Create Pandas Series in Python

You can create pandas Series in multiple ways for example creating from the python list, converting dictionary to Series, create series from numpy array, and initializing from the series constructor. In this article, I will cover all these different ways to initialize the Series with examples.

Key Points –

A Pandas Series is a one-dimensional labeled array that can store data of any type (integers, floats, strings, etc.)
Series can be created from various data structures such as lists, NumPy arrays, dictionaries, and scalar values.
The dtype parameter allows you to define the data type of the Series explicitly.
Series support vectorized operations, meaning operations are applied element-wise across all values.
The .squeeze() method can convert DataFrames with a single column or row into a Series.
Series can be created directly from a DataFrame column or row using index labels.

What is the Pandas Series?

In Python's Pandas library, a Series is a one-dimensional array-like object that can hold any data type such as integers, floats, strings, or even Python objects. It's similar to a list or an array, but with additional functionalities and capabilities. Each element in a Series has a label called an index. This index helps in accessing and manipulating data efficiently.

Quick Examples of Create Pandas Series

If you hurry below are quick examples of creating the Pandas Series.


# Quick examples of create pandas series

# Example 1: Create empty Series
ser = pd.Series()

# Example 2: Create Series from array
data = np.array(['python', 'php', 'java'])
series = pd.Series(data)

# Example 3: Create Pandas Series with custom index
series = pd.Series(data=data, index=['r1', 'r2', 'r3'])

# Example 4: Create Pandas Series using list
data = ['python', 'php', 'java']
series = pd.Series(data, index=['r1', 'r2','r3'])

# Example 5: Create Series from Dict
data = {'Courses' :"pandas", 'Fees' : 20000, 'Duration' : "30days"}
series = pd.Series(data)

# Example 6: Convert Single column DataFrame to Series
data =  ["Python", "PHP", "Java"]
df = pd.DataFrame(data, columns = ['Courses'])
series = df.squeeze()

# Example 7: Create Series from Pandas DataFrame column
series = df['Fee'].squeeze()

# Example 8: Create Series from Pandas DataFrame row
series = df.iloc[2].squeeze()

Pandas.series() Constructor

Below is the syntax of pandas Series Constructor.


# Pandas Series Constructor Syntax
Pandas.series(data, index, dtype, copy)

data - The data to be stored in the Series. It can be a scalar, list-like, dict, or other Series.
Index - The index for the Series. If not provided, a default integer index will be used.
dtype - By default, the data type of Series is float.
copy - Whether to copy the input data. Defaults to False.

Create Empty Pandas Series from Constructor

Pandas provide Series() constructor to create or initialize a Series. Default constructor without any arguments creates an empty Series object. This function takes data as an argument that accepts ndarray, list, dict, and constants.


# Imports
import pandas as pd

# Create empty pandas series
ser = pd.Series()
print(ser)

# Output :
# Series([], dtype: float64)

Create a Series From NumPy Array

NumPy array is a data structure (usually numbers) that contains values of the same type, similar to a list. But arrays are more efficient than Python lists and also much more compact. Sometimes you would be required to create pandas Series from the NumPy array. Let's create Numpy array using numpy.array() function then, create a Series by passing an array into series() function.


# Imports
import pandas as pd
import numpy as np

# Create Series from array
data = np.array(['python', 'php', 'java'])
series = pd.Series(data)
print (series)

Yields below output.


# Output:
0    python
1       php
2      java
dtype: object

By default, the index of the Series starts from 0, if we want to customize the index of the Series, we can change it by providing a list of values to the index parameter to the Series() function.

Customize the index of Series.

Now, let’s see how to create a pandas Series with a custom Index. To do so, will use index param which takes a list of index values. Make sure the index list matches the data size. For example,


# Create pandas DataFrame with custom index
series = pd.Series(data=data, index=['r1', 'r2', 'r3'])
print(series)

Yields below output.


# Output:
r1    python
r2       php
r3      java
dtype: object

Create Series from List

If you have a Python list it can be easily converted into Pandas Series by passing the list object as an argument to series() function. In case if you wanted to convert the Pandas Series to a list use Series.tolist().


# Create Pandas Series from list
data = ['python', 'php', 'java']
series = pd.Series(data, index=['r1', 'r2','r3'])
print(series)

Yields below output.


# Output:
r1 python
r2 php
r3 java
dtype:object

Create a Series using a Dictionary

A Python dictionary can be utilized to create a Pandas Series. In this process, the dictionary keys are used as the index labels, and the dictionary values are used as the data for the Series. If you want to convert a Pandas Series to a dictionary, use the Series.to_dict() method.


# Create a Dict from a input
data = {'Courses' :"pandas", 'Fees' : 20000, 'Duration' : "30days"}
series = pd.Series(data)
print (series)

Yields below output.


# Output:
Courses     pandas
Fees         20000
Duration    30days
dtype: object

Create pandas Series from DataFrame

In this section let's see how to convert DataFrame to Series. Note that every column in a DataFrame is a Series. hence, we can convert single or multiple columns into Series.

Single DataFrame column into a Series (from a single-column DataFrame)
Specific DataFrame column into a Series (from a multi-column DataFrame)
Single row in the DataFrame into a Series

Create a Series from single DataFrame column

Let’s create a DataFrame with a single column. By using DataFrame.squeeze() to create Series from the DataFrame. For example,


# Create DataFrame with single column
data =  ["Python", "PHP", "Java"]
df = pd.DataFrame(data, columns = ['Courses'])
series = df.squeeze()
print(series)
print (type(series))

Yields below output.


# Output:
0    Python
1       PHP
2      Java
Name: Courses, dtype: object

Create a Series From a specific DataFrame column

If you have a DataFrame with multiple columns, use the below example to convert a specific column into a series. Let's create Pandas DataFrame with multiple columns.


# create DataFrame with multiple columns
import pandas as pd
data = {'Courses': ['Spark', 'PySpark', 'Python'],
        'Duration':['30 days', '40 days', '50 days'],
        'Fee':[20000, 25000, 26000]
        }
df = pd.DataFrame(data, columns = ['Courses', 'Duration', 'Fee'])
print(df)
print (type(df))

Let's convert the Fee column into a Series.


# Create Series using Pandas DataFrame
series = df['Fee'].squeeze()

Yields below output.


# Output:
0    20000
1    25000
2    26000
Name: Fee, dtype: int64

Create a Series from DataFrame Row

Above, we have seen converting DataFrame column into Series, here, I will explain converting rows into Series. For example,


# Create Series using Pandas DataFrame row
series = df.iloc[2].squeeze()
print(series)
print (type(series))

Yields below output.


# Output:
Courses      Python
Duration    50 days
Fee             NaN
Name: 2, dtype: object
<class 'pandas.core.series.Series'>

Conclusion

In this article, you have learned the Pandas Series() method and using this function how we can create Pandas Series. You can create Series from the NumPy array, list, dictionary, DataFrame column/rows e.t.c

References

https://pandas.pydata.org/docs/user_guide/10min.html

Create Pandas Series in Python

What is the Pandas Series?

Quick Examples of Create Pandas Series

Pandas.series() Constructor

Create Empty Pandas Series from Constructor

Create a Series From NumPy Array

Customize the index of Series.

Create Series from List

Create a Series using a Dictionary

Create pandas Series from DataFrame

Create a Series from single DataFrame column

Create a Series From a specific DataFrame column

Create a Series from DataFrame Row

Conclusion

Related Articles

References