From d4c473a2992271e9dfa27180fc985f0586b6c699 Mon Sep 17 00:00:00 2001 From: Becky Christiansen Date: Mon, 27 Jan 2025 22:46:02 -0600 Subject: [PATCH 1/2] commit try again number 3 --- Notebooks/02_data_wrangling.ipynb | 4483 +++++++++++++++++++---------- 1 file changed, 3037 insertions(+), 1446 deletions(-) diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb index a52eb6c24..d783f785e 100644 --- a/Notebooks/02_data_wrangling.ipynb +++ b/Notebooks/02_data_wrangling.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2.1 Contents\n", + "## Contents\n", "* [2 Data wrangling](#2_Data_wrangling)\n", " * [2.1 Contents](#2.1_Contents)\n", " * [2.2 Introduction](#2.2_Introduction)\n", @@ -120,18 +120,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 18, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "#Code task 1#\n", "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n", - "import ___ as pd\n", - "import ___ as plt\n", - "import ___ as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "import os\n", - "\n", - "from library.sb_utils import save_file\n" + "from sb_utils import save_file\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "save_file" ] }, { @@ -162,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -179,13 +187,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 2#\n", "#Call the info method on ski_data to see a summary of the data\n", - "ski_data.___" + "ski_data.info" ] }, { @@ -204,211 +288,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "scrolled": true }, - "outputs": [], - "source": [ - "#Code task 3#\n", - "#Call the head method on ski_data to print the first several rows of the data\n", - "ski_data.___" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.6 Explore The Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.1 Find Your Resort Of Interest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your resort of interest is called Big Mountain Resort. Check it's in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 4#\n", - "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n", - "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n", - "#transpose method, but you can access this conveniently with the `T` property.\n", - "ski_data[ski_data.Name == ___].___" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's good that your resort doesn't appear to have any missing values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.2 Number Of Missing Values By Column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Count the number of missing values in each column and sort them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 5#\n", - "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", - "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", - "#Order them (increasing or decreasing) using sort_values\n", - "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", - "missing = ___([ski_data.___.___, 100 * ski_data.___.___], axis=1)\n", - "missing.columns=[___, ___]\n", - "missing.___(by=___)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.3 Categorical Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 6#\n", - "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", - "ski_data.___(___)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", - "\n", - "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", - "* Is `Region` always the same as `state`?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.1 Unique Resort Names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 7#\n", - "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", - "ski_data['Name'].___.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have a duplicated resort name: Crystal Mountain." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 8#\n", - "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", - "(ski_data[___] + ', ' + ski_data[___]).___.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 9#\n", - "#Concatenate 'Name' and 'state' and count the values again (as above)\n", - "(ski_data[___] + ', ' + ski_data[___]).___.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A: 1** Your answer here" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, "outputs": [ { "data": { @@ -456,409 +339,499 @@ " \n", " \n", " \n", - " 104\n", - " Crystal Mountain\n", - " Michigan\n", - " Michigan\n", - " 1132\n", - " 375\n", - " 757\n", - " 0\n", + " 0\n", + " Alyeska Resort\n", + " Alaska\n", + " Alaska\n", + " 3939\n", + " 2500\n", + " 250\n", + " 1\n", " 0.0\n", " 0\n", - " 1\n", + " 2\n", " ...\n", - " 0.3\n", - " 102.0\n", - " 96.0\n", - " 120.0\n", - " 63.0\n", - " 132.0\n", - " 54.0\n", - " 64.0\n", - " 135.0\n", - " 56.0\n", + " 1.0\n", + " 1610.0\n", + " 113.0\n", + " 150.0\n", + " 60.0\n", + " 669.0\n", + " 65.0\n", + " 85.0\n", + " 150.0\n", + " 550.0\n", " \n", " \n", - " 295\n", - " Crystal Mountain\n", - " Washington\n", - " Washington\n", - " 7012\n", - " 3100\n", - " 4400\n", - " 1\n", - " NaN\n", - " 2\n", - " 2\n", + " 1\n", + " Eaglecrest Ski Area\n", + " Alaska\n", + " Alaska\n", + " 2600\n", + " 1540\n", + " 1200\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0\n", " ...\n", - " 2.5\n", - " 2600.0\n", - " 10.0\n", - " NaN\n", - " 57.0\n", - " 486.0\n", - " 99.0\n", - " 99.0\n", - " NaN\n", + " 2.0\n", + " 640.0\n", + " 60.0\n", + " 45.0\n", + " 44.0\n", + " 350.0\n", + " 47.0\n", + " 53.0\n", + " 90.0\n", " NaN\n", " \n", - " \n", - "\n", - "

2 rows × 27 columns

\n", - "" - ], - "text/plain": [ - " Name Region state summit_elev vertical_drop \\\n", - "104 Crystal Mountain Michigan Michigan 1132 375 \n", - "295 Crystal Mountain Washington Washington 7012 3100 \n", - "\n", - " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", - "104 757 0 0.0 0 1 ... 0.3 \n", - "295 4400 1 NaN 2 2 ... 2.5 \n", - "\n", - " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", - "104 102.0 96.0 120.0 63.0 \n", - "295 2600.0 10.0 NaN 57.0 \n", - "\n", - " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", - "104 132.0 54.0 64.0 135.0 \n", - "295 486.0 99.0 99.0 NaN \n", + " \n", + " 2\n", + " Hilltop Ski Area\n", + " Alaska\n", + " Alaska\n", + " 2090\n", + " 294\n", + " 1796\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0\n", + " ...\n", + " 1.0\n", + " 30.0\n", + " 30.0\n", + " 150.0\n", + " 36.0\n", + " 69.0\n", + " 30.0\n", + " 34.0\n", + " 152.0\n", + " 30.0\n", + " \n", + " \n", + " 3\n", + " Arizona Snowbowl\n", + " Arizona\n", + " Arizona\n", + " 11500\n", + " 2300\n", + " 9200\n", + " 0\n", + " 0.0\n", + " 1\n", + " 0\n", + " ...\n", + " 2.0\n", + " 777.0\n", + " 104.0\n", + " 122.0\n", + " 81.0\n", + " 260.0\n", + " 89.0\n", + " 89.0\n", + " 122.0\n", + " NaN\n", + " \n", + " \n", + " 4\n", + " Sunrise Park Resort\n", + " Arizona\n", + " Arizona\n", + " 11100\n", + " 1800\n", + " 9200\n", + " 0\n", + " NaN\n", + " 0\n", + " 1\n", + " ...\n", + " 1.2\n", + " 800.0\n", + " 80.0\n", + " 115.0\n", + " 49.0\n", + " 250.0\n", + " 74.0\n", + " 78.0\n", + " 104.0\n", + " 80.0\n", + " \n", + " \n", + "\n", + "

5 rows × 27 columns

\n", + "" + ], + "text/plain": [ + " Name Region state summit_elev vertical_drop \\\n", + "0 Alyeska Resort Alaska Alaska 3939 2500 \n", + "1 Eaglecrest Ski Area Alaska Alaska 2600 1540 \n", + "2 Hilltop Ski Area Alaska Alaska 2090 294 \n", + "3 Arizona Snowbowl Arizona Arizona 11500 2300 \n", + "4 Sunrise Park Resort Arizona Arizona 11100 1800 \n", "\n", - " NightSkiing_ac \n", - "104 56.0 \n", - "295 NaN \n", + " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", + "0 250 1 0.0 0 2 ... 1.0 \n", + "1 1200 0 0.0 0 0 ... 2.0 \n", + "2 1796 0 0.0 0 0 ... 1.0 \n", + "3 9200 0 0.0 1 0 ... 2.0 \n", + "4 9200 0 NaN 0 1 ... 1.2 \n", "\n", - "[2 rows x 27 columns]" + " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", + "0 1610.0 113.0 150.0 60.0 \n", + "1 640.0 60.0 45.0 44.0 \n", + "2 30.0 30.0 150.0 36.0 \n", + "3 777.0 104.0 122.0 81.0 \n", + "4 800.0 80.0 115.0 49.0 \n", + "\n", + " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", + "0 669.0 65.0 85.0 150.0 \n", + "1 350.0 47.0 53.0 90.0 \n", + "2 69.0 30.0 34.0 152.0 \n", + "3 260.0 89.0 89.0 122.0 \n", + "4 250.0 74.0 78.0 104.0 \n", + "\n", + " NightSkiing_ac \n", + "0 550.0 \n", + "1 NaN \n", + "2 30.0 \n", + "3 NaN \n", + "4 80.0 \n", + "\n", + "[5 rows x 27 columns]" ] }, - "execution_count": 11, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ski_data[ski_data['Name'] == 'Crystal Mountain']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row." + "#Code task 3#\n", + "#Call the head method on ski_data to print the first several rows of the data\n", + "ski_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2.6.3.2 Region And State" + "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What's the relationship between region and state?" + "## 2.6 Explore The Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 10#\n", - "#Calculate the number of times Region does not equal state\n", - "(ski_data.Region ___ ski_data.state).___" + "### 2.6.1 Find Your Resort Of Interest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`." + "Your resort of interest is called Big Mountain Resort. Check it's in the data:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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NameBig Mountain Resort
RegionMontana
stateMontana
summit_elev6817
vertical_drop2353
base_elev4464
trams0
fastEight0.0
fastSixes0
fastQuads3
quad2
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TerrainParks4.0
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Snow Making_ac600.0
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yearsOpen72.0
averageSnowfall333.0
AdultWeekday81.0
AdultWeekend81.0
projectedDaysOpen123.0
NightSkiing_ac600.0
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" + ], "text/plain": [ - "New York 33\n", - "Michigan 29\n", - "Sierra Nevada 22\n", - "Colorado 22\n", - "Pennsylvania 19\n", - "Wisconsin 16\n", - "New Hampshire 16\n", - "Vermont 15\n", - "Minnesota 14\n", - "Montana 12\n", - "Idaho 12\n", - "Massachusetts 11\n", - "Washington 10\n", - "Maine 9\n", - "New Mexico 9\n", - "Wyoming 8\n", - "Utah 7\n", - "Oregon 6\n", - "Salt Lake City 6\n", - "North Carolina 6\n", - "Connecticut 5\n", - "Ohio 5\n", - "West Virginia 4\n", - "Virginia 4\n", - "Mt. Hood 4\n", - "Illinois 4\n", - "Alaska 3\n", - "Iowa 3\n", - "Missouri 2\n", - "Arizona 2\n", - "Indiana 2\n", - "South Dakota 2\n", - "New Jersey 2\n", - "Nevada 2\n", - "Rhode Island 1\n", - "Maryland 1\n", - "Tennessee 1\n", - "Northern California 1\n", - "Name: Region, dtype: int64" + " 151\n", + "Name Big Mountain Resort\n", + "Region Montana\n", + "state Montana\n", + "summit_elev 6817\n", + "vertical_drop 2353\n", + "base_elev 4464\n", + "trams 0\n", + "fastEight 0.0\n", + "fastSixes 0\n", + "fastQuads 3\n", + "quad 2\n", + "triple 6\n", + "double 0\n", + "surface 3\n", + "total_chairs 14\n", + "Runs 105.0\n", + "TerrainParks 4.0\n", + "LongestRun_mi 3.3\n", + "SkiableTerrain_ac 3000.0\n", + "Snow Making_ac 600.0\n", + "daysOpenLastYear 123.0\n", + "yearsOpen 72.0\n", + "averageSnowfall 333.0\n", + "AdultWeekday 81.0\n", + "AdultWeekend 81.0\n", + "projectedDaysOpen 123.0\n", + "NightSkiing_ac 600.0" ] }, - "execution_count": 13, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ski_data['Region'].value_counts()" + "#Code task 4#\n", + "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n", + "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n", + "#transpose method, but you can access this conveniently with the `T` property.\n", + "ski_data[ski_data.Name == 'Big Mountain Resort'].T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 11#\n", - "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n", - "#group that by 'state' and perform `value_counts` on the 'Region'\n", - "(ski_data[ski_data.___ ___ ski_data.___]\n", - " .groupby(___)[___]\n", - " .value_counts())" + "It's good that your resort doesn't appear to have any missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one referred to as Northern California." + "### 2.6.2 Number Of Missing Values By Column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2.6.3.3 Number of distinct regions and states" + "Count the number of missing values in each column and sort them." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " count %\n", + "Name 0 0.000000\n", + "total_chairs 0 0.000000\n", + "double 0 0.000000\n", + "triple 0 0.000000\n", + "quad 0 0.000000\n", + "fastQuads 0 0.000000\n", + "fastSixes 0 0.000000\n", + "surface 0 0.000000\n", + "trams 0 0.000000\n", + "base_elev 0 0.000000\n", + "vertical_drop 0 0.000000\n", + "summit_elev 0 0.000000\n", + "state 0 0.000000\n", + "Region 0 0.000000\n", + "yearsOpen 1 0.303030\n", + "SkiableTerrain_ac 3 0.909091\n", + "Runs 4 1.212121\n", + "LongestRun_mi 5 1.515152\n", + "averageSnowfall 14 4.242424\n", + "Snow Making_ac 46 13.939394\n", + "projectedDaysOpen 47 14.242424\n", + "TerrainParks 51 15.454545\n", + "daysOpenLastYear 51 15.454545\n", + "AdultWeekend 51 15.454545\n", + "AdultWeekday 54 16.363636\n", + "NightSkiing_ac 143 43.333333\n", + "fastEight 166 50.303030\n" + ] + } + ], "source": [ - "#Code task 12#\n", - "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n", - "#the number of unique values in each\n", - "ski_data[[___, ___]].___" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because a few states are split across multiple named regions, there are slightly more unique regions than states." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.4 Distribution Of Resorts By Region And State" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 13#\n", - "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n", - "fig, ax = plt.subplots(___, ___, figsize=(___))\n", - "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.Region.value_counts().plot(kind=___, ax=ax[0])\n", - "#Give the plot a helpful title of 'Region'\n", - "ax[0].set_title(___)\n", - "#Label the xaxis 'Count'\n", - "ax[0].set_xlabel(___)\n", - "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.state.value_counts().plot(kind=___, ax=ax[1])\n", - "#Give the plot a helpful title of 'state'\n", - "ax[1].set_title(___)\n", - "#Label the xaxis 'Count'\n", - "ax[1].set_xlabel(___)\n", - "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n", - "plt.subplots_adjust(wspace=___);\n", - "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n", - "# as the importance of easy-to-read and informative figures is frequently understated\n", - "# and you will find the ability to tweak figures invaluable later on" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.5 Distribution Of Ticket Price By State" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?" + "#Code task 5#\n", + "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", + "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", + "#Order them (increasing or decreasing) using sort_values\n", + "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", + "\n", + "#I wanted to try it my own way before just filing in the blanks\n", + "\n", + "#missing_example = pd.concat([ski_data.isnull().sum(), 100*ski_data.isnull().mean()], axis=1)\n", + "#missing_example.columns=['count','%']\n", + "#print(missing_example.sort_values(by='%'))\n", + "\n", + "\n", + "missing_count = ski_data.isnull().sum()\n", + "missing_percentage = ski_data.isnull().mean()*100\n", + "missing = pd.DataFrame({'count': missing_count, '%': missing_percentage})\n", + "print(missing.sort_values(by=['%']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 2.6.3.5.1 Average weekend and weekday price by state" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 14#\n", - "# Calculate average weekday and weekend price by state and sort by the average of the two\n", - "# Hint: use the pattern dataframe.groupby()[].mean()\n", - "state_price_means = ski_data.___(___)[[___, ___]].mean()\n", - "state_price_means.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n", - "# Compare the index order you get from\n", - "# state_price_means.index\n", - "# with\n", - "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n", - "# See how this expression simply sits within the reindex()\n", - "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n", - " .sort_values(ascending=False)\n", - " .index)\n", - " .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n", - "plt.xlabel('Price ($)');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?" + "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 2.6.3.5.2 Distribution of weekday and weekend price by state" + "### 2.6.3 Categorical Features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 15#\n", - "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n", - "#specify 'state' for `id_vars`\n", - "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n", - "#call the resultant price column 'Price' via the `value_name` argument,\n", - "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n", - "ticket_prices = pd.melt(ski_data[[___, ___, ___]], \n", - " id_vars=___, \n", - " var_name=___, \n", - " value_vars=[___, ___], \n", - " value_name=___)" + "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -882,271 +855,2211 @@ " \n", " \n", " \n", + " Name\n", + " Region\n", " state\n", - " Ticket\n", - " Price\n", " \n", " \n", " \n", " \n", " 0\n", + " Alyeska Resort\n", + " Alaska\n", " Alaska\n", - " AdultWeekday\n", - " 65.0\n", " \n", " \n", " 1\n", + " Eaglecrest Ski Area\n", + " Alaska\n", " Alaska\n", - " AdultWeekday\n", - " 47.0\n", " \n", " \n", " 2\n", + " Hilltop Ski Area\n", + " Alaska\n", " Alaska\n", - " AdultWeekday\n", - " 30.0\n", " \n", " \n", " 3\n", + " Arizona Snowbowl\n", + " Arizona\n", " Arizona\n", - " AdultWeekday\n", - " 89.0\n", " \n", " \n", " 4\n", + " Sunrise Park Resort\n", " Arizona\n", - " AdultWeekday\n", - " 74.0\n", + " Arizona\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 325\n", + " Meadowlark Ski Lodge\n", + " Wyoming\n", + " Wyoming\n", + " \n", + " \n", + " 326\n", + " Sleeping Giant Ski Resort\n", + " Wyoming\n", + " Wyoming\n", + " \n", + " \n", + " 327\n", + " Snow King Resort\n", + " Wyoming\n", + " Wyoming\n", + " \n", + " \n", + " 328\n", + " Snowy Range Ski & Recreation Area\n", + " Wyoming\n", + " Wyoming\n", + " \n", + " \n", + " 329\n", + " White Pine Ski Area\n", + " Wyoming\n", + " Wyoming\n", " \n", " \n", "\n", + "

330 rows × 3 columns

\n", "" ], "text/plain": [ - " state Ticket Price\n", - "0 Alaska AdultWeekday 65.0\n", - "1 Alaska AdultWeekday 47.0\n", - "2 Alaska AdultWeekday 30.0\n", - "3 Arizona AdultWeekday 89.0\n", - "4 Arizona AdultWeekday 74.0" + " Name Region state\n", + "0 Alyeska Resort Alaska Alaska\n", + "1 Eaglecrest Ski Area Alaska Alaska\n", + "2 Hilltop Ski Area Alaska Alaska\n", + "3 Arizona Snowbowl Arizona Arizona\n", + "4 Sunrise Park Resort Arizona Arizona\n", + ".. ... ... ...\n", + "325 Meadowlark Ski Lodge Wyoming Wyoming\n", + "326 Sleeping Giant Ski Resort Wyoming Wyoming\n", + "327 Snow King Resort Wyoming Wyoming\n", + "328 Snowy Range Ski & Recreation Area Wyoming Wyoming\n", + "329 White Pine Ski Area Wyoming Wyoming\n", + "\n", + "[330 rows x 3 columns]" ] }, - "execution_count": 20, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ticket_prices.head()" + "#Code task 6#\n", + "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", + "ski_data.select_dtypes(object)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 16#\n", - "#Create a seaborn boxplot of the ticket price dataframe we created above,\n", - "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n", - "#This will use boxplot's x, y, hue, and data arguments.\n", - "plt.subplots(figsize=(12, 8))\n", - "sns.boxplot(x=___, y=___, hue=___, data=ticket_prices)\n", - "plt.xticks(rotation='vertical')\n", - "plt.ylabel('Price ($)')\n", - "plt.xlabel('State');" + "#### 2.6.3.1 Unique Resort Names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n", - "\n", - "* disregard State completely\n", - "* retain all State information\n", - "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n", + "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", "\n", - "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday." + "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", + "* Is `Region` always the same as `state`?" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 52, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name\n", + "Crystal Mountain 2\n", + "Alyeska Resort 1\n", + "Brandywine 1\n", + "Boston Mills 1\n", + "Alpine Valley 1\n", + "Name: count, dtype: int64\n" + ] + } + ], "source": [ - "Thus we currently have two main questions you want to resolve:\n", - "\n", - "* What do you do about the two types of ticket price?\n", - "* What do you do about the state information?" + "#Code task 7#\n", + "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", + "print(ski_data['Name'].value_counts().head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2.6.4 Numeric Features" + "You have a duplicated resort name: Crystal Mountain." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features." + "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 57, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### 2.6.4.1 Numeric data summary" + "#Code task 8#\n", + "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", + "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], - "source": [ - "#Code task 17#\n", - "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n", - "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n", + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 9#\n", + "#Concatenate 'Name' and 'state' and count the values again (as above)\n", + "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "104 Crystal Mountain, Michigan, Michigan\n", + "295 Crystal Mountain, Washington, Washington\n", + "dtype: object\n" + ] + } + ], + "source": [ + "#I also wanted to find out what region and state Crystal Mountain were in.\n", + "print(ski_data.loc[ski_data['Name'] == 'Crystal Mountain', 'Name'] + ', '+ ski_data.loc[ski_data['Name'] == 'Crystal Mountain', 'Region'] + ', '+ ski_data.loc[ski_data['Name'] == 'Crystal Mountain', 'state'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A: 1** Your answer here\n", + "\n", + "**Becky's Answer:** Yes they are 2 unique resorts - One in Michigan and another in Washington" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.2 Region And State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What's the relationship between region and state?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 10#\n", + "#Calculate the number of times Region does not equal state\n", + "(ski_data.Region != ski_data.state).sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Becky's Answer:** There are 33 entries where the Region and state are different." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Region\n", + "New York 33\n", + "Michigan 29\n", + "Sierra Nevada 22\n", + "Colorado 22\n", + "Pennsylvania 19\n", + "Wisconsin 16\n", + "New Hampshire 16\n", + "Vermont 15\n", + "Minnesota 14\n", + "Idaho 12\n", + "Montana 12\n", + "Massachusetts 11\n", + "Washington 10\n", + "New Mexico 9\n", + "Maine 9\n", + "Wyoming 8\n", + "Utah 7\n", + "Salt Lake City 6\n", + "North Carolina 6\n", + "Oregon 6\n", + "Connecticut 5\n", + "Ohio 5\n", + "Virginia 4\n", + "West Virginia 4\n", + "Illinois 4\n", + "Mt. Hood 4\n", + "Alaska 3\n", + "Iowa 3\n", + "South Dakota 2\n", + "Arizona 2\n", + "Nevada 2\n", + "Missouri 2\n", + "Indiana 2\n", + "New Jersey 2\n", + "Rhode Island 1\n", + "Tennessee 1\n", + "Maryland 1\n", + "Northern California 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data['Region'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "state Region \n", + "California Sierra Nevada 20\n", + " Northern California 1\n", + "Nevada Sierra Nevada 2\n", + "Oregon Mt. Hood 4\n", + "Utah Salt Lake City 6\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 11#\n", + "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n", + "#group that by 'state' and perform `value_counts` on the 'Region'\n", + "(ski_data[ski_data.Region != ski_data.state]\n", + " .groupby('state')\n", + " ['Region'].value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one referred to as Northern California." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.3 Number of distinct regions and states" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Region 38\n", + "state 35\n", + "dtype: int64" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 12#\n", + "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n", + "#the number of unique values in each\n", + "ski_data[['Region', 'state']].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because a few states are split across multiple named regions, there are slightly more unique regions than states." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.4 Distribution Of Resorts By Region And State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 13#\n", + "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n", + "fig, ax = plt.subplots(1, 2, figsize=(12,8))\n", + "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", + "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n", + "#Give the plot a helpful title of 'Region'\n", + "ax[0].set_title('Region')\n", + "#Label the xaxis 'Count'\n", + "ax[0].set_xlabel('Count')\n", + "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", + "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n", + "#Give the plot a helpful title of 'state'\n", + "ax[1].set_title('state')\n", + "#Label the xaxis 'Count'\n", + "ax[1].set_xlabel('Count')\n", + "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n", + "plt.subplots_adjust(wspace=0.5)\n", + "plt.show()\n", + "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n", + "# as the importance of easy-to-read and informative figures is frequently understated\n", + "# and you will find the ability to tweak figures invaluable later on" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.5 Distribution Of Ticket Price By State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.3.5.1 Average weekend and weekday price by state" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AdultWeekdayAdultWeekend
state
Alaska47.33333357.333333
Arizona81.50000083.500000
California78.21428681.416667
Colorado90.71428690.714286
Connecticut47.80000056.800000
\n", + "
" + ], + "text/plain": [ + " AdultWeekday AdultWeekend\n", + "state \n", + "Alaska 47.333333 57.333333\n", + "Arizona 81.500000 83.500000\n", + "California 78.214286 81.416667\n", + "Colorado 90.714286 90.714286\n", + "Connecticut 47.800000 56.800000" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 14#\n", + "# Calculate average weekday and weekend price by state and sort by the average of the two\n", + "# Hint: use the pattern dataframe.groupby()[].mean()\n", + "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n", + "state_price_means.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n", + "# Compare the index order you get from\n", + "# state_price_means.index\n", + "# with\n", + "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n", + "# See how this expression simply sits within the reindex()\n", + "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n", + " .sort_values(ascending=False)\n", + " .index)\n", + " .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n", + "plt.xlabel('Price ($)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.3.5.2 Distribution of weekday and weekend price by state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 15#\n", + "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n", + "#specify 'state' for `id_vars`\n", + "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n", + "#call the resultant price column 'Price' via the `value_name` argument,\n", + "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n", + "ticket_prices = pd.melt(ski_data[['state','AdultWeekday' , 'AdultWeekend']], \n", + " id_vars='state', \n", + " var_name='Ticket', \n", + " value_vars=['AdultWeekday', 'AdultWeekend'], \n", + " value_name='Price')" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateTicketPrice
0AlaskaAdultWeekday65.0
1AlaskaAdultWeekday47.0
2AlaskaAdultWeekday30.0
3ArizonaAdultWeekday89.0
4ArizonaAdultWeekday74.0
\n", + "
" + ], + "text/plain": [ + " state Ticket Price\n", + "0 Alaska AdultWeekday 65.0\n", + "1 Alaska AdultWeekday 47.0\n", + "2 Alaska AdultWeekday 30.0\n", + "3 Arizona AdultWeekday 89.0\n", + "4 Arizona AdultWeekday 74.0" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ticket_prices.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 16#\n", + "#Create a seaborn boxplot of the ticket price dataframe we created above,\n", + "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n", + "#This will use boxplot's x, y, hue, and data arguments.\n", + "plt.subplots(figsize=(12, 8))\n", + "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n", + "plt.xticks(rotation='vertical')\n", + "plt.ylabel('Price ($)')\n", + "plt.xlabel('State');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n", + "\n", + "* disregard State completely\n", + "* retain all State information\n", + "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n", + "\n", + "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus we currently have two main questions you want to resolve:\n", + "\n", + "* What do you do about the two types of ticket price?\n", + "* What do you do about the state information?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.4 Numeric Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.4.1 Numeric data summary" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
summit_elev330.04591.8181823735.535934315.01403.753127.57806.0013487.0
vertical_drop330.01215.427273947.86455760.0461.25964.51800.004425.0
base_elev330.03374.0000003117.12162170.0869.001561.56325.2510800.0
trams330.00.1727270.5599460.00.000.00.004.0
fastEight164.00.0060980.0780870.00.000.00.001.0
fastSixes330.00.1848480.6516850.00.000.00.006.0
fastQuads330.01.0181822.1982940.00.000.01.0015.0
quad330.00.9333331.3122450.00.000.01.008.0
triple330.01.5000001.6191300.00.001.02.008.0
double330.01.8333331.8150280.01.001.03.0014.0
surface330.02.6212122.0596360.01.002.03.0015.0
total_chairs330.08.2666675.7986830.05.007.010.0041.0
Runs326.048.21472446.3640773.019.0033.060.00341.0
TerrainParks279.02.8207892.0081131.01.002.04.0014.0
LongestRun_mi325.01.4332311.1561710.00.501.02.006.0
SkiableTerrain_ac327.0739.8012231816.1674418.085.00200.0690.0026819.0
Snow Making_ac284.0174.873239261.3361252.050.00100.0200.503379.0
daysOpenLastYear279.0115.10394335.0632513.097.00114.0135.00305.0
yearsOpen329.063.656535109.4299286.050.0058.069.002019.0
averageSnowfall316.0185.316456136.35684218.069.00150.0300.00669.0
AdultWeekday276.057.91695726.14012615.040.0050.071.00179.0
AdultWeekend279.064.16681024.55458417.047.0060.077.50179.0
projectedDaysOpen283.0120.05300431.04596330.0100.00120.0139.50305.0
NightSkiing_ac187.0100.395722105.1696202.040.0072.0114.00650.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% 50% \\\n", + "summit_elev 330.0 4591.818182 3735.535934 315.0 1403.75 3127.5 \n", + "vertical_drop 330.0 1215.427273 947.864557 60.0 461.25 964.5 \n", + "base_elev 330.0 3374.000000 3117.121621 70.0 869.00 1561.5 \n", + "trams 330.0 0.172727 0.559946 0.0 0.00 0.0 \n", + "fastEight 164.0 0.006098 0.078087 0.0 0.00 0.0 \n", + "fastSixes 330.0 0.184848 0.651685 0.0 0.00 0.0 \n", + "fastQuads 330.0 1.018182 2.198294 0.0 0.00 0.0 \n", + "quad 330.0 0.933333 1.312245 0.0 0.00 0.0 \n", + "triple 330.0 1.500000 1.619130 0.0 0.00 1.0 \n", + "double 330.0 1.833333 1.815028 0.0 1.00 1.0 \n", + "surface 330.0 2.621212 2.059636 0.0 1.00 2.0 \n", + "total_chairs 330.0 8.266667 5.798683 0.0 5.00 7.0 \n", + "Runs 326.0 48.214724 46.364077 3.0 19.00 33.0 \n", + "TerrainParks 279.0 2.820789 2.008113 1.0 1.00 2.0 \n", + "LongestRun_mi 325.0 1.433231 1.156171 0.0 0.50 1.0 \n", + "SkiableTerrain_ac 327.0 739.801223 1816.167441 8.0 85.00 200.0 \n", + "Snow Making_ac 284.0 174.873239 261.336125 2.0 50.00 100.0 \n", + "daysOpenLastYear 279.0 115.103943 35.063251 3.0 97.00 114.0 \n", + "yearsOpen 329.0 63.656535 109.429928 6.0 50.00 58.0 \n", + "averageSnowfall 316.0 185.316456 136.356842 18.0 69.00 150.0 \n", + "AdultWeekday 276.0 57.916957 26.140126 15.0 40.00 50.0 \n", + "AdultWeekend 279.0 64.166810 24.554584 17.0 47.00 60.0 \n", + "projectedDaysOpen 283.0 120.053004 31.045963 30.0 100.00 120.0 \n", + "NightSkiing_ac 187.0 100.395722 105.169620 2.0 40.00 72.0 \n", + "\n", + " 75% max \n", + "summit_elev 7806.00 13487.0 \n", + "vertical_drop 1800.00 4425.0 \n", + "base_elev 6325.25 10800.0 \n", + "trams 0.00 4.0 \n", + "fastEight 0.00 1.0 \n", + "fastSixes 0.00 6.0 \n", + "fastQuads 1.00 15.0 \n", + "quad 1.00 8.0 \n", + "triple 2.00 8.0 \n", + "double 3.00 14.0 \n", + "surface 3.00 15.0 \n", + "total_chairs 10.00 41.0 \n", + "Runs 60.00 341.0 \n", + "TerrainParks 4.00 14.0 \n", + "LongestRun_mi 2.00 6.0 \n", + "SkiableTerrain_ac 690.00 26819.0 \n", + "Snow Making_ac 200.50 3379.0 \n", + "daysOpenLastYear 135.00 305.0 \n", + "yearsOpen 69.00 2019.0 \n", + "averageSnowfall 300.00 669.0 \n", + "AdultWeekday 71.00 179.0 \n", + "AdultWeekend 77.50 179.0 \n", + "projectedDaysOpen 139.50 305.0 \n", + "NightSkiing_ac 114.00 650.0 " + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 17#\n", + "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n", + "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n", "#will be useful again\n", - "ski_data.___.___" + "ski_data.describe().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have." + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 82.424242\n", + "2 14.242424\n", + "1 3.333333\n", + "Name: count, dtype: float64" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", + "missing_price.value_counts()/len(missing_price) * 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.4.2 Distributions Of Feature Values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dfPPNRTLmC506dcrS09PtzJkzrrJPP/3UwsLCbMSIEa6y7LsA7r///hyZ38K2ceNGGz16tKWmprqSJJ988onVrFmzyMYM7+GN7VBerFu3zoKCgiwqKsoqVqxoYWFhNmbMGNuzZ49lZmbaxIkTrXTp0kXqMyEviuP5Kq7nCkWHt73GiJd4L8Ubr2UBAMVT9u9Br7zyirVo0SLHMFPJyck2duxYa968uR07dszMzF5//XXbvn17ocf6e8TuGcTuGWfOnLFXX33VFi1aZGbnRwDKLUFy9OhR69WrlzVv3tyOHj2ar3GQHClAiYmJVq5cOYuNjTWz8y/YXbt22X//+1/XWGlnz561OXPmWEBAgNWoUcPq1q1r1atX91j3+MTERAsICLDOnTvbPffcY8HBwXbLLbfY9OnTzez8D+ItW7a0gIAAe+2112z+/Pn22GOPWWBgoMfuWlu7dq35+fnZ6NGjc6zLysqylStXWqtWrYpUzBfasGGDtWrVypo1a2bh4eE2c+ZM27lzp2VmZtqLL75o11xzjd1///1u2/Tr188GDBhgGRkZHrkzYO3atebj42OTJ092Kz927Jg9//zzVrdu3SIXM7yHN7ZDeXH8+HFr0qSJPfroo66LkkmTJtktt9xid955p6tba1H6TMiL4ni+iuu5QtHhba8x4iXeS/HGa1kAQPH366+/WpUqVWzQoEGu3pjZ9u/fb6VKlbL333/fQ9FdGrF7BrEXvt8PB7lo0SJzOBw2ZswYO3LkiJmdnzvs+PHjdvTo0UvOS3KlSI4UkBMnTlh0dLSFhYW5ynr27GmRkZFWrlw5u+aaa+zNN9+05ORkMzPbu3evffHFF/bVV1/Znj17PBJzenq63XvvvTZkyBBX2a5du+zBBx+066+/3p577jkzOz9uXUxMjF177bVWr149a9Giha1evdojMW/ZssWcTqdNnDjRdQwffPCBzZgxwxYsWODKJqakpBSZmC+0fft2q1Spkj300EM2d+5cGz9+vFWvXt169+5tq1atsnPnztmsWbMsJCTErr/+erv//vutb9++Vr58edu4caNHYl63bp35+fnZo48+6lae3R3vyJEj9tprr1loaGiRiRnewxvbobzatWuX1axZ07766iu38nnz5lnLli2tb9++rvmpisJnQl4U1/NVHM8VihZve40Rb8Hytngv5I3XsgCAkuPbb781p9Npw4cPd7sT/ciRI9akSRP77rvvPBfcHyB2zyB2z7jwhpmFCxe6epDs27fPYmJirFu3bm5DtOYnkiMF6Pnnn7dbb73V7r33XmvSpIl17tzZPvjgA9uyZYsNGDDA6tSpY++9956nw3TTvn17Gzx4sJn9r2vW/v37LSYmxm688UZ79913XXX37dtnx48ftxMnTngk1qysLHvkkUesUqVK9s9//tPMzDp27GiNGze2a665xsqUKWOdO3e2ZcuWFZmYf2/atGnWsmVLt7IPPvjAoqOjrVu3brZhwwYzOz80zcCBA+3uu++2gQMHeuzLZPYX4L59+5rZ+fGkJ0+ebPfee6/dcccd9sknn1hmZqZlZWUVmZjhfbypHboce/futWuvvdbmzJljZubqQWhm9o9//MMaNmxo8+bN81B0V644nq/du3cXy3OFosPb2gNve0/s2bOHeAuJt13LAgBKno8//ticTqd1797dFixYYBs3brTHHnvMgoKCisyE2rkhds8gds/IyspyDdu/aNEiK1OmjNWrV898fHwK9OZKkiMF4MJJql9++WWrX7++3XHHHTm6/tx+++05JsvxhOw7/s+ePWuDBg2y7t27