From 02180eb56a294ecae6701b6191a1b861142e0f9a Mon Sep 17 00:00:00 2001 From: harshit333-exe Date: Tue, 8 Oct 2024 14:04:39 +0530 Subject: [PATCH 1/3] nothing --- .../02-Strings.ipynb | 775 ++++++++++++++++-- .../03-Print Formatting with Strings.ipynb | 69 +- .../hello.py | 9 + .../new.py | 13 + 4 files changed, 781 insertions(+), 85 deletions(-) create mode 100644 00-Python Object and Data Structure Basics/hello.py create mode 100644 00-Python Object and Data Structure Basics/new.py diff --git a/00-Python Object and Data Structure Basics/02-Strings.ipynb b/00-Python Object and Data Structure Basics/02-Strings.ipynb index 28a42ec48..6a227bcb1 100644 --- a/00-Python Object and Data Structure Basics/02-Strings.ipynb +++ b/00-Python Object and Data Structure Basics/02-Strings.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ "'hello'" ] }, - "execution_count": 1, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ "'This is also a string'" ] }, - "execution_count": 2, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -97,7 +97,7 @@ "'String built with double quotes'" ] }, - "execution_count": 3, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -109,15 +109,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 90, "metadata": {}, "outputs": [ { "ename": "SyntaxError", - "evalue": "invalid syntax (, line 2)", + "evalue": "unterminated string literal (detected at line 2) (2053197537.py, line 2)", "output_type": "error", "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m ' I'm using single quotes, but this will create an error'\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + "\u001b[1;36m Cell \u001b[1;32mIn[90], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m ' I'm using single quotes, but this will create an error'\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 2)\n" ] } ], @@ -299,47 +299,47 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Assign s as a string\n", - "s = 'Hello World'" + "s = '''kolkata'''\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World'" + "str" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Check\n", - "s" + "type(s)\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hello World\n" + "kolkata\n" ] } ], @@ -665,24 +665,23 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "'str' object does not support item assignment", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Let's try to change the first letter to 'x'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" - ] + "data": { + "text/plain": [ + "'khello'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# Let's try to change the first letter to 'x'\n", - "s[0] = 'x'" + "s[0]+\"hello\"\n" ] }, { @@ -711,6 +710,7 @@ } ], "source": [ + "\n", "s" ] }, @@ -819,166 +819,787 @@ } ], "source": [ - "letter*10" + "letter*10\n", + "\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 13, "metadata": {}, + "outputs": [], "source": [ - "## Basic Built-in String methods\n", - "\n", - "Objects in Python usually have built-in methods. These methods are functions inside the object (we will learn about these in much more depth later) that can perform actions or commands on the object itself.\n", - "\n", - "We call methods with a period and then the method name. Methods are in the form:\n", - "\n", - "object.method(parameters)\n", - "\n", - "Where parameters are extra arguments we can pass into the method. Don't worry if the details don't make 100% sense right now. Later on we will be creating our own objects and functions!\n", - "\n", - "Here are some examples of built-in methods in strings:" + "s='kolkata'" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "m=[1,2,3,4,5,6]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "m.append(8)" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World concatenate me!'" + "[1, 2, 3, 4, 5, 6, 8]" ] }, - "execution_count": 34, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s" + "m\n" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "m.extend('k')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'HELLO WORLD CONCATENATE ME!'" + "[1, 2, 3, 4, 5, 6, 8, 'h', 'h', 'h', 'k', 'k']" ] }, - "execution_count": 35, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Upper Case a string\n", - "s.upper()" + "m" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'hello world concatenate me!'" + "'k'" ] }, - "execution_count": 36, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Lower case\n", - "s.lower()" + "m.pop()" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Hello', 'World', 'concatenate', 'me!']" + "'h'" ] }, - "execution_count": 37, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Split a string by blank space (this is the default)\n", - "s.split()" + "m.pop(8)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Hello ', 'orld concatenate me!']" + "'h'" ] }, - "execution_count": 38, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Split by a specific element (doesn't include the element that was split on)\n", - "s.split('W')" + "m.pop(\n", + " \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 8, 'h']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "m.remove('h')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 8]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" ] }, { "cell_type": "markdown", "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], "source": [ - "There are many more methods than the ones covered here. Visit the Advanced String section to find out more!" + "m=[4,3,63,56,34,324]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "m.sort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Print Formatting\n", - "\n", - "We can use the .format() method to add formatted objects to printed string statements. \n", - "\n", - "The easiest way to show this is through an example:" + "method---->m.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 4, 34, 56, 63, 324]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "sorted(m) is function " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 4, 34, 56, 63, 324]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "m.sort(reverse =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[324, 63, 56, 34, 4, 3]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.index(324)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "m.append(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Insert another string with curly brackets: The inserted string'" + "2" ] }, - "execution_count": 39, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.count(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "m=['agra','lucknow','kanpur','delhi']" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "m1=[i for i in m if 'a' in i ]" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['agra', 'kanpur']" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "m=[2,3,4,5]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "m=[1,23,4,5,6]" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "s='s'+s[1:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Built-in String methods\n", + "\n", + "Objects in Python usually have built-in methods. These methods are functions inside the object (we will learn about these in much more depth later) that can perform actions or commands on the object itself.\n", + "\n", + "We call methods with a period and then the method name. Methods are in the form:\n", + "\n", + "object.method(parameters)\n", + "\n", + "Where parameters are extra arguments we can pass into the method. Don't worry if the details don't make 100% sense right now. Later on we will be creating our own objects and functions!\n", + "\n", + "Here are some examples of built-in methods in strings:" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 's' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[128], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s\n", + "\u001b[1;31mNameError\u001b[0m: name 's' is not defined" + ] + } + ], + "source": [ + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "s='p'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "x='h'+'a'+s+s+'y'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'happy'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "s='s'" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'x' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[48], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m x\n", + "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined" + ] + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'y'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'h', 'p', 'p', 'y']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "140720717175160" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "id(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "capitalise first letter " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Happy'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.capitalize()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.count('s')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'HELLO WORLD CONCATENATE ME!'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Upper Case a string\n", + "s.upper()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Lower case\n", + "s.lower()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['happy']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Split a string by blank space (this is the default)\n", + "x.split()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['', 'appy']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Split by a specific element (doesn't include the element that was split on)\n", + "x.split('h')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many more methods than the ones covered here. Visit the Advanced String section to find out more!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Print Formatting\n", + "\n", + "We can use the .format() method to add formatted objects to printed string statements. \n", + "\n", + "The easiest way to show this is through an example:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Insert another string with curly brackets: fuck you'" + ] + }, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "'Insert another string with curly brackets: {}'.format('The inserted string')" + "'Insert another string with curly brackets: {}'.format('fuck you')" ] }, { @@ -998,7 +1619,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1012,7 +1633,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/00-Python Object and Data Structure Basics/03-Print Formatting with Strings.ipynb b/00-Python Object and Data Structure Basics/03-Print Formatting with Strings.ipynb index 7d1e2d568..a21b55e1d 100644 --- a/00-Python Object and Data Structure Basics/03-Print Formatting with Strings.ipynb +++ b/00-Python Object and Data Structure Basics/03-Print Formatting with Strings.ipynb @@ -47,17 +47,70 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "s=\"dick\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I'm going to inject something here.\n" + "I'm going to inject dick here.\n" ] } ], "source": [ - "print(\"I'm going to inject %s here.\" %'something')" + "print(\"I'm going to inject %s here.\" %s)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "x=7" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "y=5" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "z=x+y" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n" + ] + } + ], + "source": [ + "print(z)" ] }, { @@ -69,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -132,8 +185,7 @@ } ], "source": [ - "print('He said his name was %s.' %'Fred')\n", - "print('He said his name was %r.' %'Fred')" + "print" ] }, { @@ -570,7 +622,8 @@ "source": [ "name = 'Fred'\n", "\n", - "print(f\"He said his name is {name}.\")" + "print(f\"He\n", + " said his name is {name}.\")" ] }, { @@ -702,7 +755,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -716,7 +769,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/00-Python Object and Data Structure Basics/hello.py b/00-Python Object and Data Structure Basics/hello.py new file mode 100644 index 000000000..efe985951 --- /dev/null +++ b/00-Python Object and Data Structure Basics/hello.py @@ -0,0 +1,9 @@ +z=['abc','xyz','aba','1221'] +count=0 +for i in z: + if i[0]==i[-1]: + count=count+1 + +print(count) + + \ No newline at end of file diff --git a/00-Python Object and Data Structure Basics/new.py b/00-Python Object and Data Structure Basics/new.py new file mode 100644 index 000000000..bf407d7f9 --- /dev/null +++ b/00-Python Object and Data Structure Basics/new.py @@ -0,0 +1,13 @@ +x=int(input("enter the no 1")) +y=int(input("enter the no 2")) +z=int(input("enter the no 3")) +if(x!=7 and y!=7 and z!=7): + print (x*y*z) +elif(x==7): + print(y*z) +elif(y==7): + print(x*z) +elif(z==7): + print(y*x) +elif(x==7 and y==7 and z==7): + print \ No newline at end of file From 043b7962b47f4a0dff4459988256bae2a682baee Mon Sep 17 00:00:00 2001 From: harshit333-exe Date: Tue, 8 Oct 2024 14:04:40 +0530 Subject: [PATCH 2/3] nothing From 28bb4c86ec3f23835ee5f24b50f0fa58cb4d3757 Mon Sep 17 00:00:00 2001 From: harshit333-exe Date: Wed, 18 Dec 2024 09:54:34 +0530 Subject: [PATCH 3/3] graphs learning --- .../01-Numbers-checkpoint.ipynb | 4 +- .../01-Variable Assignment.ipynb | 4 +- .../02-Strings.ipynb | 282 ++++--- .../04-Lists.ipynb | 263 +++--- .../07-Sets and Booleans.ipynb | 763 +++++++++++++++++- .../08-Files.ipynb | 45 +- .../new.ipynb | 96 +++ .../.ipynb_checkpoints/HEK.ipynb | 24 + .../dictionary.ipynb | 551 +++++++++++++ python programming tutorials /error.ipynb | 63 ++ .../file handling.ipynb | 67 ++ .../functions.ipynb | 288 +++++++ python programming tutorials /graph.ipynb | 303 +++++++ python programming tutorials /matrixmul.py | 8 + python programming tutorials /new.txt | 0 python programming tutorials /set.ipynb | 646 +++++++++++++++ 16 files changed, 3070 insertions(+), 337 deletions(-) create mode 100644 00-Python Object and Data Structure Basics/new.ipynb create mode 100644 08-Milestone Project - 2/.ipynb_checkpoints/HEK.ipynb create mode 100644 python programming tutorials /dictionary.ipynb create mode 100644 python programming tutorials /error.ipynb create mode 100644 python programming tutorials /file handling.ipynb create mode 100644 python programming tutorials /functions.ipynb create mode 100644 python programming tutorials /graph.ipynb create mode 100644 python programming tutorials /matrixmul.py create mode 100644 python programming tutorials /new.txt create mode 100644 python programming tutorials /set.ipynb diff --git a/00-Python Object and Data Structure Basics/.ipynb_checkpoints/01-Numbers-checkpoint.ipynb b/00-Python Object and Data Structure Basics/.ipynb_checkpoints/01-Numbers-checkpoint.ipynb index d840c0184..6411ee919 100644 --- a/00-Python Object and Data Structure Basics/.ipynb_checkpoints/01-Numbers-checkpoint.ipynb +++ b/00-Python Object and Data Structure Basics/.ipynb_checkpoints/01-Numbers-checkpoint.ipynb @@ -542,7 +542,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -556,7 +556,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/00-Python Object and Data Structure Basics/01-Variable Assignment.ipynb b/00-Python Object and Data Structure Basics/01-Variable Assignment.ipynb index c869c3ab6..1efe1eb1a 100644 --- a/00-Python Object and Data Structure Basics/01-Variable Assignment.ipynb +++ b/00-Python Object and Data Structure Basics/01-Variable Assignment.ipynb @@ -444,7 +444,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -458,7 +458,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/00-Python Object and Data Structure Basics/02-Strings.ipynb b/00-Python Object and Data Structure Basics/02-Strings.ipynb index 6a227bcb1..41d387b09 100644 --- a/00-Python Object and Data Structure Basics/02-Strings.ipynb +++ b/00-Python Object and Data Structure Basics/02-Strings.ipynb @@ -109,21 +109,12 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "unterminated string literal (detected at line 2) (2053197537.py, line 2)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m Cell \u001b[1;32mIn[90], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m ' I'm using single quotes, but this will create an error'\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 2)\n" - ] - } - ], + "outputs": [], "source": [ "# Be careful with quotes!\n", - "' I'm using single quotes, but this will create an error'" + "v=\"I'm using single quotes, but this will create an error\"" ] }, { @@ -135,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -144,7 +135,7 @@ "\"Now I'm ready to use the single quotes inside a string!\"" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -171,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -180,7 +171,7 @@ "'Hello World'" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -192,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -201,7 +192,7 @@ "'Hello World 2'" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -221,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -262,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -271,7 +262,7 @@ "11" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -299,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -311,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -320,7 +311,7 @@ "str" ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -332,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -357,37 +348,38 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 34, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'H'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" + "ename": "TypeError", + "evalue": "'str' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[34], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Show first element (in this case a letter)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m s[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml\u001b[39m\u001b[38;5;124m'\u001b[39m\n", + "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" + ] } ], "source": [ "# Show first element (in this case a letter)\n", - "s[0]" + "s[0]='l'" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'e'" + "'o'" ] }, - "execution_count": 14, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -407,7 +399,7 @@ "'l'" ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -425,16 +417,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'ello World'" + "'olkata'" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -446,16 +438,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World'" + "'kolkata'" ] }, - "execution_count": 17, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -467,16 +459,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hel'" + "'kol'" ] }, - "execution_count": 18, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -495,16 +487,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World'" + "'kolkata'" ] }, - "execution_count": 19, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -523,16 +515,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'d'" + "'a'" ] }, - "execution_count": 20, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -544,16 +536,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello Worl'" + "'kolkat'" ] }, - "execution_count": 21, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -572,16 +564,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World'" + "'kolkata'" ] }, - "execution_count": 22, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -593,16 +585,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'HloWrd'" + "'klaa'" ] }, - "execution_count": 23, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -614,16 +606,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'dlroW olleH'" + "'ataklok'" ] }, - "execution_count": 24, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -645,16 +637,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World'" + "'kolkata'" ] }, - "execution_count": 25, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -665,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -674,7 +666,7 @@ "'khello'" ] }, - "execution_count": 12, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -695,16 +687,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World'" + "'kolkata'" ] }, - "execution_count": 27, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -716,16 +708,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World concatenate me!'" + "'kolkata concatenate me!'" ] }, - "execution_count": 28, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -737,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -749,14 +741,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hello World concatenate me!\n" + "kolkata concatenate me!\n" ] } ], @@ -766,16 +758,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Hello World concatenate me!'" + "'kolkata concatenate me!'" ] }, - "execution_count": 31, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -793,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -804,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -813,7 +805,7 @@ "'zzzzzzzzzz'" ] }, - "execution_count": 33, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -825,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -834,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -843,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -852,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -861,7 +853,7 @@ "[1, 2, 3, 4, 5, 6, 8]" ] }, - "execution_count": 3, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -872,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -881,16 +873,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 2, 3, 4, 5, 6, 8, 'h', 'h', 'h', 'k', 'k']" + "[1, 2, 3, 4, 5, 6, 8, 'k']" ] }, - "execution_count": 14, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -901,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -910,7 +902,7 @@ "'k'" ] }, - "execution_count": 17, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -921,18 +913,19 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 33, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'h'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" + "ename": "IndexError", + "evalue": "pop index out of range", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[33], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;241m8\u001b[39m)\n", + "\u001b[1;31mIndexError\u001b[0m: pop index out of range" + ] } ], "source": [ @@ -1293,7 +1286,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1309,7 +1302,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1318,7 +1311,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1327,7 +1320,7 @@ "'happy'" ] }, - "execution_count": 5, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1338,7 +1331,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1347,19 +1340,18 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 39, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'x' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[48], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m x\n", - "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined" - ] + "data": { + "text/plain": [ + "'happy'" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1368,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1377,7 +1369,7 @@ "'y'" ] }, - "execution_count": 8, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1388,7 +1380,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1397,7 +1389,7 @@ "['a', 'h', 'p', 'p', 'y']" ] }, - "execution_count": 9, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1408,16 +1400,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "140720717175160" + "140703288334712" ] }, - "execution_count": 10, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1435,7 +1427,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1444,7 +1436,7 @@ "'Happy'" ] }, - "execution_count": 11, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1455,16 +1447,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2" + "0" ] }, - "execution_count": 35, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1475,16 +1467,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'HELLO WORLD CONCATENATE ME!'" + "'S'" ] }, - "execution_count": 35, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1503,7 +1495,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1512,7 +1504,7 @@ "'s'" ] }, - "execution_count": 12, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1524,7 +1516,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1533,7 +1525,7 @@ "['happy']" ] }, - "execution_count": 15, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1545,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1554,7 +1546,7 @@ "['', 'appy']" ] }, - "execution_count": 21, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1584,7 +1576,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1593,7 +1585,7 @@ "'Insert another string with curly brackets: fuck you'" ] }, - "execution_count": 22, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } diff --git a/00-Python Object and Data Structure Basics/04-Lists.ipynb b/00-Python Object and Data Structure Basics/04-Lists.ipynb index 860c2e71c..64dda9034 100644 --- a/00-Python Object and Data Structure Basics/04-Lists.ipynb +++ b/00-Python Object and Data Structure Basics/04-Lists.ipynb @@ -11,6 +11,13 @@ "
