In this article, We will get very handy Python Pandas Series Notes. We all know Python pandas is one of the most important library in python programming used to manage large and complex data using various data structures such as Panel, Series, Dataframes. This article titled ‘ python pandas series notes ‘ talks about how to declare and manage python pandas series with examples.
Please refer following table of content listed all the topics for python pandas series notes.
Contents
- 1 Series Data Structure
- 2 Create or Declare Series object
- 3 Creating Series object using List-
- 4 Creating Series object Numpy Array
- 5 Creating Series object using Scalar value
- 6 Creating Series object using String
- 7 Creating Series object using Dictionaries
- 8 Specifying Missing values in Series object
- 8.0.1 Exp-7: Create a series object using using dictionary that stores no of votes secured by each party in election 21-22. Make sure Nota is included with missing value.
- 8.0.2 Exp-8: Create a series object using tuple that stores month wise percentage of absent students of class 12th . Missing value should be represented properly
- 9 Mathematical Expressions to Create Data in Series
- 10 Accessing Elements of Series Object
- 11 Slicing
- 12 Mathematical Operation on Series object
- 13 Attributes of Series Object
- 13.0.1 Exp-9 : Retrieving Index of Series object
- 13.0.2 Exp-10 : Retrieving Labelled Index of Series object
- 13.0.3 Exp-11 : returning Series as numpy array
- 13.0.4 Exp-12 : Returning total no of elements including missing values as tuple of Series object
- 13.0.5 Exp-13 : Returning total no of elements including missing values as tuple of Series object
- 13.0.6 Exp-14 : Returning total no of elements including missing values as integer of Series object
- 13.0.7 Exp-15 : Check for any missing value in Series object
- 13.0.8 Exp-16 : Check Series object is empty or not
- 13.0.9 Exp-17 : Check data type of individual element of Series object
- 13.0.10 Exp-18 : Check data type of individual element of Series object
- 14 Renaming Index of Series object
- 15 Modifying Series Elements
- 16 Python Pandas Series MCQs
Series Data Structure
- It is 1D data structure
- It is size immutable
- It is value mutable
- It stores homogeneous data
- It supports explicit indexing
Create or Declare Series object
To create series object we need to do followings
Import pandas library in your program as per syntax given below:
import pandas as <object name>
For exp –
import pandas as pd
Use Series() method of pandas library to create series object as per syntax given below:
<Series object> = <panda_object>.Series( data = <value>, index = <value>)
For exp –
S = pd.Series(data = [12,23,34],index = [‘a’,’b’,’c’])
While creating Series object using Series() method, following are the points we should keep in our mind:
- Series() can have two arguments data and index
- Series() arguments can be taken in any order.
- Index argument is optional
- It can be made on any data such as python sequence (List, Tuple, String), ndarray, dictionary, scalar value
- We can assign value of any data type as Index.
- We can skip keyword data for assigning values to series object.
Creating Series object using List-
Syntax:
<Series object> = <panda_object>.Series(<any list of values>)
Without Indexing-
Exp-1:
import pandas as pd S = pd.Series([23,34,55]) print(“My Series is”) print(S)
Output –
My Series is 0 23 1 34 2 55 dtype : int64
With index –
Exp-2:
import pandas as pd S = pd.Series([23,34,55], index = [‘jan’,’feb’,’mar’]) print(“My Series is”) print(S)
Output –
My Series is jan 23 feb 34 mar 55 dtype : int64
Exp-1: Create a Series object using a list that stores the total no of students of three sections ‘A’,’B’,’C.
import pandas as pd section = [‘A’,’B’,‘C’] students = [40,50,45] S = pd.Series(students, index = section) print(“My Series is”) print(S)
Output –
My Series is A 40 B 50 C 45 dtype : int64
Creating Series object Numpy Array
To use Numpy array as data for Series object make sure Numpy library is imported as per syntax given below:
import numpy as np
Syntax:
<Series object> = <panda_object>.Series(data = <nparray>, index = <sequence>)
Without Indexing-
Prog-1:
import pandas as pd import Numpy as np n = np.array([10.8,12.6,8.2]) S = pd.Series(n) print(“My Series is”) print(S)
Output –
My Series is 0 10.8 1 12.6 2 8.2 dtype : float64
With index –
Prog-2:
import pandas as pd import numpy as np n = np.arrange(10,50,10) S = pd.Series(n, index = range(1,5)) print(“My Series is”) print(S)
Output –
My Series is 1 10 2 20 3 30 4 40 dtype : int32
Exp2: Create a Series object that stores amount paid as values and name of customer as index. Amount paid is taken as nparray.
import pandas as pd import numpy as np amount = np.array([ 200.6, 350.4, 760.2]) names = [‘raj’, ‘sunil’, ‘kamal’] S = pd.Series(amount, index = names) print(“My Series is”) print(S)
Output –
My Series is Raj 200.6 Sunil 350.4 kamal 760.2 dtype : float64
Creating Series object using Scalar value
- Scalar value refers to single value passed for creating series object.
