Fundamental Data Structure :¶

The fundamental data structure in python includes

Primitive type ( *Integer, Float, String, and Boolean*) and
Non-Primitive type ( *Array, List, Tuples, Dictionary, Set, and File*)

In this tutorial, we are going to discudd about List, Tuples, Set and Dictionary.

List¶

List is built in data structure in python. It is

Mutable i.e., we can change or edite the size of the list by appending, inserting and deleting the elements.
List can hold heterogeneous objects (e.g., integer, string, boolean)

Lets try to understand the List:

To initiate a blank List.

In [62]:

l = []

To find the type of the object.

In [63]:

type(l)

Out[63]:

list

To create a list from scratch.

In [97]:

L = [1,2,3,4,5,6,342,34]

In [98]:

Out[98]:

[1, 2, 3, 4, 5, 6, 342, 34]

Indexing of list.

In [66]:

L[0],L[1],L[5]

Out[66]:

(1, 2, 6)

Revers indexing is also possible.

In [67]:

L[-1],L[-2],L[-3]

Out[67]:

(6, 5, 4)

To find the length of list.

In [68]:

len(L)

Out[68]:

To add the element from last.

In [69]:

L.append(12)
L

Out[69]:

[1, 2, 3, 4, 5, 6, 12]

To find the sum of the elements (if they are of same types like int. double etc)

In [8]:

sum(L)

Out[8]:

To find maximum and minimum of the list

In [10]:

max(L), min(L)

Out[10]:

(12, 1)

To create a list of heterogeneous element types.

In [11]:

L = [1,2.0,3,4,5,"Apple",True, False]

To find the type of elements of a list.

In [12]:

type(L[1]),type(L[5])

Out[12]:

(float, str)

To create a list of list.

In [71]:

L = [[1,2,3],[3,4,5],[5,7,9]]

To find list inside a list.

In [14]:

L[0]

Out[14]:

[1, 2, 3]

In [15]:

L[0][1]

Out[15]:

To add two list. It is not as ususal addition. The elements are accumulated.

In [16]:

L1 = [1,2,3] ; L2 = [2,4,6]
L1+L2, set(L1+L2)

Out[16]:

([1, 2, 3, 2, 4, 6], {1, 2, 3, 4, 6})

To add element from end of the list

In [72]:

L = [1,4,2,3,5,6,7]
L.append(100)
L

Out[72]:

[1, 4, 2, 3, 5, 6, 7, 100]

To insert element (100) at specific index (1)

In [29]:

L = [1,4,2,3,5,6,7]
L.insert(1,100)
L

Out[29]:

[1, 100, 4, 2, 3, 5, 6, 7]

To remove specific element form list. It will remove the first occurance.

In [34]:

L = [1,4,2,3,5,6,7,4]
L.remove(4)
L

Out[34]:

[1, 2, 3, 5, 6, 7, 4]

To remove the element from specific index

In [35]:

L = [1,4,2,3,5,6,7]
L.pop(-1)
L

Out[35]:

[1, 4, 2, 3, 5, 6]

To sort the list

In [36]:

L = [1,10,2,30,5,60,7]
L.sort()
L

Out[36]:

[1, 2, 5, 7, 10, 30, 60]

To reverse the list

In [37]:

L = [1,4,2,3,5,6,7]
L.reverse()
L

Out[37]:

[7, 6, 5, 3, 2, 4, 1]

List comprehension

In [74]:

L = [x for x in range(100)]
print(L)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]

In [75]:

L = [x for x in range(100) if x%2==0]
print(L)

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98]

In [79]:

import random as rn
rn.randint(0,100)

Out[79]:

In [83]:

import random as rn
R = [rn.randint(0,100) for k in range(200)]
print(R)

[89, 28, 17, 21, 30, 12, 2, 18, 44, 35, 39, 34, 36, 15, 62, 31, 29, 67, 89, 84, 52, 99, 12, 2, 65, 93, 37, 60, 65, 65, 67, 65, 83, 96, 6, 16, 96, 38, 43, 28, 38, 4, 60, 48, 5, 50, 82, 27, 75, 94, 3, 11, 55, 87, 56, 48, 71, 40, 7, 20, 45, 29, 100, 4, 23, 86, 0, 46, 69, 60, 85, 27, 45, 93, 6, 68, 13, 66, 89, 59, 46, 89, 41, 84, 69, 11, 22, 38, 24, 74, 82, 6, 12, 84, 14, 68, 16, 2, 91, 62, 57, 72, 98, 4, 98, 64, 72, 57, 73, 12, 78, 40, 0, 10, 36, 52, 71, 42, 27, 94, 32, 82, 22, 89, 37, 26, 55, 80, 49, 48, 69, 34, 48, 97, 39, 82, 78, 97, 11, 38, 77, 62, 55, 95, 52, 42, 61, 94, 100, 86, 28, 41, 75, 13, 40, 22, 7, 78, 2, 51, 84, 30, 45, 39, 54, 72, 88, 48, 10, 78, 9, 44, 18, 36, 23, 83, 1, 88, 37, 87, 85, 4, 65, 55, 11, 97, 90, 77, 1, 41, 75, 37, 43, 3, 45, 38, 4, 16, 87, 47]

In [45]:

import collections
#High Performance Counting
C = collections.Counter(R)
print(C)

Counter({37: 5, 17: 5, 67: 5, 15: 4, 99: 4, 88: 4, 61: 4, 54: 4, 96: 4, 80: 4, 51: 4, 83: 4, 14: 4, 10: 3, 78: 3, 27: 3, 49: 3, 62: 3, 38: 3, 48: 3, 94: 3, 66: 3, 81: 3, 72: 3, 95: 3, 1: 3, 21: 3, 100: 3, 40: 3, 28: 3, 43: 3, 29: 2, 16: 2, 7: 2, 74: 2, 60: 2, 64: 2, 85: 2, 50: 2, 39: 2, 44: 2, 8: 2, 90: 2, 58: 2, 0: 2, 79: 2, 24: 2, 98: 2, 93: 2, 22: 2, 68: 2, 42: 2, 86: 2, 30: 2, 41: 2, 77: 2, 59: 2, 34: 2, 4: 2, 31: 2, 57: 2, 6: 2, 73: 2, 45: 1, 69: 1, 91: 1, 71: 1, 75: 1, 87: 1, 70: 1, 47: 1, 82: 1, 18: 1, 9: 1, 26: 1, 55: 1, 56: 1, 23: 1, 52: 1, 33: 1, 13: 1, 2: 1, 46: 1, 36: 1, 53: 1, 92: 1, 84: 1, 12: 1, 20: 1, 5: 1})

In [84]:

R = [rn.choice(['A','T','G','C']) for i in range(200)]
print(R)

['A', 'T', 'C', 'T', 'T', 'C', 'A', 'C', 'G', 'G', 'A', 'T', 'G', 'G', 'C', 'T', 'G', 'C', 'A', 'T', 'A', 'T', 'T', 'G', 'G', 'G', 'A', 'C', 'G', 'G', 'C', 'C', 'A', 'T', 'A', 'A', 'G', 'G', 'G', 'A', 'A', 'G', 'C', 'C', 'A', 'A', 'C', 'G', 'C', 'G', 'T', 'A', 'A', 'A', 'G', 'G', 'T', 'T', 'A', 'G', 'T', 'T', 'G', 'A', 'C', 'G', 'C', 'C', 'G', 'T', 'C', 'T', 'A', 'C', 'G', 'G', 'G', 'T', 'G', 'A', 'A', 'G', 'C', 'T', 'T', 'C', 'C', 'A', 'A', 'G', 'A', 'C', 'G', 'C', 'T', 'T', 'T', 'T', 'A', 'A', 'T', 'C', 'C', 'T', 'G', 'A', 'C', 'G', 'C', 'A', 'G', 'A', 'C', 'C', 'C', 'A', 'G', 'T', 'A', 'G', 'C', 'A', 'G', 'C', 'G', 'G', 'G', 'T', 'T', 'T', 'A', 'A', 'C', 'T', 'G', 'A', 'T', 'A', 'G', 'G', 'G', 'C', 'G', 'T', 'C', 'C', 'T', 'G', 'G', 'C', 'G', 'A', 'G', 'C', 'C', 'A', 'T', 'A', 'T', 'T', 'C', 'A', 'C', 'T', 'G', 'T', 'G', 'T', 'T', 'T', 'C', 'T', 'G', 'C', 'A', 'G', 'G', 'C', 'T', 'T', 'C', 'G', 'C', 'T', 'C', 'G', 'A', 'G', 'T', 'G', 'A', 'C', 'C', 'T', 'A', 'G', 'C', 'T', 'T', 'T']

In [85]:

DNA = ''.join(R)
DNA

Out[85]:

'ATCTTCACGGATGGCTGCATATTGGGACGGCCATAAGGGAAGCCAACGCGTAAAGGTTAGTTGACGCCGTCTACGGGTGAAGCTTCCAAGACGCTTTTAATCCTGACGCAGACCCAGTAGCAGCGGGTTTAACTGATAGGGCGTCCTGGCGAGCCATATTCACTGTGTTTCTGCAGGCTTCGCTCGAGTGACCTAGCTTT'

In [86]:

DNA.count('A'), DNA.count('AT'), DNA.count('ATG')

Out[86]:

(44, 9, 1)

Mini Assignment:¶

Create a DNA string of 10,000 characters and count the following: A,T,G,C,all combination of two charaters, all combinations of three characters.

In [ ]:

Tuples¶

Tuples are non-mutable, which means we can ot add or remove elements once tuple is defind.

To define a tuples from scratch

In [87]:

t = (2,3,4,5)

Find type

In [88]:

type(t)

Out[88]:

tuple

Indexing

In [24]:

t[1]

Out[24]:

In [1]:

L = [(1,2),(2,3),(3,4)]

In [3]:

L[0][0]

Out[3]:

Create a list of tuples

In [46]:

L = [(1,2),("a","b"),(True, False)]
L

Out[46]:

[(1, 2), ('a', 'b'), (True, False)]

Dictionary¶

Dictionary organizes the data with key-value pair. Dictionary can be nested with other data types.

To initiate a dictionary

In [38]:

D = dict()
DD = {}

Create a dictionary from scratch

In [89]:

D = {"fruit":'apple',
    "vegetable" : 'carrot',
    "rice": 2.0,
    'milk': 10,}

What are keys?

In [90]:

D.keys()

Out[90]:

dict_keys(['fruit', 'vegetable', 'rice', 'milk'])

What are values?

In [91]:

D.values()

Out[91]:

dict_values(['apple', 'carrot', 2.0, 10])

Indexing

In [42]:

D['fruit'], D["rice"]

Out[42]:

('apple', 2.0)

Iteration over key and values

In [28]:

for key,value in D.items():
    print(key,value)

fruit apple
vegetable carrot
rice 2.0
milk 10

To update a dictionary

In [29]:

D.update({"salt": 2.0})
D

Out[29]:

{'fruit': 'apple', 'vegetable': 'carrot', 'rice': 2.0, 'milk': 10, 'salt': 2.0}

To create a list form a Dictionary. Only keys are collected.

In [43]:

list(D)

Out[43]:

['fruit', 'vegetable', 'rice', 'milk']

To create a list of keys only

In [44]:

list(D.keys())

Out[44]:

['fruit', 'vegetable', 'rice', 'milk']

To create a list of values

In [32]:

list(D.values())

Out[32]:

['apple', 'carrot', 2.0, 10, 2.0]

To create Dictionary of with list, tuples and dictionary

In [48]:

DD = {"names":("John","Harry", "Brat"),\
      "roll no": [1,2,3],\
      "plan":{"first":[12,34,56],"second":[1,3,5]}}

DD

Out[48]:

{'names': ['John', 'Harry', 'Brat'],
 'roll no': (1, 2, 3),
 'plan': {'first': [12, 34, 56], 'second': [1, 3, 5]}}

In [92]:

import numpy as np
X = np.arange(0,np.pi,0.1)
print(X)

[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7
 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.  3.1]

In [93]:

import numpy as np
X = np.arange(0,np.pi,0.1)
M = {"sin": [np.sin(x) for x in X],\
     "cos": [np.cos(x) for x in X],\
     "plo":[(x*x+x+1) for x in X],\
     "trig": [np.cos(x) + np.sin(x) for x in X]}

In [94]:

print(M)

