Pandas Data Series: Add some data to an existing Series

Last update on April 03 2025 13:11:41 (UTC/GMT +8 hours)

12. Append Data to 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'.

For more Practice: Solve these Related Problems:

• Write a Pandas program to append a new Series to an existing Series and remove duplicates.
• Write a Pandas program to add a scalar value to an existing Series and then compute the cumulative sum.
• Write a Pandas program to add new elements to a Series at specified positions using index reordering.
• Write a Pandas program to concatenate two Series with overlapping indexes and then fill missing values.

Python-Pandas Code Editor:

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