- Index argument must be passed while creating series object using scalar value.
Syntax:
<Series object> = <panda_object>.Series(scalar value, index = <sequence>)
Prog-1:
import pandas as pd S = pd.Series(2021, index= [‘jan’,’feb’,’mar’]) print(“My Series is”) print(S)
Output –
My Series is Jan 2021 Feb 2021 Mar 2021 dtype : int64
Prog-2:
import pandas as pd S = pd.Series(2000, index= range(5)) print(“My Series is”) print(S)
Output –
My Series is 0 2000 1 2000 2 2000 3 2000 dtype : int64
Creating Series object using String
Syntax:
<Series object> = <panda_object>.Series(<string value>, index = <sequence>)
Exp-3: Create a series object using individual characters
import pandas as pd S = pd.Series([‘m’,’o’,’l’]) print(“My Series is”) print(S)
Output –
My Series is 0 m 1 o 2 l dtype : object
Exp-4: Create a series object using String
import pandas as pd S = pd.Series(‘techtipnow’) print(“My Series is”) print(S)
Output –
My Series is 0 techtipnow dtype : object
Exp-5: Create a series object using multiple Strings
import pandas as pd S = pd.Series([‘I’,’am’,’Indian’l, index = [1,2,3]) print(“My Series is”) print(S)
Output –
My Series is 1 I 2 am 3 Indian dtype : object
Creating Series object using Dictionaries
- Key of Dictionary is always treated as index of Series object
- Values of Dictionary are always treated as data of Series object.
Syntax:
<Series object> = <panda_object>.Series(<dictionary object>)
Exp-6: Create a series object using using dictionary that stores no of votes secured by each party in election 21-22
import pandas as pd
d={‘bjp’:234,’inc’:210,’jdu’:80}
S = pd.Series(d)
print(“My Series is”)
print(S)
Output –
My Series is Bjp 234 INC 210 jdu 80 dtype : int64
Specifying Missing values in Series object
To represent missing values for Series objects, We can use –
- None
- np.NaN
Exp-7: Create a series object using using dictionary that stores no of votes secured by each party in election 21-22. Make sure Nota is included with missing value.
import pandas as pd
d={‘bjp’:234,’inc’:210,’jdu’:80, ‘nota’: None}
S = pd.Series(d)
print(“My Series is”)
print(S)
Output –
My Series is Bjp 234 INC 210 jdu 80 nota NaN dtype : float64
Exp-8: Create a series object using tuple that stores month wise percentage of absent students of class 12th . Missing value should be represented properly
import pandas as pd import numpy as np month = [‘jan’,’feb’,’mar’] attend = (50,np.nan,70) S = pd.Series(d) print(“My Series is”) print(S)
Output –
My Series is Jan 50 Feb NaN Mar 70 dtype : float64
Mathematical Expressions to Create Data in Series
We can provide data for Series() method by implementing mathematical expression that can calculate values for Data sequence as per syntax given below:
<Series object> = <panda_object>.Series(<mathematical expression>, index = <sequence>)
Prog-1:
import pandas as pd import numpy as np n = np.array([12.2,23.3,40.0]) S = pd.Series(n*2, index = range(1,4)) print(“My Series is”) print(S)
Output –
My Series is 1 24.4 2 46.6 3 80.0 dtype : float64
Note: In numpy array n*2 will be applied to each value of n.
Prog-2:
import pandas as pd L = [12,23,None] S = pd.Series(L*2) print(“My Series is”) print(S)
Output –
My Series is 0 12.0 1 23.0 2 NaN 3 12.0 4 23.0 5 NaN dtype : float64
Note: Here L*2 will replicate values of L two times.
Accessing Elements of Series Object
We can access elements of Series object in two ways –
- Index: Using Index we can access
- Using Indexing we can access Individual element of Series object.
