Creating a Series df = pd.Series([1, 2, 3, 4, 5, 10, 11, 21, 4]) # This will prefix ’Row_’ in # each row of the series df = df.add_prefix(’Row_’) # Print the Series df
