🧱 Pandas Creating and Accessing Series – Build & Explore 1D Labeled Data
🧲 Introduction – Why Learn Series Creation and Access?
A Pandas Series is the foundation of data handling in Pandas. Before moving to complex DataFrames or transformations, you must master how to create, access, and interact with Series objects effectively.
🎯 In this guide, you’ll learn:
- Various methods to create a Series in Pandas
- How to assign custom indexes to Series
- Different ways to access elements using labels and positions
- Perform slicing, conditional access, and indexing
🛠️ 1. Create a Pandas Series from a List
import pandas as pd
data = [10, 20, 30, 40]
series = pd.Series(data)
print(series)
👉 Output:
0 10
1 20
2 30
3 40
dtype: int64
✅ A default integer index (0 to n-1) is assigned.
🧾 2. Create Series with Custom Index
series = pd.Series([100, 200, 300], index=['a', 'b', 'c'])
print(series)
👉 Output:
a 100
b 200
c 300
dtype: int64
✅ Index can be strings, numbers, or even datetime objects.
📚 3. Create Series from a Dictionary
data = {'apple': 3, 'banana': 5, 'orange': 2}
series = pd.Series(data)
print(series)
👉 Output:
apple 3
banana 5
orange 2
dtype: int64
✅ Keys become index labels, and values become data.
🏷️ 4. Create Series with Scalar Value
series = pd.Series(7, index=['x', 'y', 'z'])
print(series)
👉 Output:
x 7
y 7
z 7
dtype: int64
✅ A scalar is broadcasted across all index values.
🔍 5. Access Elements by Position (.iloc)
print(series.iloc[0]) # First element
print(series.iloc[-1]) # Last element
✅ .iloc[] is used for integer-based indexing.
🔎 6. Access Elements by Label (.loc)
print(series.loc['x']) # Access using index label
✅ .loc[] is used for label-based indexing.
✂️ 7. Slice a Series
print(series[1:3]) # Slicing by position
print(series['x':'y']) # Slicing by label (inclusive)
✅ Label slicing in Series is inclusive of end index.
🎯 8. Conditional Access
print(series[series > 6])
👉 Output:
x 7
y 7
z 7
dtype: int64
✅ Return only values matching the condition.
📋 9. Useful Series Attributes & Methods
| Attribute / Method | Purpose |
|---|---|
series.index | Return the index object |
series.values | Return values as NumPy array |
series.dtype | Data type of the Series |
series.head() | First 5 elements |
series.tail() | Last 5 elements |
📌 Summary – Recap & Next Steps
Creating and accessing Pandas Series is an essential step before moving on to complex analysis. With just a few lines, you can build and interact with labeled data for real-world tasks like filtering, preprocessing, and feature extraction.
🔍 Key Takeaways:
- Series can be created from lists, dictionaries, and scalar values
- You can use custom or default indexing
- Access data using
.iloc[],.loc[], or slicing - Series supports conditional and vectorized access
⚙️ Real-world relevance: Common in preprocessing steps like creating target vectors, handling labels, and managing 1D data columns.
❓ FAQs – Creating and Accessing Series
❓ Can a Series have duplicate index labels?
✅ Yes, but accessing such labels returns multiple values.
❓ How do I change the index of a Series?
Use:
series.index = ['a', 'b', 'c']
❓ What happens if I access a missing label using .loc[]?
❌ It will raise a KeyError.
❓ Can I slice Series with non-integer indexes?
✅ Yes, if labels are sorted. Label slicing is inclusive.
❓ Are Series faster than lists?
✅ Yes, due to underlying NumPy optimization.
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