Learning record (6th day) #Set type #Dictionary type #Mutual conversion of list tuple set #ndarray type #Pandas (DataFrame type)

content of study

• Set type
• Dictionary type
• Mutual conversion of list tuple sets
• ndarray type
• Pandas (DataFrame type)

Set type

A data type that can store multiple data, similar to lists and tuples. It has the following features.

• Data with the same value cannot be saved more than once
• Multiple stored data are not ordered
• Defined by {}.

Description example

a = {1,0,2,9,8,3,7,5,4,6}
print('a = ',a)

b = {2,4,4,6,5,2,1,0,8,7,9,3,6}
print('b = ', b)

Execution result

a = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} b = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}

No matter what order you specify the data like the execution result, it will be sorted and output.

Dictionary type

A data type that can store multiple data as well as lists, tuples, and sets. It has the following features.

• Put data with different labels for multiple data.
• Define as {Label 1: Data 1, Label 2: Data 2, Label 3: Data 3}.
• keys () gives a list of labels and ʻitems ()` gives a list of values.

In lists and tuples, multiple data are specified by indexes, but in dictionaries, they are specified by labels.

Description example

c = {'l1':12, 'l2':45, 'l3':36, 'l4':58}

print('c = ', c)
print("c['l2'] = ", c['l2'])
print("c['l4'] = ", c['l4'])
print(c.keys())
print(c.values())

Execution result

c = {'l1': 12, 'l2': 45, 'l3': 36, 'l4': 58} c['l2'] = 45 c['l4'] = 58 dict_keys(['l1', 'l2', 'l3', 'l4']) dict_values([12, 45, 36, 58])

Mutual conversion of lists, tuples and sets

Lists, tuples, and sets can be converted to each other using the methods list (), tuple (), and set (), respectively.

Description example

# A program that uses mutual conversion to remove duplicate data from a list
d = [0,2,6,4,2,8,0,2]

e = list(set(d))
print('e = ', e)

Execution result

e = [0, 2, 4, 6, 8]

ndarray type

The basic data type used by Numpy in a typical numerical calculation package.

Description example

# Creating ndarray type
import numpy

f = [0, 1, 2, 3]
 g = numpy.array (f) # Create ndarray type from list
print('g = ', g)

h = [1, 2]
i = [2, 3]
j = [3, 4]
 k = numpy.array ([h, i, j]) # Create a two-dimensional ndarray type
print('k = ', k)

 l = numpy.array (a, dtype = numpy.float16) #Create ndarray by specifying data type
print('l = ', l)

Execution result

g = [0 1 2 3] k = [[1 2] [2 3] [3 4]] l = [0. 1. 2. 3.]

In addition, various numerical operations can be performed on ndarray type data and lists by using the functions provided by Numpy. The representative ones are introduced below.

import numpy

m = numpy.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

 n = numpy.median (m) # median
 o = numpy.mean (m) #mean
 p = numpy.std (m) # standard deviation
 q = numpy.var (m) # Distributed

print(n)
print(o)
print(p)
print(q)

Execution result

4.5 4.5 2.8722813232690143 8.25

Pandas

Basic data type

• DataFrame type (represents table data)
• Seriez type (representing row or column data)

DataFrame type

Create as follows.

Description example

import pandas

r = pandas.DataFrame([1, 11.1], [2, 22.2], [3, 33.3])

You can specify column and row subscripts with column and ʻindex`, respectively.

Description example

r.colums = ['C1', 'C2']
r.index = ['A', 'B', 'C']

As with the index type, you can get column data by specifying a subscript.

Description example

 s = r ['C1'] # Extract only the data in the first column
 t = s ['A'] # Extract the data in the first column and the first row