- Using Indexing we can access multiple elements of Series object that may not be contiguous element.
- Indexing can be used in two ways : Positional Index, Labelled Index
- In Positional Index an Integer value is taken which represent specific element.
- In Labelled Index any user defined label as index is taken.
- Slice: Using Slice we can access
- Subset of Series object contain multiple elements are always contiguous element.
Accessing Individual Element
To access individual element, we have to provide index no of the element within square bracket of Series object.
Syntax:
<Series object>[<Index>]
Prog-1
import pandas as pd S = pd.Series(range(10,101,10)) print(“We have Accessed”) print(S[4])
Output –
We have Accessed 50
Accessing Multiple Elements Using Index
To access multiple elements, we have to provide index no of the each element as List within square bracket of Series object .
Syntax:
<Series object>[[<Index, Index,…>]]
Prog-1
import pandas as pd S = pd.Series([12,23,34,45,55],index = [‘m1’,’m2’,’m3’,’m4’,’m5’]) print(“We have Accessed”) print(S[[‘m1’,’m4’]])
Output –
We have Accessed m1 12 m2 45 dtype: int64
Slicing
- Extracting a specific part of Series object is called Slicing.
- Subset occurred after slicing contains contiguous elements.
- Slicing is done using positions not Index of the Series object.
- In Positional slicing value of end index is excluded.
- If labels are used in slicing, than value at end index label is also included.
- Slicing can also be used to extract slice elements in reverse order.
We can retrieve subset of series object as per syntax given below –
<Series object>[start_index : end_index : step-value]
Prog-1
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[2:6])
Output –
Slicing Demo 2 34 3 45 4 55 5 76 dtype: int64
Prog-2
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[:])
Output –
Slicing Demo 0 12 1 23 2 34 3 45 4 55 5 76 6 80 7 92 8 41 9 69 10 56 dtype: int64
Prog-3
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[:4])
Output –
Slicing Demo 0 12 1 23 2 34 3 45 dtype: int64
Prog-4
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[5:])
Output –
Slicing Demo 5 76 6 80 7 92 8 41 9 69 10 56 dtype: int64
Prog-5
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[1:9:2])
Output –
Slicing Demo 1 23 3 45 5 76 7 92 dtype: int64
Prog-6
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[7:15])
Output –
Slicing Demo 7 92 8 41 9 69 10 56 dtype: int64
Note – if end index is out of bound, even though slicing produces subset
Prog-7
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[3:-5])
Output –
Slicing Demo 3 45 4 55 5 76 dtype: int64
Prog-8
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[:-6])
Output –
Slicing Demo 0 12 1 23 2 34 3 45 4 55 dtype: int64
Prog-9
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[-4:])
Output –
Slicing Demo 7 92 8 41 9 69 10 56 dtype: int64
Prog-10
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[-7:-2])
Output –
Slicing Demo 4 55 5 76 6 80 7 92 8 41 dtype: int64
Prog-11
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[-4:])
Output –
Slicing Demo 7 92 8 41 9 69 10 56 dtype: int64
Prog-12
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[::3])
Output –
Slicing Demo 0 12 3 45 6 80 9 69 dtype: int64
Prog-13
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Slicing demo”) print(S[::-3])
Output –
Slicing Demo 10 56 7 92 4 55 1 23 dtype: int64
Mathematical Operation on Series object
- We can do arithmetic operations ( +, -, *, /) on more than one series objects.
- The arithmetic operation is performed only on matching index.
- For non-matching index it produces NaN values.
- If data items of matching indexes are not compatible for the operation, it produces NaN values as a result.
