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30-Days-Of-Python/numpy.ipynb
Asabeneh 25e2986816 day 24
2019-12-12 23:59:37 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Author: Asabeneh Yetayeh\n",
"\n",
"# Numpy(Numberic Python)\n",
"\n",
"In this note book you will cover all what you need to know about Numpy. Inditon to numpy we will see how to use matplot lib python library which will help us to draw graphs and visualize data.\n",
"\n",
"So far, we have been using vscode but from now on I would recommend using Jupter Notebook. To access jupter notebook let's install [anaconda](https://www.anaconda.com/)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Installation anconda numpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to import numpy (Numeric Python)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# A numpy array must have all items to be of the same data type, unlike lists.\n",
"# This is another significant difference."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# How to import numpy\n",
"import numpy as np\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to check package version in python"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "numpy: 1.17.2\n"
}
],
"source": [
"# How to check the version of the numpy package\n",
"print('numpy:', np.__version__)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "['ALLOW_THREADS', 'AxisError', 'BUFSIZE', 'CLIP', 'ComplexWarning', 'DataSource', 'ERR_CALL', 'ERR_DEFAULT', 'ERR_IGNORE', 'ERR_LOG', 'ERR_PRINT', 'ERR_RAISE', 'ERR_WARN', 'FLOATING_POINT_SUPPORT', 'FPE_DIVIDEBYZERO', 'FPE_INVALID', 'FPE_OVERFLOW', 'FPE_UNDERFLOW', 'False_', 'Inf', 'Infinity', 'MAXDIMS', 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'MachAr', 'ModuleDeprecationWarning', 'NAN', 'NINF', 'NZERO', 'NaN', 'PINF', 'PZERO', 'RAISE', 'RankWarning', 'SHIFT_DIVIDEBYZERO', 'SHIFT_INVALID', 'SHIFT_OVERFLOW', 'SHIFT_UNDERFLOW', 'ScalarType', 'Tester', 'TooHardError', 'True_', 'UFUNC_BUFSIZE_DEFAULT', 'UFUNC_PYVALS_NAME', 'VisibleDeprecationWarning', 'WRAP', '_NoValue', '_UFUNC_API', '__NUMPY_SETUP__', '__all__', '__builtins__', '__cached__', '__config__', '__doc__', '__file__', '__git_revision__', '__loader__', '__mkl_version__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_add_newdoc_ufunc', '_distributor_init', '_globals', '_mat', '_pytesttester', 'abs', 'absolute', 'absolute_import', 'add', 'add_docstring', 'add_newdoc', 'add_newdoc_ufunc', 'alen', 'all', 'allclose', 'alltrue', 'amax', 'amin', 'angle', 'any', 'append', 'apply_along_axis', 'apply_over_axes', 'arange', 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh', 'argmax', 'argmin', 'argpartition', 'argsort', 'argwhere', 'around', 'array', 'array2string', 'array_equal', 'array_equiv', 'array_repr', 'array_split', 'array_str', 'asanyarray', 'asarray', 'asarray_chkfinite', 'ascontiguousarray', 'asfarray', 'asfortranarray', 'asmatrix', 'asscalar', 'atleast_1d', 'atleast_2d', 'atleast_3d', 'average', 'bartlett', 'base_repr', 'binary_repr', 'bincount', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'blackman', 'block', 'bmat', 'bool', 'bool8', 'bool_', 'broadcast', 'broadcast_arrays', 'broadcast_to', 'busday_count', 'busday_offset', 'busdaycalendar', 'byte', 'byte_bounds', 'bytes0', 'bytes_', 'c_', 'can_cast', 'cast', 'cbrt', 'cdouble', 'ceil', 'cfloat', 'char', 'character', 'chararray', 'choose', 'clip', 'clongdouble', 'clongfloat', 'column_stack', 'common_type', 'compare_chararrays', 'compat', 'complex', 'complex128', 'complex256', 'complex64', 'complex_', 'complexfloating', 'compress', 'concatenate', 'conj', 'conjugate', 'convolve', 'copy', 'copysign', 'copyto', 'core', 'corrcoef', 'correlate', 'cos', 'cosh', 'count_nonzero', 'cov', 'cross', 'csingle', 'ctypeslib', 'cumprod', 'cumproduct', 'cumsum', 'datetime64', 'datetime_as_string', 'datetime_data', 'deg2rad', 'degrees', 'delete', 'deprecate', 'deprecate_with_doc', 'diag', 'diag_indices', 'diag_indices_from', 'diagflat', 'diagonal', 'diff', 'digitize', 'disp', 'divide', 'division', 'divmod', 'dot', 'double', 'dsplit', 'dstack', 'dtype', 'e', 'ediff1d', 'einsum', 'einsum_path', 'emath', 'empty', 'empty_like', 'equal', 'errstate', 'euler_gamma', 'exp', 'exp2', 'expand_dims', 'expm1', 'extract', 'eye', 'fabs', 'fastCopyAndTranspose', 'fft', 'fill_diagonal', 'find_common_type', 'finfo', 'fix', 'flatiter', 'flatnonzero', 'flexible', 'flip', 'fliplr', 'flipud', 'float', 'float128', 'float16', 'float32', 'float64', 'float_', 'float_power', 'floating', 'floor', 'floor_divide', 'fmax', 'fmin', 'fmod', 'format_float_positional', 'format_float_scientific', 'format_parser', 'frexp', 'frombuffer', 'fromfile', 'fromfunction', 'fromiter', 'frompyfunc', 'fromregex', 'fromstring', 'full', 'full_like', 'fv', 'gcd', 'generic', 'genfromtxt', 'geomspace', 'get_array_wrap', 'get_include', 'get_printoptions', 'getbufsize', 'geterr', 'geterrcall', 'geterrobj', 'gradient', 'greater', 'greater_equal', 'half', 'hamming', 'hanning', 'heaviside', 'histogram', 'histogram2d', 