🧬 Pandas Series – 1D Labeled Array for Powerful Data Handling
🧲 Introduction – What is a Pandas Series?
A Pandas Series is a one-dimensional labeled array capable of holding any data type—integers, floats, strings, objects, or even Python functions. It’s the fundamental building block of Pandas and is often used to store a single column of data in a DataFrame.
🎯 In this guide, you’ll learn:
- How to create and manipulate Pandas Series
- Accessing and slicing Series data
- Using custom indexes and performing operations
- Handling missing values and applying functions
🛠️ 1. Creating a Pandas Series
import pandas as pd
data = [10, 20, 30]
series = pd.Series(data)
print(series)
👉 Output:
0 10
1 20
2 30
dtype: int64
✅ Default index is auto-generated from 0 to n-1.
🔖 With Custom Index
series = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(series)
👉 Output:
a 10
b 20
c 30
dtype: int64
✅ Index can be any list-like object: strings, dates, numbers.
🔍 2. Accessing Data in Series
🧪 Access by Label or Position
print(series['b']) # Access by label
print(series[1]) # Access by position
🎯 Slicing Series
print(series['a':'c']) # Label-based slice (inclusive)
print(series[0:2]) # Position-based slice (exclusive of end)
➕ 3. Series Operations
Pandas Series supports vectorized arithmetic and broadcasting:
print(series + 5) # Add 5 to each element
print(series * 2) # Multiply each element by 2
You can also perform element-wise operations between two Series:
s1 = pd.Series([1, 2, 3])
s2 = pd.Series([4, 5, 6])
print(s1 + s2)
🧼 4. Handling Missing Data
series = pd.Series([1, None, 3])
print(series.isnull()) # Detect missing
print(series.fillna(0)) # Replace missing with 0
✅ Missing values are represented as NaN
🧠 5. Common Series Methods
| Method | Description |
|---|---|
series.head(n) | First n values |
series.tail(n) | Last n values |
series.dtype | Data type of elements |
series.index | Index labels |
series.values | Underlying data as NumPy array |
series.sort_values() | Sort values ascending |
series.unique() | Return unique values |
series.value_counts() | Frequency count of unique values |
📊 6. Applying Functions to Series
You can apply custom or built-in functions to Series data.
✅ Using apply()
print(series.apply(lambda x: x * 2))
✅ Using NumPy functions
import numpy as np
print(np.sqrt(series))
📌 Summary – Recap & Next Steps
Pandas Series offers fast and flexible handling of labeled 1D data. It behaves like a NumPy array but with the added power of labels, missing data support, and function application.
🔍 Key Takeaways:
- Series is a 1D labeled array; ideal for single-column or vector-style data
- Supports indexing, slicing, arithmetic, and broadcasting
- Integrates well with NumPy and Python functions
- Useful for exploration, transformation, and analysis
⚙️ Real-world relevance: Series is often used to handle single columns in datasets, compute stats, clean data, or perform quick transformations during preprocessing.
❓ FAQs – Pandas Series
❓ What is the difference between a Series and a DataFrame?
✅ A Series is 1D, like a single column; a DataFrame is 2D, like a full table.
❓ Can Series hold different data types?
❌ No. A Series must be of a single dtype, although it can be object to mix types.
❓ How can I change the index of a Series?
Use:
series.index = ['x', 'y', 'z']
❓ What if I perform operations between Series with different indexes?
✅ Pandas aligns indexes automatically and fills unmatched values with NaN.
❓ How to convert Series to list or array?
Use .tolist() for list and .values for NumPy array:
series.tolist()
series.values
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