{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Practical example of Bitwise operators in Python\n", "\n", "Trainer: Md. Jalal Uddin\n", " \n", "Founder and director of Research Society https://researchsociety20.org/founder-and-director/\n", "\n", "email: 20205103002@nuist.edu.cn, dmjalal90@gmail.com, " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What is Bitwise operator in Python?\n", "\n", "convert integers into binary\n", "\n", "bit by bit operation\n", "\n", "output as decimal format\n", "\n", "decimal [base 10 (0-9)]\n", "\n", "binary [base 2 (0-1)]\n", "\n", "binary = prefix 0b\n", "\n", "\n", "Python's \"and\", \"or\" and \"not\" logical operators are designed to work with \"scalars\".\n", "\n", "Read more: https://stackoverflow.com/questions/21415661/logical-operators-for-boolean-indexing-in-pandas\n", "\n", "However, \"&\", \"|\" and \"~\" Bitwise operators are designed to work with both \"scalars\" and \"vectors\".\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learn more from geeksforgeeks\n", "\n", "https://www.geeksforgeeks.org/python-bitwise-operators/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# &\n", "\n", "If both the bits are 1, it returns 1; otherwise, it returns 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# |\n", "\n", "If either of the bit is 1, it returns 1; otherwise, it returns 0\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ~\n", "\n", "It returns one’s complement of the number\n", "\n", "For example:\n", "\n", "NOT x = -x − 1 \n", "\n", "https://www.pyblog.in/programming/bitwise-operators-in-python/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# define variable" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "a = 4\n", "b = 12" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "decimal to binary for a = 0b100\n", "decimal to binary for b = 0b1100\n" ] } ], "source": [ "print(\"decimal to binary for a =\",bin(a))\n", "\n", "print(\"decimal to binary for b =\",bin(b))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a & b = 4\n", "a | b = 12\n", "~a = -5\n" ] } ], "source": [ "print(\"a & b =\", a&b) \n", " \n", "print(\"a | b =\", a | b) \n", " \n", "print(\"~a =\", ~a) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Function" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a & b = 4\n", "a | b = 12\n" ] } ], "source": [ "import operator as op\n", "\n", "print(\"a & b =\", op.__and__(a, b))\n", "\n", "print(\"a | b =\", op.__or__(a, b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example for real data\n", "\n", "We will monitor drought (water scarcity) for Rangpur station in Bangladesh from 1994 to 1995\n", "\n", "The example has been adapted from Uddin et al. (2020). https://link.springer.com/article/10.1007/s12517-020-05302-0" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Year and monthSPI
01983-01-0.285149
11983-02-0.299666
21983-03-0.229297
31983-04-0.382996
41983-05-0.060437
\n", "
" ], "text/plain": [ " Year and month SPI\n", "0 1983-01 -0.285149\n", "1 1983-02 -0.299666\n", "2 1983-03 -0.229297\n", "3 1983-04 -0.382996\n", "4 1983-05 -0.060437" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "dataset = pd.read_excel('drought_data.xlsx')\n", "\n", "dataset.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "133 0.289110\n", "134 0.291538\n", "135 0.238511\n", "136 0.236418\n", "137 -0.093880\n", "Name: SPI, dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SPI = dataset.SPI[133:156] # 1994-1995\n", "\n", "SPI.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "The climate was wet if the SPI values are greater than 1 and less than 2. Similarly, if the SPI values are less than 1 and less than equal -2, the climate was dry.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wet climate = \n", " 133 False\n", "134 False\n", "135 False\n", "136 False\n", "137 False\n", "138 False\n", "139 False\n", "140 False\n", "141 False\n", "142 False\n", "143 False\n", "144 False\n", "145 False\n", "146 False\n", "147 False\n", "148 False\n", "149 False\n", "150 False\n", "151 False\n", "152 False\n", "153 False\n", "154 False\n", "155 False\n", "Name: SPI, dtype: bool\n" ] } ], "source": [ "print(\"wet climate = \\n\",SPI.gt(1) & SPI.lt(2))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dry climate = \n", " 133 True\n", "134 True\n", "135 True\n", "136 True\n", "137 True\n", "138 True\n", "139 True\n", "140 True\n", "141 True\n", "142 True\n", "143 True\n", "144 True\n", "145 True\n", "146 True\n", "147 True\n", "148 True\n", "149 True\n", "150 True\n", "151 True\n", "152 True\n", "153 True\n", "154 True\n", "155 True\n", "Name: SPI, dtype: bool\n" ] } ], "source": [ "print(\"dry climate = \\n\",SPI.lt(1) | SPI.le(-2))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "not extreme wet climate = \n", " 133 True\n", "134 True\n", "135 True\n", "136 True\n", "137 True\n", "138 True\n", "139 True\n", "140 True\n", "141 True\n", "142 True\n", "143 True\n", "144 True\n", "145 True\n", "146 True\n", "147 True\n", "148 True\n", "149 True\n", "150 True\n", "151 True\n", "152 True\n", "153 True\n", "154 True\n", "155 True\n", "Name: SPI, dtype: bool\n" ] } ], "source": [ "print(\"not extreme wet climate = \\n\", ~(SPI>2))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wet climate = \n", " 133 False\n", "134 False\n", "135 False\n", "136 False\n", "137 False\n", "138 False\n", "139 False\n", "140 False\n", "141 False\n", "142 False\n", "143 False\n", "144 False\n", "145 False\n", "146 False\n", "147 False\n", "148 False\n", "149 False\n", "150 False\n", "151 False\n", "152 False\n", "153 False\n", "154 False\n", "155 False\n", "Name: SPI, dtype: bool\n" ] } ], "source": [ "print(\"wet climate = \\n\",op.__and__(SPI.gt(1), SPI.lt(2)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dry climate = \n", " 133 True\n", "134 True\n", "135 True\n", "136 True\n", "137 True\n", "138 True\n", "139 True\n", "140 True\n", "141 True\n", "142 True\n", "143 True\n", "144 True\n", "145 True\n", "146 True\n", "147 True\n", "148 True\n", "149 True\n", "150 True\n", "151 True\n", "152 True\n", "153 True\n", "154 True\n", "155 True\n", "Name: SPI, dtype: bool\n" ] } ], "source": [ "print(\"dry climate = \\n\",op.__or__(SPI.lt(1), SPI.le(-2)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }