Pandas Data Series: Add some data to an existing Series
Write a Pandas program to add some data to an existing Series.
Sample Solution:
Python Code:
import pandas as pd
s = pd.Series(['100', '200', 'python', '300.12', '400'])
print("Original Data Series:")
print(s)
print("\nData Series after adding some data:")
new_s = pd.concat([s, pd.Series([500, "php"])], ignore_index=True)
print(new_s)
Sample Output:
Original Data Series: 0 100 1 200 2 python 3 300.12 4 400 dtype: object Data Series after adding some data: 0 100 1 200 2 python 3 300.12 4 400 5 500 6 php dtype: object
Explanation:
In the above exercise -
s = pd.Series(['100', '200', 'python', '300.12', '400']): This code creates a Pandas Series object 's' containing a sequence of five string values: ['100', '200', 'python', '300.12', '400'].
new_s = pd.concat([s, pd.Series([500, "php"])], ignore_index=True): This line creates a new Pandas Series object 'new_s' by concatenating the original Series object 's' with a new Pandas Series object containing two additional values: [500, "php"]. The pd.concat() function is used to concatenate the two Series objects, with the 'ignore_index' parameter set to True to reset the index of the resulting Series object 'new_s'.
Python-Pandas Code Editor:
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