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Python pandas cheat sheets

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Python pandas cheat sheets

Pandas Practice Code -Set 1

Pandas Practice Code -Set 2

Pandas Practice Code -Set 3

Pandas Practice Code -Set 4

Pandas Practice Code -Set 5

Pandas Practice Code -Set 6

Pandas Practice Code -Set 7

Pandas Practice Code -Set 8

Pandas Practice Code -Set 9

Pandas Practice Code -Set 10

Pandas Practice Code -Set 11

Pandas Practice Code -Set 12

5 faster alternatives to Pandas DataFrame apply()

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Python pandas cheat sheets

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Pandas Practice Code (Set - 1)

2 /6 / 2 2 , 1 1 :4 2 A M P a n d a s P r a ctice Co d e - S e t 1 - Ju p y te r No te b o o k loca lh o st: 8 8 8 8 /n o t e b o o k s/P a n d a s P ract ice Cod e - S e t 1 . i p y n b 1 /4 In [1 ] :  In [3 ] :  Se r ie s ([ ] , d ty p e: fl o at 6 4) <i p yt h on - in p ut - 1- c 46 5 11 a d4 f ca > :1 2 : D ep r e ca t io n Wa r ni n g: Th e d e fa u lt dt y pe f or em p ty Se r ie s w i ll be 'o b je c t' in s te a d o f ' f lo a t6 4 ' i n a f u tu r e v er s io n . Sp e ci f y a d t yp e e x pl i ci t ly to si l en c e t h is wa r ni n g. m y _s e ri e s = p d .S e ri e s( ) 0 1 0 1 2 0 2 3 0 3 4 0 4 5 0 dt y pe : i n t3 2 # A P a nd a s s er i es is a d at a s t ru c tu r e t h at st o re s d a ta in th e f o rm # o f a c o lu m n. A s er i es is no r ma l ly us e d t o s t or e i n fo r ma t io n # a bo u t a p a rt i cu l ar at t ri b ut e i n y o ur d at a

Numpy tutorials Part 2

Click here for part 1 👇 Numpy tutorials part 1 Numpy tutorials part 2 mean, variance and standard deviation 15. mean >>>import numpy >>>my_array = numpy.array([ [1, 2], [3, 4] ]) >>>print numpy.mean(my_array, axis = 0) #Output : [ 2. 3.] >>>print numpy.mean(my_array, axis = 1) #Output : [ 1.5 3.5] >>>print numpy.mean(my_array, axis = None) #Output : 2.5 >>>print numpy.mean(my_array) #Output : 2.5 16. var >>>import numpy >>>my_array = numpy.array([ [1, 2], [3, 4] ]) >>>print numpy.var(my_array, axis = 0) #Output : [ 1. 1.] >>>print numpy.var(my_array, axis = 1) #Output : [ 0.25 0.25] >>>print numpy.var(my_array, axis = None) #Output : 1.25 >>>print numpy.var(my_array) #Output : 1.25 17. std >>>import numpy >>>my_array = numpy.array([ [1, 2], [3, 4] ]) >>>print numpy.std(my_ar

Numpy tutorials - Part 1

1. Array to Numpy import numpy a = numpy.array([1,2,3,4,5]) print(a[1]) output: 2 2.Using shape to change array dimensions import numpy change_array = numpy.array([1,2,3,4,5,6]) change_array.shape = (3, 2) print change_array #Output [[1 2] [3 4] [5 6]] 3.reshape import numpy my_array = numpy.array([1,2,3,4,5,6]) print numpy.reshape(my_array,(3,2)) #Output [[1 2] [3 4] [5 6]] 4. Transpose import numpy my_array = numpy.array([[1,2,3], [4,5,6]]) print numpy.transpose(my_array) #Output [[1 4] [2 5] [3 6]] 5. Flatten import numpy my_array = numpy.array([[1,2,3], [4,5,6]]) print my_array.flatten() #Output [1 2 3 4 5 6] 6. Concatenation import numpy array_1 = numpy.array([1,2,3]) array_2 = numpy.array([4,5,6]) array_3 = numpy.array([7,8,9]) print numpy.concatenate((array_1, array_2, array_3)) #Output [1 2 3 4 5 6 7 8 9] import numpy array_1 = numpy.array([[1,2,3],[0,0,0]]) array_2 = numpy.array([[0,0,0],[7,8,9]]) print numpy.concate

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