W2pqqtuLcteuXdaxY0fr2rWraztPdoHfs2eP/frrr7Z792574IEH7KabbrKwsDDr1KmTbdmyxTIyMmzDhg3WoEEDu+uuu4pEzBfzwgsvWMOGDS0lJcXtdfPpp59akyZN7KGHHrLTp0+7bePJCZM++eQTq1Gjhj3yyCO2cuVKa9u2rbVt29buvvtu69Gjh5UqVcomT55s6enpbtt5epIneIeMjAxLT0/3mnboSnTp0sWuv/56O378uJm5/wB29913W4sWLTwU2ZVJS0uzQYMGWbdu3bz+fO3fv98SExNdjzt37lyszhWKnqLeHnjbe+L06dNu1x9du3Yt0vH+XlF/fnPjbdeyAICSadWqVda6dWu7+uqrrXbt2lavXr0i3ZP9QsTuGcTuGVlZWa7fDW699VYLDAy09evXF+jfJDmSj06dOmUpKSmuobKyvf322/bpp5+6Tm72l51x48Z5PDmyatUqa9mypWuMt2XLllnp0qXt5ZdfdtXJ/qLz888/m8PhsDVr1ngiVJeNGzdajRo1LCYmxszMNm/ebP3797fOnTvb1q1b3ep+/fXX5nA4CvyNdKUWLVpk5cqVs5UrV5rZ/yapNDs/lEKZMmVc67J5+sfFBQsW2A033GDBwcHWuXNnO3jwoOs1PW3aNCtTpkyORtfTMaNo+/2PJN7QDuXFxT4TDh8+bOHh4da+fXu397uZ2RtvvGE33XRTjvKi5ujRo7Z582ZXe/vjjz96/fnau3evVa5c2bp3724//vijmZ0/V7Vq1fLqc4Wiw9vaA297T2zYsMG6du1q33//veuatig/v3v27LFFixbZ+++/77pmKsrxXoo3XssCAEqm5ORk27Fjh23YsCHHZM9FHbF7BrF7RkZGho0cOdIcDoetW7euwP9eKSFfbNq0ST169FDr1q113XXX6d1331VmZqYkqX///urQoYMcDockycfHR5K0b98+NWjQQFlZWTKzQo953bp1atWqlZo1a6by5cvLzNS6dWvFx8dr5MiRev311yVJpUqdf5lcddVVql+/vvz8/Ao91gtjvvHGG1WmTBktXLhQSUlJuvbaa/XMM8/ooYceUq1atSRJdj7xp7Nnz6pu3boKCgryWMyX0qtXL3Xo0EHdu3fXoUOHVLZsWaWlpUmS7r33XkVEROibb75x2yb7deQpffr00ZgxY3TjjTfqySefVLVq1Vyv6UceeUSVK1fWsmXL3LbxdMwourZu3arp06frwIEDrrLWrVtrypQpGjlypN58801JRasdyouLfSZkZWWpSpUqWrBggX755Rd16NBBW7Zs0dmzZyVJP//8s/z9/T3yeZBXGzduVLt27dSzZ081aNBAkyZN0k033aTnnntOI0eO1BtvvCHJ+87X1q1blZycrOTkZM2aNUsJCQmqUqWKFi5cqI0bN+rWW2/1unOFosMb2wNvek8kJiaqVatWqlGjhmrXrq3y5ctLkuv5TUxMLFLP74YNG3TLLbfohRde0PDhwxUbG6tt27a54t28eXORivePeOO1LACgZKpQoYJq1aqlyMhIValSxdPhXBZi9wxi95wGDRpo9erVatSoUcH/sQJPv5QAiYmJVrlyZRs5cqQtWLDARo0aZWXKlMn1TtnU1FSbOHGiVa1a1X755ZfCDfb/W7dunZUvXz7HpNqpqalmZvbcc89ZqVKlbPz48bZy5Uo7fPiwjRs3zmrXru2aELKwrV271nx9fW3ChAl2+PBha9CggT3zzDOuu84vdhfa2LFj7bbbbisS49v/8ssvNnLkSOvVq5fFx8dbQkKCmZ0fg7l58+YWHh7uNv5famqqNWvWzGbPnu2pkG379u02bdo0GzVqlC1atMht3aZNm1yvl2w7duyw66+/3r744ovCDBNeatu2bRYYGGgOh8PGjx/vdjfD6dOnbdKkSeZwOGzixIlFph3Ki9w+Ey7sUbVhwwZr2LCh1alTx5o2bWpdunQxf39/W7t2rQcjv7Ts4xozZowlJibaCy+8YA6Hw3bv3m3nzp2z2NhY17n0pvNldr43TNeuXe0f//iH3XDDDda3b1/btGmTmZ3/vLzlllusdu3aXnOuUHR4a3vgLe+JU6dOWYcOHexvf/ubq2zz5s22Zs0a27t3r5md73Fcv379IvH87ty506pXr27jxo2zU6dO2eeff27BwcH2888/u+oUpXh/zxuvZQEAAOB9CrOnscOsCN6C5EWOHTumPn366Nprr9XLL7/sKr/11lvVsGFDvfzyyzIz1x1Sn3/+uaZNm6ZffvlFn3zyiaKiogo95qSkJEVFRalx48b68ssvlZmZqZEjR2rr1q3atm2bBg0apI4dO2rv3r3629/+JkkKCAjQyZMnPRbz+vXrdeONN2r06NF69tlnlZWVpV69emnXrl36+eefJcnteU5MTNTChQs1Y8YM/fDDD2rYsGGhx3yhTZs2KTo6Wi1btlTFihX19ddf65prrtHdd9+tRx55RImJifrb3/6m9evXKz4+XhUqVNCGDRv0xhtv6Oeff1adOnUKPeYNGzboL3/5i6699lqdOXNGP/30k5577jk9+uijuW7z+OOP6+OPP9aXX36p0NDQQowW3ub06dN6+OGHlZWVpaZNm2rEiBEaM2aMHn30UVWtWlWSlJWVpXfffVdjx45VqVKlVKFCBY+2Q3lxuZ8Jr776qvbu3StfX1/16tVL9erV81Tol3TkyBHdddddioqK0vTp0yWdb3M7duyop556Sn5+fqpUqZLWrVunoUOHysxUsWLFIn++JCkzM1PHjh3TLbfcom+//VY///yz4uPj1bhxY23atEnXXHON5s2bpxkzZmj//v1F/lyh6PDW9sCb3hNpaWlq166dXnnlFTVq1EidOnXSsWPH9Msvv6h+/fq6//77NWTIEEnSzJkztW/fPo/G+49//EOLFi3St99+6zrvnTp10p133imn06maNWuqTZs2klQknt8LeeO1LAAAAPBHfDwdgLc7d+6cTpw4obvvvlvS+R/zSpUqpdq1a+vo0aOS3LuOt2nTRmvWrNGrr77q0S85LVq00J49e/Tvf/9bf//735WRkaEbb7xRkZGReu+997Ru3Tq99dZb+umnn7Rz506lpaWpfv36ql69ukfiTUtL09ixY/X000+7nuPJkyerefPmmjVrlv72t7+5nuedO3dq9OjR+u2337R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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 18#\n", + "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n", + "#Try passing it an argument figsize=(15,10)\n", + "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n", + "#It's important you create legible and easy-to-read plots\n", + "ski_data.hist(figsize=(20,20),xrot=45, grid=False)\n", + "plt.subplots_adjust(wspace = 0.25, hspace=.75);\n", + "plt.show\n", + "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Becky's Note** - I played around with the spacing and tried a few different sizes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What features do we have possible cause for concern about and why?\n", + "\n", + "* SkiableTerrain_ac because values are clustered down the low end,\n", + "* Snow Making_ac for the same reason,\n", + "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n", + "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n", + "* trams also may get an amber flag for the same reason,\n", + "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.4.2.1 SkiableTerrain_ac" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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13KirkwoodSierra NevadaCalifornia98002000780000.002...2.52300.0200.0200.047.0354.0NaNNaN167.0NaN
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299Stevens Pass ResortWashingtonWashington58451800406100.003...1.01125.0NaN116.082.0460.0NaNNaN145.0450.0
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324Jackson HoleWyomingWyoming104504139631130.004...4.52500.0195.0130.054.0459.0NaNNaN133.0NaN
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66 rows × 27 columns

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" + ], + "text/plain": [ + " Name Region state summit_elev \\\n", + "0 Alyeska Resort Alaska Alaska 3939 \n", + "7 Bear Valley Sierra Nevada California 8500 \n", + "11 Heavenly Mountain Resort Sierra Nevada California 10067 \n", + "12 June Mountain Sierra Nevada California 10090 \n", + "13 Kirkwood Sierra Nevada California 9800 \n", + ".. ... ... ... ... \n", + "299 Stevens Pass Resort Washington Washington 5845 \n", + "300 The Summit at Snoqualmie Washington Washington 3865 \n", + "301 White Pass Washington Washington 6550 \n", + "322 Grand Targhee Resort Wyoming Wyoming 9920 \n", + "324 Jackson Hole Wyoming Wyoming 10450 \n", + "\n", + " vertical_drop base_elev trams fastEight fastSixes fastQuads ... \\\n", + "0 2500 250 1 0.0 0 2 ... \n", + "7 1900 6600 0 0.0 1 1 ... \n", + "11 3500 7170 2 0.0 2 7 ... \n", + "12 2590 7545 0 NaN 0 2 ... \n", + "13 2000 7800 0 0.0 0 2 ... \n", + ".. ... ... ... ... ... ... ... \n", + "299 1800 4061 0 0.0 0 3 ... \n", + "300 1025 2840 0 NaN 0 3 ... \n", + "301 2050 4500 0 NaN 0 2 ... \n", + "322 2270 7851 0 0.0 0 2 ... \n", + "324 4139 6311 3 0.0 0 4 ... \n", + "\n", + " LongestRun_mi SkiableTerrain_ac Snow Making_ac daysOpenLastYear \\\n", + "0 1.0 1610.0 113.0 150.0 \n", + "7 1.2 1680.0 100.0 165.0 \n", + "11 5.5 4800.0 3379.0 155.0 \n", + "12 2.0 1500.0 NaN NaN \n", + "13 2.5 2300.0 200.0 200.0 \n", + ".. ... ... ... ... \n", + "299 1.0 1125.0 NaN 116.0 \n", + "300 0.8 1994.0 5.0 120.0 \n", + "301 2.5 1402.0 30.0 148.0 \n", + "322 2.7 2602.0 NaN 152.0 \n", + "324 4.5 2500.0 195.0 130.0 \n", + "\n", + " yearsOpen averageSnowfall AdultWeekday AdultWeekend \\\n", + "0 60.0 669.0 65.0 85.0 \n", + "7 52.0 359.0 NaN NaN \n", + "11 64.0 360.0 NaN NaN \n", + "12 58.0 250.0 NaN NaN \n", + "13 47.0 354.0 NaN NaN \n", + ".. ... ... ... ... \n", + "299 82.0 460.0 NaN NaN \n", + "300 82.0 428.0 85.0 95.0 \n", + "301 67.0 400.0 69.0 69.0 \n", + "322 50.0 500.0 90.0 90.0 \n", + "324 54.0 459.0 NaN NaN \n", + "\n", + " projectedDaysOpen NightSkiing_ac \n", + "0 150.0 550.0 \n", + "7 151.0 NaN \n", + "11 157.0 NaN \n", + "12 128.0 NaN \n", + "13 167.0 NaN \n", + ".. ... ... \n", + "299 145.0 450.0 \n", + "300 140.0 541.0 \n", + "301 144.0 90.0 \n", + "322 152.0 NaN \n", + "324 133.0 NaN \n", + "\n", + "[66 rows x 27 columns]" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 19#\n", + "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n", + "ski_data.loc[ski_data.SkiableTerrain_ac > 1000]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have." + "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 143, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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11273945140231266267
NameHeavenly Mountain ResortAspen / SnowmassSilverton MountainVailBig Sky ResortMt. BachelorPark CityPowder Mountain
RegionSierra NevadaColoradoColoradoColoradoMontanaOregonSalt Lake CityUtah
stateCaliforniaColoradoColoradoColoradoMontanaOregonUtahUtah
summit_elev10067125101348711570111669065100009422
vertical_drop35004406308734504350336532002522
base_elev717081041040081207500570068006900
trams23021040
fastEight0.00.00.00.01.00.00.00.0
fastSixes21032060
fastQuads71501558101
quad14013044
triple53017371
double35105040
surface890912063
total_chairs28401313611419
Runs97.0336.0NaN195.0317.0101.0341.0167.0
TerrainParks3.010.0NaN3.08.05.08.02.0
LongestRun_mi5.55.31.54.06.04.03.53.5
SkiableTerrain_ac4800.05517.026819.05289.05800.04318.07300.08464.0
Snow Making_ac3379.0658.0NaN461.0400.020.0750.0NaN
daysOpenLastYear155.0138.0175.0149.0144.0185.0142.0120.0
yearsOpen64.072.017.057.046.061.056.047.0
averageSnowfall360.0300.0400.0354.0400.0462.0355.0500.0
AdultWeekdayNaN179.079.0NaNNaN99.0NaN88.0
AdultWeekendNaN179.079.0NaNNaN99.0NaN88.0
projectedDaysOpen157.0138.0181.0142.0144.0185.0143.0146.0
NightSkiing_acNaNNaNNaNNaNNaNNaNNaN300.0
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" + ], "text/plain": [ - "0 82.424242\n", - "2 14.242424\n", - "1 3.333333\n", - "dtype: float64" + " 11 27 \\\n", + "Name Heavenly Mountain Resort Aspen / Snowmass \n", + "Region Sierra Nevada Colorado \n", + "state California Colorado \n", + "summit_elev 10067 12510 \n", + "vertical_drop 3500 4406 \n", + "base_elev 7170 8104 \n", + "trams 2 3 \n", + "fastEight 0.0 0.0 \n", + "fastSixes 2 1 \n", + "fastQuads 7 15 \n", + "quad 1 4 \n", + "triple 5 3 \n", + "double 3 5 \n", + "surface 8 9 \n", + "total_chairs 28 40 \n", + "Runs 97.0 336.0 \n", + "TerrainParks 3.0 10.0 \n", + "LongestRun_mi 5.5 5.3 \n", + "SkiableTerrain_ac 4800.0 5517.0 \n", + "Snow Making_ac 3379.0 658.0 \n", + "daysOpenLastYear 155.0 138.0 \n", + "yearsOpen 64.0 72.0 \n", + "averageSnowfall 360.0 300.0 \n", + "AdultWeekday NaN 179.0 \n", + "AdultWeekend NaN 179.0 \n", + "projectedDaysOpen 157.0 138.0 \n", + "NightSkiing_ac NaN NaN \n", + "\n", + " 39 45 140 231 \\\n", + "Name Silverton Mountain Vail Big Sky Resort Mt. Bachelor \n", + "Region Colorado Colorado Montana Oregon \n", + "state Colorado Colorado Montana Oregon \n", + "summit_elev 13487 11570 11166 9065 \n", + "vertical_drop 3087 3450 4350 3365 \n", + "base_elev 10400 8120 7500 5700 \n", + "trams 0 2 1 0 \n", + "fastEight 0.0 0.0 1.0 0.0 \n", + "fastSixes 0 3 2 0 \n", + "fastQuads 0 15 5 8 \n", + "quad 0 1 3 0 \n", + "triple 0 1 7 3 \n", + "double 1 0 5 0 \n", + "surface 0 9 12 0 \n", + "total_chairs 1 31 36 11 \n", + "Runs NaN 195.0 317.0 101.0 \n", + "TerrainParks NaN 3.0 8.0 5.0 \n", + "LongestRun_mi 1.5 4.0 6.0 4.0 \n", + "SkiableTerrain_ac 26819.0 5289.0 5800.0 4318.0 \n", + "Snow Making_ac NaN 461.0 400.0 20.0 \n", + "daysOpenLastYear 175.0 149.0 144.0 185.0 \n", + "yearsOpen 17.0 57.0 46.0 61.0 \n", + "averageSnowfall 400.0 354.0 400.0 462.0 \n", + "AdultWeekday 79.0 NaN NaN 99.0 \n", + "AdultWeekend 79.0 NaN NaN 99.0 \n", + "projectedDaysOpen 181.0 142.0 144.0 185.0 \n", + "NightSkiing_ac NaN NaN NaN NaN \n", + "\n", + " 266 267 \n", + "Name Park City Powder Mountain \n", + "Region Salt Lake City Utah \n", + "state Utah Utah \n", + "summit_elev 10000 9422 \n", + "vertical_drop 3200 2522 \n", + "base_elev 6800 6900 \n", + "trams 4 0 \n", + "fastEight 0.0 0.0 \n", + "fastSixes 6 0 \n", + "fastQuads 10 1 \n", + "quad 4 4 \n", + "triple 7 1 \n", + "double 4 0 \n", + "surface 6 3 \n", + "total_chairs 41 9 \n", + "Runs 341.0 167.0 \n", + "TerrainParks 8.0 2.0 \n", + "LongestRun_mi 3.5 3.5 \n", + "SkiableTerrain_ac 7300.0 8464.0 \n", + "Snow Making_ac 750.0 NaN \n", + "daysOpenLastYear 142.0 120.0 \n", + "yearsOpen 56.0 47.0 \n", + "averageSnowfall 355.0 500.0 \n", + "AdultWeekday NaN 88.0 \n", + "AdultWeekend NaN 88.0 \n", + "projectedDaysOpen 143.0 146.0 \n", + "NightSkiing_ac NaN 300.0 " ] }, - "execution_count": 23, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", - "missing_price.value_counts()/len(missing_price) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.4.2 Distributions Of Feature Values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 18#\n", - "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n", - "#Try passing it an argument figsize=(15,10)\n", - "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n", - "#It's important you create legible and easy-to-read plots\n", - "ski_data.___(___)\n", - "#plt.subplots_adjust(hspace=___);\n", - "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What features do we have possible cause for concern about and why?\n", - "\n", - "* SkiableTerrain_ac because values are clustered down the low end,\n", - "* Snow Making_ac for the same reason,\n", - "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n", - "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n", - "* trams also may get an amber flag for the same reason,\n", - "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.4.2.1 SkiableTerrain_ac" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 19#\n", - "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n", - "ski_data.___[ski_data.___ > ___]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "#Code task 20#\n", "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n", "#Hint: don't forget the transpose will be helpful here\n", - "ski_data[ski_data.___ > ___].___" + "ski_data[ski_data.SkiableTerrain_ac > 4000].T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**A: 2** Your answer here" + "**A: 2** Your answer here\n", + "\n", + "**Becky's Answer:** Silverton Mountain" ] }, { @@ -1167,7 +3080,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![Silverton Mountain information](images/silverton_mountain_info.png)" + "![Silverton Mountain Info](../images/silverton_mountain_info.png)" ] }, { @@ -1179,35 +3092,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 162, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "26819.0" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 21#\n", "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n", - "ski_data.___[39, 'SkiableTerrain_ac']" + "ski_data.loc[39, 'SkiableTerrain_ac']" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ "#Code task 22#\n", "#Use the .loc accessor again to modify this value with the correct value of 1819\n", - "ski_data.___[39, 'SkiableTerrain_ac'] = ___" + "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 168, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1819.0" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 23#\n", "#Use the .loc accessor a final time to verify that the value has been modified\n", - "ski_data.___[39, 'SkiableTerrain_ac']" + "ski_data.loc[39, 'SkiableTerrain_ac']" ] }, { @@ -1226,19 +3161,17 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 172, "metadata": {}, "outputs": [ { "data": { - "image/png": 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alnS/nesjYirpCt5HA+Rh00nXdtoLOE2+SZmZ2YjTtsIUEYsj4vb8/EXSlaInki5NcU4e7RzSvWHI7bMi4uWImE+6PMcu7YrPzMzKaUh+Y5I0BXgz6cKEXZFuMU3++7o82kRWvpz8Ipq/NYSZma0m2n5WXr4y96WkO1W+kC6SXXvUGm2v+e/ffP2zGQBdXV1UKpWW4urt7W152pHA+WnM+WnM+WnM+WmsrYUpX3b9UuD8iLgsNy+RNCEiFufrWj2Z2xex8r1Tat7TJV//bCZAd3d3tHrKZadP1yw756cx56cx56cx56exdp6VJ9Jl+edFxCmFQVeR7m1P/ntloX16vhfLFqQLMN7arvjMzKyc2rnHtBvp3ix3SZqb244FTgIulnQo8CjwUYCIuEfSxaT70CwDPpfvBWNmZiNI2wpTRNxE/dvw7l6rMSJOBE5sV0zVphz906bGW3DSvm2OxMzM+vjKD2ZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViouTGZmViptK0ySzpL0pKS7C20XSZqbHwskzc3tUyT9qTDsjHbFZWZm5Ta6jX2fDfwAOLevISI+3vdc0snA0sL4D0XEjm2Mx8zMhoG2FaaIuFHSlFrDJAn4GPCeds3fzMyGJ0VE+zpPhemaiNi+qv1dwCkR0V0Y7x7gAeAF4PiI+HWdPmcAMwC6urp2mjVrVkux9fb2Mn/p8qbG3WHiBi3NYzjr7e1l7NixnQ6jtJyfxpyfxjqdn2nTps3p+/wto3YeymvkAODCwuvFwOYR8YyknYArJG0XES9UTxgRM4GZAN3d3dHT09NSAJVKhZNveqmpcRcc2No8hrNKpUKruR0JnJ/GnJ/GnJ/GhvysPEmjgQ8DF/W1RcTLEfFMfj4HeAjYaqhjMzOzzuvE6eJ7APdFxKK+BkmbSBqVn28JTAUe7kBsZmbWYe08XfxC4GZga0mLJB2aB01n5cN4AO8C7pR0B3AJcHhEPNuu2MzMrLzaeVbeAXXaD6nRdilwabtiMTOz4cNXfjAzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1Jp563Vz5L0pKS7C20nSHpM0tz82Kcw7BhJD0q6X9Ke7YrLzMzKrZ17TGcDe9VoPzUidsyPnwFI2haYDmyXpzlN0qg2xmZmZiXVtsIUETcCzzY5+v7ArIh4OSLmAw8Cu7QrNjMzK6/RHZjn5yV9EpgNHBkRzwETgVsK4yzKba8haQYwA6Crq4tKpdJSEL29vRy5w/Kmxm11HsNZb2/viFzuZjk/jTk/jTk/jQ11YTod+DYQ+e/JwKcA1Rg3anUQETOBmQDd3d3R09PTUiCVSoWTb3qpqXEXHNjaPIazSqVCq7kdCZyfxpyfxpyfxob0rLyIWBIRyyPir8APWXG4bhEwqTDqZsDjQxmbmZmVw5AWJkkTCi8/BPSdsXcVMF3S2pK2AKYCtw5lbGZmVg5tO5Qn6UKgBxgvaRHwDaBH0o6kw3QLgMMAIuIeSRcD9wLLgM9FRHM/AJmZ2WqlbYUpIg6o0Xxmg/FPBE5sVzxmZjY8+MoPZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKi5MZmZWKm0rTJLOkvSkpLsLbd+VdJ+kOyVdLmnD3D5F0p8kzc2PM9oVl5mZlVs795jOBvaqarsO2D4i3gg8ABxTGPZQROyYH4e3MS4zMyuxthWmiLgReLaq7dqIWJZf3gJs1q75m5nZ8KSIaF/n0hTgmojYvsawq4GLIuK8PN49pL2oF4DjI+LXdfqcAcwA6Orq2mnWrFktxdbb28v8pcubGneHiRu0NI/hrLe3l7Fjx3Y6jNJyfhpzfhrrdH6mTZs2JyK6OxZAP0Z3YqaSjgOWAefnpsXA5hHxjKSdgCskbRcRL1RPGxEzgZkA3d3d0dPT01IMlUqFk296qalxFxzY2jyGs0qlQqu5HQmcn8acn8acn8aG/Kw8SQcD+wEHRt5di4iXI+KZ/HwO8BCw1VDHZmZmnTekhUnSXsDXgA9ExB8L7ZtIGpWfbwlMBR4eytjMzKwc2nYoT9KFQA8wXtIi4Buks/DWBq6TBHBLPgPvXcC3JC0DlgOHR8SzNTs2M7PVWtsKU0QcUKP5zDrjXgpc2q5YzMxs+PCVH8zMrFRcmMzMrFRcmMzMrFRcmMzMrFRcmMzMrFSaKkySdmumzczMbFU1u8f0n022mZmZrZKG/8ck6W3A24FNJH25MGh9YFQ7AzMzs5Gpv3+wXQsYm8cbV2h/AfhIu4IyM7ORq2FhiohfAb+SdHZEPDJEMZmZ2QjW7CWJ1pY0E5hSnCYi3tOOoMzMbORqtjD9BDgD+BHpIqtmZmZt0WxhWhYRp7c1EjMzM5o/XfxqSZ+VNEHSxn2PtkZmZmYjUrN7TAfnv18ptAWw5eCGY2ZmI11ThSkitmh3IGZmZtBkYZL0yVrtEXHu4IZjZmYjXbO/Me1ceLwTOAH4QKMJJJ0l6UlJdxfaNpZ0naQ/5L8bFYYdI+lBSfdL2nPAS2JmZquFpgpTRPxj4fEZ4M2kq0I0cjawV1Xb0cD1ETEVuD6/RtK2wHRguzzNaZJ8ySMzsxGo1dte/BGY2miEiLgReLaqeX/gnPz8HOCDhfZZEfFyRMwHHgR2aTE2MzMbxpr9jelq0ll4kC7eug1wcQvz64qIxQARsVjS63L7ROCWwniLclutWGYAMwC6urqoVCothAG9vb0cuUNz/yvc6jyGs97e3hG53M1yfhpzfhpzfhpr9nTx7xWeLwMeiYhFgxiHarRFjTYiYiYwE6C7uzt6enpammGlUuHkm15qatwFB7Y2j+GsUqnQam5HAuenMeenMeensWZ/Y/oVcB/pCuMbAa+0OL8lkiYA5L9P5vZFwKTCeJsBj7c4DzMzG8aavYPtx4BbgY8CHwN+J6mV215cxYp/1j0YuLLQPl3S2pK2IP1+dWsL/ZuZ2TDX7KG844CdI+JJAEmbAL8ELqk3gaQLgR5gvKRFwDeAk4CLJR0KPEoqdETEPZIuBu4lHSr8XET4YrFmZiNQs4Vpjb6ilD1DP3tbEXFAnUG71xn/RODEJuMxM7PVVLOF6eeSfgFcmF9/HPhZe0IyM7ORrGFhkvR60ineX5H0YeAdpDPobgbOH4L4zMxshOnv5IfvAy8CRMRlEfHliPgSaW/p++0OzszMRp7+CtOUiLizujEiZpNus25mZjao+itM6zQYtu5gBmJmZgb9F6bbJH2mujGf7j2nPSGZmdlI1t9ZeUcAl0s6kBWFqJt0ZfEPtTMwMzMbmRoWpohYArxd0jRg+9z804j4v7ZHZmZmI1Kzt1a/AbihzbGYmZm1fD8mMzOztnBhMjOzUnFhMjOzUnFhMjOzUnFhMjOzUnFhMjOzUnFhMjOzUnFhMjOzUmn2RoGDRtLWwEWFpi2BrwMbAp8Bnsrtx0aEb0ZoZjbCDHlhioj7gR0BJI0CHgMuB/4eODUivjfUMZmZWXl0+lDe7sBDEfFIh+MwM7OS6HRhmg5cWHj9eUl3SjpL0kadCsrMzDpHEdGZGUtrAY8D20XEEkldwNNAAN8GJkTEp2pMNwOYAdDV1bXTrFmzWpp/b28v85cub2rcHSZu0NI8hrPe3l7Gjh3b6TBKy/lpzPlprNP5mTZt2pyI6O5YAP3oZGHaH/hcRLyvxrApwDURsX31sKLu7u6YPXt2S/OvVCoc8vOXmhp3wUn7tjSP4axSqdDT09PpMErL+WnM+Wms0/mRVOrC1MlDeQdQOIwnaUJh2IeAu4c8IjMz67ghPysPQNIY4L3AYYXm70jakXQob0HVMDMzGyE6Upgi4o/A31S1HdSJWMzMrFw6fVaemZnZSlyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVDpya3VJC4AXgeXAsojolrQxcBEwBVgAfCwinutEfGZm1jmd3GOaFhE7RkR3fn00cH1ETAWuz6/NzGyEKdOhvP2Bc/Lzc4APdjAWMzPrEEXE0M9Umg88BwTw3xExU9LzEbFhYZznImKjGtPOAGYAdHV17TRr1qyWYujt7WX+0uVNjbvDxA1amsdw1tvby9ixYzsdRmk5P405P411Oj/Tpk2bUzhaVTod+Y0J2C0iHpf0OuA6Sfc1O2FEzARmAnR3d0dPT09LAVQqFU6+6aWmxl1wYGvzGM4qlQqt5nYkcH4ac34ac34a68ihvIh4PP99Ergc2AVYImkCQP77ZCdiMzOzzhrywiRpPUnj+p4D7wPuBq4CDs6jHQxcOdSxmZlZ53XiUF4XcLmkvvlfEBE/l3QbcLGkQ4FHgY92IDYzM+uwIS9MEfEw8KYa7c8Auw91PGZmVi5lOl3czMzMhcnMzMrFhcnMzErFhcnMzErFhcnMzErFhcnMzErFhcnMzErFhcnMzErFhcnMzErFhcnMzErFhcnMzEqlU/djGlamHP3TpsZbcNK+bY7EzGz15z0mMzMrFRcmMzMrFRcmMzMrFRcmMzMrFRcmMzMrlSEvTJImSbpB0jxJ90j6Ym4/QdJjkubmxz5DHZuZmXVeJ04XXwYcGRG3SxoHzJF0XR52akR8rwMxmZlZSQx5YYqIxcDi/PxFSfOAiUMdh5mZlZMionMzl6YANwLbA18GDgFeAGaT9qqeqzHNDGAGQFdX106zZs1qad69vb3MX7q8pWnr2WHiBoPaXyf19vYyduzYTodRWs5PY85PY53Oz7Rp0+ZERHfHAuhHxwqTpLHAr4ATI+IySV3A00AA3wYmRMSnGvXR3d0ds2fPbmn+lUqFQ37+UkvT1rM6XfmhUqnQ09PT6TBKy/lpzPlprNP5kVTqwtSRSxJJWhO4FDg/Ii4DiIglheE/BK7pRGyrwpcuMjNbdZ04K0/AmcC8iDil0D6hMNqHgLuHOjYzM+u8Tuwx7QYcBNwlaW5uOxY4QNKOpEN5C4DDOhCbmZl1WCfOyrsJUI1BPxvqWMzMrHx85QczMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMysVFyYzMyuVjlySyAaXL4VkZqsTF6YOcCExM6vPh/LMzKxUXJjMzKxUXJjMzKxU/BtTiTX7W1Q73PXYUg5pYv7+HczMBpv3mMzMrFRcmMzMrFR8KG8EGcihwSN3GNw+mz3k51Ppzcx7TGZmVireY7Ih0ckTOcxseCndHpOkvSTdL+lBSUd3Oh4zMxtapdpjkjQK+C/gvcAi4DZJV0XEvZ2NzMrGv0WZrb5KVZiAXYAHI+JhAEmzgP0BFyZrSScLWKdODOmksn8RGA5faIZDjO2miOh0DK+S9BFgr4j4dH59ELBrRHy+MM4MYEZ+uTVwf4uzGw88vQrhru6cn8acn8acn8Y6nZ/JEbFJB+ffUNn2mFSjbaXKGREzgZmrPCNpdkR0r2o/qyvnpzHnpzHnpzHnp7GynfywCJhUeL0Z8HiHYjEzsw4oW2G6DZgqaQtJawHTgas6HJOZmQ2hUh3Ki4hlkj4P/AIYBZwVEfe0aXarfDhwNef8NOb8NOb8NOb8NFCqkx/MzMzKdijPzMxGOBcmMzMrlRFZmEbiZY8kTZJ0g6R5ku6R9MXcvrGk6yT9If/dqDDNMTlH90vas9C+k6S78rD/kFTrNP9hSdIoSb+XdE1+7fxkkjaUdImk+/J69DbnZwVJX8rb1t2SLpS0jvPToogYUQ/SSRUPAVsCawF3ANt2Oq4hWO4JwFvy83HAA8C2wHeAo3P70eHrpesAAAZNSURBVMC/5efb5tysDWyRczYqD7sVeBvp/87+F9i708s3iHn6MnABcE1+7fysyM05wKfz87WADZ2fV3MzEZgPrJtfXwwc4vy09hiJe0yvXvYoIl4B+i57tFqLiMURcXt+/iIwj7Qx7U/6wCH//WB+vj8wKyJejoj5wIPALpImAOtHxM2RtqJzC9MMa5I2A/YFflRodn4ASesD7wLOBIiIVyLieZyfotHAupJGA2NI/4Pp/LRgJBamicDCwutFuW3EkDQFeDPwO6ArIhZDKl7A6/Jo9fI0MT+vbl8dfB/4KvDXQpvzk2wJPAX8Tz7U+SNJ6+H8ABARjwHfAx4FFgNLI+JanJ+WjMTC1O9lj1ZnksYClwJHRMQLjUat0RYN2oc1SfsBT0bEnGYnqdG22uaHtDfwFuD0iHgz8BLp0FQ9Iyo/+bej/UmH5TYF1pP0iUaT1GhbbfMzUCOxMI3Yyx5JWpNUlM6PiMty85J8+ID898ncXi9Pi/Lz6vbhbjfgA5IWkA7vvkfSeTg/fRYBiyLid/n1JaRC5fwkewDzI+KpiPgLcBnwdpyflozEwjQiL3uUz+w5E5gXEacUBl0FHJyfHwxcWWifLmltSVsAU4Fb8+GIFyW9Nff5ycI0w1ZEHBMRm0XEFNI68X8R8QmcHwAi4glgoaStc9PupNvROD/Jo8BbJY3Jy7U76Xdc56cVnT77ohMPYB/SWWkPAcd1Op4hWuZ3kA4J3AnMzY99gL8Brgf+kP9uXJjmuJyj+ymcGQR0A3fnYT8gX0FkdXkAPaw4K8/5WbFcOwKz8zp0BbCR87NSfr4J3JeX7cekM+6cnxYeviSRmZmVykg8lGdmZiXmwmRmZqXiwmRmZqXiwmRmZqXiwmRmZqXiwmRmZqXiwmTDjqTj8u0F7pQ0V9KukhZIGl9j3N/209cUSXfXGVaR1N1g2svz/B+UtDQ/nyvp7QNfqrrz2FTSJYPVn9lwMLrTAZgNhKS3AfuRbuHxci5Ga9UbPyIGrUjU6PtDOaYe4KiI2K+Z6SSNjohl9V5XzeNx4CODEK7ZsOE9JhtuJgBPR8TLABHxdP7wBkDSupJ+Lukz+XVv/jtW0vWSbs83YSve6mS0pHPyHtglksZUz1TS+yTdnKf/Sb4Y7mtI2kTSpZJuy4/dcvsJkmZKuhY4t8brKZJ+nfu/vW+vq7hHJ+kQSZfl5fuDpO80SpSk0yXNznuX3yy07yzpt5LukHSrpHHNJN5syHT60hN++DGQBzCWdDmlB4DTgHfn9gXAFOCXwCcL4/fmv6NJ97kBGE+6/43yNAHsloedRdr7AaiQLg8zHrgRWC+3fw34emEePay4hNEFwDvy881J1yYEOAGYw4obyVW/HgOsk59PBWbn51OAu/PzQ4CHgQ2AdYBHgEkNcrVx/jsqL8sbSXuXDwM752HrA6M7/b764Ufx4UN5NqxERK+knYB3AtOAiyT13X7hSuA7EXF+jUkF/Iukd5HutzQR6MrDFkbEb/Lz84AvkO6t0+etpDuO/iZdV5O1gJvrhLgHsK1W3A17/cIeyVUR8afCuMXXawI/kLQjsBzYqk7/10fEUgBJ9wKTWfm+PkUfkzSDVJQn5GUIYHFE3AYQjW99YtYRLkw27ETEctIeQEXSXay4evNvgL0lXRAR1ReBPBDYBNgpIv6Sb2+xTl+X1bOoei3guog4oInw1gDeVlWAyIXqpapxi6+/BCwB3pT7+HOd/l8uPF9OnW04X7H6KNKe0XOSziYtrxiB9/ex4cW/MdmwImlrSVMLTTuSDmkBfB14hnSIr9oGpBsB/kXSNNKeRp/N80kVAAcAN1VNewuwm6TX5xjGSKq3R3Mt8PlCvDs2sVh98S2OiL8CB5EOv62K9UmFb6mkLmDv3H4fsKmknXN845RuBW5WGi5MNtyMBc6RdK+kO0mHp04oDD8CWKfGiQHnA92SZpP2nu4rDJsHHJz72xg4vThhRDxF+n3nwjzOLcAb6sT3hTyfO/OhtsObXK7Tcgy3kA7jVe9dDUhE3AH8HriH9LvZb3L7K8DHgf+UdAdwHSv2HM1Kwbe9MDOzUvEek5mZlYqPLZsNc5J+R7pbatFBEXFXJ+IxW1U+lGdmZqXiQ3lmZlYqLkxmZlYqLkxmZlYqLkxmZlYq/x8GqBhObMyWNQAAAABJRU5ErkJggg==\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1265,7 +3198,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -1276,7 +3209,7 @@ "Name: Snow Making_ac, dtype: float64" ] }, - "execution_count": 31, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -1287,7 +3220,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -1345,7 +3278,7 @@ " \n", " \n", " fastEight\n", - " 0\n", + " 0.0\n", " \n", " \n", " fastSixes\n", @@ -1377,11 +3310,11 @@ " \n", " \n", " Runs\n", - " 97\n", + " 97.0\n", " \n", " \n", " TerrainParks\n", - " 3\n", + " 3.0\n", " \n", " \n", " LongestRun_mi\n", @@ -1389,23 +3322,23 @@ " \n", " \n", " SkiableTerrain_ac\n", - " 4800\n", + " 4800.0\n", " \n", " \n", " Snow Making_ac\n", - " 3379\n", + " 3379.0\n", " \n", " \n", " daysOpenLastYear\n", - " 155\n", + " 155.0\n", " \n", " \n", " yearsOpen\n", - " 64\n", + " 64.0\n", " \n", " \n", " averageSnowfall\n", - " 360\n", + " 360.0\n", " \n", " \n", " AdultWeekday\n", @@ -1417,7 +3350,7 @@ " \n", " \n", " projectedDaysOpen\n", - " 157\n", + " 157.0\n", " \n", " \n", " NightSkiing_ac\n", @@ -1436,7 +3369,7 @@ "vertical_drop 3500\n", "base_elev 7170\n", "trams 2\n", - "fastEight 0\n", + "fastEight 0.0\n", "fastSixes 2\n", "fastQuads 7\n", "quad 1\n", @@ -1444,21 +3377,21 @@ "double 3\n", "surface 8\n", "total_chairs 28\n", - "Runs 97\n", - "TerrainParks 3\n", + "Runs 97.0\n", + "TerrainParks 3.0\n", "LongestRun_mi 5.5\n", - "SkiableTerrain_ac 4800\n", - "Snow Making_ac 3379\n", - "daysOpenLastYear 155\n", - "yearsOpen 64\n", - "averageSnowfall 360\n", + "SkiableTerrain_ac 4800.0\n", + "Snow Making_ac 3379.0\n", + "daysOpenLastYear 155.0\n", + "yearsOpen 64.0\n", + "averageSnowfall 360.0\n", "AdultWeekday NaN\n", "AdultWeekend NaN\n", - "projectedDaysOpen 157\n", + "projectedDaysOpen 157.0\n", "NightSkiing_ac NaN" ] }, - "execution_count": 32, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -1471,7 +3404,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails." + "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails.\n", + "\n", + "**Becky's Question:** - Where can I find that snowmaking covers 60% of the trails? I don't see this info anywhere. " ] }, { @@ -1481,129 +3416,257 @@ "What, then, is your rough guess for the area covered by snowmaking?" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ".6 * 4800\n", + "\n", + "**Becky's Answer:** 2880" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.4.2.3 fastEight" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Look at the different fastEight values more closely:" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "fastEight\n", + "0.0 163\n", + "1.0 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data.fastEight.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column." + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 24#\n", + "#Drop the 'fastEight' column from ski_data. Use inplace=True\n", + "ski_data.drop(columns='fastEight', inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?" + ] + }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 201, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastSixesfastQuadsquad...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
34Howelsen HillColoradoColorado713644066960000...6.050.025.0100.0104.0150.025.025.0100.010.0
115Pine Knob Ski ResortMichiganMichigan130830010090000...1.080.080.0NaN2019.0NaN49.057.0NaNNaN
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2 rows × 26 columns

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" + ], "text/plain": [ - "2880.0" + " Name Region state summit_elev vertical_drop \\\n", + "34 Howelsen Hill Colorado Colorado 7136 440 \n", + "115 Pine Knob Ski Resort Michigan Michigan 1308 300 \n", + "\n", + " base_elev trams fastSixes fastQuads quad ... LongestRun_mi \\\n", + "34 6696 0 0 0 0 ... 6.0 \n", + "115 1009 0 0 0 0 ... 1.0 \n", + "\n", + " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", + "34 50.0 25.0 100.0 104.0 \n", + "115 80.0 80.0 NaN 2019.0 \n", + "\n", + " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", + "34 150.0 25.0 25.0 100.0 \n", + "115 NaN 49.0 57.0 NaN \n", + "\n", + " NightSkiing_ac \n", + "34 10.0 \n", + "115 NaN \n", + "\n", + "[2 rows x 26 columns]" ] }, - "execution_count": 33, + "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ - ".6 * 4800" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!" + "#Code task 25#\n", + "#Filter the 'yearsOpen' column for values greater than 100\n", + "ski_data.loc[ski_data.yearsOpen > 100]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 2.6.4.2.3 fastEight" + "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Look at the different fastEight values more closely:" + "What does the distribution of yearsOpen look like if you exclude just the obviously wrong one?" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 207, "metadata": {}, "outputs": [ { "data": { + "image/png": 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tWk2cOFGpqamKj4/XNddcU+Z9IiMjVVRUpF9//dUt0BhjlJ2d7drjU8qb50yDBg0UEhKiN998s8xtVsfvtirbKT3Z9pJLLtGQIUPUsWNH1atXT9L/nXu2d+/eStcRHBzs8fqQ/niN/LmGyMjICvtXHerVqyebzVbm+TGV2U7pJdSfffaZPv74Y3Xp0sVjzqWXXipJ2rx5s1q2bOkaLyoq0g8//KA777yzCo/gjxOQFy1apJycHGVnZ6tJkyYKCQnR9OnTdcstt1RpnSgfe2bgld27d2vEiBGKiIgo8wqYsgQGBqpdu3Z66aWXJMm1+96bvRGVsXXrVn333XduYxkZGQoLC3PtYSi9ouLUE+/mz5/vsT5v/pou1aVLF61YscIVKkr97//+r0JDQ3X11Vd7+zDKVbt2bbVr105z5851q6ukpESzZ89Wo0aNPA7XVMaQIUO0cOFCjR49WlFRUbr11lvdlvfo0UNbtmxRs2bN1LZtW4+f0jBjs9kUFBTktqfg+PHj+ve//12luhwOh5KSkjRp0iRJ0rffflvu3NJ/pE4NYh9++KGOHTvm8Y+YN8+ZHj166Oeff1ZkZGSZj7u6PgyuMtt5/fXXNXv2bE2bNk3z58/X4cOH3a7CufDCC9WsWTO9+eabZQaTisTHx3u8Pn788UePQ5idO3cut3/VpXbt2mrbtq0++ugj18m00h9X4Xl7hVjpHpkVK1boww8/VNeuXcuc165dO8XExOitt95yG//ggw909OhRj8+aqayGDRuqVatWioiI0CuvvKJjx45V6mRpeIc9M/CwZcsW13H7nJwcffnll5o5c6YCAwM1b948j135f/bKK69oxYoV6t69uxo3bqwTJ064/uK84YYbJElhYWFq0qSJ6y+l+vXrq0GDBlX+xyE2NlZ/+9vf5HQ6FRMTo9mzZyszM1OTJk1SaGiopD/Oxbjooos0YsQIFRUVqV69epo3b55WrVrlsb5LL71Uc+fO1csvv6w2bdooICBAbdu2LXPb48aNc51TMnbsWNWvX19vv/22Fi5c6HGV0JmYOHGikpOT1blzZ40YMUK1atXS9OnTtWXLFr3zzjtVOq5f6u6779bo0aP1xRdf6IknnnA7R0GSJkyYoMzMTLVv315DhgzRRRddpBMnTmjnzp1atGiRXnnlFTVq1Ejdu3fXlClTlJqaqocfflgHDx7Uc889V+4VVWUZO3as9u7dqy5duqhRo0Y6fPiwXnjhBdnt9go//C85OVldu3bVqFGjlJubqw4dOriuZrr88svVt29ft/nePGfS0tL04Ycf6rrrrtOwYcPUqlUrlZSUaPfu3Vq2bJn+53/+x+OQRVV4u53NmzdryJAh6tevnyvAvPHGG7rllls0depU16XXL730knr27Kmrr75aw4YNU+PGjbV7924tXbpUb7/9drl19O3bV3fffbcGDBigv//979q1a5cmT57s8XpPS0vTm2++qe7du+upp55yXc30ww8/nHEv/mzChAnq3r27unbtqqFDh6q4uFjPPvus6tSpo99///2097/lllu0ePFijRkzRpGRkVq7dq1rWXh4uGsvTGBgoCZPnqy+ffuqf//+uvPOO/XTTz9p5MiRSk5O1o033ui23sWLF+vYsWOuS9G///57ffDBB5L+uOKz9Pnz2muvSfrj0Pvhw4e1ePFivfHGG0pPT3c7jItq4sOTj+FnSq9wKP2pVauWadiwoUlKSjLp6ekmJyfH4z6nXmG0Zs0ac/PNN5smTZoYh8NhIiMjTVJSkpk/f77b/T799FNz+eWXG4fDYSS5rqIoXd+vv/562m0Z838fmvfBBx+YSy65xNSqVcvEx8ebKVOmeNz/xx9/NCkpKSY8PNycd955ZvDgwWbhwoUeV2D8/vvv5pZbbjF169Y1NpvNbZsq4+qFzZs3m549e5qIiAhTq1Ytc9lll5mZM2e6zSm9mun99993Gy+9+ujU+WX58ssvzfXXX29q165tQkJCzNVXX20++eSTMtfnzdVMf3bvvfeaoKAgs3fv3jKX//rrr2bIkCEmISHB2O12U79+fdOmTRszZswYc/ToUde8N99801x00UXG4XCYpk2bmokTJ5o33nijzCtnyvqwwwULFpibbrrJnH/++a7nX7du3cyXX3552sdw/PhxM2rUKNOkSRNjt9tNTEyMefTRR82hQ4fc5lXmOXP06FHzxBNPmIsuusjUqlXLREREmEsvvdQMGzbM7cMjJZmBAwd63L+8K4Qqu52jR4+aiy++2LRs2dLjQyQHDhxo7Ha7+frrr11ja9asMTfddJOJiIgwDofDNGvWzAwbNsy1vKyrmUpKSszkyZNN06ZNTXBwsGnbtq1ZsWKFx9VMxhjz/fffm+TkZBMcHGzq169vHnjgAfPxxx97fTWTt72aN2+eufTSS02tWrVM48aNzTPPPGOGDBli6tWrd9qe/vm97NSfUx+PMX98oF2rVq1MrVq1THR0tBkyZIjbVU9/rrO89f65n6+++qpp0aKFCQ0NNXXq1DEdO3Y0H3300WnrRtXYjDnN5SkAzmknT55UfHy8rr32Wr333nu+Luesi4+PV2JioteHK+A/CgsLXSfQVtfn5uDcwGEm4C/q119/1fbt2zVz5kwdOHBAjz32mK9LAtw88MADSk5OVkxMjLKzs/XKK69o27Ztlb46Euc+wgzwF7Vw4ULdd999iomJ0fTp0zmOD7+Tl5enESNG6Ndff5XdbtcVV1yhRYsWuc6/A0pxmAkAAFgal2YDAABLI8wAAABLI8wAAABLO+dPAC4pKdEvv/yisLCwM/pgMQAAUHOMMcrLy1NsbOxpv5zznA8zv/zyi8c3GgMAAGvYs2fPab889ZwPM6VfM79nzx6Fh4f7uJpzS2FhoZYtW6aUlBTZ7XZfl3POo981i37XLPpds6zQ79zcXMXFxbn+Ha/IOR9mSg8thYeHE2aqWWFhoUJDQxUeHu63L4ZzCf2uWfS7ZtHvmmWlfntziggnAAMAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsL8nUBAHAm4h9bWOX77nymezVWAsBXfL5nZt++fbr77rsVGRmp0NBQtW7dWuvXr3ctN8bI6XQqNjZWISEh6tSpk7Zu3erDigEAgD/xaZg5dOiQOnToILvdrsWLF+v777/X888/r7p167rmTJ48WVOmTNG0adOUlZWl6OhoJScnKy8vz3eFAwAAv+HTw0yTJk1SXFycZs6c6RqLj493/b8xRlOnTtWYMWPUp08fSdKsWbMUFRWljIwM9e/fv6ZLBgAAfsanYWb+/Pnq2rWrbr31Vq1cuVLnn3++BgwYoIceekiStGPHDmVnZyslJcV1H4fDoaSkJK1evbrMMFNQUKCCggLX7dzcXElSYWGhCgsLz/Ij+msp7Sd9rRn0u2yOQFPl+1bUS/pds+h3zbJCvytTm80YU/V3gjMUHBwsSRo+fLhuvfVWffPNN0pLS9Orr76qe+65R6tXr1aHDh20b98+xcbGuu738MMPa9euXVq6dKnHOp1Op8aPH+8xnpGRodDQ0LP3YAAAQLXJz89Xamqqjhw5ovDw8Arn+nTPTElJidq2bav09HRJ0uWXX66tW7fq5Zdf1j333OOaZ7PZ3O5njPEYKzV69GgNHz7cdTs3N1dxcXFKSUk5bTNQOYWFhcrMzFRycrLsdruvyznn0e+yJTo9/6jx1hZn13KX0e+aRb9rlhX6XXpkxRs+DTMxMTFq2bKl21iLFi304YcfSpKio6MlSdnZ2YqJiXHNycnJUVRUVJnrdDgccjgcHuN2u91vf2FWR29rFv12V1Bc9h823vCmj/S7ZtHvmuXP/a5MXT69mqlDhw7avn2729iPP/6oJk2aSJISEhIUHR2tzMxM1/KTJ09q5cqVat++fY3WCgAA/JNP98wMGzZM7du3V3p6um677TZ98803mjFjhmbMmCHpj8NLaWlpSk9PV/PmzdW8eXOlp6crNDRUqampviwdAAD4CZ+GmSuvvFLz5s3T6NGjNWHCBCUkJGjq1Km66667XHNGjhyp48ePa8CAATp06JDatWunZcuWKSwszIeVAwAAf+HzrzPo0aOHevToUe5ym80mp9Mpp9NZc0UBAADL8PnXGQAAAJwJwgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0n4YZp9Mpm83m9hMdHe1aboyR0+lUbGysQkJC1KlTJ23dutWHFQMAAH/j8z0zl1xyifbv3+/62bx5s2vZ5MmTNWXKFE2bNk1ZWVmKjo5WcnKy8vLyfFgxAADwJz4PM0FBQYqOjnb9nHfeeZL+2CszdepUjRkzRn369FFiYqJmzZql/Px8ZWRk+LhqAADgL4J8XcBPP/2k2NhYORwOtWvXTunp6WratKl27Nih7OxspaSkuOY6HA4lJSVp9erV6t+/f5nrKygoUEFBget2bm6uJKmwsFCFhYVn98H8xZT2k77WDPpdNkegqfJ9K+ol/a5Z9LtmWaHflanNZoyp+jvBGVq8eLHy8/N14YUX6sCBA3rqqaf0ww8/aOvWrdq+fbs6dOigffv2KTY21nWfhx9+WLt27dLSpUvLXKfT6dT48eM9xjMyMhQaGnrWHgsAAKg++fn5Sk1N1ZEjRxQeHl7hXJ+GmVMdO3ZMzZo108iRI3X11VerQ4cO+uWXXxQTE+Oa89BDD2nPnj1asmRJmesoa89MXFycfvvtt9M2A5VTWFiozMxMJScny263+7qccx79Lluis+w/bLyxxdm13GX0u2bR75plhX7n5uaqQYMGXoUZnx9m+rPatWvr0ksv1U8//aTevXtLkrKzs93CTE5OjqKiospdh8PhkMPh8Bi32+1++wuzOnpbs+i3u4JiW5Xv600f6XfNot81y5/7XZm6fH4C8J8VFBRo27ZtiomJUUJCgqKjo5WZmelafvLkSa1cuVLt27f3YZUAAMCf+HTPzIgRI9SzZ081btxYOTk5euqpp5Sbm6t+/frJZrMpLS1N6enpat68uZo3b6709HSFhoYqNTXVl2UDAAA/4tMws3fvXt1555367bffdN555+nqq6/W2rVr1aRJE0nSyJEjdfz4cQ0YMECHDh1Su3bttGzZMoWFhfmybAAA4Ed8GmbmzJlT4XKbzSan0ymn01kzBQEAAMvxq3NmAAAAKoswAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALM1vwszEiRNls9mUlpbmGjPGyOl0KjY2ViEhIerUqZO2bt3quyIBAIDf8Yswk5WVpRkzZqhVq1Zu45MnT9aUKVM0bdo0ZWVlKTo6WsnJycrLy/NRpQAAwN/4PMwcPXpUd911l1577TXVq1fPNW6M0dSpUzVmzBj16dNHiYmJmjVrlvLz85WRkeHDigEAgD8J8nUBAwcOVPfu3XXDDTfoqaeeco3v2LFD2dnZSklJcY05HA4lJSVp9erV6t+/f5nrKygoUEFBget2bm6uJKmwsFCFhYVn6VH8NZX2k77WDPpdNkegqfJ9K+ol/a5Z9LtmWaHflanNp2Fmzpw52rBhg7KysjyWZWdnS5KioqLcxqOiorRr165y1zlx4kSNHz/eY3zZsmUKDQ09w4pRlszMTF+X8JdCv91Nvqrq9120aNFp59DvmkW/a5Y/9zs/P9/ruT4LM3v27NHQoUO1bNkyBQcHlzvPZrO53TbGeIz92ejRozV8+HDX7dzcXMXFxSklJUXh4eFnXjhcCgsLlZmZqeTkZNntdl+Xc86j32VLdC6t8n23OLuWu4x+1yz6XbOs0O/SIyve8FmYWb9+vXJyctSmTRvXWHFxsb744gtNmzZN27dvl/THHpqYmBjXnJycHI+9NX/mcDjkcDg8xu12u9/+wqyO3tYs+u2uoLj8P25Ox5s+0u+aRb9rlj/3uzJ1+ewE4C5dumjz5s3auHGj66dt27a66667tHHjRjVt2lTR0dFuu8BOnjyplStXqn379r4qGwAA+Bmf7ZkJCwtTYmKi21jt2rUVGRnpGk9LS1N6erqaN2+u5s2bKz09XaGhoUpNTfVFyQAAwA/5/GqmiowcOVLHjx/XgAEDdOjQIbVr107Lli1TWFiYr0sDAAB+wq/CzOeff+5222azyel0yul0+qQeAADg/3z+oXkAAABngjADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsrUphpmnTpjp48KDH+OHDh9W0adMzLgoAAMBbVQozO3fuVHFxscd4QUGB9u3bd8ZFAQAAeCuoMpPnz5/v+v+lS5cqIiLCdbu4uFjLly9XfHx8tRUHAABwOpUKM71795Yk2Ww29evXz22Z3W5XfHy8nn/++WorDgAA4HQqFWZKSkokSQkJCcrKylKDBg3OSlEAAADeqlSYKbVjx47qrgMAAKBKqhRmJGn58uVavny5cnJyXHtsSr355ptnXBgAAIA3qhRmxo8frwkTJqht27aKiYmRzWar7roAAAC8UqUw88orr+itt95S3759q7seAACASqnS58ycPHlS7du3r+5aAAAAKq1KYebBBx9URkZGddcCAABQaVU6zHTixAnNmDFDn376qVq1aiW73e62fMqUKdVSHAAAwOlUKcxs2rRJrVu3liRt2bLFbRknAwMAgJpUpTDz2WefVXcdAAAAVVKlc2YAAAD8RZX2zHTu3LnCw0krVqyockEAAACVUaUwU3q+TKnCwkJt3LhRW7Zs8fgCSgAAgLOpSmHmn//8Z5njTqdTR48ePaOCAAAAKqPK381UlrvvvltXXXWVnnvuuepcLQCcFfGPLSx3mSPQaPJVUqJzqQqKPQ+r73ym+9ksDUAlVOsJwGvWrFFwcHB1rhIAAKBCVdoz06dPH7fbxhjt379f69at05NPPlkthQEAAHijSmEmIiLC7XZAQIAuuugiTZgwQSkpKdVSGAAAgDeqFGZmzpxZ3XUAAABUyRmdALx+/Xpt27ZNNptNLVu21OWXX15ddQEAAHilSmEmJydHd9xxhz7//HPVrVtXxhgdOXJEnTt31pw5c3TeeedVd50AAABlqtLVTIMHD1Zubq62bt2q33//XYcOHdKWLVuUm5urIUOGeL2el19+Wa1atVJ4eLjCw8N1zTXXaPHixa7lxhg5nU7FxsYqJCREnTp10tatW6tSMgAAOEdVKcwsWbJEL7/8slq0aOEaa9mypV566SW3MHI6jRo10jPPPKN169Zp3bp1uv7669WrVy9XYJk8ebKmTJmiadOmKSsrS9HR0UpOTlZeXl5VygYAAOegKoWZkpIS2e12j3G73a6SkhKv19OzZ09169ZNF154oS688EI9/fTTqlOnjtauXStjjKZOnaoxY8aoT58+SkxM1KxZs5Sfn6+MjIyqlA0AAM5BVQoz119/vYYOHapffvnFNbZv3z4NGzZMXbp0qVIhxcXFmjNnjo4dO6ZrrrlGO3bsUHZ2ttul3g6HQ0lJSVq9enWVtgEAAM49VToBeNq0aerVq5fi4+MVFxcnm82m3bt369JLL9Xs2bMrta7Nmzfrmmuu0YkTJ1SnTh3NmzdPLVu2dAWWqKgot/lRUVHatWtXuesrKChQQUGB63Zubq6kP74Ms7CwsFK1oWKl/aSvNYN+l80RaM7OegOM239Pxe+hevH8rllW6HdlarMZY6r8TpCZmakffvhBxhi1bNlSN9xwQ6XXcfLkSe3evVuHDx/Whx9+qNdff10rV67U4cOH1aFDB/3yyy+KiYlxzX/ooYe0Z88eLVmypMz1OZ1OjR8/3mM8IyNDoaGhla4PAADUvPz8fKWmpurIkSMKDw+vcG6lwsyKFSs0aNAgrV271mPFR44cUfv27fXKK6+oY8eOVatc0g033KBmzZpp1KhRatasmTZs2OD2+TW9evVS3bp1NWvWrDLvX9aembi4OP3222+nbQYqp7CwUJmZmUpOTi7zHCpUL/pdtkTn0rOyXkeA0T/alujJdQEqKPH8osktzq5nZbt/VTy/a5YV+p2bm6sGDRp4FWYqdZhp6tSpeuihh8pcaUREhPr3768pU6acUZgxxqigoEAJCQmKjo5WZmamK8ycPHlSK1eu1KRJk8q9v8PhkMPh8Bi32+1++wuzOnpbs+i3u7K+0bpa119iK3Mb/A7ODp7fNcuf+12Zuip1AvB3332nG2+8sdzlKSkpWr9+vdfre/zxx/Xll19q586d