'histogram_bin_edges', 'histogramdd', 'hsplit', 'hstack', 'hypot', 'i0', 'identity', 'iinfo', 'imag', 'in1d', 'index_exp', 'indices', 'inexact', 'inf', 'info', 'infty', 'inner', 'insert', 'int', 'int0', 'int16', 'int32', 'int64', 'int8', 'int_', 'int_asbuffer', 'intc', 'integer', 'interp', 'intersect1d', 'intp', 'invert', 'ipmt', 'irr', 'is_busday', 'isclose', 'iscomplex', 'iscomplexobj', 'isfinite', 'isfortran', 'isin', 'isinf', 'isnan', 'isnat', 'isneginf', 'isposinf', 'isreal', 'isrealobj', 'isscalar', 'issctype', 'issubclass_', 'issubdtype', 'issubsctype', 'iterable', 'ix_', 'kaiser', 'kron', 'lcm', 'ldexp', 'left_shift', 'less', 'less_equal', 'lexsort', 'lib', 'linalg', 'linspace', 'little_endian', 'load', 'loads', 'loadtxt', 'log', 'log10', 'log1p', 'log2', 'logaddexp', 'logaddexp2', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'logspace', 'long', 'longcomplex', 'longdouble', 'longfloat', 'longlong', 'lookfor', 'ma', 'mafromtxt', 'mask_indices', 'mat', 'math', 'matmul', 'matrix', 'matrixlib', 'max', 'maximum', 'maximum_sctype', 'may_share_memory', 'mean', 'median', 'memmap', 'meshgrid', 'mgrid', 'min', 'min_scalar_type', 'minimum', 'mintypecode', 'mirr', 'mkl', 'mod', 'modf', 'moveaxis', 'msort', 'multiply', 'nan', 'nan_to_num', 'nanargmax', 'nanargmin', 'nancumprod', 'nancumsum', 'nanmax', 'nanmean', 'nanmedian', 'nanmin', 'nanpercentile', 'nanprod', 'nanquantile', 'nanstd', 'nansum', 'nanvar', 'nbytes', 'ndarray', 'ndenumerate', 'ndfromtxt', 'ndim', 'ndindex', 'nditer', 'negative', 'nested_iters', 'newaxis', 'nextafter', 'nonzero', 'not_equal', 'nper', 'npv', 'numarray', 'number', 'obj2sctype', 'object', 'object0', 'object_', 'ogrid', 'oldnumeric', 'ones', 'ones_like', 'os', 'outer', 'packbits', 'pad', 'partition', 'percentile', 'pi', 'piecewise', 'place', 'pmt', 'poly', 'poly1d', 'polyadd', 'polyder', 'polydiv', 'polyfit', 'polyint', 'polymul', 'polynomial', 'polysub', 'polyval', 'positive', 'power', 'ppmt', 'print_function', 'printoptions', 'prod', 'product', 'promote_types', 'ptp', 'put', 'put_along_axis', 'putmask', 'pv', 'quantile', 'r_', 'rad2deg', 'radians', 'random', 'rank', 'rate', 'ravel', 'ravel_multi_index', 'real', 'real_if_close', 'rec', 'recarray', 'recfromcsv', 'recfromtxt', 'reciprocal', 'record', 'remainder', 'repeat', 'require', 'reshape', 'resize', 'result_type', 'right_shift', 'rint', 'roll', 'rollaxis', 'roots', 'rot90', 'round', 'round_', 'row_stack', 's_', 'safe_eval', 'save', 'savetxt', 'savez', 'savez_compressed', 'sctype2char', 'sctypeDict', 'sctypeNA', 'sctypes', 'searchsorted', 'select', 'set_numeric_ops', 'set_printoptions', 'set_string_function', 'setbufsize', 'setdiff1d', 'seterr', 'seterrcall', 'seterrobj', 'setxor1d', 'shape', 'shares_memory', 'short', 'show_config', 'sign', 'signbit', 'signedinteger', 'sin', 'sinc', 'single', 'singlecomplex', 'sinh', 'size', 'sometrue', 'sort', 'sort_complex', 'source', 'spacing', 'split', 'sqrt', 'square', 'squeeze', 'stack', 'std', 'str', 'str0', 'str_', 'string_', 'subtract', 'sum', 'swapaxes', 'sys', 'take', 'take_along_axis', 'tan', 'tanh', 'tensordot', 'test', 'testing', 'tile', 'timedelta64', 'trace', 'tracemalloc_domain', 'transpose', 'trapz', 'tri', 'tril', 'tril_indices', 'tril_indices_from', 'trim_zeros', 'triu', 'triu_indices', 'triu_indices_from', 'true_divide', 'trunc', 'typeDict', 'typeNA', 'typecodes', 'typename', 'ubyte', 'ufunc', 'uint', 'uint0', 'uint16', 'uint32', 'uint64', 'uint8', 'uintc', 'uintp', 'ulonglong', 'unicode', 'unicode_', 'union1d', 'unique', 'unpackbits', 'unravel_index', 'unsignedinteger', 'unwrap', 'ushort', 'vander', 'var', 'vdot', 'vectorize', 'version', 'void', 'void0', 'vsplit', 'vstack', 'warnings', 'where', 'who', 'zeros', 'zeros_like']\n"
}
],
"source": [
"# Checking the available methods\n",
"print(dir(np))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "0.3010299956639812\nFalse\n"
}
],
"source": [
"print(np.log10(2))\n",
"print(np.not_equal(10, 10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a list in python"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "<class 'list'>\n[1, 2, 3, 4, 5]\n[[0, 1, 2], [3, 4, 5], [6, 7, 8]]\n"
}
],
"source": [
"# Creating python List\n",
"\n",
"python_list = [1,2,3,4,5]\n",
"# Checking data types\n",
"print(type (python_list))\n",
"print(python_list)\n",
"two_dimensional_list = [[0,1,2], [3,4,5], [6,7,8]]\n",
"print(two_dimensional_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating numpy array using numpy"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "<class 'numpy.ndarray'>\n[1 2 3 4 5]\n"
}
],
"source": [
"# Creating Numpy(Numerical Python) array from python list\n",
"numpy_array_from_list = np.array(python_list)\n",
"print(type (numpy_array_from_list))\n",
"print(numpy_array_from_list)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1, 2, 3, 4, 5])"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numy_array_from_list2 = np.array(python_list)\n",
"numy_array_from_list2"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1., 2., 3., 4., 5.])"
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numy_array_from_list2 = np.array(python_list, dtype=float)\n",
"numy_array_from_list2"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([False, True, True, False, False])"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_bool_array = np.array([0, 1, -1, 0, 0], dtype=bool)\n",
"numpy_bool_array"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "<class 'numpy.ndarray'>\n[[0 1 2]\n [3 4 5]\n [6 7 8]]\n"
}
],
"source": [
"\n",
"numpy_two_dimensional_list = np.array(two_dimensional_list)\n",
"print(type (numpy_two_dimensional_list))\n",
"print(numpy_two_dimensional_list)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1, 2, 3, 4, 5])"
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_array_from_list"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_two_dimensional_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Converting numpy array to list"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "<class 'list'>\none dimensional array: [1, 2, 3, 4, 5]\ntwo dimensional array: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]\n"
}
],
"source": [
"# We can always convert an array back to a python list using tolist().\n",
"np_to_list = numpy_array_from_list.tolist()\n",
"print(type (np_to_list))\n",
"print('one dimensional array:', np_to_list)\n",
"print('two dimensional array: ', numpy_two_dimensional_list.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])"
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_two_dimensional_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating numpy array from tuple"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "<class 'tuple'>\npython_tuple: (1, 2, 3, 4, 5)\n<class 'numpy.ndarray'>\nnumpy_array_from_tuple: [1 2 3 4 5]\n"
}
],
"source": [
"# Numpy array from tuple\n",
"\n",
"# Creating tuple in Python\n",
"\n",
"python_tuple = (1,2,3,4,5)\n",
"print(type (python_tuple))\n",
"print('python_tuple: ', python_tuple)\n",
"\n",
"numpy_array_from_tuple = np.array(python_tuple)\n",
"print(type (numpy_array_from_tuple))\n",
"print('numpy_array_from_tuple: ', numpy_array_from_tuple)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1, 2, 3, 4, 5])"
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_array_from_tuple"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shape of numpy array\n",
"The shape method provide the shape of the array as a tuple. The first is the row and the second is the column"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[1 2 3 4 5]\nshape of nums: (5,)\n[[0 1 2]\n [3 4 5]\n [6 7 8]]\nshape of numpy_two_dimensional_list: (3, 3)\n(3, 4)\n"
}
],
"source": [
"nums = np.array([1, 2, 3, 4, 5])\n",
"print(nums)\n",
"print('shape of nums: ', nums.shape)\n",
"print(numpy_two_dimensional_list)\n",
"print('shape of numpy_two_dimensional_list: ', numpy_two_dimensional_list.shape)\n",
"three_by_four_array = np.array([[0, 1, 2, 3],\n",
" [4,5,6,7],\n",
" [8,9,10, 11]])\n",
"print(three_by_four_array.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data type of numpy array\n",
"\n",
"Type of data types: str, int, float, complex, bool, list, None"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[-3 -2 -1 0 1 2 3]\nint64\n[-3. -2. -1. 0. 1. 2. 3.]\nfloat64\n"
}
],
"source": [
"int_lists = [-3, -2, -1, 0, 1, 2,3]\n",
"int_array = np.array(int_lists)\n",
"float_array = np.array(int_lists, dtype=float)\n",
"\n",
"print(int_array)\n",
"print(int_array.dtype)\n",
"print(float_array)\n",
"print(float_array.dtype)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Size of a numpy array\n",
"Instead of len size is used to get the length of items in a numpy array"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "5"
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_array_from_list.size"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "9"
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_two_dimensional_list.size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mathematical Operation\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "original array: [1 2 3 4 5]\n[11 12 13 14 15]\n[-9 -8 -7 -6 -5]\n[10 20 30 40 50]\n"
}
],
"source": [
"# Mathematical Operation\n",
"\n",
"# Addition\n",
"print('original array: ', numpy_array_from_list)\n",
"ten_plus_original = numpy_array_from_list + 10\n",
"print(ten_plus_original)\n",
"ten_minus_original = numpy_array_from_list - 10\n",
"print(ten_minus_original)\n",
"# Multiplication\n",
"ten_times_original = numpy_array_from_list * 10\n",
"print(ten_times_original)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "int64\nfloat64\nfloat64\n"
}
],
"source": [
"# Float numbers\n",
"\n",
"numpy_int_list = np.array([1,2,3,4])\n",
"numpy_float_list = np.array([1.1, 2.0,3.2])\n",
"numpy_float_list2 = np.array([1.1,2.0,3.2])\n",
"\n",
"print(numpy_int_list.dtype)\n",
"print(numpy_float_list2.dtype)\n",
"print(numpy_float_list.dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Converting type from float to int"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1, 2, 3])"
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Converting type from float to int\n",
"numpy_float_list.astype('int')\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array(['1', '2', '3'], dtype='<U21')"
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Converting type from int to str\n",
"numpy_float_list.astype('int').astype('str')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dimensional Arrays"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "<class 'numpy.ndarray'>\n[[1 2 3]\n [4 5 6]\n [7 8 9]]\nShape: (3, 3)\nSize: 9\nData type: int64\n"
}
],
"source": [
"# 2 Dimension Array\n",
"two_dimension_array = np.array([(1,2,3),(4,5,6), (7,8,9)])\n",
"print(type (two_dimension_array))\n",
"print(two_dimension_array)\n",
"print('Shape: ', two_dimension_array.shape)\n",
"print('Size:', two_dimension_array.size)\n",
"print('Data type:', two_dimension_array.dtype)\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])"
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two_dimension_array"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to extract specific items from an array?"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "First row: [1 2 3]\nSecond row: [4 5 6]\nThird row: [7 8 9]\n"
}
],
"source": [
"first_row = two_dimension_array[0]\n",
"second_row = two_dimension_array[1]\n",
"third_row = two_dimension_array[2]\n",
"print('First row:', first_row)\n",
"print('Second row:', second_row)\n",
"print('Third row: ', third_row)\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "First column: [1 4 7]\nSecond column: [2 5 8]\nThird column: [3 6 9]\n[[1 2 3]\n [4 5 6]\n [7 8 9]]\n"
}
],