Prog-1
import pandas as pd S1 = pd.Series([12,23,34]) S2 = pd.Series([10,20,10]) print(“Addition of Series with matching indexes”) print(S1 + S2)
Output –
Addition of Series with matching indexes 0 22 1 43 2 44 dtype: int64
Prog-2
import pandas as pd S1 = pd.Series([12,23,34,56]) S2 = pd.Series([10,20,10]) print(“Addition of Series of Different sizes”) print(S1 + S2)
Output –
Addition of Series of Different sizes 0 22 1 43 2 44 3 NaN dtype: int64
Prog-3
import pandas as pd S1 = pd.Series([12,23,34]) S2 = pd.Series([10,20,10],index=[‘a’,’b’,’c’]) print(“Addition of Series With Non Matching Index”) print(S1 + S2)
Output –
Addition of Series With Non Matching Index 0 NaN 1 NaN 2 NaN a NaN b NaN c NaN dtype: float64
Attributes of Series Object
| Attribute | Description |
| index | Returns index of Series object |
| values | Returns series as nparray |
| shape | Returns size of Series object as tuple |
| size | Return no of element of Series object |
| hasnans | Returns True if any NaN value exists in Series object otherwise False |
| empty | Returns empty if Series object has no element |
| dtype | Returns the data type of Series object |
Exp-9 : Retrieving Index of Series object
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Retrieving Index Array”) print(S.index)
Output –
Retrieving Index Array RangeIndex(start=0, stop=11, step=1)
Exp-10 : Retrieving Labelled Index of Series object
import pandas as pd S = pd.Series([10,20,10],index=[‘a’,’b’,’c’]) print(“Retrieving Label of Series OBject”) print(S.index)
Output –
Retrieving Label of Series OBject Index(['a', 'b', 'c'], dtype='object')
Exp-11 : returning Series as numpy array
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Returning Series As Numpy Array”) print(S.values)
Output –
Returning Series As Numpy Array array([12, 23, 34, 45, 55, 76, 80, 92, 41, 69, 56], dtype=int64)
Exp-12 : Returning total no of elements including missing values as tuple of Series object
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Returning Shape of Series”) print(S.shape)
Output –
Returning Shape of Series (11,)
Exp-13 : Returning total no of elements including missing values as tuple of Series object
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Returning Shape of Series”) print(S.shape)
Output –
Returning Shape of Series (11,)
Exp-14 : Returning total no of elements including missing values as integer of Series object
import pandas as pd S = pd.Series([12,23,34,45,55,76,80,92,41,69,56]) print(“Returning Size of Series”) print(S.size)
Output –
Returning Size of Series 11
Exp-15 : Check for any missing value in Series object
import pandas as pd S = pd.Series([12,23,34,None,56]) print(“Check Missing Value”) print(S.hasnans)
Output –
Check for Missing value True
Exp-16 : Check Series object is empty or not
import pandas as pd S = pd.Series([12,23,34,None,56]) print(“Series Emptiness”) print(S.empty)
Output –
Series Emptiness False
Exp-17 : Check data type of individual element of Series object
import pandas as pd S = pd.Series([12,23,34,65,56]) print(“Check data type of Series object”) print(S.dtype)
Output –
Check data type of Series object
dtype('int64')
Exp-18 : Check data type of individual element of Series object
import pandas as pd S = pd.Series([12,23,None,65,56]) print(“Check data type of Series object”) print(S.dtype)
Output –
Check data type of Series object
dtype('float64')
Renaming Index of Series object
We can rename existing index of Series object with new one as per syntax given below:
<series_object>.index = <new index array>
Exp-19 : Renaming Index of Series object
import pandas as pd S = pd.Series([10,20,10],index=[‘a’,’b’,’c’]) print(“Series object with Original Index”) print(S) S.index = [101,102,103] print(“Series object After renaming Index”) Print(S)
Output –
Series object with Original Index a 10 b 20 c 10 Series object After renaming Index 101 10 102 20 103 10
Modifying Series Elements
We can modify series elements in two ways:
Using Index
Using Index we can modify only one element at a time as given below:
import pandas as pd S = pd.Series([10,20,10],index=[‘a’,’b’,’c’]) print(“Series object with Original Elements”) print(S) S[‘c’]=30 print(“Series object with modified Elements”) Print(S)
Output –
Series object with Original Elements a 10 b 20 c 10 Series object with Modified Elements a 10 b 20 c 30
Using Slicing
We can modify multiple adjacent elements with single value using slicing of Series object as given below:
import pandas as pd S = pd.Series([10,20,10,45,32,12,76]) print(“Series object with Original Elements”) print(S) S[2:5]=30 print(“Series object with modified Elements”) Print(S)
Output –
Series object with Original Elements 0 10 1 20 2 10 3 45 4 32 5 12 6 76 Series object with Modified Elements 0 10 1 20 2 30 3 30 4 30 5 12 6 76
thank you so much for this, this is really helpful…
Thanks for appreciation..
Keep Learning and Best Wishes!!
My heartfelt thanks for sharing this information in free of cost.
Keep Learning..All Best!!
Thanks a bunch
thank you