{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Exam1Solutions.ipynb", "provenance": [], "authorship_tag": "ABX9TyOhhBTxhpIQv/2cI2HjFP+d", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "# Exam 1 Solutions" ], "metadata": { "id": "29Ca3Uzggxxq" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RKYvWyW4gxL2", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "95fef0e8-6c74-46e9-dccc-2555aac9895b" }, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "\n", "
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MountainMetresFeetRangeLocation and Notes
0Mount Everest884829029HimalayasNepal/China
1K2861128251KarakoramPakistan/China
2Kangchenjunga858628169HimalayasNepal/India
3Lhotse851627940HimalayasNepal – Climbers ascend Lhotse Face in climbin...
4Makalu848527838HimalayasNepal
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MountainMetresFeetRangeLocation and Notes
0Mount Everest884829029HimalayasNepal/China
2Kangchenjunga858628169HimalayasNepal/India
3Lhotse851627940HimalayasNepal – Climbers ascend Lhotse Face in climbin...
4Makalu848527838HimalayasNepal
5Cho Oyu818826864HimalayasNepal – Considered \"easiest\" eight-thousander
6Dhaulagiri816726795HimalayasNepal – Presumed world's highest from 1808-1838
7Manaslu816326781HimalayasNepal
8Nanga Parbat812626660HimalayasPakistan
9Annapurna809126545HimalayasNepal – First eight-thousander to be climbed (...
13Shishapangma802726335HimalayasChina
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China\n", "\n", "[10 rows x 5 columns]" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "df.groupby('Range')['Range'].count().plot(kind= 'bar')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 341 }, "id": "H6Qw2l3ghXDH", "outputId": "cb3749fd-5e15-4469-9df8-9357e20fb6ed" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 10 }, { "output_type": "display_data", "data": { "image/png": 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MetresFeet
meanmaxstdmeanmaxstd
Range
Himalayas8319.708848270.10041327295.629029886.239521
Karakoram8194.258611278.45690926884.028251913.363382
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total_rows
0198792903
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payment_typeavg_trip_totaltotal_num_of_trips
0Prepaid22.3776621805
1Credit Card20.52489280421932
2Mobile20.339910698557
3Prcard20.320682955795
4Split18.7880393442
5Unknown17.649893932066
6Way2ride16.639366142
7No Charge15.836177817699
8Dispute15.49658883309
9Cash12.265548114841282
10Pcard10.11693136874
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\n", " " ], "text/plain": [ " payment_type avg_trip_total total_num_of_trips\n", "0 Prepaid 22.377662 1805\n", "1 Credit Card 20.524892 80421932\n", "2 Mobile 20.339910 698557\n", "3 Prcard 20.320682 955795\n", "4 Split 18.788039 3442\n", "5 Unknown 17.649893 932066\n", "6 Way2ride 16.639366 142\n", "7 No Charge 15.836177 817699\n", "8 Dispute 15.496588 83309\n", "9 Cash 12.265548 114841282\n", "10 Pcard 10.116931 36874" ] }, "metadata": {}, "execution_count": 22 } ] }, { "cell_type": "code", "source": [ "%%bigquery --project pic-math\n", "\n", "SELECT COUNT(*) as num_trips_over_average_time\n", "FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips` as s\n", "WHERE s.trip_seconds > (SELECT AVG(trip_seconds) as avg_trip_seconds\n", " FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`\n", " WHERE trip_seconds >0)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81 }, "id": "B1jew_gZj7F0", "outputId": "250ec89c-0399-4d2e-dcff-c3fd21865e3b" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "\n", "
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num_trips_over_average_time
060750508
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