"source": [
"first_column= two_dimension_array[:,0]\n",
"second_column = two_dimension_array[:,1]\n",
"third_column = two_dimension_array[:,2]\n",
"print('First column:', first_column)\n",
"print('Second column:', second_column)\n",
"print('Third column: ', third_column)\n",
"print(two_dimension_array)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Slicing in numpy is similar to list"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[1, 2],\n [4, 5]])"
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first_two_rows_and_columns = two_dimension_array[0:2, 0:2]\n",
"first_two_rows_and_columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to reverse the rows and the whole array?"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])"
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two_dimension_array[::]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reverse only the row positions"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[7, 8, 9],\n [4, 5, 6],\n [1, 2, 3]])"
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two_dimension_array[::-1,]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reverse the row and column positions"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[9, 8, 7],\n [6, 5, 4],\n [3, 2, 1]])"
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two_dimension_array[::-1,::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to represent missing values and infinite?"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[[1 2 3]\n [4 5 6]\n [7 8 9]]\n[[ 1 2 3]\n [ 4 55 44]\n [ 7 8 9]]\n"
}
],
"source": [
"print(two_dimension_array)\n",
"two_dimension_array[1,1] = 55\n",
"two_dimension_array[1,2] =44\n",
"print(two_dimension_array)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[0, 0, 0],\n [0, 0, 0],\n [0, 0, 0]])"
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Numpy Zeroes\n",
"# numpy.zeros(shape, dtype=float, order='C')\n",
"numpy_zeroes = np.zeros((3,3),dtype=int,order='C')\n",
"numpy_zeroes"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[[1 1 1]\n [1 1 1]\n [1 1 1]]\n"
}
],
"source": [
"# Numpy Zeroes\n",
"numpy_ones = np.ones((3,3),dtype=int,order='C')\n",
"print(numpy_ones)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"twoes = numpy_ones * 2"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[[1 2 3]\n [4 5 6]]\n[[1 2]\n [3 4]\n [5 6]]\n"
}
],
"source": [
"# Reshape\n",
"# numpy.reshape(), numpy.flatten()\n",
"first_shape = np.array([(1,2,3), (4,5,6)])\n",
"print(first_shape)\n",
"reshaped = first_shape.reshape(3,2)\n",
"print(reshaped)\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1, 2, 3, 4, 5, 6])"
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flattened = reshaped.flatten()\n",
"flattened"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[5 7 9]\nHorizontal Append: [1 2 3 4 5 6]\n"
}
],
"source": [
"## Horitzontal Stack\n",
"np_list_one = np.array([1,2,3])\n",
"np_list_two = np.array([4,5,6])\n",
"\n",
"print(np_list_one + np_list_two)\n",
"\n",
"print('Horizontal Append:', np.hstack((np_list_one, np_list_two)))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Vertical Append: [[1 2 3]\n [4 5 6]]\n"
}
],
"source": [
"## Vertical Stack\n",
"print('Vertical Append:', np.vstack((np_list_one, np_list_two)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Generating Random Numbers"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0.1477747854886351"
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generate a random float number\n",
"random_float = np.random.random()\n",
"random_float"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([0.66880516, 0.58036922, 0.32565217, 0.75833764, 0.33492522])"
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generate a random float number\n",
"random_floats = np.random.random(5)\n",
"random_floats"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "1"
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generating a random integers between 0 and 10\n",
"random_int = np.random.randint(0, 11)\n",
"random_int"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([6, 7, 6, 5])"
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generating a random integers between 2 and 11, and creating a one row array\n",
"random_int = np.random.randint(2,10, size=4)\n",
"random_int"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[4, 8, 7],\n [6, 3, 8],\n [8, 3, 4]])"
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generating a random integers between 0 and 10\n",
"random_int = np.random.randint(2,10, size=(3,3))\n",
"random_int"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 78.06228226, 80.47997605, 67.16871012, 100.01106251,\n 73.43427077, 70.90858509, 68.18228819, 88.71233066,\n 86.23943257, 99.66232512, 80.46786746, 105.15418964,\n 78.37084644, 74.10533462, 81.86581832, 78.34056027,\n 72.13209789, 72.69912968, 111.64843341, 96.02940931,\n 80.82983836, 49.2603406 , 82.11783362, 82.00473287,\n 68.36971774, 83.44601599, 75.22668092, 67.16102449,\n 84.12544108, 84.89425903, 69.87305909, 96.80967774,\n 80.89504255, 33.82173197, 65.9476567 , 114.69465093,\n 87.58440669, 59.44914567, 69.09711011, 81.28734677,\n 54.02167729, 76.24325763, 81.07595137, 78.25321296,\n 56.06354536, 74.88790644, 65.37671466, 73.0608308 ,\n 83.53329124, 56.11613468, 69.70534252, 105.03153668,\n 92.09774157, 76.27471759, 66.30470031, 75.26872422,\n 104.47856237, 69.43495655, 78.47940174, 66.33033948,\n 88.47445255, 53.07577185, 106.87650496, 84.80340513,\n 92.70276459, 66.26854244, 92.80079253, 90.54206113,\n 76.68091973, 88.37271982, 99.28200027, 85.85283546,\n 81.3903595 , 64.08758723, 56.9362159 , 51.233451 ,\n 72.95037141, 81.21256832, 95.22519781, 69.18242214,\n 47.90222857, 87.08328607, 80.42971413, 94.36731459,\n 52.62876844, 65.72917254, 62.00665453, 60.2468513 ,\n 89.17802841, 95.21163386, 87.46425376, 110.2230449 ,\n 72.34068564, 69.43244175, 79.42739361, 91.96167318,\n 88.71546515, 46.20839431, 93.30432944, 83.55539894,\n 88.63548129, 73.50049682, 59.96380522, 93.58951555,\n 120.13068226, 70.06924206, 65.69839628, 83.23774129,\n 79.25570985, 71.89321258, 80.64905645, 76.07107192,\n 89.34416292, 98.82861126, 73.4790967 , 63.35301086,\n 83.76185379, 100.64189068, 70.13596249, 64.50577384,\n 75.04897329, 78.99902225, 85.14751918, 69.71124641,\n 65.90879778, 90.30143113, 104.27014623, 84.86204287,\n 74.41819024, 62.6483838 , 90.39531176, 52.18361377,\n 56.10889369, 105.07634101, 74.60664275, 80.48560286,\n 93.92703692, 89.4721985 , 76.69992387, 87.55989167,\n 74.42497904, 93.34549716, 85.56512669, 91.27006247,\n 76.99063702, 74.51895242, 68.31335173, 61.82950247,\n 74.27565476, 75.47967285, 56.99448404, 77.83995971,\n 88.23766005, 