Unit 5 Lab 3: Comparing Electric Bikes Importing and Accessing Data In this project, you will use a small table about electric bikes. You will build simple selector blocks that help you pull out headings, bike records, single values, and whole columns. Start a New Project called U5L3-ElectricBikes 1. This starter project already has a sample electric bike table. 2. Look at the table watcher on the stage. The first row contains the column headings. 3. Try item () of list with different numbers to inspect rows in the table. 4. Talk with your partner: How is item (1) different from the other items? 5. Try all but first of list. What part of the table does it remove? 6. Build these selector blocks: - headings of bike table () reports only the headings. - bikes in table () reports only the bike data, not the headings. Records, Fields, and Columns A record is one row of data about one bike. A field is one value inside a record. A column is one kind of value for every bike. 7. Build bike () from table () and field () from bike () blocks. 8. Build bike column () from bikes () to report one column from the data. 9. Use one column to answer a simple question, such as: Which bike has the longest range? What is the average price? Which bike is the lightest? Cleaning Data Data can be messy. One bike might list range in miles while another lists range in kilometers. A brand name might be spelled two different ways. A price might be missing. Cleaning data means making the data consistent without changing what it means. Write Out Your Thoughts If your class collected data about electric bikes, what are two problems that could make the data hard to 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+Ao2vAMKHMNmzueqks18l0nyVTFjMme5Ka0Iee/UjRFTxDiWnABUA2X1CuJi2aaxb+rKk0z5myeKcOPIyQoIBUjBLq+BEuL8KjWI95y7UljPdD54P6Qlte5czS1GfdB89J9AODhj6/9tprzqISedyLmkn9GGmuSRPRgAuA9vRn/3Wv/xCO/5ZBJcKZay8BQM0DIBDXzIgYE7jLiMfjoDeDT5kfAQoiT5566il59dVXhc9wEEZFGwJSuT+GAqFT8CBCr7CuvfL4qitfaGWNSH7s2LHq8xzUxzeMYcIzcU9AxgePcM/hrXHKwn+sr2Pg46equSZNRAMuANoGsK8sPBPs4REk6lvBZrK4BxD4XFmLMViWFVk3EpDKjEUwqF3V9y9+ZixbDBXqYTXz+QxmRrwhuNzYXwTsgALPClssmZmZ6hsyTqFYxwQzMKPyY8DSxd3WsmVLdSiJU3I8D54X/r8TZmxAik/Y61nB7ccZZad8k0+eBlwAZF0DcDTp27C2YpskKDATKxgrFQuSyBUCSS0rAj6sWcqIOmZvdjvGKwAABtFJREFUjjparjPFNcbshOuOY5dYzgSePv3004IlTJ7D8RdccIH61BqWMds6zGza5abl0R+sZKxgtl3oP7Mpdc8//3z1jeibb75ZVad/BDrwGbhzzz1XCHhVBcX/AHyOlHKp9UKKruAZSkwDHgDaarWnwYiCgwWzvsJyTAYxW3rl4HHx8uK59pMVTzu/On5WMHqJUbB+TEn8GnABMKbckutAp8h0tYLRgdYR+RiZXFk14AGgvxgsYAJAcWX51cB9FU9A6oQJE9SJNz8ZmoeLDK/Es88+q/5fErwceXl5ol+ZvCp5LWM08LkO3S4oxR3HqTeMDe0f5h4YHhhIvJY5cBXUHj7GCingIzWUPA24AIhYlAyR14RhQBwfJ9Y0LyzFnYbHgvUT9XilEkvIuo1oZ3hBxIk31nBY0HyWQxs+rEEBHP/7Jp4Sfgx8dDJIjubTl7vuukuIzmYNSkwga1m2i7DMqcdeH2kQ0XfK0IumT2b+Im9PmiwZu0TWupQbKr0GPACMvHq1kp3i/PbTdDnxdOQtKzwgFUsYnzJ1wwh5xOkBXKzYrKwsAbwEkQJsZkBce2zLhMmhDEuXGRB5bMlglCxevFh96JKQe2TzGWDqBhGb0pRpvZBa1arLliT95znITldyARDFavIqhG0PL09fAxj8qWx34O5yWroMsA5IxYqlrm4XlFIHwDCDEnSKDAIMsFY5n0GkM5Zvpr0FEyRD82lHnlkVK5cIaaxaAI1c/MaAlDpOqlYtphq2bXSZ1k9O9zYyYthQ+XeH2RPUuilLGtOy3do2gO1/zV80wGsXsANUrGt4gI/UUPI04AIgYlGyps2btwivQV5RTuKAEmuxZBAfnuRA0KRJk1TkCsYMkSmkmignTxgWKUQdzeca4jQeKcTG+JNPPinkIQwQUt2Gcj