Content Copyright by Pierian Data
" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -41,7 +48,24 @@ "outputs": [], "source": [ "# Assign a list to an variable named my_list\n", - "my_list = [1,2,3]" + "l=[1,2,3,4,5,6,7,8,9]" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9, 8, 7, 6, 5, 4, 3, 2]\n" + ] + } + ], + "source": [ + "print(l[:-9:-1])" ] }, { @@ -53,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 289, "metadata": { "collapsed": true }, @@ -71,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 290, "metadata": {}, "outputs": [ { @@ -80,7 +104,7 @@ "4" ] }, - "execution_count": 3, + "execution_count": 290, "metadata": {}, "output_type": "execute_result" } @@ -99,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 291, "metadata": { "collapsed": true }, @@ -110,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 292, "metadata": {}, "outputs": [ { @@ -119,7 +143,7 @@ "'one'" ] }, - "execution_count": 5, + "execution_count": 292, "metadata": {}, "output_type": "execute_result" } @@ -131,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 293, "metadata": {}, "outputs": [ { @@ -140,7 +164,7 @@ "['two', 'three', 4, 5]" ] }, - "execution_count": 6, + "execution_count": 293, "metadata": {}, "output_type": "execute_result" } @@ -152,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 294, "metadata": {}, "outputs": [ { @@ -161,7 +185,7 @@ "['one', 'two', 'three']" ] }, - "execution_count": 7, + "execution_count": 294, "metadata": {}, "output_type": "execute_result" } @@ -180,24 +204,51 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "['one', 'two', 'three', 4, 5, 'new item']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'my_list' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m my_list \u001b[38;5;241m+\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnew item\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[1;31mNameError\u001b[0m: name 'my_list' is not defined" + ] } ], "source": [ "my_list + ['new item']" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "def is_leap(year):\n", + " \n", + " if ((year/4==0 and year/100!=0)or year/400==0):\n", + " leap = True\n", + " else:\n", + " leap = False\n", + " return leap\n", + "\n", + "year = int(input())\n", + "print(is_leap(year))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -207,18 +258,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "['one', 'two', 'three', 4, 5]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'my_list' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m my_list\n", + "\u001b[1;31mNameError\u001b[0m: name 'my_list' is not defined" + ] } ], "source": [ @@ -234,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 297, "metadata": { "collapsed": true }, @@ -246,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 298, "metadata": {}, "outputs": [ { @@ -255,7 +307,7 @@ "['one', 'two', 'three', 4, 5, 'add new item permanently']" ] }, - "execution_count": 11, + "execution_count": 298, "metadata": {}, "output_type": "execute_result" } @@ -273,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 299, "metadata": {}, "outputs": [ { @@ -293,7 +345,7 @@ " 'add new item permanently']" ] }, - "execution_count": 12, + "execution_count": 299, "metadata": {}, "output_type": "execute_result" } @@ -305,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 300, "metadata": {}, "outputs": [ { @@ -314,7 +366,7 @@ "['one', 'two', 'three', 4, 5, 'add new item permanently']" ] }, - "execution_count": 13, + "execution_count": 300, "metadata": {}, "output_type": "execute_result" } @@ -337,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 301, "metadata": { "collapsed": true }, @@ -356,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 302, "metadata": { "collapsed": true }, @@ -368,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 303, "metadata": {}, "outputs": [ { @@ -377,7 +429,7 @@ "[1, 2, 3, 'append me!']" ] }, - "execution_count": 16, + "execution_count": 303, "metadata": {}, "output_type": "execute_result" } @@ -396,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 304, "metadata": {}, "outputs": [ { @@ -405,7 +457,7 @@ "1" ] }, - "execution_count": 17, + "execution_count": 304, "metadata": {}, "output_type": "execute_result" } @@ -417,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 305, "metadata": {}, "outputs": [ { @@ -426,7 +478,7 @@ "[2, 3, 'append me!']" ] }, - "execution_count": 18, + "execution_count": 305, "metadata": {}, "output_type": "execute_result" } @@ -438,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 306, "metadata": { "collapsed": true }, @@ -450,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 307, "metadata": {}, "outputs": [ { @@ -459,7 +511,7 @@ "'append me!'" ] }, - "execution_count": 20, + "execution_count": 307, "metadata": {}, "output_type": "execute_result" } @@ -470,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 308, "metadata": {}, "outputs": [ { @@ -479,7 +531,7 @@ "[2, 3]" ] }, - "execution_count": 21, + "execution_count": 308, "metadata": {}, "output_type": "execute_result" } @@ -498,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 309, "metadata": {}, "outputs": [ { @@ -508,7 +560,7 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlist1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "Cell \u001b[1;32mIn[309], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m list1[\u001b[38;5;241m100\u001b[39m]\n", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } @@ -537,20 +589,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['a', 'e', 'x', 'b', 'c']" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Show\n", "new_list" @@ -570,20 +611,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['c', 'b', 'x', 'e', 'a']" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "new_list" ] @@ -602,20 +632,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['a', 'b', 'c', 'e', 'x']" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "new_list" ] @@ -649,20 +668,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Show\n", "matrix" @@ -677,20 +685,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Grab first item in matrix object\n", "matrix[0]" @@ -698,20 +695,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Grab first item of the first item in the matrix object\n", "matrix[0][0]" @@ -741,20 +727,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 4, 7]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "first_col" ] @@ -771,7 +746,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -785,7 +760,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/00-Python Object and Data Structure Basics/07-Sets and Booleans.ipynb b/00-Python Object and Data Structure Basics/07-Sets and Booleans.ipynb index 6951ed42b..877ee4e8f 100644 --- a/00-Python Object and Data Structure Basics/07-Sets and Booleans.ipynb +++ b/00-Python Object and Data Structure Basics/07-Sets and Booleans.