64.25073627, 84.21727063, 72.73365378,\n 90.56998368, 61.90746621, 104.32735616, 33.51801517,\n 38.17830252, 91.00363097, 79.96743202, 92.59940851,\n 74.31161094, 78.25334346, 87.49652045, 65.10876645,\n 97.29702422, 71.21703521, 71.57154296, 55.67862433,\n 78.63567388, 77.39172292, 85.85382211, 76.1303165 ,\n 79.93907903, 55.86163862, 65.03007833, 98.06472484,\n 98.18297161, 86.35617188, 73.95928996, 81.58243288,\n 72.13009418, 85.57473551, 73.31333927, 90.54931228,\n 92.84532205, 84.5451769 , 86.73824099, 84.29718499,\n 108.54889407, 82.05896086, 64.74466701, 87.31905746,\n 70.86189615, 74.17689346, 98.68855732, 73.47579279,\n 112.8709868 , 73.95676101, 62.34891052, 89.75718241,\n 86.84831209, 84.76483197, 75.00712483, 89.66930311,\n 79.02327324, 111.2672887 , 65.93375863, 57.32720674,\n 55.36834689, 80.69909018, 96.25708549, 86.41302494,\n 106.71440179, 87.34716208, 53.87588225, 67.60260121,\n 71.74959277, 69.64175761, 61.62553359, 92.84685364,\n 88.39326749, 67.82375745, 78.88508202, 63.41683055,\n 83.33247265, 61.78182943, 69.47178609, 94.29415174,\n 71.34896978, 42.97502617, 94.36712783, 66.99952686,\n 71.18875098, 61.51192175, 58.25951442, 67.83145087,\n 93.1965335 , 78.841944 , 90.02731255, 108.67011039,\n 87.9282376 , 62.27183736, 84.46821305, 93.10995068,\n 81.79071208, 40.0635728 , 102.20724488, 88.6488238 ,\n 92.91848738, 93.32901714, 74.89123998, 102.44537103,\n 58.84904865, 80.17557578, 69.60840343, 65.11810179,\n 73.57184954, 103.42197477, 94.51878907, 76.34101893,\n 58.20236603, 76.15803306, 85.01092471, 102.62825199,\n 70.57778869, 64.55384487, 75.92847535, 108.29656951,\n 65.09564337, 71.97746096, 67.91685793, 98.37480773,\n 78.55584328, 58.79508008, 83.78716705, 85.51819492,\n 85.39472497, 86.48911498, 86.74878694, 88.82191939,\n 87.56668016, 68.18363072, 72.65452689, 86.89008749,\n 74.10734973, 55.4094851 , 66.84354188, 58.51715121,\n 90.78804647, 66.26231617, 83.23663878, 121.92253573,\n 94.17040645, 85.300603 , 83.28102031, 88.92437677,\n 47.92432906, 74.9123454 , 74.82372863, 102.34755849,\n 63.97023146, 91.42597585, 76.97286274, 89.99561505,\n 104.27334052, 65.28530687, 71.76722311, 76.99942849,\n 93.12862908, 88.29252813, 74.40020522, 59.83777803,\n 86.2692467 , 57.72956079, 92.21270412, 76.32248068,\n 74.39526899, 54.8414521 , 48.888437 , 106.77555705,\n 104.07932258, 62.61412927, 91.17935874, 69.99817706,\n 80.27041286, 85.57510819, 87.32432908, 94.01658731,\n 87.54240264, 68.81017962, 91.37078862, 82.47632639,\n 97.58252432, 87.48121013, 74.6234212 , 74.6986795 ,\n 76.65946615, 74.36683032, 86.98593152, 54.16182721,\n 95.90072974, 68.90556443, 88.8115489 , 61.33034803,\n 75.03376482, 85.67012786, 69.61414988, 77.9329127 ,\n 74.73350745, 50.98154773, 70.32859405, 81.82842611,\n 79.34651799, 56.61456886, 108.10555204, 88.22287037,\n 74.39100199, 76.41366393, 62.04351567, 83.27601537,\n 86.99514943, 71.91082253, 64.87485275, 95.53195338,\n 75.98938226, 50.46800926, 99.48823496, 87.43692335,\n 77.78589128, 70.70840352, 103.66404784, 98.84563267,\n 83.84939092, 63.35894729, 86.21033577, 113.4329368 ,\n 71.96158217, 85.28699953, 58.00992511, 79.71669981,\n 89.32418303, 39.37997338, 86.20379324, 65.66118568,\n 78.98887722, 116.59556808, 96.71728972, 62.82439269,\n 81.48105406, 50.10899439, 85.05778363, 75.00743273,\n 91.94666309, 56.79855211, 63.95730649, 62.68945327,\n 85.85109342, 83.27385995, 83.27264177, 73.24183525,\n 56.87217993, 68.80304416, 91.42472878, 96.91813936,\n 61.43286456, 60.19787993, 76.10324428, 76.02346429,\n 70.30878903, 111.39327281, 83.58888893, 100.87217758,\n 86.65284275, 80.57122349, 84.30929287, 83.29940119,\n 77.22425347, 83.70996324, 76.18292731, 70.66577399,\n 82.87365549, 60.97611143, 84.00291165, 73.75303205,\n 62.36634484, 81.03059977, 78.79641201, 66.80902035,\n 68.81283634, 95.40175522, 100.9812039 , 87.22874071,\n 66.37434254, 107.62870114, 89.95113086, 78.9459868 ,\n 55.74164355, 82.3878577 , 103.00808091, 103.92471237,\n 54.66425635, 79.29420733, 69.96882481, 72.17070955,\n 54.36635876, 90.92566622, 58.22358622, 53.1620387 ,\n 80.39951312, 82.04658674, 67.2881847 , 65.67690213,\n 108.66137872, 89.3519222 , 68.34664527, 94.67503978,\n 94.97036282, 86.17659241, 86.84161366, 74.67551798,\n 66.7113065 , 71.45805551, 100.196514 , 93.67226563,\n 56.40934843, 118.21100209, 82.9799281 , 88.62491982,\n 87.03019073, 57.79816924, 74.28981915, 82.88973048,\n 91.8436542 , 96.00113316, 69.95094311, 68.13895371,\n 52.58979642, 98.57099807, 80.22167709, 94.40292506,\n 83.31105998, 59.9926828 , 77.74859503, 94.56173455,\n 56.63442708, 70.99578669, 61.27584441, 71.64922331,\n 79.88262193, 74.69845801, 75.82961051, 87.75033865,\n 108.55912336, 57.56875358, 89.19231781, 58.2379943 ,\n 96.58276029, 75.04521478, 86.66189835, 74.0184621 ,\n 84.1612496 , 78.80287021, 82.40061125, 71.61704823,\n 96.52547727, 83.92205443, 89.18972954, 77.78097677,\n 79.54274882, 80.90208256, 114.55056822, 84.75557082,\n 75.76282328, 91.98124081, 74.84037922, 82.71645645,\n 83.13525585, 95.57640112, 69.43862495, 76.3638732 ,\n 78.8258523 , 82.79968036, 79.53328299, 97.14114949,\n 83.06939074, 77.62484757, 82.36762999, 67.32343718,\n 79.53488316, 110.55065547, 73.45111082, 74.11521981,\n 53.55164117, 97.21629361, 70.65778072, 81.77179281,\n 93.09918786, 84.64189076, 73.01267275, 82.05924077,\n 104.27148836, 73.18202528, 82.20035143, 70.11261095,\n 76.3956408 , 80.59874089, 76.60682684, 92.96876414,\n 77.70067312, 62.19425744, 74.23546655, 86.09897646,\n 95.3922493 , 65.96070337, 78.14115335, 88.77127978,\n 66.50819906, 69.67195613, 77.17798553, 73.6445897 ,\n 76.45981325, 91.34264205, 86.27955532, 70.29966357,\n 104.36673101, 97.05018014, 60.04493436, 85.11496696,\n 65.81390189, 84.86482526, 52.97518977, 74.57566331,\n 78.75310135, 85.77492172, 71.35722871, 106.11068985,\n 124.97210481, 64.21057686, 66.52047736, 106.94144838,\n 117.34122374, 88.50176593, 88.91385192, 107.97612164,\n 93.15004781, 58.76140505, 52.03809202, 92.62254407,\n 80.86381554, 100.43323091, 93.42890753, 72.523524 ,\n 80.29641867, 75.20448137, 74.66556513, 94.98379828,\n 74.78647058, 76.98338228, 74.84876293, 82.95665001,\n 88.35626775, 94.32698817, 71.14073011, 112.07773057,\n 80.00015636, 68.07692698, 113.49304812, 108.92762648,\n 68.67420516, 98.73515549, 84.32042451, 91.72139165,\n 66.4137705 , 69.93003756, 63.89062414, 82.64679468,\n 68.66601776, 64.30864809, 80.24191016, 53.43056906,\n 81.69719392, 87.34441433, 91.38770101, 59.16322365,\n 83.19726315, 61.75218797, 67.48675016, 64.09508776,\n 72.08408273, 80.05154611, 90.39502598, 89.20492664,\n 79.43005379, 103.62785263, 31.38686493, 72.00221598,\n 84.90436196, 87.71163029, 60.24661018, 98.14319475,\n 104.27390629, 82.13872268, 