73Bg8i4JaU6BkMFp6bVz5bTqRe3YiYmQ+dJEoeAMbWgBHBRer/62AmcBKGAXFzfkTApx8/iGdZkUU8ljTxhtTjuy14PfCOEKVCUCpeEtZ+7M/hWcEyBSS47ghKda4HaccrnDbIY63H83A4ScccEuPI/86k5XFaj3qsOUk5GIULkrMnBL7SXv8wIykcQ4lqwAXATZs2K18wQlEyhgeDi1sLf248KWuseOppecyiWKN8lBLLFCsZdxuAAIT0BdcbKeu4goIC9V8+cM2saVmWioi2rAiQ4ePKQwYuNtqScs3zADx8xBxQ4rSelgdQcc/RntcuVrYGHyl8CL1ALVq05NJQghpwATCzbTv1QW8U3LxTd2m0YYns+8dU6bHxa+m56Zto2nvbLMm2Fkg/mV8i7bRyii8/qH6vLTMFeUfU+EleOOdAGZfdWG4f0ErGZlWXS7pnSLcNX8m4nMYyvPladT24QaHk7r5CRnfYKhPH9JX2Kz6UB4d0lO4bp0u7wvcl89d3pe/2OdK/9mK5vFdt2W/d5zKo/jIZ2Wqd9LPmyxMnd5Vzu+xQfOoOLpY3pvN2OaRonoztVl0afv+iHLxjrhSMv1z1jZT1MXoR+8/KVaulSdOmds78TVQDJQDYNauHFC5fLkv+3CaftT5dVu09VNZ0Geai3zucIAVtjkkKLd1zoBS0tmVpQi55nZIPI0c9r6z1Wae6ZYfJcZQtan6UfLqjsyzc4zCZ8k8nlT63pI7635pWr1krA4/NTVTvpn2xBlwAhNe+Q0cZecooGXP+xTJgUK4c0X+gIVsHp51xttLJkBNHSNDhLPRnqHQaKAHA0jU3tY0GEtNA5QVgYs9tWqeIBgwAU2Qg0rUbBoDpOvIp8twGgCkyEOnaDQPAdB35FHluA8AUGYh07YYBYOUb+SrVYwPAKjWcle9hDAAr35hVqR4bAFap4ax8D2MAWPnGrEr12ACwSg1n5XsYA8DKN2ZVqselAmCVenLzMCmhAQPAlBiG9O2EAWD6jn1KPLkBYEoMQ/p2wgAwfcc+JZ7cADAlhiF9O2EAGNfYm0rlpQEDwPLSrJEblwYMAONSk6lUXhowACwvzRq5cWnAADAuNZlK5aUBA8Dy0qyRG5cGDADjUlP6VirvJzcALG8NG/mhGjAADFWPKSxvDRgAlreGjfxQDRgAhqrHFJa3BgwAy1vDRn6oBgwAQ9VjCstbA6kLwPJ+ciM/JTRgAJgSw5C+nTAATN+xT4knNwBMiWFI304YAKbv2KfEkxsApsQwpG8nDABTb+zTqkcGgGk13Kn3sAaAqTcmadUjA8C0Gu7Ue1gDwNQbk7TqkQFgWg136j2sAWDqjUla9cgFwLR6cvOwKaEBA8CUGIb07YQBYPqOfUo8uQFgSgxD+nbCADB9xz4lnvx/AAAA//96HY5rAAAABklEQVQDABUFfB034lQLAAAAAElFTkSuQmCCUnit 5 Lab 3: Comparing Electric Bikes Importing and Accessing Data In this project, you will use a small table about electric bikes. You will build simple selector blocks that help you pull out headings, bike records, single values, and whole columns. Start a New Project called U5L3-ElectricBikes 1. This starter project already has a sample electric bike table. 2. Look at the table watcher on the stage. The first row contains the column headings. 3. Try item () of list with different numbers to inspect rows in the table. 4. Talk with your partner: How is item (1) different from the other items? 5. Try all but first of list. What part of the table does it remove? 6. Build these selector blocks: - headings of bike table () reports only the headings. - bikes in table () reports only the bike data, not the headings. Records, Fields, and Columns A record is one row of data about one bike. A field is one value inside a record. A column is one kind of value for every bike. 7. Build bike () from table () and field () from bike () blocks. 8. Build bike column () from bikes () to report one column from the data. 9. Use one column to answer a simple question, such as: Which bike has the longest range? What is the average price? Which bike is the lightest? Cleaning Data Data can be messy. One bike might list range in miles while another lists range in kilometers. A brand name might be spelled two different ways. A price might be missing. Cleaning data means making the data consistent without changing what it means. Write Out Your Thoughts If your class collected data about electric bikes, what are two problems that could make the data hard to compare?

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