Series
A Series is a one-dimensional labelled array — a single column of data.
Syntax
pd.Series(data, index=labels)A Series is a 1D array with an index (labels). Think of it as one column of a spreadsheet.
Index power
Because each value has a label, you can look items up by name, not just position. Vectorized math works just like NumPy.
Example
import pandas as pd
s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(s['b']) # 20 (label lookup)
print(s * 2) # each value doubled
print(s.mean()) # 20.0
# a 10
# b 20
# c 30
# dtype: int64When to use it
- A data analyst stores a column of customer ages as a pandas Series to compute the mean, median, and value counts in one line each.
- An ML engineer extracts the target label column from a DataFrame as a Series to pass directly to scikit-learn's fit method.
- A financial analyst creates a Series with a DatetimeIndex to represent monthly revenue, enabling time-based slicing and resampling.
More examples
Create a Series from a list
Creates a named Series from a Python list and computes its mean, showing the basic structure of pandas' 1D labelled array.
import pandas as pd
ages = pd.Series([25, 32, 47, 19, 38], name='age')
print(ages)
print(ages.mean())Series with a custom index
Assigns meaningful string labels as the index, then accesses a single quarter and filters for quarters above a threshold using boolean indexing.
import pandas as pd
revenue = pd.Series(
[12000, 15400, 9800, 18200],
index=['Q1', 'Q2', 'Q3', 'Q4'],
name='revenue'
)
print(revenue['Q3'])
print(revenue[revenue > 12000])Series operations and value counts
Counts occurrences of each category with value_counts and lists unique labels, two essential steps in exploratory analysis of a label column.
import pandas as pd
categories = pd.Series(['cat', 'dog', 'cat', 'bird', 'dog', 'cat'])
print(categories.value_counts())
print(categories.unique())
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