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -37,7 +37,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "ms=(1,2,'agra')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "a=[1,3]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "collapsed": true }, @@ -49,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -58,7 +76,7 @@ "{1}" ] }, - "execution_count": 3, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -79,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -91,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -100,7 +118,7 @@ "{1, 2}" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -112,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -124,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -133,7 +151,7 @@ "{1, 2}" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -152,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -164,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -173,7 +191,7 @@ "{1, 2, 3, 4, 5, 6}" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -194,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -206,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -215,7 +233,7 @@ "True" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -234,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -243,7 +261,7 @@ "False" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -262,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -274,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -296,11 +314,712 @@ "source": [ "Thats it! You should now have a basic understanding of Python objects and data structure types. Next, go ahead and do the assessment test!" ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "ms={1,2,'agra'}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 2, 'agra'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "m=list(ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 'agra']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unhashable type: 'set'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[23], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ms\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;28;01mTrue\u001b[39;00m,\u001b[38;5;28;01mFalse\u001b[39;00m,\u001b[38;5;241m0\u001b[39m,{\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m}}\n", + "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'set'" + ] + } + ], + "source": [ + "ms={'s','g',1,2,True,False,0,{2,3}}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unhashable type: 'list'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[17], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m ms\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;28;01mTrue\u001b[39;00m,\u001b[38;5;28;01mFalse\u001b[39;00m,\u001b[38;5;241m0\u001b[39m,[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m]}\n", + "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" + ] + } + ], + "source": [ + "ms={'s','g',1,2,True,False,0,[1,2]}" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "ms={'s','g',2,True,1,False,0,(1,2)}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "here 0=false and 1 =true\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(1, 2), 2, False, True, 'g', 's'}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "ms.add('hello')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(1, 2), 2, False, True, 'g', 'hello', 's'}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "ms.update(\"bro\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(1, 2), 2, False, True, 'b', 'g', 'hello', 'o', 'r', 's'}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "m={1,2,3,4}" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "m.update([11,23])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 2, 3, 4, 11, 23}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "ms.update(m)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(1, 2), 11, 2, 23, 3, 4, False, True, 'b', 'g', 'hello', 'o', 'r', 's'}" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "ms.clear()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "ms={(1, 2), 11, 2, 23, 3, 4, False, True, 'b', 'g', 'hello', 'o', 'r', 's'}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "ms.remove('s')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(1, 2), 11, 2, 23, 3, 4, False, True, 'b', 'g', 'hello', 'o', 'r'}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "ms.discard('g')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(1, 2), 11, 2, 23, 3, 4, False, True, 'b', 'hello', 'o', 'r'}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "m=\"i like python\"" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "x=len(str(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i\n", + " \n", + "l\n", + "i\n", + "k\n", + "e\n", + " \n", + "p\n", + "y\n", + "t\n", + "h\n", + "o\n", + "n\n" + ] + } + ], + "source": [ + "for i in m:\n", + " print(i)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "list(m).remove(' ')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'i like python'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "x=list(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['i', ' ', 'l', 'i', 'k', 'e', ' ', 'p', 'y', 't', 'h', 'o', 'n']" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "x.remove(' ')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "msg1=\"I like Python\"\n", + "msg2=\"Java is a very popular language\"\n", + "m=[]\n", + "for x in msg1:\n", + " if x in msg2:\n", + " m.append(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[' ', 'l', 'i', 'e', ' ', 'y', 'o', 'n']" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "z=str(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l\n", + "i\n", + "e\n", + "y\n", + "o\n", + "n\n" + ] + } + ], + "source": [ + "for x in m:\n", + " if(x==\" \"):\n", + " continue\n", + " else:\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "#lex_auth_012693825794351104168\n", + "\n", + "def find_common_characters(msg1,msg2):\n", + " m=[]\n", + " for x in msg1:\n", + " if x in msg2:\n", + " if(x==\" \"):\n", + " continue\n", + " else:\n", + " m.append(x)\n", + " \n", + "\n", + "\n", + "#Provide different values for msg1,msg2 and test your program\n", + "msg1=\"I like Python\"\n", + "msg2=\"Java is a very popular language\"\n", + "common_characters=find_common_characters(msg1,msg2)\n", + "print(common_characters)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "a=[1,3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "b=a" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 3]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "a[0]=4\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "a=[1,3]\n", + "b=a\n", + "a[0]=4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4, 3]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b\n" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -314,7 +1033,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/00-Python Object and Data Structure Basics/08-Files.ipynb b/00-Python Object and Data Structure Basics/08-Files.ipynb index 8bc0c1551..3bf12dfdd 100644 --- a/00-Python Object and Data Structure Basics/08-Files.ipynb +++ b/00-Python Object and Data Structure Basics/08-Files.ipynb @@ -66,7 +66,8 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmyfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whoops.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m myfile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwhoops.txt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\Harshit\\anaconda\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[1;34m(file, *args, **kwargs)\u001b[0m\n\u001b[0;32m 317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[0;32m 318\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 319\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 320\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 321\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 322\u001b[0m )\n\u001b[1;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m io_open(file, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'whoops.txt'" ] } @@ -84,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -119,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -131,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -152,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -180,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -201,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -229,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -258,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": true }, @@ -278,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": true }, @@ -300,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -321,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -343,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": true }, @@ -362,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -384,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -404,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": true }, @@ -423,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -459,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -485,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -516,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -545,7 +546,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -559,7 +560,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/00-Python Object and Data Structure Basics/new.ipynb b/00-Python Object and Data Structure Basics/new.ipynb new file mode 100644 index 000000000..c19f874fc --- /dev/null +++ b/00-Python Object and Data Structure Basics/new.