A Pandas Series can be created from different Python data structures like
lists, dictionaries, and NumPy arrays.
This flexibility makes it easy to bring existing data into Pandas for analysis.
🔹 Creating Series from a List
When you pass a Python list to pd.Series(), Pandas creates a Series with a default integer index starting from 0.
import pandas as pd
data = [10, 20, 30, 40]
s = pd.Series(data)
print(s)
👉 Output:
0 10
1 20
2 30
3 40
dtype: int64
🔹 Creating Series with Custom Index
s = pd.Series([10, 20, 30], index=["a", "b", "c"])
print(s)
👉 Output:
a 10
b 20
c 30
dtype: int64
🔹 Creating Series from a Dictionary
When using a dictionary, the keys become the index, and the values become the data.
data = {"x": 100, "y": 200, "z": 300}
s = pd.Series(data)
print(s)
👉 Output:
x 100
y 200
z 300
dtype: int64
🔹 Creating Series from a NumPy Array
Pandas works closely with NumPy, so you can directly create a Series from a NumPy array.
import numpy as np
arr = np.array([5, 10, 15, 20])
s = pd.Series(arr, index=["A", "B", "C", "D"])
print(s)
👉 Output:
A 5
B 10
C 15
D 20
dtype: int64
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