Creating a Series

Play with the builder below to preview each source, then run the real code in the panels underneath.

import pandas as pd
cities = ['Mumbai', 'Delhi', 'Bengaluru', 'Chennai']
s = pd.Series(cities)
print(s)

1Method 1 — The empty Series

Sometimes you need to start with nothing and fill it in later. Calling pd.Series() with no arguments creates an empty Series.

import pandas as pd

# Creates an empty Series
empty_s = pd.Series()
print(empty_s)

2Method 2 — From a Python list

This is the most common way. Pandas automatically creates a default integer index starting from 0.

import pandas as pd

# Series from a list of Indian cities
cities = ['Mumbai', 'Delhi', 'Bengaluru', 'Chennai']
s_from_list = pd.Series(cities)
print(s_from_list)

Beyond the default: pass index=... to give every value a meaningful label of your own.

import pandas as pd

marks = [98, 67, 86, 92]
labels = ['Amit', 'Divya', 'Vinay', 'Sara']
s = pd.Series(data=marks, index=labels)
print(s)

3Method 3 — From a NumPy ndarray

Pandas is built on top of NumPy, so turning an array into a Series is seamless and efficient.

import pandas as pd
import numpy as np

# Series from a NumPy array of cricket scores
scores = np.array([102, 88, 95, 74])
s_from_ndarray = pd.Series(scores)
print(s_from_ndarray)

4Method 4 — From a dictionary

This one is powerful: the dictionary's keys automatically become the Series' index labels, and the values become the data.

import pandas as pd

# Series from a dictionary of population (in millions)
population = {'Mumbai': 12.5, 'Delhi': 11.0, 'Kolkata': 4.5}
s_from_dict = pd.Series(population)
print(s_from_dict)

5Method 5 — From a scalar (single value)

Want a Series where every element is the same? Give a single value and an index. Pandas repeats the value once for every label.

import pandas as pd

# The value 'Holiday' repeated for several dates
s_from_scalar = pd.Series(
    'Holiday',
    index=['2025-01-26', '2025-08-15', '2025-10-02']
)
print(s_from_scalar)

The index does double duty here: it sets both the labels and the length. Three labels give three copies.

What if you leave the index out? There is no error. You get a Series with just one value, at the default label 0:

import pandas as pd

marks = pd.Series(9)
print(marks)

6Practice — find the errors

Board papers often give a short Pandas program with mistakes in it and ask you to rewrite the correct code and underline the corrections. Here are two of them.

One thing to know first: Python stops at the first mistake it finds. So run the program, fix that mistake, and run it again. The next mistake then shows itself.

Error hunt 1 (2025 board paper)

Rohit wants a Series of three numbers with the labels x, y and z. Run his program and read the error.

import pandas
data = [50, 15, 40]
series = pd.series(data, Index=['x', 'y', 'z'])
Print(series)

Fix one mistake at a time, and this is what Python says after each run:

Four corrections. The correct program and its output:

import pandas as pd
data = [50, 15, 40]
series = pd.Series(data, index=['x', 'y', 'z'])
print(series)

x    50
y    15
z    40
dtype: int64

Error hunt 2 (2024 board paper)

This program should make a Series from a student's details. Run it.

import Pandas as pd
stud=['Name':'Ramya','Class':11,'House':'Red']
s=p.Series(s)
print(s)

This time Python does not even start running. Line 2 is a SyntaxError: key:value pairs belong inside curly brackets, so it must be a dictionary. Fix that, and the other mistakes appear one by one:

import pandas as pd
stud={'Name':'Ramya','Class':11,'House':'Red'}
s=pd.Series(stud)
print(s)

Name     Ramya
Class       11
House      Red
dtype: object

7Recap — five ways in