{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## NumPy\n", "For each question below, use NumPy functionality to execute the logic of your answer. (**Do not use ```For```...

Hello! I have one question here that I need some help with answering. I am just starting to learn numPy and could use some assistance


{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## NumPy\n", "For each question below, use NumPy functionality to execute the logic of your answer. (**Do not use ```For``` loops!**)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generating Random Data and Filtering That Data\n", "\n", "Please run the two cells below to set your random seed. (This makes the 'randomness' of the array the same for everyone)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "np.random.seed(25)\n", "ar = np.random.randn(1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "# Do NOT change this code!\n", "ar = ar * 100\n", "ar = ar.astype('int8')\n", "ar" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A. Write code in the function reshape that takes an array of size 1000 and reshapes the array into a set of five columns with 200 rows. Return this new array and replace the original ar array with it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def reshape(my_array):\n", " # YOUR CODE HERE\n", " \n", "#Check\n", "ar = reshape(ar)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "B. Now that ar is a matrix, create a function named row_min_max to get the maximum and minimum values from the matrix, row wise. This function returns a tuple of a list of row max values and a list of row min values (in that order). Be sure to remember how many total values you should be outputting (one for every row).\n", "\n", "For example: if my_matrix is:\n", "\n", "4 1 5\n", "5 6 7\n", "4 5 6\n", "The returned value should be:\n", "\n", "([5,7,6], [1,5,4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def row_min_max(my_matrix):\n", " # YOUR CODE HERE\n", "\n", "#Check\n", "row_min_max(ar)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "C. Write a function named less_than_mean that returns the total number of values in the ar matrix that are less than the mean of all values in the matrix. This should be a single number." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def less_than_mean(my_matrix):\n", " # YOUR CODE HERE\n", "\n", " \n", "#Check\n", "less_than_mean(ar)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "D. Write a function called max_position that returns the position (as a tuple) of the max value in the passed matrix.\n", "\n", "For example: if my_matrix is:\n", "\n", "4 1 5\n", "5 6 7\n", "4 5 6\n", "The returned value should be:\n", "\n", "(1, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def max_position(my_matrix):\n", " # YOUR CODE HERE\n", "\n", " \n", "#Check\n", "max_position(ar)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "E. Lastly, write a function named select_sum that returns the sum of all the values of a boolean array indexed ar matrix. Use the boolean array index of the ar matrix by:\n", "\n", "values greater than 100 -boolean or-\n", "values less than 5 -boolean and- greater than or equal to -20" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def select_sum(my_matrix):\n", " # YOUR CODE HERE\n", "\n", "#Check\n", "select_sum(ar)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }