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Python pandas: How to Convert a Dictionary to a Pandas Series

Learn how to convert a Python dictionary to a pandas Series using pd.Series(), customize index order, handle missing keys, and force data types for data analysis workflows.

A pandas Series is a one-dimensional labeled array that can hold any data type - integers, floats, strings, or Python objects. Since Python dictionaries also store data as key-value pairs, converting between these two structures is a natural and frequent operation in data analysis.

In this guide, you'll learn how to convert a dictionary to a pandas Series using pd.Series(), control the index order, handle mismatched keys, and apply this conversion in practical scenarios.

Basic Conversion with pd.Series()

The simplest way to convert a dictionary to a Series is to pass it directly to pd.Series(). The dictionary keys become the Series index, and the values become the data.

import pandas as pd

data = {'a': 100, 'b': 200, 'c': 300, 'd': 400, 'e': 500}

series = pd.Series(data)
print(series)

Output:

a    100
b    200
c    300
d    400
e    500
dtype: int64

The resulting Series preserves the insertion order of the dictionary keys (guaranteed in Python 3.7+).

Specifying a Custom Index Order

You can control the order of the Series by passing an index parameter. The Series will be reordered to match the index you specify:

import pandas as pd

data = {'a': 10, 'b': 20, 'c': 40, 'd': 80, 'e': 160}

series = pd.Series(data, index=['e', 'b', 'd', 'a', 'c'])
print(series)

Output:

e    160
b     20
d     80
a     10
c     40
dtype: int64

The values are rearranged to match the order specified in the index list, not the original dictionary order.

Handling Mismatched Index and Keys

When the index list contains keys that don't exist in the dictionary, pandas assigns NaN to those positions. Conversely, dictionary keys that aren't in the index list are excluded from the Series.

import pandas as pd

data = {'a': 10, 'b': 20, 'c': 40, 'd': 80}

series = pd.Series(data, index=['b', 'd', 'e', 'a', 'c'])
print(series)

Output:

b    20.0
d    80.0
e     NaN
a    10.0
c    40.0
dtype: float64

Converting Different Value Types

Dictionaries with various value types are handled naturally by pandas:

String Values

import pandas as pd

capitals = {'France': 'Paris', 'Germany': 'Berlin', 'Japan': 'Tokyo', 'India': 'New Delhi'}

series = pd.Series(capitals)
print(series)

Output:

France         Paris
Germany       Berlin
Japan          Tokyo
India      New Delhi
dtype: object

Float Values

import pandas as pd

prices = {'apple': 1.50, 'banana': 0.75, 'cherry': 3.25, 'date': 5.00}

series = pd.Series(prices)
print(series)
print(f"\nDtype: {series.dtype}")

Output:

apple     1.50
banana    0.75
cherry    3.25
date      5.00
dtype: float64

Dtype: float64

Specifying the Data Type

You can force a specific data type using the dtype parameter:

import pandas as pd

data = {'x': 1, 'y': 2, 'z': 3}

series_float = pd.Series(data, dtype=float)
print(series_float)

Output:

x    1.0
y    2.0
z    3.0
dtype: float64

Naming the Series

You can assign a name to the Series and its index during creation, which is useful when the Series will later become part of a DataFrame:

import pandas as pd

scores = {'Alice': 92, 'Bob': 85, 'Charlie': 78, 'Diana': 95}

series = pd.Series(scores, name='exam_score')
series.index.name = 'student'

print(series)

Output:

student
Alice      92
Bob        85
Charlie    78
Diana      95
Name: exam_score, dtype: int64

Converting a Nested Dictionary

If your dictionary has nested values, each value becomes a single element in the Series (stored as an object):

import pandas as pd

data = {
    'user_1': {'name': 'Alice', 'age': 30},
    'user_2': {'name': 'Bob', 'age': 25}
}

series = pd.Series(data)
print(series)
print(f"\nType of first element: {type(series['user_1'])}")

Output:

user_1    {'name': 'Alice', 'age': 30}
user_2      {'name': 'Bob', 'age': 25}
dtype: object

Type of first element: <class 'dict'>

Practical Example: Converting and Analyzing Data

Here's a real-world example of converting a dictionary to a Series and performing analysis:

import pandas as pd

monthly_sales = {
    'January': 15000,
    'February': 18000,
    'March': 22000,
    'April': 19500,
    'May': 25000,
    'June': 21000
}

sales = pd.Series(monthly_sales, name='revenue')

print("Monthly Sales:")
print(sales)
print(f"\nTotal revenue:   ${sales.sum():,}")
print(f"Average revenue: ${sales.mean():,.0f}")
print(f"Best month:      {sales.idxmax()} (${sales.max():,})")
print(f"Worst month:     {sales.idxmin()} (${sales.min():,})")
print(f"\nAbove average months:")
print(sales[sales > sales.mean()])

Output:

Monthly Sales:
January     15000
February    18000
March       22000
April       19500
May         25000
June        21000
Name: revenue, dtype: int64

Total revenue:   $120,500
Average revenue: $20,083
Best month:      May ($25,000)
Worst month:     January ($15,000)

Above average months:
March    22000
May      25000
June     21000
Name: revenue, dtype: int64

Converting Back: Series to Dictionary

To convert a Series back to a dictionary, use the to_dict() method:

import pandas as pd

series = pd.Series({'a': 1, 'b': 2, 'c': 3})

back_to_dict = series.to_dict()
print(back_to_dict)
print(type(back_to_dict))

Output:

{'a': 1, 'b': 2, 'c': 3}
<class 'dict'>

Summary of Key Parameters

Parameter Description Example
data The dictionary to convert pd.Series({'a': 1})
index Custom index order; missing keys get NaN pd.Series(d, index=['b', 'a'])
dtype Force a specific data type pd.Series(d, dtype=float)
name Assign a name to the Series pd.Series(d, name='scores')

Conclusion

Converting a dictionary to a pandas Series is straightforward with pd.Series():

  • Pass the dictionary directly to create a Series where dictionary keys become the index and values become the data.
  • Use the index parameter to control the order or select a subset of keys.
  • Missing keys in the index produce NaN values, and the dtype is automatically promoted to float64 to accommodate them.
  • Use dtype to explicitly set the data type of the resulting Series.

This conversion is one of the most fundamental operations in pandas and serves as a building block for constructing DataFrames, performing analysis, and transforming data between different formats.

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