72.46160603, 75.6806385 ,\n 78.20961222, 67.04506469, 67.50202444, 90.78465477,\n 69.78798088, 84.57134186, 56.87529573, 84.77391649,\n 89.53774771, 65.85042351, 78.30539835, 78.39971562,\n 54.17582106, 66.14233741, 95.30687232, 80.38434415,\n 89.20059439, 60.08421512, 55.049331 , 101.52734573,\n 62.43599109, 78.53915989, 72.19896564, 116.79354989,\n 76.80593363, 70.02409537, 38.49989574, 73.04644811,\n 98.85180903, 79.5240704 , 66.31040168, 92.4980046 ,\n 83.34256192, 66.99625849, 96.27910494, 95.358351 ,\n 79.84098206, 83.14032522, 42.40925601, 54.34194303,\n 84.06615973, 82.17376391, 70.67851012, 85.09767893,\n 80.32794527, 85.17526058, 97.30100338, 66.91553603,\n 76.07403987, 80.76033764, 95.91036179, 82.08802473,\n 98.55080876, 97.10028467, 101.13288782, 81.91445241,\n 66.74373645, 90.14807268, 90.10897824, 67.71922793,\n 72.12689907, 62.45830276, 69.12897818, 75.65268046,\n 84.49480148, 100.63329536, 87.37431219, 105.89171019,\n 75.28306231, 70.00937135, 89.03671396, 74.51401316,\n 73.79273308, 100.41392714, 68.69070397, 88.20822103,\n 76.00259474, 106.61250194, 82.88183663, 76.27135957,\n 74.95698129, 93.63350012, 88.9619559 , 62.85201103,\n 119.69006733, 85.86409883, 55.75743606, 75.00489182,\n 58.06456391, 52.26432328, 66.66447096, 75.07032703,\n 84.59732897, 112.54736571, 65.34570336, 102.17736386,\n 40.37932797, 83.04954136, 77.69724422, 105.90650084,\n 80.45674399, 71.68883905, 98.73184451, 76.13710261,\n 66.20507679, 88.74955062, 80.0510249 , 76.22040964,\n 93.95306912, 52.35187424, 70.61081767, 95.22725407,\n 108.94784107, 72.24788481, 88.79595569, 50.5979318 ,\n 62.04383021, 82.92286625, 73.21143122, 53.05418957,\n 87.40469357, 94.69362199, 78.83425313, 103.18034128,\n 91.77577212, 67.46671285, 68.03901009, 62.2977924 ,\n 97.5852646 , 79.90403623, 77.95140453, 55.37994377,\n 89.23457844, 90.37887043, 89.85164318, 73.76839666,\n 87.96911602, 68.63450746, 93.21846679, 53.30857204,\n 94.15702001, 90.69365635, 96.38657258, 81.77837411,\n 79.47635508, 67.02666039, 84.07348833, 110.67955405,\n 91.05912686, 85.44147472, 65.16901929, 84.73650976,\n 81.89662136, 55.35194992, 72.66891519, 87.76721338,\n 90.88372285, 57.96348731, 89.68013973, 62.74481238,\n 114.52531551, 92.67234425, 87.31186873, 78.60200183,\n 86.59058664, 66.94814514, 77.3853412 , 91.71363056,\n 95.70888858, 52.45127237, 88.63758894, 97.57111284,\n 102.01999059, 75.96281703, 88.36166644, 93.34628342,\n 73.53905254, 59.92934719, 66.59948145, 56.78496989,\n 105.70030441, 72.86444968, 52.39540333, 74.45779479,\n 45.66426504, 56.3253082 , 61.62810162, 83.84735818,\n 80.53413791, 74.71040154, 92.85498595, 94.88784482,\n 96.30760768, 57.32765239, 82.83004411, 79.46457279,\n 49.90957363, 60.65314868, 90.93552368, 106.21061945,\n 97.13211144, 71.44100454, 90.05329611, 91.53492172,\n 71.46036606, 68.34357157, 80.42264456, 52.26701607,\n 48.28971326, 50.09431862, 52.27223857, 53.59897973,\n 81.96496589, 59.92479546, 64.00557478, 113.15028134,\n 48.87116061, 81.9481499 , 95.142522 , 76.16819745,\n 99.58493419, 94.06452704, 83.05951515, 64.0776772 ,\n 98.85617806, 53.79063833, 67.38281794, 88.12377387,\n 89.03606168, 71.96180971, 94.82115345, 75.35454868,\n 85.71719855, 77.85575022, 55.38749871, 88.81850218,\n 98.28893289, 71.79790379, 86.74856436, 100.86891044,\n 106.94848343, 68.17002494, 95.00452271, 96.1588338 ,\n 87.02101749, 79.34469034, 93.11547607, 81.4201332 ,\n 79.61242788, 80.14276989, 66.31126804, 73.81342493,\n 84.47742817, 87.65835947, 65.64731682, 86.66836892,\n 80.54654032, 84.83075141, 72.89043985, 61.3376971 ,\n 84.94655925, 87.38518507, 76.41231247, 106.23342075,\n 59.71539452, 70.96316145, 86.07725176, 109.16186329,\n 77.98466482, 61.86883949, 93.33239525, 71.42031957,\n 55.60559768, 105.19788732, 76.43995609, 101.1628942 ,\n 58.91082047, 67.00069345, 76.45884388, 73.54853117,\n 69.62413178, 74.36808765, 91.04834957, 69.78617132,\n 75.60903984, 89.53931323, 83.58114772, 79.9388412 ,\n 55.10438037, 87.65131276, 49.84807055, 52.41289143,\n 73.60261926, 86.15417227, 84.3558208 , 100.35194259,\n 78.19168592, 73.3648356 , 86.51749328, 80.33272539,\n 80.30453794, 51.27924021, 76.11370381, 74.36769789,\n 79.69049874, 77.0311673 , 92.05156246, 70.55832463,\n 71.77061074, 102.05166634, 54.32326398, 65.78529409,\n 70.88825347, 75.95280562, 59.98473392, 78.69470919,\n 56.4382883 , 89.81907081, 39.71499043, 62.08117787,\n 73.71371881, 79.24108502, 60.78772328, 72.11092866,\n 85.52384131, 73.39225323, 81.39602368, 64.41522371,\n 76.49433703, 65.1950397 , 90.5638382 , 94.66606541,\n 77.59274208, 85.57891274, 87.78522439, 108.15796205,\n 84.58319919, 108.12112397, 72.02513902, 76.48664057,\n 68.59305846, 84.35962562, 70.61111213, 87.87455868,\n 52.1882934 , 81.87445995, 48.17687999, 53.51759063,\n 105.03063346, 65.52206369, 109.02272228, 84.14593548])"
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generate random numbers\n",
"# np.random.normal(mu, sigma, size)\n",
"normal_array = np.random.normal(79, 15, 1000)\n",
"normal_array\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy and Statistics"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "(array([ 1., 2., 0., 2., 4., 1., 1., 2., 3., 7., 6., 22., 18.,\n 20., 16., 17., 27., 19., 41., 36., 37., 41., 50., 57., 39., 49.,\n 47., 49., 50., 48., 50., 33., 32., 32., 24., 21., 14., 13., 14.,\n 12., 10., 10., 6., 6., 3., 3., 1., 2., 1., 1.]),\n array([ 31.38686493, 33.25856972, 35.13027452, 37.00197932,\n 38.87368412, 40.74538891, 42.61709371, 44.48879851,\n 46.36050331, 48.2322081 , 50.1039129 , 51.9756177 ,\n 53.8473225 , 55.71902729, 57.59073209, 59.46243689,\n 61.33414169, 63.20584649, 65.07755128, 66.94925608,\n 68.82096088, 70.69266568, 72.56437047, 74.43607527,\n 76.30778007, 78.17948487, 80.05118966, 81.92289446,\n 83.79459926, 85.66630406, 87.53800885, 89.40971365,\n 91.28141845, 93.15312325, 95.02482805, 96.89653284,\n 98.76823764, 100.63994244, 102.51164724, 104.38335203,\n 106.25505683, 108.12676163, 109.99846643, 111.87017122,\n 113.74187602, 115.61358082, 117.48528562, 119.35699041,\n 121.22869521, 123.10040001, 124.97210481]),\n <a list of 50 Patch objects>)"
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set()\n",
"plt.hist(normal_array, color=\"grey\", bins=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# numpy.asarray()\n",
"# Asarray\n",
"# The asarray()function is used when you want to convert an input to an array. \n",
"# The input could be a lists, tuple, ndarray, etc."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"four_by_four_matrix = np.matrix(np.ones((4,4), dtype=float))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "matrix([[1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [1., 1., 1., 1.]])"