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "def is_leap(year):\n", + " \n", + " if ((year/4==0 and year/100!=0)or year/400==0):\n", + " leap = True\n", + " else:\n", + " leap = False\n", + " return leap\n", + "\n", + "year = int(input())\n", + "print(is_leap(year))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "list1=[1,5,9,3,2]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x=list1.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 5, 9]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list1\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/08-Milestone Project - 2/.ipynb_checkpoints/HEK.ipynb b/08-Milestone Project - 2/.ipynb_checkpoints/HEK.ipynb new file mode 100644 index 000000000..b54f1e815 --- /dev/null +++ b/08-Milestone Project - 2/.ipynb_checkpoints/HEK.ipynb @@ -0,0 +1,24 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python programming tutorials /dictionary.ipynb b/python programming tutorials /dictionary.ipynb new file mode 100644 index 000000000..2873c220b --- /dev/null +++ b/python programming tutorials /dictionary.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **DICTIONARY** \n", + "are used to store data values in key:value pairs \n", + "\n", + "A DISCTIONARY is a collectiion which is ordered , changeable and allow dupplicates \n", + "\n", + "dictionaries are written with curly brackets and have keys and value \n", + "\n", + "![**dict were previously unodered but after some version it is odered or indexed**](google/com\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "j" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "d={'name':'harshit','roll no':21\n", + " \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2217248560832" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "id(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d['roll no']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "d['age']=20\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'harshit', 'roll no': 21, 'age': 20}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "incomplete input (2452419986.py, line 7)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m Cell \u001b[1;32mIn[38], line 7\u001b[1;36m\u001b[0m\n\u001b[1;33m \u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m incomplete input\n" + ] + } + ], + "source": [ + "d={}\n", + "for i in range(0,26):\n", + " d[chr(ord('a')+i)]=i+1\n", + "s=str(input())\n", + "count=0\n", + "for i in range(0,3):\n", + " if(s[i+1]-s[0])\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 1,\n", + " 'b': 2,\n", + " 'c': 3,\n", + " 'd': 4,\n", + " 'e': 5,\n", + " 'f': 6,\n", + " 'g': 7,\n", + " 'h': 8,\n", + " 'i': 9,\n", + " 'j': 10,\n", + " 'k': 11,\n", + " 'l': 12,\n", + " 'm': 13,\n", + " 'n': 14,\n", + " 'o': 15,\n", + " 'p': 16,\n", + " 'q': 17,\n", + " 'r': 18,\n", + " 's': 19,\n", + " 't': 20,\n", + " 'u': 21,\n", + " 'v': 22,\n", + " 'w': 23,\n", + " 'x': 24,\n", + " 'y': 25,\n", + " 'z': 26}" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "s='acz'" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a'" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "d={\"ba\":2,\"ab\":3}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "arr={}" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "arr={key:d[key] for key in sorted(d)}\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "record={'mohan':[30,40,60],\"rohan\":[60,70,80],\"sohan\":[60,90,40]}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n" + ] + } + ], + "source": [ + "\n", + "print(record['mohan'][0])\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.0\n" + ] + } + ], + "source": [ + "score=0\n", + "x=str(input())\n", + "for i in range(0,3):\n", + " score=score+(record[x][i]*(((i+1)*10)/100))\n", + "print(score)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56088\n" + ] + } + ], + "source": [ + "x=str(input(\"num1--\"))\n", + "y=str(input(\"num2--\"))\n", + "pr=int(x)*int(y)\n", + "print(str(pr))" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "56088" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((ord('3')-48)+((ord('2')-48)*10)+((ord('1')-48)*100))*((ord('6')-48)+((ord('5')-48)*10)+((ord('4')-48)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "string indices must be integers, not 'str'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[168], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum1\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m x:\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(x[i])\n", + "\u001b[1;31mTypeError\u001b[0m: string indices must be integers, not 'str'" + ] + } + ], + "source": [ + "x=str(input(\"num1\"))\n", + "sum=0\n", + "while(i>=0):\n", + " ord(x%10)-48" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'float' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[234], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mpow\u001b[39m(\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n", + "\u001b[1;31mTypeError\u001b[0m: 'float' object is not callable" + ] + } + ], + "source": [ + "print(pow(2,8))" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56088\n" + ] + } + ], + "source": [ + "num={'0':0,'1':1,'2':2,'3':3,'4':4,'5':5,'6':6,'7':7,'8':8,'9':9}\n", + "x=str(input(\"num1--\"))\n", + "y=str(input(\"num2--\"))\n", + "n1=0\n", + "n2=0\n", + "count1=len(x)-1\n", + "count2=len(y)-1\n", + "for i in x:\n", + " n1=n1+(num[i]*(pow(10,count1)))\n", + " count1=count1-1\n", + "for i in y:\n", + " n2=n2+(num[i]*(pow(10,count2)))\n", + " count2=count2-1\n", + "print(str(n1*n2))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "56088" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "123*456\n" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00390625" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def pon(x,y):\n", + " p=1\n", + " if(y>0):\n", + " while(y>=1):\n", + " p=p*x\n", + " y=y-1\n", + " return p\n", + " else:\n", + " while(y<0):\n", + " p=p*x\n", + " np=1/p\n", + " y=y+1\n", + " return np\n", + "pon(2,-8)\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'float' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[233], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mpow\u001b[39m(\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n", + "\u001b[1;31mTypeError\u001b[0m: 'float' object is not callable" + ] + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python programming tutorials /error.ipynb b/python programming tutorials /error.ipynb new file mode 100644 index 000000000..59bab1d96 --- /dev/null +++ b/python programming tutorials /error.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "x= int(input('dividend'))\n", + "y= int(input('divisor'))\n", + "if(y<0 or x<0):\n", + " ay=abs(y)\n", + " ax=abs(x)\n", + "sum=0\n", + "count=0\n", + "while (ax>sum):\n", + " sum=sum+ay\n", + " count= count+1\n", + " \n", + " \n", + " \n", + "if(x<0 ^ y<0):\n", + " print('-',(count-1))\n", + "else:\n", + " print (count-1)\n", + "\n", + "\n", + "\n", + " \n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python programming tutorials /file handling.ipynb b/python programming tutorials /file handling.ipynb new file mode 100644 index 000000000..12a2b26ad --- /dev/null +++ b/python programming tutorials /file handling.