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.
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.
# Below are the quick examples
# 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()
print(series)
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
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