Series Object¶

Core Structure¶

1. Definition¶

A Series is a 1D labeled array:

import pandas as pd

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(type(s))  # <class 'pandas.core.series.Series'>

2. Components¶

print(s.values)  # NumPy array [10 20 30]
print(s.index)   # Index(['a', 'b', 'c'])
print(s.dtype)   # dtype('int64')

3. vs NumPy Array¶

Series = Values (ndarray) + Index + Name

Construction¶

1. From List¶

s = pd.Series([1, 2, 3, 4])
print(s.index)  # RangeIndex(0, 4)

2. From Dict¶

data = {'a': 10, 'b': 20, 'c': 30}
s = pd.Series(data)

3. From Scalar¶

s = pd.Series(5, index=['a', 'b', 'c'])
# a    5
# b    5
# c    5

Indexing¶

1. Label-based¶

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(s['a'])  # 10
print(s[['a', 'c']])  # Multiple labels

2. Position-based¶

print(s.iloc[0])  # 10
print(s.iloc[0:2])  # First two

3. Boolean Indexing¶

print(s[s > 15])  # Elements > 15

Operations¶

1. Arithmetic¶

s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([10, 20, 30], index=['a', 'b', 'd'])
result = s1 + s2
# Aligns on index, NaN for 'c' and 'd'

2. Methods¶

s = pd.Series([1, 2, 3, 4, 5])
print(s.mean())  # 3.0
print(s.std())   # 1.58
print(s.sum())   # 15

3. String Methods¶

s = pd.Series(['hello', 'world', 'pandas'])
print(s.str.upper())
print(s.str.len())