2rx5s8aMGaPPP/9cd911l2w2m9LS0pSenq558+Zpy5YtuvfeexUaGqrU1NTKlA0AAM5hldozc+DAgQqTUlBQkH799ddKra9v377av3+/IiIi1KpVKy1ZskTJycmSpJEjR+r48eMaMGCADh06pHbt2mnZsmUKCwurTNkAAOAcVqkwc/7552vz5s264IILyly+adMmt5N1T+eNN96ocLnNZpPT6ZTT6axMmQAA4C+kUoeZunXrprFjx+rEiRMey44fP65x48apR48e1VYcAADA6VRqz8wTTzyhuXPn6sILL9SgQYN00UUXyWazadu2bXrppZdUXFysMWPGnK1aAQAAPFQqzERFRWn16tV69NFHNXr0aJVe1W2z2dS1a1dNnz7d40PuAAAAzqZKfwJwkyZNtGjRIh06dEj/+c9/ZIxR8+bNVa9evbNRHwAAQIWq9HUGklSvXj1deeWV1VkLAABApVU5zABAdYl/bKGvSwBgYVX61mwAAAB/QZgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWxnczAQD81pl8b9fOZ7pXYyXwZ+yZAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlhbk6wIAAOe2+McWnnaOI9Bo8lVSonOpCoptNVAVziXsmQEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJbG1UwAUAXeXKFTnp3PdK/GSgD4dM/MxIkTdeWVVyosLEwNGzZU7969tX37drc5xhg5nU7FxsYqJCREnTp10tatW31UMQAA8Dc+DTMrV67UwIEDtXbtWmVmZqqoqEgpKSk6duyYa87kyZM1ZcoUTZs2TVlZWYqOjlZycrLy8vJ8WDkAAPAXPj3MtGTJErfbM2fOVMOGDbV+/Xpdd911MsZo6tSpGjNmjPr06SNJmjVrlqKiopSRkaH+/fv7omwAAOBH/OqcmSNHjkiS6tevL0nasWOHsrOzlZKS4prjcDiUlJSk1atXlxlmCgoKVFBQ4Lqdm5srSSosLFRhYeHZLP8vp7Sf9LVmnMv9dgQaX5fgwRFg3P5bnc7F32FFvPn9no1+/9X6XBlWeD+pTG02Y4xfvIsYY9SrVy8dOnRIX375pSRp9erV6tChg/bt26fY2FjX3Icffli7du3S0qVLPdbjdDo1fvx4j/GMjAyFhoaevQcAAACqTX5+vlJTU3XkyBGFh4dXONdv9swMGjRImzZt0qpVqzyW2Wzu39NhjPEYKzV69GgNHz7cdTs3N1dxcXFKSUk5bTNQOYWFhcrMzFRycrLsdruvyznn1US/E52efyB4a4uzq0+2e7Y4Aoz+0bZET64LUEFJ9X5X0Jn0yoq8+f2ejX7/1fpcGVZ4/y49suINvwgzgwcP1vz58/XFF1+oUaNGrvHo6GhJUnZ2tmJiYlzjOTk5ioqKKnNdDodDDofDY9xut/vtL8zq6G3NOpv9PpMv+DuTmvz5iwULSmzVXt9f7fVSmf5VZ7//an2uCn9+/65MXT69mskYo0GDBmnu3LlasWKFEhIS3JYnJCQoOjpamZmZrrGTJ09q5cqVat++fU2XCwAA/JBP98wMHDhQGRkZ+vjjjxUWFqbs7GxJUkREhEJCQmSz2ZSWlqb09HQ1b95czZs3V3p6ukJDQ5WamurL0gEAgJ/waZh5+eWXJUmdOnVyG585c6buvfdeSdLIkSN1/PhxDRgwQIcOHVK7du20bNkyhYWF1XC1AADAH/k0zHhzIZXNZpPT6ZTT6Tz7BQEAAMvhiyYBAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClBfm6AACA9+IfW1jl++58pns1VgL4D/bMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAAS+NqJgDV4kyusvmroVdA9WLPDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDS+zgA4x/BR+SjPmTw3dj7TvRorAaoXe2YAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAICl+TTMfPHFF+rZs6diY2Nls9n00UcfuS03xsjpdCo2NlYhISHq1KmTtm7d6ptiAQCAX/JpmDl27Jguu+wyTZs2rczlkydP1pQpUzRt2jRlZWUpOjpaycnJysvLq+FKAQCAv/LpJwDfdNNNuummm8pcZozR1KlTNWbMGPXp00eSNGvWLEVFRSkjI0P9+/evyVIBAICf8tuvM9ixY4eys7OVkpLiGnM4HEpKStLq1avLDTMFBQUqKChw3c7NzZUkFRYWqrCw8OwW/RdT2k/6WjO87bcj0NREOec8R4Bx++9f3Zm8zr15Tp6NfvPeVD4rvH9XpjabMcYvXqk2m03z5s1T7969JUmrV69Whw4dtG/fPsXGxrrmPfzww9q1a5eWLl1a5nqcTqfGjx/vMZ6RkaHQ0NCzUjsAAKhe+fn5Sk1N1ZEjRxQeHl7hXL/dM1PKZrO53TbGeIz92ejRozV8+HDX7dzcXMXFxSklJeW0zfirSHSWHQS9scXZ1fX/hYWFyszMVHJysux2e3WUhgp42+8z+f3i/zgCjP7RtkRPrgtQQUn57zl/FX9+7VeWN8/Js9HvM6n5XGeF9+/SIyve8NswEx0dLUnKzs5WTEyMazwnJ0dRUVHl3s/hcMjhcHiM2+12v/2F1bSC4qq/UZTVQ3pbs07X7zP5/cJTQYmNnqrs1763KtO/6uw370un58/v35Wpy28/ZyYhIUHR0dHKzMx0jZ08eVIrV65U+/btfVgZAADwJz7dM3P06FH95z//cd3esWOHNm7cqPr166tx48ZKS0tTenq6mjdvrubNmys9PV2hoaFKTU31YdUAAMCf+DTMrFu3Tp07d3bdLj3XpV+/fnrrrbc0cuRIHT9+XAMGDNChQ4fUrl07LVu2TGFhYb4qGQAA+BmfhplOnTqpooupbDabnE6nnE5nzRUFAAAsxW/PmQEAAPAGYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFia334CMGB18Y8trPJ9dz7TvRorAYBzG3tmAACApRFmAACApRFmAACApRFmAACApXECMOCHyjp52BFoNPkqKdG5VAXFNh9UBQD+iT0zAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0riaCajAmXwlAQCgZrBnBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWFqQrwuwuvjHFlb5vjuf6V6NlQAA/oz3578O9swAAABLI8wAAABLI8wAAABLI8wAAABLI8wAAABL42omizqTs/Sra7uOQKPJV0mJzqUqKLb5pB5vcFUCcOZ89Z4DeIM9MwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNK4msmHuDqgZtBnAFbhq++Tsvr3WFliz8z06dOVkJCg4OBgtWnTRl9++aWvSwIAAH7C78PMu+++q7S0NI0ZM0bffvutOnbsqJtuukm7d+/2dWkAAMAP+H2YmTJlih544AE9+OCDatGihaZOnaq4uDi9/PLLvi4NAAD4Ab8OMydPntT69euVkpLiNp6SkqLVq1f7qCoAAOBP/PoE4N9++03FxcWKiopyG4+KilJ2dnaZ9ykoKFBBQYHr9pEjRyRJv//+uwoLC6u9xqCiY9W+TqsIKjHKzy9RUGGAikv89+sMzhX0u2bR75rlb/0+ePCgT7Z7Jv+mVKbmwsJC5efn6+DBg7Lb7TW23crIy8uTJBljTjvXr8NMKZvN/YltjPEYKzVx4kSNHz/eYzwhIeGs1PZXl+rrAv5i6HfNot81y5/63eB5X1dQeb6q+WxvNy8vTxERERXO8esw06BBAwUGBnrshcnJyfHYW1Nq9OjRGj58uOt2SUmJfv/9d0VGRpYbgFA1ubm5iouL0549exQeHu7rcs559Ltm0e+aRb9rlhX6bYxRXl6eYmNjTzvXr8NMrVq11KZNG2VmZurmm292jWdmZqpXr15l3sfhcMjhcLiN1a1b92yW+ZcXHh7uty+GcxH9rln0u2bR75rl7/0+3R6ZUn4dZiRp+PDh6tu3r9q2batrrrlGM2bM0O7du/XII4/4ujQAAOAH/D7M3H777Tp48KAmTJig/fv3KzExUYsWLVKTJk18XRoAAPADfh9mJGnAgAEaMGCAr8vAKRwOh8aNG+dxWA9nB/2uWfS7ZtHvmnWu9dtmvLnmCQAAwE/59YfmAQAAnA5hBgAAWBphBgAAWBphBgAAWBphBhWaOHGirrzySoWFhalhw4bq3bu3tm/f7jbHGCOn06nY2FiFhISoU6dO2rp1q48qPrdMnDhRNptNaWlprjH6Xb327dunu+++W5GRkQoNDVXr1q21fv1613L6XX2Kior0xBNPKCEhQSEhIWratKkmTJigkpIS1xz6XXVffPGFevbsqdjYWNlsNn300Uduy73pbUFBgQYPHqwGDRqodu3a+tvf/qa9e/fW4KOoGsIMKrRy5UoNHDhQa9euVWZmpoqKipSSkqJjx/7vS8kmT56sKVOmaNq0acrKylJ0dLSSk5NdXxKGqsnKytKMGTPUqlUrt3H6XX0OHTqkDh06yG63a/Hixfr+++/1/PPPu31qOP2uPpMmTdIrr7yiadOmadu2bZo8ebKeffZZ/etf/3LNod9Vd+zYMV122WWaNm1amcu96W1aWprmzZunOXPmaNWqVTp69Kh69Oih4uLimnoYVWOASsjJyTGSzMqVK40xxpSUlJjo6GjzzDPPuOacOHHCREREmFdeecVXZVpeXl6ead68ucnMzDRJSUlm6NChxhj6Xd1GjRplrr322nKX0+/q1b17d3P//fe7jfXp08fcfffdxhj6XZ0kmXnz5rlue9Pbw4cPG7vdbubMmeOas2/fPhMQEGCWLFlSY7VXBXtmUClHjhyRJNWvX1+StGPHDmVnZyslJcU1x+FwKCkpSatXr/ZJjeeCgQMHqnv37rrhhhvcxul39Zo/f77atm2rW2+9VQ0bNtTll1+u1157zbWcfleva6+9VsuXL9ePP/4oSfruu++0atUqdevWTRL9Ppu86e369etVWFjoNic2NlaJiYl+339LfAIw/IMxRsOHD9e1116rxMRESXJ9o/mp32IeFRWlXbt21XiN54I5c+Zow4YNysrK8lhGv6vXf//7X7388ssaPny4Hn/8cX3zzTcaMmSIHA6H7rnnHvpdzUaNGqUjR47o4osvVmBgoIqLi/X000/rzjvvlMTz+2zyprfZ2dmqVauW6tWr5zGn9P7+ijADrw0aNEibNm3SqlWrPJbZbDa328YYjzGc3p49ezR06FAtW7ZMwcHB5c6j39WjpKREbdu2VXp6uiTp8ssv19atW/Xyyy/rnnvucc2j39Xj3Xff1ezZs5WRkaFLLrlEGzduVFpammJjY9WvXz/XPPp99lSlt1boP4eZ4JXBgwdr/vz5+uyzz9SoUSPXeHR0tCR5pPacnByPvwBweuvXr1dOTo7atGmjoKAgBQUFaeXKlXrxxRcVFBTk6in9rh4xMTFq2bKl21iLFi20e/duSTy/q9v/+3//T4899pjuuOMOXXrpperbt6+GDRumiRMnSqLfZ5M3vY2OjtbJkyd16NChcuf4K8IMKmSM0aBBgzR37lytWLFCCQkJbssTEhIUHR2tzMxM19jJkye1cuVKtW/fvqbLtbwuXbpo8+bN2rhxo+unbdu2uuuuu7Rx40Y1bdqUflejDh06eHzUwI8//qgmTZpI4vld3fLz8xUQ4P7PTmBgoOvSbPp99njT2zZt2shut7vN2b9/v7Zs2eL//ffducewgkcffdRERESYzz//3Ozfv9/1k5+f75rzzDPPmIiICDN37lyzefNmc+edd5qYmBiTm5vrw8rPHX++mskY+l2dvvnmGxMUFGSefvpp89NPP5m3337bhIaGmtmzZ7vm0O/q069fP3P++eebBQsWmB07dpi5c+eaBg0amJEjR7rm0O+qy8vLM99++6359ttvjSQzZcoU8+2335pdu3YZY7zr7SOPPGIaNWpkPv30U7NhwwZz/fXXm8suu8wUFRX56mF5hTCDCkkq82fmzJmuOSUlJWbcuHEmOjraOBwOc91115nNmzf7ruhzzKlhhn5Xr08++cQkJiYah8NhLr74YjNjxgy35fS7+uTm5pqhQ4eaxo0bm+DgYNO0aVMzZswYU1BQ4JpDv6vus88+K/P9ul+/fsYY73p7/PhxM2jQIFO/fn0TEhJievToYXbv3u2DR1M5NmOM8c0+IQAAgDPHOTMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAvGKM0Q033KCuXbt6LJs+fboiIiJcX9AIADWJMAPAKzabTTNnztTXX3+tV1991TW+Y8cOjRo1Si+88IIaN25crdssLCys1vUBODcRZgB4LS4uTi+88IJGjBihHTt2yBijBx54QF26dNFVV12lbt26qU6dOoqKilLfvn3122+/ue67ZMkSXXvttapbt64iIyPVo0cP/fzzz67lO3fulM1m03vvvadOnTopODhYs2fP1q5du9SzZ0/Vq1dPtWvX1iWXXKJFixaVW+OhQ4d0zz33qF69egoNDdVNN92kn376ybX8rbfeUt26dfXRRx/pwgsvVHBwsJKTk7Vnzx639XzyySdq06aNgoOD1bRpU40fP15FRUWu5TabTa+//rpuvvlmhYaGqnnz5po/f351tBlAJRFmAFRKv3791KVLF913332aNm2atmzZohdeeEFJSUlq3bq11q1bpyVLlujAgQO67bbbXPc7duyYhg8frqysLC1fvlwBAQG6+eabVVJS4rb+UaNGaciQIdq2bZu6du2qgQMHqqCgQF988YU2b96sSZMmqU6dOuXWd++992rdunWaP3++1qxZI2OMunXr5raXJz8/X08//bRmzZqlr776Srm5ubrjjjtcy5cuXaq7775bQ4YM0ffff69XX31Vb731lp5++mm3bY0fP1633XabNm3apG7duumuu+7S77//fqYtBlBZPv2aSwCWdODAAXPeeeeZgIAAM3fuXPPkk0+alJQUtzl79uwxksz27dvLXEdOTo6R5PrW3h07dhhJZurUqW7zLr30UuN0Or2q68cffzSSzFdffeUa++2330xISIh57733jDHGzJw500gya9eudc3Ztm2bkWS+/vprY4wxHTt2NOnp6W7r/ve//21iYmJctyWZJ554wnX76NGjxmazmcWLF3tVK4Dqw54ZAJXWsGFDPfzww2rRooVuvvlmrV+/Xp999pnq1Knj+rn44oslyXUo6eeff1ZqaqqaNm2q8PBwJSQkSJLHScNt27Z1uz1kyBA99dRT6tChg8aNG6dNmzaVW9e2bdsUFBSkdu3aucYiIyN10UUXadu2ba6xoKAgt+1cfPHFqlu3rmvO+vXrNWHCBLfH89BDD2n//v3Kz8933a9Vq1au/69du7bCwsKUk5PjXRMBVJsgXxcAwJqCgoIUFPTHW0hJSYl69uypSZMmecyLiYmRJPXs2VNxcXF67bXXFBsbq5KSEiUmJurkyZNu82vXru12+8EHH1TXrl21cOFCLVu2TBMnTtTzzz+vwYMHe2zLGFNmrcYY2Ww2t7FTb/95rKSkROPHj1efPn085gQHB7v+3263e9z/1MNmAM4+wgyAM3bFFVfoww8/VHx8vCvg/NnBgwe1bds2vfrqq+rYsaMkadWqVV6vPy4uTo888ogeeeQRjR49Wq+99lqZYaZly5YqKirS119/rfbt27u2/eOPP6pFixaueUVFRVq3bp2uuuoqSdL27dt1+PBh196kK664Qtu3b9cFF1zgfRMA+AyHmQCcsYEDB+r333/XnXfeqW+++Ub//e9/tWzZMt1///0qLi5WvXr1FBkZqRkzZug///mPVqxYoeHDh3u17rS0NC1dulQ7duzQhg0btGLFCrdg8mfNmzdXr1699NBDD2nVqlX67rvvdPfdd+v8889Xr169XPPsdrsGDx6sr7/+Whs2bNB9992nq6++2hVuxo4dq//93/+V0+nU1q1btW3bNr377rt64oknzrxZAKodYQbAGYuNjdVXX32l4uJide3aVYmJiRo6dKgiIiIUEBCggIAAzZkzR+vXr1diYqKGDRumZ5991qt1FxcXa+DAgWrRooVuvPFGXXTRRZo+fXq582fOnKk2bdqoR48euuaaa2SM0aJFi9wOCYWGhmrUqFFKTU3VNddco5CQEM2ZM8e1vGvXrlqwYIEyMzN15ZVX6uqrr9aUKVPUpEmTqjcJwFljM+UdZAaAc9Bbb72ltLQ0HT582NelAKgm7JkBAACWRpgBAACWxmEmAABgaeyZAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlvb/AWUWxIW29sxOAAAAAElFTkSuQmCC", "text/plain": [ - "0.0 163\n", - "1.0 1\n", - "Name: fastEight, dtype: int64" + "
" ] }, - "execution_count": 34, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], - "source": [ - "ski_data.fastEight.value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 24#\n", - "#Drop the 'fastEight' column from ski_data. Use inplace=True\n", - "ski_data.drop(columns=___, inplace=___)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 25#\n", - "#Filter the 'yearsOpen' column for values greater than 100\n", - "ski_data.___[ski_data.___ > ___]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What does the distribution of yearsOpen look like if you exclude just the obviously wrong one?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "#Code task 26#\n", "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n", "#Pass the argument bins=30 to hist(), but feel free to explore other values\n", - "ski_data.___[ski_data.___ < ___].hist(___)\n", + "ski_data.loc[ski_data.yearsOpen < 1000].yearsOpen.hist(bins=30)\n", "plt.xlabel('Years open')\n", "plt.ylabel('Count')\n", "plt.title('Distribution of years open excluding 2019');" @@ -1625,7 +3688,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -1642,7 +3705,7 @@ "Name: yearsOpen, dtype: float64" ] }, - "execution_count": 38, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -1660,11 +3723,85 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 224, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ - "ski_data = ski_data[ski_data.yearsOpen < 1000]" + "ski_data = ski_data[ski_data.yearsOpen < 1000]\n", + "print(ski_data.info)" ] }, { @@ -1719,10 +3856,8 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 27#\n", "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n", @@ -1741,55 +3876,37 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "## 2.8 Drop Rows With No Price Data" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing." ] }, { - "cell_type": "code", - "execution_count": 41, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 82.317073\n", - "2 14.329268\n", - "1 3.353659\n", - "dtype: float64" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", "missing_price.value_counts()/len(missing_price) * 100" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 28#\n", "#Use `missing_price` to remove rows from ski_data where both price values are missing\n", @@ -1797,293 +3914,83 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "## 2.9 Review distributions" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], "source": [ "ski_data.hist(figsize=(15, 10))\n", "plt.subplots_adjust(hspace=0.5);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values. Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.10 Population data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 29#\n", - "#Use pandas' `read_html` method to read the table from the URL below\n", - "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n", - "usa_states = pd.___(___)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "list" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(usa_states)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(usa_states)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Name &postal abbs. [1]CitiesEstablished[upper-alpha 1]Population[upper-alpha 2][3]Total area[4]Land area[4]Water area[4]Numberof Reps.
