1 / 38

INTRODUCING PYTHON PANDAS:-SERIES

INTRODUCING PYTHON PANDAS:-SERIES. SUBMITTED TO:-VIJETA DARA MAM SUBMITTED BY:-AMAN SAINI. PANDAS DATA STRUCTURE. DATA STRUCTURE:- It refers to specialized way of storing data so as to apply a specific type of functionality on them.

kinney
Télécharger la présentation

INTRODUCING PYTHON PANDAS:-SERIES

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. INTRODUCING PYTHON PANDAS:-SERIES SUBMITTED TO:-VIJETA DARA MAM SUBMITTED BY:-AMAN SAINI

  2. PANDAS DATA STRUCTURE DATA STRUCTURE:- It refers to specialized way of storing data so as to apply a specific type of functionality on them. SERIES:- A series is pandas data structure that represent a one dimensional array like object containing an array of data (of any NumPy data type) and an associated array of data labels, called is index

  3. CREATING SERIES OBJECT

  4. 1. CREATING EMPTY SERIES

  5. 2.CREATING SERIES USING ARRAY

  6. USING ZEROS()

  7. USING ONES()

  8. USING ARANGE()

  9. 3. USING DICTIONARY

  10. 4.Using scalar values

  11. ADDITIONAL FUNCTION

  12. 1.ADDING NaN VALUES

  13. 2.SPECIFY INDEXES AS WELL AS DATA

  14. 3.SPECIFY DATA WITH DATA TYPE

  15. 4.USING MATHEMATICAL EXPRESSION/FUNCTION

  16. SERIES OBJECT ATTRIBUTES

  17. Series.index:-The size of the series

  18. Values:- return series as nd array

  19. Dtype:- returns datatype of list

  20. Shape:- return shape of series data

  21. Nbytes:- return number of bytes

  22. Ndim:-returns number of dimension

  23. Size:-returns number of element

  24. Itemsize:-return size of datatype

  25. Hasnans:-return true if there ia any nan values

  26. Empty:-return true if series object ia empty

  27. ACCESSING INDIVIDUAL ELEMENT

  28. SLICES FROM SERIES OBJECT

  29. HEAD() AND TAIL() FUNCTION:- First 5 or last 5 element is taken as default HEAD TAIL

  30. VECTOR OPERATION ON SERIES sr+2 sr*3

  31. Sr1=sr**2 Sr>15

  32. ARITHMETIC ON SERIES OBJECT Sr+sr1(if index are same) Sr+sr1(if index are not same)

  33. DIFFERENCE BETWEEN NUMPY AND SERIES

  34. Assignments • What is the significance of pandas library. • Name some common data structure of python pandas library . • If a python list is having 7 integer and a numpy array is also having 7 integer, then how are these two data structure similar or different from one another ? • Given a list=[3,4,5] and an ndarray N having elements 3,4,5.What will be the result produced by : (a)L*3 (b)N*3 (c)L+L (d)N+N 5. Write code to create an ndarray having six zeros in it. Write statements to change 3rd and 5th elements of this ndarray to 15 and 25 respectively

  35. Application based question 1.Consider following series object namely S: 0 0.430271 1 0.617328 2 -0.265421 3 -0.836113 What will be returned by following statement ? (a) S*100 (b) S>0 (c)S1=pd.Series(S) (d)S3=pd.Series(S1)+3 What will be the values of Series object S1 and S3 created above ?

  36. 2.Consider the same series object S, given in previous question. What output will be produced by following code ? S.index=[‘AMZN’,’AAPL’,’MSFT’,’GOOG’] print(S) print(S[‘AMZN’]) S=[‘AMZN’]=1.5 print(S[‘AMZN’]) print(S)3.What will be the output of following code ? Stationery=[‘pencils’,’notebooks’,’scales’,’erasers’] s=pd.Series([20,33,52,10],index=Stationery) s1=pd.Series([17,13,31,32],index=Stationery) print(s+s1) s=s+s2 print(s+s2)4.What will be the output produced by following code, considering the Series object s given above ? (a) print(s[1:1]) (b) print(s[0:1]) (c) print(s[0:2]) (d) s[0:2]=12 (e) print(s.index) print(s) print(s.values)

  37. 5.Find the error: (a) s2=pd.Series([101,102,103,104]) print(s2.index) s2.index=[0,1,2,3,4,5] s2[5]=220 print(s2) (b) s=pd.Series(2,3,4,5,index=range(4) ) (c) s1=pd.Series(1,2,3,4,index=range(7) ) (d) s2=pd.Series([1,2,3,4],index=range(4) )6. Find the error and correct it: data=np.array([‘a, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’]) s=pd.Series(data,index=[100,101,102,103,104,105] print(s[102,103,104])7. Why does the following code cause error ? S=pd.Series(range(1,15,3),index=list(‘abcd’))8. Why does the following code cause error ? S=pd.Series(range(1,15,3),index=list(‘ababa’)) print(s[‘ab’])9. If Ser is a series type object having 30 values, then how r statement (a),(b),(c) and (d) similar and different ? (a) print(Ser.head()) (b) print(Ser.head(8)) (c) print(Ser,tail()) (d) print(Ser.tail(8))

  38. THE END

More Related