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1. Pandas- Creating Series Object Class XII Informatics Practices

2. Series Data Structure Represents One- Dimensional Array Homogeneous – Same data types Size immutable There are two components in Series i.e. Index & Series January February March April 1200 1500 2000 1900

3. Creating Series Object Series Object = pandas.Series () Coding for Empty Series import pandas as pd obj1= pd.Series () Import pandas as pd obj1= pd.Series ([5,10,15]) List Tuple String Dictionary

4. Creating Series Object Series Object = pandas.Series () Coding import pandas as pd obj1= pd.Series (range(5)) print(obj1) Coding for list import pandas as pd obj1= pd.Series ([5,10,15,20]) print(obj1) Index Data 1 1 2 2 3 3 4 4 Index Data 5 1 10 2 15 3 20

5. Series Data Structures ndarray Using function arange ( start,end,step value) import pandas as pd import numpy as np nda1= np.arange (3,13,3.5) obj1= pd.Series (nda1) print(obj1) 2. import pandas as pd import numpy as np obj1= pd.Series ( np.arange (3,13,3.5)) print(obj1) Index Data 3 1 6.5 2 10

6. Series Data Structures ndarray Using function linspace ( start,end,no . of elements) 1. import pandas as pd import numpy as np obj1= pd.Series ( np.linspace (24,64,5)) print(obj1) Index Data 24 1 34 2 44 3 54 4 64

7. Series Data Structures ndarray Using function tile ([list elements], no. of occurance / repeatition ) 1. import pandas as pd import numpy as np obj1= pd.Series ( np.tile ([3,5],2)) print(obj1) Index Data 3 1 5 2 3 3 5

8. Series Data Structures Data as python sequence Data as an ndarray Data as python dictionary

9. Data as python dictionary import pandas as pd obj1= pd.Series ({‘Jan’:31,’Feb’:28,’Mar’:31}) print(obj1) index data

10. Data as Scaler Value import pandas as pd obj1= pd.Series (50000,index=range(2020,2029,2)) print(obj1) data

11. Using Mathematical Function import pandas as pd import numpy as np a= np.arange (9,13) print(a) obj4= pd.Series (index= a,data =a*2) print(obj4)

12. Series Attributes import pandas as pd import numpy as np arr =[31,28,31,30] mon =[' Jan','Feb','Mar','Apr '] obj1= pd.Series (data= arr,index = mon ) obj2= pd.Series ([3.5,5,6.5,8]) obj3= pd.Series ([6.5,np.NaN,2.34]) print(obj3.values)

13. Series Attributes index values dtype shape size and itemsize hasnans dim

14. Indexing | Slicing | Manipulating Accessing individual Element obj1[2]=____ obj1[3]=____ obj1[4]=____ Slicing of Series object obj1[1:3]=____ obj1[1: ]=____ obj1[ : 2]=____ import pandas as pd obj1= pd.Series ([5,10,15,20]) print(obj1) Output - 0 5 1 10 2 15 3 20

15. Indexing | Slicing | Manipulating Modifying Series object’s value obj1[2]=40 obj1[3]=80 obj1[1:3]=50 Renaming an index obj1.index=[‘A’,’B’,’C’,’D’] import pandas as pd obj1= pd.Series ([5,10,15,20]) print(obj1) Output - 0 5 1 10 2 15 3 20

16. Head() & Tail() function obj1.head(2) = 5,10 obj1.tail(2)=15,20 obj1.head() obj1.tail() In this case it will give by default the first 5 elements of head and last 5 elements of tail import pandas as pd obj1= pd.Series ([5,10,15,20]) print(obj1) Output - 0 5 1 10 2 15 3 20

17. Vector Operations obj1+2 obj1*2 Output- 0 7 10 1 12 20 2 17 30 3 22 40 import pandas as pd obj1= pd.Series ([5,10,15,20]) print(obj1) Output - 0 5 1 10 2 15 3 20

18. Arithmetic on Series object obj1+obj2 Output- 0 7 1 15 2 19 23 obj2+obj3 Output- 0 5 1 11 2 NaN NaN import pandas as pd obj1= pd.Series ([5,10,15,20]) print(obj1) import pandas as pd obj2= pd.Series ([2,5,4,3]) print(obj2) import pandas as pd obj3= pd.Series ([3,6]) print(obj3)

19. Filtering Entries On the basis of True & False it filters the data or it checks the conditions and gives the output. import pandas as pd info= pd.Series ([30,41,52]) print(info>40) print(info[info>40])