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"four_by_four_matrix"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "matrix([[1., 1., 1., 1.],\n [1., 1., 1., 1.],\n [2., 2., 2., 2.],\n [1., 1., 1., 1.]])"
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.asarray(four_by_four_matrix)[2] = 2\n",
"four_by_four_matrix"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# numpy.arange() in Python with Example\n",
"# Whay is Arrange?\n",
"# Sometimes, you want to create values that are evenly spaced within a defined interval. \n",
"# For instance, you want to create values from 1 to 10; you can use numpy.arange() function\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "range(0, 11, 2)"
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# creating list using range(starting, stop, step)\n",
"lst = range(0, 11, 2)\n",
"lst"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "0\n2\n4\n6\n8\n10\n"
}
],
"source": [
"for l in lst:\n",
" print(l)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n 17, 18, 19])"
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Similar to range arange numpy.arange(start, stop, step)\n",
"whole_numbers = np.arange(0, 20, 1)\n",
"whole_numbers"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n 18, 19])"
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"natural_numbers = np.arange(1, 20, 1)\n",
"natural_numbers"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 1, 3, 5, 7, 9, 11, 13, 15, 17, 19])"
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"odd_numbers = np.arange(1, 20, 2)\n",
"odd_numbers"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 2, 4, 6, 8, 10, 12, 14, 16, 18])"
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"even_numbers = np.arange(2, 20, 2)\n",
"even_numbers"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1. , 1.44444444, 1.88888889, 2.33333333, 2.77777778,\n 3.22222222, 3.66666667, 4.11111111, 4.55555556, 5. ])"
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# numpy.linspace()\n",
"# numpy.logspace() in Python with Example\n",
"# For instance, it can be used to create 10 values from 1 to 5 evenly spaced.\n",
"np.linspace(1.0, 5.0, num=10)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1. , 1.8, 2.6, 3.4, 4.2])"
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# not to include the last value in the interval\n",
"np.linspace(1.0, 5.0, num=5, endpoint=False)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"# LogSpace\n",
"# LogSpace returns even spaced numbers on a log scale. Logspace has the same parameters as np.linspace.\n",
"\n",
"# Syntax:\n",
"\n",
"# numpy.logspace(start, stop, num, endpoint)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 100. , 464.15888336, 2154.43469003, 10000. ])"
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.logspace(2, 4.0, num=4)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# to check the size of an array\n",
"x = np.array([1,2,3], dtype=np.complex128)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([1.+0.j, 2.+0.j, 3.+0.j])"
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "16"
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.itemsize"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[1, 2, 3],\n [4, 5, 6]])"
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# indexing and Slicing NumPy Arrays in Python \n",
"\n",
"np_list = np.array([(1,2,3), (4,5,6)])\n",
"np_list\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "First row: [1 2 3]\nSecond row: [4 5 6]\n"
}
],
"source": [
"print('First row: ', np_list[0])\n",
"print('Second row: ', np_list[1])\n"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "First column: [1 4]\nSecond column: [2 5]\nThird column: [3 6]\n"
}
],
"source": [
"print('First column: ', np_list[:,0])\n",
"print('Second column: ', np_list[:,1])\n",
"print('Third column: ', np_list[:,2])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## NumPy Statistical Functions with Example\n",
"NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc from the given elements in the array.\n",
"The functions are explained as follows −\n",
"Statistical function\n",
"Numpy is equipped with the robust statistical function as listed below\n",
"\n",
"- Numpy Functions\n",
" - Min\tnp.min()\n",
" - Max\tnp.max()\n",
" - Mean\tnp.mean()\n",
" - Median\tnp.median()\n",
" - Standard deviation\tnp.std()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "min: 1\nmax: 55\nmean: 14.777777777777779\nsd: 18.913709183069525\n"
}
],
"source": [
"np_normal_dis = np.random.normal(5, 0.5, 100)\n",
"np_normal_dis\n",
"## min, max, mean, median, sd\n",
"print('min: ', two_dimension_array.min())\n",
"print('max: ', two_dimension_array.max())\n",
"print('mean: ',two_dimension_array.mean())\n",
"# print('median: ', two_dimension_array.median())\n",
"print('sd: ', two_dimension_array.std())"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[[ 1 2 3]\n [ 4 55 44]\n [ 7 8 9]]\nColumn with minimum: [1 2 3]\nColumn with maximum: [ 7 55 44]\n=== Row ==\nRow with minimum: [1 4 7]\nRow with maximum: [ 3 55 9]\n"
}
],
"source": [
"print(two_dimension_array)\n",
"print('Column with minimum: ', np.amin(two_dimension_array,axis=0))\n",
"print('Column with maximum: ', np.amax(two_dimension_array,axis=0))\n",
"print('=== Row ==')\n",
"print('Row with minimum: ', np.amin(two_dimension_array,axis=1))\n",
"print('Row with maximum: ', np.amax(two_dimension_array,axis=1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to create repeating sequences?\n"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Tile: [1 2 3 1 2 3]\nRepeat: [1 1 2 2 3 3]\n"
}
],
"source": [
"a = [1,2,3] \n",
"\n",
"# Repeat whole of 'a' two times\n",
"print('Tile: ', np.tile(a, 2))\n",
"\n",
"# Repeat each element of 'a' two times\n",
"print('Repeat: ', np.repeat(a, 2))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to generate random numbers?"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "0.9476490025120005\n"
}
],
"source": [
"# One random number between [0,1)\n",
"one_random_num = np.random.random()\n",
"one_random_in = np.random\n",
"print(one_random_num)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[[0.85805543 0.94673813 0.11857992]\n [0.39495196 0.99733241 0.90835007]]\n"
}
],
"source": [
"# Random numbers between [0,1) of shape 2,3\n",
"r = np.random.random(size=[2,3])\n",
"print(r)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "['a' 'e' 'u' 'i' 'a' 'u' 'o' 'a' 'a' 'i']\n"
}
],
"source": [
"print(np.random.choice(['a', 'e', 'i', 'o', 'u'], size=10)) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[0.20980067, 0.48290936],\n [0.57274305, 0.36054907]])"
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Random numbers between [0, 1] of shape 2, 2\n",
"rand = np.random.rand(2,2)\n",
"rand"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[-1.46836664, 0.61438705],\n [ 0.51575582, 0.89551303]])"