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\Users\\\\Harshit\\\\AppData\\\\Local\\\\Programs\\\\Microsoft VS Code'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pwd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'tuple' object has no attribute 'read'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[16], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m f\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mHarshit\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mAppData\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mLocal\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mPrograms\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mMicrosoft VS Code\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mhello! my name is harshit patel.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(f\u001b[38;5;241m.\u001b[39mread())\n", + "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'read'" + ] + } + ], + "source": [ + "f=(\"C:\\\\Users\\\\Harshit\\\\AppData\\\\Local\\\\Programs\\\\Microsoft VS Code\\\\hello! my name is harshit patel.txt\",\"r\")\n", + "print(f.read())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python programming tutorials /functions.ipynb b/python programming tutorials /functions.ipynb new file mode 100644 index 000000000..668404ae1 --- /dev/null +++ b/python programming tutorials /functions.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "def fun_name(arguments):\n", + "\n", + " -----\n", + "fun_call()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "my name is ajay\n" + ] + } + ], + "source": [ + "def fun(s):\n", + " print(\"my name is \",s)\n", + "fun(\"ajay\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "my name is ajay\n" + ] + } + ], + "source": [ + "def fun(*s):\n", + " print(\"my name is \",s[0])\n", + "fun('ajay','ram','tarun')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "my name is ram\n" + ] + } + ], + "source": [ + "def fun(s,r,t):\n", + " print(\"my name is \",s)\n", + "fun(r='ajay',s='ram',t='tarun')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "my name is ram\n", + "my name is shyam\n" + ] + } + ], + "source": [ + "def fun(s=\"ram\"):\n", + " print(\"my name is \",s)\n", + "fun()\n", + "fun(\"shyam\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5790" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def fun1(a,b):\n", + " s=a+b\n", + " return s\n", + " \n", + "fun1(2345,3445)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 7, 9]\n" + ] + } + ], + "source": [ + "def tupsum(t1,t2):\n", + " x=[]\n", + " for i in range(0,len(t1)):\n", + " sum=t1[i]+t2[i]\n", + " x.append(sum)\n", + " print(x)\n", + " \n", + " \n", + "tupsum((1,2,3),(4,5,6))\n", + " \n", + " \n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 7, 9, 10]\n" + ] + } + ], + "source": [ + "def fun1(a,b):\n", + " s=a+b\n", + " return s\n", + " \n", + "z=map(fun1,(1,2,3,5),(4,5,6,5))\n", + "print(list(z))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['kanpur', 'delhi']\n" + ] + } + ], + "source": [ + "def fun2(a):\n", + " if(len(a)>=3):\n", + " return a\n", + " else:\n", + " return False\n", + "x=('kanpur','xy','delhi')\n", + "z=filter(fun2,x)\n", + "print(list(z))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['xy']\n" + ] + } + ], + "source": [ + "def fun2(a):\n", + " if(len(a)>=3):\n", + " return False\n", + " else:\n", + " return True\n", + "x=('kanpur','xy','delhi')\n", + "z=filter(fun2,x)\n", + "print(list(z))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['kanpur', 'delhi']\n" + ] + } + ], + "source": [ + "def fun2(a):\n", + " if(len(a)<=3):\n", + " return False\n", + " else:\n", + " return True\n", + "x=('kanpur','xy','delhi')\n", + "z=filter(fun2,x)\n", + "print(list(z))" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.0\n" + ] + } + ], + "source": [ + "z=lambda a,b,c:(a+b)/c\n", + "print(z(5,10,3))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python programming tutorials /graph.ipynb b/python programming tutorials /graph.ipynb new file mode 100644 index 000000000..b3105508d --- /dev/null +++ b/python programming tutorials /graph.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: ''", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 6\u001b[0m\n\u001b[0;32m 4\u001b[0m y\u001b[38;5;241m=\u001b[39m[]\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m5\u001b[39m):\n\u001b[1;32m----> 6\u001b[0m a\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m(\u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menter x coordinate \u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m 7\u001b[0m x\u001b[38;5;241m.\u001b[39mappend(a)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m5\u001b[39m):\n", + "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: ''" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "x=[]\n", + "y=[]\n", + "for i in range(0,5):\n", + " a=int(input(\"enter x coordinate \"))\n", + " x.append(a)\n", + "for i in range(0,5):\n", + " b=int(input(\"enter y coordinate \"))\n", + " y.append(b)\n", + "\n", + "plt.plot(x,y*2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "x=[1,2,3,4,5,6]\n", + "y=[12,11,10,9,8,7]\n", + "b=[1,2,3,4,5,6]\n", + "a=[12,11,10,9,8,7]\n", + "\n", + "plt.scatter(a,b)\n", + "\n", + "plt.plot(a,b)\n", + "plt.scatter(x,y)\n", + "\n", + "plt.plot(x,y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Generating sample data\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "r = np.random.rand(100) # Random radius values\n", + "colors = np.random.rand(100) # Random colors\n", + "\n", + "# Creating the polar scatter plot\n", + "plt.figure(figsize=(8, 8))\n", + "ax = plt.subplot(111, polar=True)\n", + "ax.scatter(theta, r, c=colors, s=100, cmap='hsv', alpha=0.75)\n", + "\n", + "plt.title('Scatter Plot on Polar Axis', fontsize=15)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[17], line 13\u001b[0m\n\u001b[0;32m 10\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_yscale(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msymlog\u001b[39m\u001b[38;5;124m'\u001b[39m, linthresh\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500000\u001b[39m)\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Add bars\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m ANGLES \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0.05\u001b[39m, \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mpi \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m0.05\u001b[39m, \u001b[38;5;28mlen\u001b[39m(df), endpoint \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 14\u001b[0m LENGTHS \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStudents\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m 15\u001b[0m ax\u001b[38;5;241m.\u001b[39mbar(ANGLES, LENGTHS,\n\u001b[0;32m 16\u001b[0m color\u001b[38;5;241m=\u001b[39mCOLORS, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m,\n\u001b[0;32m 17\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.3\u001b[39m, zorder\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m11\u001b[39m,\n\u001b[0;32m 18\u001b[0m label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSpanish Learners\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Initialize layout in polar coordinates\n", + "fig, ax = plt.subplots(figsize=(7, 12.6), subplot_kw={\"projection\": \"polar\"})\n", + "\n", + "# Set background color to white, both axis and figure.