{'sin': [0.0, 0.09983341664682815, 0.19866933079506122, 0.2955202066613396, 0.3894183423086505, 0.479425538604203, 0.5646424733950355, 0.6442176872376911, 0.7173560908995228, 0.7833269096274834, 0.8414709848078965, 0.8912073600614354, 0.9320390859672264, 0.963558185417193, 0.9854497299884603, 0.9974949866040544, 0.9995736030415051, 0.9916648104524686, 0.9738476308781951, 0.9463000876874145, 0.9092974268256817, 0.8632093666488737, 0.8084964038195901, 0.74570521217672, 0.6754631805511506, 0.5984721441039564, 0.5155013718214642, 0.4273798802338298, 0.33498815015590466, 0.23924932921398198, 0.1411200080598672, 0.04158066243329049], 'cos': [1.0, 0.9950041652780258, 0.9800665778412416, 0.955336489125606, 0.9210609940028851, 0.8775825618903728, 0.8253356149096782, 0.7648421872844884, 0.6967067093471654, 0.6216099682706644, 0.5403023058681398, 0.4535961214255773, 0.3623577544766734, 0.26749882862458735, 0.16996714290024081, 0.0707372016677029, -0.029199522301288815, -0.12884449429552486, -0.2272020946930871, -0.3232895668635036, -0.4161468365471424, -0.5048461045998576, -0.5885011172553458, -0.6662760212798244, -0.7373937155412458, -0.8011436155469337, -0.8568887533689473, -0.9040721420170612, -0.9422223406686583, -0.9709581651495907, -0.9899924966004454, -0.9991351502732795], 'plo': [1.0, 1.11, 1.24, 1.3900000000000001, 1.56, 1.75, 1.9600000000000002, 2.1900000000000004, 2.4400000000000004, 2.71, 3.0, 3.3100000000000005, 3.6400000000000006, 3.99, 4.36, 4.75, 5.16, 5.590000000000001, 6.04, 6.510000000000001, 7.0, 7.51, 8.040000000000001, 8.590000000000002, 9.160000000000002, 9.75, 10.360000000000001, 10.990000000000002, 11.640000000000002, 12.310000000000002, 13.0, 13.71], 'trig': [1.0, 1.094837581924854, 1.1787359086363027, 1.2508566957869456, 1.3104793363115357, 1.3570081004945758, 1.3899780883047137, 1.4090598745221796, 1.4140628002466882, 1.4049368778981477, 1.3817732906760363, 1.3448034814870127, 1.2943968404438997, 1.2310570140417803, 1.155416872888701, 1.0682321882717574, 0.9703740807402162, 0.8628203161569437, 0.7466455361851081, 0.623010520823911, 0.4931505902785393, 0.35836326204901614, 0.21999528656424427, 0.0794291908968956, -0.061930534990095154, -0.20267147144297726, -0.34138738154748316, -0.47669226178323143, -0.6072341905127536, -0.7317088359356086, -0.8488724885405782, -0.957554487839989]}

In [95]:

import pandas as pd
DF = pd.DataFrame(M)
DF

Out[95]:

	sin	cos	plo	trig
0	0.000000	1.000000	1.00	1.000000
1	0.099833	0.995004	1.11	1.094838
2	0.198669	0.980067	1.24	1.178736
3	0.295520	0.955336	1.39	1.250857
4	0.389418	0.921061	1.56	1.310479
5	0.479426	0.877583	1.75	1.357008
6	0.564642	0.825336	1.96	1.389978
7	0.644218	0.764842	2.19	1.409060
8	0.717356	0.696707	2.44	1.414063
9	0.783327	0.621610	2.71	1.404937
10	0.841471	0.540302	3.00	1.381773
11	0.891207	0.453596	3.31	1.344803
12	0.932039	0.362358	3.64	1.294397
13	0.963558	0.267499	3.99	1.231057
14	0.985450	0.169967	4.36	1.155417
15	0.997495	0.070737	4.75	1.068232
16	0.999574	-0.029200	5.16	0.970374
17	0.991665	-0.128844	5.59	0.862820
18	0.973848	-0.227202	6.04	0.746646
19	0.946300	-0.323290	6.51	0.623011
20	0.909297	-0.416147	7.00	0.493151
21	0.863209	-0.504846	7.51	0.358363
22	0.808496	-0.588501	8.04	0.219995
23	0.745705	-0.666276	8.59	0.079429
24	0.675463	-0.737394	9.16	-0.061931
25	0.598472	-0.801144	9.75	-0.202671
26	0.515501	-0.856889	10.36	-0.341387
27	0.427380	-0.904072	10.99	-0.476692
28	0.334988	-0.942222	11.64	-0.607234
29	0.239249	-0.970958	12.31	-0.731709
30	0.141120	-0.989992	13.00	-0.848872
31	0.041581	-0.999135	13.71	-0.957554

In [96]:

%matplotlib inline
DF.plot()

Out[96]:

<matplotlib.axes._subplots.AxesSubplot at 0x211d87c8668>

References:¶

https://docs.python.org/3/tutorial/index.html