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rand2 = np.random.randn(2,2)\n",
"rand2\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[6, 2, 6],\n [3, 8, 7],\n [8, 3, 5],\n [2, 4, 3],\n [3, 7, 3]])"
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Random integers between [0, 10) of shape 2,5\n",
"rand_int = np.random.randint(0, 10, size=[5,3])\n",
"rand_int"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "min: 3.181694913603133\nmax: 6.358778013894176\nmean: 4.98118971737671\nmedian: 4.993503158562607\nmode: ModeResult(mode=array([3.18169491]), count=array([1]))\nsd: 0.4917061541395749\n"
}
],
"source": [
"from scipy import stats\n",
"np_normal_dis = np.random.normal(5, 0.5, 1000)\n",
"np_normal_dis\n",
"## min, max, mean, median, sd\n",
"print('min: ', np.min(np_normal_dis))\n",
"print('max: ', np.max(np_normal_dis))\n",
"print('mean: ', np.mean(np_normal_dis))\n",
"print('median: ', np.median(np_normal_dis))\n",
"print('mode: ', stats.mode(np_normal_dis))\n",
"print('sd: ', np.std(np_normal_dis))"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "(array([ 3., 1., 2., 4., 6., 20., 31., 44., 87., 80., 114.,\n 112., 124., 87., 110., 76., 45., 29., 21., 2., 2.]),\n array([3.18169491, 3.33298459, 3.48427426, 3.63556393, 3.7868536 ,\n 3.93814327, 4.08943294, 4.24072261, 4.39201229, 4.54330196,\n 4.69459163, 4.8458813 , 4.99717097, 5.14846064, 5.29975031,\n 5.45103999, 5.60232966, 5.75361933, 5.904909 , 6.05619867,\n 6.20748834, 6.35877801]),\n <a list of 21 Patch objects>)"
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
},
{
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"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(np_normal_dis, color=\"grey\", bins=21)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"# numpy.dot(): Dot Product in Python using Numpy\n",
"# Dot Product\n",
"# Numpy is powerful library for matrices computation. For instance, you can compute the dot product with np.dot\n",
"\n",
"# Syntax\n",
"\n",
"# numpy.dot(x, y, out=None)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "14"
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Linear algebra\n",
"### Dot product: product of two arrays\n",
"f = np.array([1,2])\n",
"g = np.array([4,5])\n",
"### 1*4+2*5\n",
"np.dot(f, g)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "23"
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Linear algebra\n",
"### Dot product: product of two arrays\n",
"f = np.array([1,2,3])\n",
"g = np.array([4,5,3])\n",
"### 1*4+2*5 + 3*6\n",
"np.dot(f, g)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"# NumPy Matrix Multiplication with np.matmul() "
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[19, 22],\n [43, 50]])"
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### Matmul: matruc product of two arrays\n",
"h = [[1,2],[3,4]] \n",
"i = [[5,6],[7,8]] \n",
"### 1*5+2*7 = 19\n",
"np.matmul(h, i)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"## Determinant 2*2 matrix\n",
"### 5*8-7*6np.linalg.det(i)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "-1.999999999999999"
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linalg.det(i)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"Z = np.zeros((8,8))\n",
"Z[1::2,::2] = 1\n",
"Z[::2,1::2] = 1"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[0., 1., 0., 1., 0., 1., 0., 1.],\n [1., 0., 1., 0., 1., 0., 1., 0.],\n [0., 1., 0., 1., 0., 1., 0., 1.],\n [1., 0., 1., 0., 1., 0., 1., 0.],\n [0., 1., 0., 1., 0., 1., 0., 1.],\n [1., 0., 1., 0., 1., 0., 1., 0.],\n [0., 1., 0., 1., 0., 1., 0., 1.],\n [1., 0., 1., 0., 1., 0., 1., 0.]])"
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Z"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"new_list = [ x + 2 for x in range(0, 11)]"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_list"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])"
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np_arr = np.array(range(0, 11))\n",
"np_arr + 2"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([ 7, 9, 11, 13, 15])"
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.array([1,2,3,4,5])\n",
"y = x * 2 + 5\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "x and y must have same first dimension, but have shapes (1000,) and (5,)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-105-7f0b46fdf1a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Temperature in oC'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Pressure in atm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Temperature vs Pressure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2793\u001b[0m return gca().plot(\n\u001b[1;32m 2794\u001b[0m *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2795\u001b[0;31m is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m 2796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1664\u001b[0m \"\"\"\n\u001b[1;32m 1665\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1666\u001b[0;31m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1667\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1668\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 269\u001b[0m raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 270\u001b[0;31m \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m 271\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 272\u001b[0m raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
"\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (1000,) and (5,)"
]
}
],
"source": [
"plt.plot(x,y)\n",
"plt.xlabel('Temperature in oC')\n",
"plt.ylabel('Pressure in atm')\n",
"plt.title('Temperature vs Pressure')\n",
"plt.xticks(np.arange(0, 6, step=0.5))\n",
"plt.savefig('./numpy_files/linear_equation.png', dpi=None, facecolor='w', edgecolor='w',\n",
" orientation='portrait', papertype=None, format=None,\n",
" transparent=False, bbox_inches=None, pad_inches=0.1,\n",
" frameon=None, metadata=None)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "[Text(0, 0.5, 'y'), Text(0.5, 0, 'x')]"
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": "<Figure size 432x288 with 1 Axes>"
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"source": [
"x = np.random.normal(size=1000)\n",
"ax = sns.distplot(x);\n",
"ax.set(xlabel=\"x\", ylabel='y')\n"
]
},
{
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"# Summery\n",
"\n",
"To summarise, the main differences with python lists are:\n",
"\n",
"1. Arrays support vectorised operations, while lists don’t.\n",
"1. Once an array is created, you cannot change its size. You will have to create a new array or overwrite the existing one.\n",
"1. Every array has one and only one dtype. All items in it should be of that dtype.\n",
"1. An equivalent numpy array occupies much less space than a python list of lists.\n",
"1. numpy arrays support boolean indexing."
]
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