Name &postal abbs. [1]Name &postal abbs. [1].1CapitalLargest[5]Established[upper-alpha 1]Population[upper-alpha 2][3]mi2km2mi2km2mi2km2Numberof Reps.
0AlabamaALMontgomeryBirminghamDec 14, 181949031855242013576750645131171177545977
1AlaskaAKJuneauAnchorageJan 3, 195973154566538417233375706411477953947432453841
2ArizonaAZPhoenixPhoenixFeb 14, 1912727871711399029523411359429420739610269
3ArkansasARLittle RockLittle RockJun 15, 183630178045317913773252035134771114329614
4CaliforniaCASacramentoLos AngelesSep 9, 18503951222316369542396715577940346679162050153
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" - ], - "text/plain": [ - " Name &postal abbs. [1] Cities \\\n", - " Name &postal abbs. [1] Name &postal abbs. [1].1 Capital Largest[5] \n", - "0 Alabama AL Montgomery Birmingham \n", - "1 Alaska AK Juneau Anchorage \n", - "2 Arizona AZ Phoenix Phoenix \n", - "3 Arkansas AR Little Rock Little Rock \n", - "4 California CA Sacramento Los Angeles \n", - "\n", - " Established[upper-alpha 1] Population[upper-alpha 2][3] Total area[4] \\\n", - " Established[upper-alpha 1] Population[upper-alpha 2][3] mi2 \n", - "0 Dec 14, 1819 4903185 52420 \n", - "1 Jan 3, 1959 731545 665384 \n", - "2 Feb 14, 1912 7278717 113990 \n", - "3 Jun 15, 1836 3017804 53179 \n", - "4 Sep 9, 1850 39512223 163695 \n", - "\n", - " Land area[4] Water area[4] Numberof Reps. \n", - " km2 mi2 km2 mi2 km2 Numberof Reps. \n", - "0 135767 50645 131171 1775 4597 7 \n", - "1 1723337 570641 1477953 94743 245384 1 \n", - "2 295234 113594 294207 396 1026 9 \n", - "3 137732 52035 134771 1143 2961 4 \n", - "4 423967 155779 403466 7916 20501 53 " - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values. Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "## 2.10 Population data" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "#Code task 29#\n", + "#Use pandas' `read_html` method to read the table from the URL below\n", + "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n", + "usa_states = pd.___(___)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "type(usa_states)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "len(usa_states)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, "source": [ "usa_states = usa_states[0]\n", "usa_states.head()" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 30#\n", "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n", @@ -2092,87 +3999,22 @@ ] }, { - "cell_type": "code", - "execution_count": 49, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Dec 14, 1819\n", - "1 Jan 3, 1959\n", - "2 Feb 14, 1912\n", - "3 Jun 15, 1836\n", - "4 Sep 9, 1850\n", - "5 Aug 1, 1876\n", - "6 Jan 9, 1788\n", - "7 Dec 7, 1787\n", - "8 Mar 3, 1845\n", - "9 Jan 2, 1788\n", - "10 Aug 21, 1959\n", - "11 Jul 3, 1890\n", - "12 Dec 3, 1818\n", - "13 Dec 11, 1816\n", - "14 Dec 28, 1846\n", - "15 Jan 29, 1861\n", - "16 Jun 1, 1792\n", - "17 Apr 30, 1812\n", - "18 Mar 15, 1820\n", - "19 Apr 28, 1788\n", - "20 Feb 6, 1788\n", - "21 Jan 26, 1837\n", - "22 May 11, 1858\n", - "23 Dec 10, 1817\n", - "24 Aug 10, 1821\n", - "25 Nov 8, 1889\n", - "26 Mar 1, 1867\n", - "27 Oct 31, 1864\n", - "28 Jun 21, 1788\n", - "29 Dec 18, 1787\n", - "30 Jan 6, 1912\n", - "31 Jul 26, 1788\n", - "32 Nov 21, 1789\n", - "33 Nov 2, 1889\n", - "34 Mar 1, 1803\n", - "35 Nov 16, 1907\n", - "36 Feb 14, 1859\n", - "37 Dec 12, 1787\n", - "38 May 29, 1790\n", - "39 May 23, 1788\n", - "40 Nov 2, 1889\n", - "41 Jun 1, 1796\n", - "42 Dec 29, 1845\n", - "43 Jan 4, 1896\n", - "44 Mar 4, 1791\n", - "45 Jun 25, 1788\n", - "46 Nov 11, 1889\n", - "47 Jun 20, 1863\n", - "48 May 29, 1848\n", - "49 Jul 10, 1890\n", - "Name: (Established[upper-alpha 1], Established[upper-alpha 1]), dtype: object" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "established" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Extract the state name, population, and total area (square miles) columns." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 31#\n", "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n", @@ -2184,17 +4026,15 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Do you have all the ski data states accounted for?" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 32#\n", "#Find the states in `state_summary` that are not in `usa_states_sub`\n", @@ -2204,56 +4044,36 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "No?? " ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:" ] }, { - "cell_type": "code", - "execution_count": 52, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "20 Massachusetts[upper-alpha 3]\n", - "37 Pennsylvania[upper-alpha 3]\n", - "38 Rhode Island[upper-alpha 4]\n", - "45 Virginia[upper-alpha 3]\n", - "47 West Virginia\n", - "Name: state, dtype: object" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Delete square brackets and their contents and try again:" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 33#\n", "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n", @@ -2267,10 +4087,8 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 34#\n", "#And now verify none of our states are missing by checking that there are no states in\n", @@ -2280,17 +4098,15 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 35#\n", "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n", @@ -2300,31 +4116,29 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it." ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "## 2.11 Target Feature" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 36#\n", "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n", @@ -2333,17 +4147,15 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "A couple of observations can be made. Firstly, there is a clear line where weekend and weekday prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distribution for weekday and weekend prices in Montana seemed equal. Is this confirmed in the actual data for each resort? Big Mountain resort is in Montana, so the relationship between these quantities in this state are particularly relevant." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "#Code task 37#\n", "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n", @@ -2351,196 +4163,65 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Is there any reason to prefer weekend or weekday prices? Which is missing the least?" ] }, { - "cell_type": "code", - "execution_count": 58, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AdultWeekend 4\n", - "AdultWeekday 7\n", - "dtype: int64" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price." ] }, { - "cell_type": "code", - "execution_count": 59, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "ski_data.drop(columns='AdultWeekday', inplace=True)\n", "ski_data.dropna(subset=['AdultWeekend'], inplace=True)" ] }, { - "cell_type": "code", - "execution_count": 60, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(277, 25)" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "ski_data.shape" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Perform a final quick check on the data." ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "### 2.11.1 Number Of Missing Values By Row - Resort" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Having dropped rows missing the desired target ticket price, what degree of missingness do you have for the remaining rows?" ] }, { - "cell_type": "code", - "execution_count": 61, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " count %\n", - "329 5 20.0\n", - "62 5 20.0\n", - "141 5 20.0\n", - "86 5 20.0\n", - "74 5 20.0\n", - "146 5 20.0\n", - "184 4 16.0\n", - "108 4 16.0\n", - "198 4 16.0\n", - "39 4 16.0" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n", "missing.columns=['count', '%']\n", @@ -2548,166 +4229,78 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "These seem possibly curiously quantized..." ] }, { - "cell_type": "code", - "execution_count": 62, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0., 4., 8., 12., 16., 20.])" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "missing['%'].unique()" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Yes, the percentage of missing values per row appear in multiples of 4." ] }, { - "cell_type": "code", - "execution_count": 63, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0 107\n", - "4.0 94\n", - "8.0 45\n", - "12.0 15\n", - "16.0 10\n", - "20.0 6\n", - "Name: %, dtype: int64" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "missing['%'].value_counts()" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features." ] }, { - "cell_type": "code", - "execution_count": 64, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 277 entries, 0 to 329\n", - "Data columns (total 25 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Name 277 non-null object \n", - " 1 Region 277 non-null object \n", - " 2 state 277 non-null object \n", - " 3 summit_elev 277 non-null int64 \n", - " 4 vertical_drop 277 non-null int64 \n", - " 5 base_elev 277 non-null int64 \n", - " 6 trams 277 non-null int64 \n", - " 7 fastSixes 277 non-null int64 \n", - " 8 fastQuads 277 non-null int64 \n", - " 9 quad 277 non-null int64 \n", - " 10 triple 277 non-null int64 \n", - " 11 double 277 non-null int64 \n", - " 12 surface 277 non-null int64 \n", - " 13 total_chairs 277 non-null int64 \n", - " 14 Runs 274 non-null float64\n", - " 15 TerrainParks 233 non-null float64\n", - " 16 LongestRun_mi 272 non-null float64\n", - " 17 SkiableTerrain_ac 275 non-null float64\n", - " 18 Snow Making_ac 240 non-null float64\n", - " 19 daysOpenLastYear 233 non-null float64\n", - " 20 yearsOpen 277 non-null float64\n", - " 21 averageSnowfall 268 non-null float64\n", - " 22 AdultWeekend 277 non-null float64\n", - " 23 projectedDaysOpen 236 non-null float64\n", - " 24 NightSkiing_ac 163 non-null float64\n", - "dtypes: float64(11), int64(11), object(3)\n", - "memory usage: 56.3+ KB\n" - ] - } - ], "source": [ "ski_data.info()" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "There are still some missing values, and it's good to be aware of this, but leave them as is for now." ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "## 2.12 Save data" ] }, { - "cell_type": "code", - "execution_count": 65, + "cell_type": "raw", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(277, 25)" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "ski_data.shape" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data." ] }, { - "cell_type": "code", - "execution_count": 66, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "# save the data to a new csv file\n", "datapath = '../data'\n", @@ -2715,10 +4308,8 @@ ] }, { - "cell_type": "code", - "execution_count": 67, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "# save the state_summary separately.\n", "datapath = '../data'\n", @@ -2726,21 +4317,21 @@ ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "## 2.13 Summary" ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way." ] }, { - "cell_type": "markdown", + "cell_type": "raw", "metadata": {}, "source": [ "**A: 3** Your answer here" @@ -2749,9 +4340,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -2763,7 +4354,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.12.7" }, "toc": { "base_numbering": 1, From 67f7d88ab9b87711dcbfed047b42c723c1977173 Mon Sep 17 00:00:00 2001 From: Becky Christiansen Date: Mon, 27 Jan 2025 22:55:18 -0600 Subject: [PATCH 2/2] Test commit of saved progress --- Notebooks/02_data_wrangling.ipynb | 103 ++++++++++-------------------- 1 file changed, 35 insertions(+), 68 deletions(-) diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb index d783f785e..115e4463e 100644 --- a/Notebooks/02_data_wrangling.ipynb +++ b/Notebooks/02_data_wrangling.ipynb @@ -3723,85 +3723,52 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 232, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n", + "Index: 328 entries, 0 to 329\n", + "Data columns (total 26 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 328 non-null object \n", + " 1 Region 328 non-null object \n", + " 2 state 328 non-null object \n", + " 3 summit_elev 328 non-null int64 \n", + " 4 vertical_drop 328 non-null int64 \n", + " 5 base_elev 328 non-null int64 \n", + " 6 trams 328 non-null int64 \n", + " 7 fastSixes 328 non-null int64 \n", + " 8 fastQuads 328 non-null int64 \n", + " 9 quad 328 non-null int64 \n", + " 10 triple 328 non-null int64 \n", + " 11 double 328 non-null int64 \n", + " 12 surface 328 non-null int64 \n", + " 13 total_chairs 328 non-null int64 \n", + " 14 Runs 324 non-null float64\n", + " 15 TerrainParks 277 non-null float64\n", + " 16 LongestRun_mi 323 non-null float64\n", + " 17 SkiableTerrain_ac 325 non-null float64\n", + " 18 Snow Making_ac 282 non-null float64\n", + " 19 daysOpenLastYear 279 non-null float64\n", + " 20 yearsOpen 328 non-null float64\n", + " 21 averageSnowfall 316 non-null float64\n", + " 22 AdultWeekday 274 non-null float64\n", + " 23 AdultWeekend 277 non-null float64\n", + " 24 projectedDaysOpen 283 non-null float64\n", + " 25 NightSkiing_ac 186 non-null float64\n", + "dtypes: float64(12), int64(11), object(3)\n", + "memory usage: 69.2+ KB\n" ] } ], "source": [ "ski_data = ski_data[ski_data.yearsOpen < 1000]\n", - "print(ski_data.info)" + "ski_data.info()" ] }, {