\n", + "fig.patch.set_facecolor(\"white\")\n", + "ax.set_facecolor(\"white\")\n", + "\n", + "ax.set_theta_offset(1.2 * np.pi / 2)\n", + "ax.set_ylim(0, 45000000)\n", + "ax.set_yscale('symlog', linthresh=500000)\n", + "\n", + "# Add bars\n", + "ANGLES = np.linspace(0.05, 2*np.pi - 0.05, len(df), endpoint = False)\n", + "LENGTHS = df['Students'].values\n", + "ax.bar(ANGLES, LENGTHS,\n", + " color=COLORS, alpha=0.5,\n", + " width=0.3, zorder=11,\n", + " label='Spanish Learners')\n", + "\n", + "# Add dashed vertical lines. These are just references\n", + "ax.vlines(ANGLES, 0, 45000000, color=\"#1f1f1f\", ls=(0, (4, 4)), zorder=11)\n", + "\n", + "# Add dots to represent the mean gain\n", + "MEAN_GAIN = df['Natives'].values\n", + "ax.scatter(ANGLES, MEAN_GAIN, s=80, color= COLORS, zorder=11, label = 'Native Spanish Speakers')\n", + "\n", + "# Add labels for the regions\n", + "REGION = [\"\\n\".join(wrap(r, 5, break_long_words=False)) for r in df['Country'].values]\n", + "\n", + "# Set the labels\n", + "ax.set_xticks(ANGLES)\n", + "ax.set_xticklabels(REGION, size=12)\n", + "ax.set_yticks(np.arange(0,45000000,\n", + " step=5000000))\n", + "\n", + "# Add title and subtile at the top of the chart\n", + "plt.suptitle('Top Countries with Spanish Learners',\n", + " size = 20, y = 0.95)\n", + "plt.title('And their Native Spanish Speaking Population',\n", + " style = 'italic', size = 14, pad = 85)\n", + "\n", + "# Add scale starting at 1M and ending at 45M\n", + "PAD = 10\n", + "ax.text(-0.75 * np.pi / 2, 1000000 + PAD, \"1M\", ha=\"right\", size=12)\n", + "ax.text(-0.75 * np.pi / 2, 5000000 + PAD, \"5M\", ha=\"right\", size=11)\n", + "ax.text(-0.75 * np.pi / 2, 10000000 + PAD, \"10M\", ha=\"right\", size=10)\n", + "ax.text(-0.75 * np.pi / 2, 20000000 + PAD, \"20M \", ha=\"right\", size=9)\n", + "ax.text(-0.75 * np.pi / 2, 30000000 + PAD, \"30M \", ha=\"right\", size=8)\n", + "ax.text(-0.75 * np.pi / 2, 46000000 + PAD, \"45M \", ha=\"right\", size=7)\n", + "XTICKS = ax.xaxis.get_major_ticks()\n", + "for tick in XTICKS:\n", + " tick.set_pad(12)\n", + "\n", + "# Add credit and sources\n", + "caption = \"\\n\".join([\"Created adapting a tutorial from Yan Holtz: https://python-graph-gallery.com/web-circular-barplot-with-matplotlib/\",\n", + " \"Data compiled from various sources including:\",\n", + " \"https://www.statista.com/statistics/991020/number-native-spanish-speakers-country-worldwide/\",\n", + " \"https://cvc.cervantes.es/lengua/espanol_lengua_viva/pdf/espanol_lengua_viva_2022.pdf\",\n", + " \"https://www.wordspath.com/spanish-speaking-countries-in-europe/#:~:text=More%20than%2084%20million%20people,them%20are%20native%20Spanish%20speakers.\"\n", + "])\n", + "fig.text(0, 0.1, caption, fontsize=10, ha=\"left\", va=\"baseline\")\n", + "\n", + "# First, make some room for the legend and the caption in the bottom.\n", + "fig.subplots_adjust(bottom=0.175)\n", + "\n", + "# Add customed legend\n", + "legend_elements = [Line2D([0], [0], marker='o', color='w', label='Native Spanish Speaking Population',\n", + " markerfacecolor='gray', markersize=12),\n", + " Line2D([0],[0] ,color = 'lightgray', lw = 3, label = 'Spanish Learners'),\n", + " mpatches.Patch(color='tan', label='North America', alpha = 0.8),\n", + " mpatches.Patch(color='#F9A03F', label='South America', alpha = 0.8),\n", + " mpatches.Patch(color='#2B3B39', label='West Europe', alpha = 0.8),\n", + " mpatches.Patch(color='#914F76', label='West Africa', alpha = 0.8),\n", + " mpatches.Patch(color='#914F76', label='Central Africa', alpha = 0.8),\n", + " mpatches.Patch(color='#4D6A67', label='North Europe', alpha = 0.8),\n", + " mpatches.Patch(color='#A2B9B6', label='East Europe', alpha = 0.8)]\n", + "ax.legend(handles=legend_elements,\n", + " loc='upper right', # location\n", + " bbox_to_anchor=(1.4, 1), # shift the legend\n", + " fontsize = 'small')\n", + "\n", + "# Display the final chart\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x=['Meat','Banana','Avocados','Sweet Potatoes','Spinach','Watermelomn','Coconut Water','Beans','Legumes','Tomato']\n", + "cal=[250,130,140,120,20,20,10,50,40,19]\n", + "pot=[40,55,20,30,40,32,10,26,25,20]\n", + "fat=[8,5,3,6,1,1.5,0,2,1.5,2.5]\n", + "plt.ylim(0,100)\n", + "# plt.scatter(x,cal,color='cyan')\n", + "# plt.scatter(x,pot,color='green')\n", + "# plt.scatter(x,fat,color='red')\n", + "plt.scatter(x,cal)\n", + "plt.scatter(x,pot)\n", + "plt.scatter(x,fat)\n", + "plt.plot(x,cal,color='cyan')\n", + "plt.plot(x,pot,color='green')\n", + "plt.plot(x,fat,color='red')\n", + "\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x=['Jan','feb','mar','apr','may','june','july','aug','sep','oct','nov','dec']\n", + "y=[100,30,70,200,50,260,70,210,90,60,110,240]\n", + "c=['r','b','g','yellow','purple']\n", + "plt.bar(x,y,color=c,alpha=0.90,width=0.5,edgecolor='black',linewidth=2,linestyle='solid')\n", + "plt.xlabel('months',fontsize=15)\n", + "plt.ylabel(\"Power consumption in Kwh\",fontsize=12)\n", + "plt.title(\"Electricity consumption of electricity\",fontsize=12)\n", + "plt.xticks(x,rotation=90)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python programming tutorials /matrixmul.py b/python programming tutorials /matrixmul.py new file mode 100644 index 000000000..33cd872eb --- /dev/null +++ b/python programming tutorials /matrixmul.py @@ -0,0 +1,8 @@ +a=[[1,2,3], + [4,5,6], + [7,8,9]] +b=[[2,3,4], + [5,6,7], + [8,9,10]] +rowa=len(a) +colsa=len(a[0]) \ No newline at end of file diff --git a/python programming tutorials /new.txt b/python programming tutorials /new.txt new file mode 100644 index 000000000..e69de29bb diff --git a/python programming tutorials /set.ipynb b/python programming tutorials /set.ipynb new file mode 100644 index 000000000..fec9906ca --- /dev/null +++ b/python programming tutorials /set.ipynb @@ -0,0 +1,646 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "s={}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "s={1,2,3}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unhashable type: 'set'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[11], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s\u001b[38;5;241m=\u001b[39m{\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,{\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m}}\n", + "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'set'" + ] + } + ], + "source": [ + "s={1,2,3,{1,2}}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "s={1,2,3,(1,2)}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "s1={7,8}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "s.update \n", + "s.discard(7) \n", + "s.remove(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "x=s.union(s,s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1.issubset(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "s={1,2,3}\n", + "s1={2,3,4,5,6}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.difference(s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{4, 5, 6}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1.difference(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "s.difference_update(s1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "functions containing update updates the value " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2, 3, 4, 5, 6}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s={1,2,3}\n", + "s1={2,3,4,5,6}" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2, 3}" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.intersection(s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 2, 3}" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "s.intersection_update(s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2, 3}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s-s1" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "s={1,2,3}\n", + "s1={2,3,4,5,6}" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 4, 5, 6}" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.symmetric_difference(s1\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "symmetric_differnce==^" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 4, 5, 6}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s^s1" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2, 3, 4, 5, 6}" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 2, 3}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 2, 3, 4, 5, 6}" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.union(s1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.isdisjoint(s1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "s.isdisjoints tells disticts values are present or not" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "s1={1,2}\n", + "s2={1,2,3}" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1.issubset(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.issubset(s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "s2.issuperset(s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "s2.pop()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "s2.pop removes random values " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2, 3}" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}