2 rows × 27 columns
\n", + "5 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", + " 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", - " 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", + " 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", - " 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", + " 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", - "104 132.0 54.0 64.0 135.0 \n", - "295 486.0 99.0 99.0 NaN \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", - "104 56.0 \n", - "295 NaN \n", + " NightSkiing_ac \n", + "0 550.0 \n", + "1 NaN \n", + "2 30.0 \n", + "3 NaN \n", + "4 80.0 \n", "\n", - "[2 rows x 27 columns]" + "[5 rows x 27 columns]" ] }, - "execution_count": 11, + "execution_count": 23, "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": 28, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + " | 151 | \n", + "
---|---|
Name | \n", + "Big Mountain Resort | \n", + "
Region | \n", + "Montana | \n", + "
state | \n", + "Montana | \n", + "
summit_elev | \n", + "6817 | \n", + "
vertical_drop | \n", + "2353 | \n", + "
base_elev | \n", + "4464 | \n", + "
trams | \n", + "0 | \n", + "
fastEight | \n", + "0.0 | \n", + "
fastSixes | \n", + "0 | \n", + "
fastQuads | \n", + "3 | \n", + "
quad | \n", + "2 | \n", + "
triple | \n", + "6 | \n", + "
double | \n", + "0 | \n", + "
surface | \n", + "3 | \n", + "
total_chairs | \n", + "14 | \n", + "
Runs | \n", + "105.0 | \n", + "
TerrainParks | \n", + "4.0 | \n", + "
LongestRun_mi | \n", + "3.3 | \n", + "
SkiableTerrain_ac | \n", + "3000.0 | \n", + "
Snow Making_ac | \n", + "600.0 | \n", + "
daysOpenLastYear | \n", + "123.0 | \n", + "
yearsOpen | \n", + "72.0 | \n", + "
averageSnowfall | \n", + "333.0 | \n", + "
AdultWeekday | \n", + "81.0 | \n", + "
AdultWeekend | \n", + "81.0 | \n", + "
projectedDaysOpen | \n", + "123.0 | \n", + "
NightSkiing_ac | \n", + "600.0 | \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": 36, "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": 39, "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": 42, "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": 42, + "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": 43, "metadata": {}, - "outputs": [], + "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": 43, + "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.___.___" + "#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": [ - "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." + "##**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": 23, + "execution_count": 45, + "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": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 82.424242\n", - "2 14.242424\n", - "1 3.333333\n", - "dtype: float64" + "33" ] }, - "execution_count": 23, + "execution_count": 51, "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" + "#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": [ - "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." + "**Becky's Answer:** There are 33 entries where the Region and state are different." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2.6.4.2 Distributions Of Feature Values" + "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": 54, + "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": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data['Region'].value_counts()" ] }, { "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." + "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, + "execution_count": 56, "metadata": {}, - "outputs": [], + "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": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "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" + "#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": [ - "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." + "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": [ - "##### 2.6.4.2.1 SkiableTerrain_ac" + "#### 2.6.3.3 Number of distinct regions and states" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Region 38\n", + "state 35\n", + "dtype: int64" + ] + }, + "execution_count": 59, + "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.___[ski_data.___ > ___]" + "#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": [ - "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" + "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, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "\n", + " | AdultWeekday | \n", + "AdultWeekend | \n", + "
---|---|---|
state | \n", + "\n", + " | \n", + " |
Alaska | \n", + "47.333333 | \n", + "57.333333 | \n", + "
Arizona | \n", + "81.500000 | \n", + "83.500000 | \n", + "
California | \n", + "78.214286 | \n", + "81.416667 | \n", + "
Colorado | \n", + "90.714286 | \n", + "90.714286 | \n", + "
Connecticut | \n", + "47.800000 | \n", + "56.800000 | \n", + "
\n", + " | state | \n", + "Ticket | \n", + "Price | \n", + "
---|---|---|---|
0 | \n", + "Alaska | \n", + "AdultWeekday | \n", + "65.0 | \n", + "
1 | \n", + "Alaska | \n", + "AdultWeekday | \n", + "47.0 | \n", + "
2 | \n", + "Alaska | \n", + "AdultWeekday | \n", + "30.0 | \n", + "
3 | \n", + "Arizona | \n", + "AdultWeekday | \n", + "89.0 | \n", + "
4 | \n", + "Arizona | \n", + "AdultWeekday | \n", + "74.0 | \n", + "
\n", + " | count | \n", + "mean | \n", + "std | \n", + "min | \n", + "25% | \n", + "50% | \n", + "75% | \n", + "max | \n", + "
---|---|---|---|---|---|---|---|---|
summit_elev | \n", + "330.0 | \n", + "4591.818182 | \n", + "3735.535934 | \n", + "315.0 | \n", + "1403.75 | \n", + "3127.5 | \n", + "7806.00 | \n", + "13487.0 | \n", + "
vertical_drop | \n", + "330.0 | \n", + "1215.427273 | \n", + "947.864557 | \n", + "60.0 | \n", + "461.25 | \n", + "964.5 | \n", + "1800.00 | \n", + "4425.0 | \n", + "
base_elev | \n", + "330.0 | \n", + "3374.000000 | \n", + "3117.121621 | \n", + "70.0 | \n", + "869.00 | \n", + "1561.5 | \n", + "6325.25 | \n", + "10800.0 | \n", + "
trams | \n", + "330.0 | \n", + "0.172727 | \n", + "0.559946 | \n", + "0.0 | \n", + "0.00 | \n", + "0.0 | \n", + "0.00 | \n", + "4.0 | \n", + "
fastEight | \n", + "164.0 | \n", + "0.006098 | \n", + "0.078087 | \n", + "0.0 | \n", + "0.00 | \n", + "0.0 | \n", + "0.00 | \n", + "1.0 | \n", + "
fastSixes | \n", + "330.0 | \n", + "0.184848 | \n", + "0.651685 | \n", + "0.0 | \n", + "0.00 | \n", + "0.0 | \n", + "0.00 | \n", + "6.0 | \n", + "
fastQuads | \n", + "330.0 | \n", + "1.018182 | \n", + "2.198294 | \n", + "0.0 | \n", + "0.00 | \n", + "0.0 | \n", + "1.00 | \n", + "15.0 | \n", + "
quad | \n", + "330.0 | \n", + "0.933333 | \n", + "1.312245 | \n", + "0.0 | \n", + "0.00 | \n", + "0.0 | \n", + "1.00 | \n", + "8.0 | \n", + "
triple | \n", + "330.0 | \n", + "1.500000 | \n", + "1.619130 | \n", + "0.0 | \n", + "0.00 | \n", + "1.0 | \n", + "2.00 | \n", + "8.0 | \n", + "
double | \n", + "330.0 | \n", + "1.833333 | \n", + "1.815028 | \n", + "0.0 | \n", + "1.00 | \n", + "1.0 | \n", + "3.00 | \n", + "14.0 | \n", + "
surface | \n", + "330.0 | \n", + "2.621212 | \n", + "2.059636 | \n", + "0.0 | \n", + "1.00 | \n", + "2.0 | \n", + "3.00 | \n", + "15.0 | \n", + "
total_chairs | \n", + "330.0 | \n", + "8.266667 | \n", + "5.798683 | \n", + "0.0 | \n", + "5.00 | \n", + "7.0 | \n", + "10.00 | \n", + "41.0 | \n", + "
Runs | \n", + "326.0 | \n", + "48.214724 | \n", + "46.364077 | \n", + "3.0 | \n", + "19.00 | \n", + "33.0 | \n", + "60.00 | \n", + "341.0 | \n", + "
TerrainParks | \n", + "279.0 | \n", + "2.820789 | \n", + "2.008113 | \n", + "1.0 | \n", + "1.00 | \n", + "2.0 | \n", + "4.00 | \n", + "14.0 | \n", + "
LongestRun_mi | \n", + "325.0 | \n", + "1.433231 | \n", + "1.156171 | \n", + "0.0 | \n", + "0.50 | \n", + "1.0 | \n", + "2.00 | \n", + "6.0 | \n", + "
SkiableTerrain_ac | \n", + "327.0 | \n", + "739.801223 | \n", + "1816.167441 | \n", + "8.0 | \n", + "85.00 | \n", + "200.0 | \n", + "690.00 | \n", + "26819.0 | \n", + "
Snow Making_ac | \n", + "284.0 | \n", + "174.873239 | \n", + "261.336125 | \n", + "2.0 | \n", + "50.00 | \n", + "100.0 | \n", + "200.50 | \n", + "3379.0 | \n", + "
daysOpenLastYear | \n", + "279.0 | \n", + "115.103943 | \n", + "35.063251 | \n", + "3.0 | \n", + "97.00 | \n", + "114.0 | \n", + "135.00 | \n", + "305.0 | \n", + "
yearsOpen | \n", + "329.0 | \n", + "63.656535 | \n", + "109.429928 | \n", + "6.0 | \n", + "50.00 | \n", + "58.0 | \n", + "69.00 | \n", + "2019.0 | \n", + "
averageSnowfall | \n", + "316.0 | \n", + "185.316456 | \n", + "136.356842 | \n", + "18.0 | \n", + "69.00 | \n", + "150.0 | \n", + "300.00 | \n", + "669.0 | \n", + "
AdultWeekday | \n", + "276.0 | \n", + "57.916957 | \n", + "26.140126 | \n", + "15.0 | \n", + "40.00 | \n", + "50.0 | \n", + "71.00 | \n", + "179.0 | \n", + "
AdultWeekend | \n", + "279.0 | \n", + "64.166810 | \n", + "24.554584 | \n", + "17.0 | \n", + "47.00 | \n", + "60.0 | \n", + "77.50 | \n", + "179.0 | \n", + "
projectedDaysOpen | \n", + "283.0 | \n", + "120.053004 | \n", + "31.045963 | \n", + "30.0 | \n", + "100.00 | \n", + "120.0 | \n", + "139.50 | \n", + "305.0 | \n", + "
NightSkiing_ac | \n", + "187.0 | \n", + "100.395722 | \n", + "105.169620 | \n", + "2.0 | \n", + "40.00 | \n", + "72.0 | \n", + "114.00 | \n", + "650.0 | \n", + "
\n", + " | Name | \n", + "Region | \n", + "state | \n", + "summit_elev | \n", + "vertical_drop | \n", + "base_elev | \n", + "trams | \n", + "fastEight | \n", + "fastSixes | \n", + "fastQuads | \n", + "... | \n", + "LongestRun_mi | \n", + "SkiableTerrain_ac | \n", + "Snow Making_ac | \n", + "daysOpenLastYear | \n", + "yearsOpen | \n", + "averageSnowfall | \n", + "AdultWeekday | \n", + "AdultWeekend | \n", + "projectedDaysOpen | \n", + "NightSkiing_ac | \n", + "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", + "Alyeska Resort | \n", + "Alaska | \n", + "Alaska | \n", + "3939 | \n", + "2500 | \n", + "250 | \n", + "1 | \n", + "0.0 | \n", + "0 | \n", + "2 | \n", + "... | \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", + "
7 | \n", + "Bear Valley | \n", + "Sierra Nevada | \n", + "California | \n", + "8500 | \n", + "1900 | \n", + "6600 | \n", + "0 | \n", + "0.0 | \n", + "1 | \n", + "1 | \n", + "... | \n", + "1.2 | \n", + "1680.0 | \n", + "100.0 | \n", + "165.0 | \n", + "52.0 | \n", + "359.0 | \n", + "NaN | \n", + "NaN | \n", + "151.0 | \n", + "NaN | \n", + "
11 | \n", + "Heavenly Mountain Resort | \n", + "Sierra Nevada | \n", + "California | \n", + "10067 | \n", + "3500 | \n", + "7170 | \n", + "2 | \n", + "0.0 | \n", + "2 | \n", + "7 | \n", + "... | \n", + "5.5 | \n", + "4800.0 | \n", + "3379.0 | \n", + "155.0 | \n", + "64.0 | \n", + "360.0 | \n", + "NaN | \n", + "NaN | \n", + "157.0 | \n", + "NaN | \n", + "
12 | \n", + "June Mountain | \n", + "Sierra Nevada | \n", + "California | \n", + "10090 | \n", + "2590 | \n", + "7545 | \n", + "0 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "... | \n", + "2.0 | \n", + "1500.0 | \n", + "NaN | \n", + "NaN | \n", + "58.0 | \n", + "250.0 | \n", + "NaN | \n", + "NaN | \n", + "128.0 | \n", + "NaN | \n", + "
13 | \n", + "Kirkwood | \n", + "Sierra Nevada | \n", + "California | \n", + "9800 | \n", + "2000 | \n", + "7800 | \n", + "0 | \n", + "0.0 | \n", + "0 | \n", + "2 | \n", + "... | \n", + "2.5 | \n", + "2300.0 | \n", + "200.0 | \n", + "200.0 | \n", + "47.0 | \n", + "354.0 | \n", + "NaN | \n", + "NaN | \n", + "167.0 | \n", + "NaN | \n", + "
... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
299 | \n", + "Stevens Pass Resort | \n", + "Washington | \n", + "Washington | \n", + "5845 | \n", + "1800 | \n", + "4061 | \n", + "0 | \n", + "0.0 | \n", + "0 | \n", + "3 | \n", + "... | \n", + "1.0 | \n", + "1125.0 | \n", + "NaN | \n", + "116.0 | \n", + "82.0 | \n", + "460.0 | \n", + "NaN | \n", + "NaN | \n", + "145.0 | \n", + "450.0 | \n", + "
300 | \n", + "The Summit at Snoqualmie | \n", + "Washington | \n", + "Washington | \n", + "3865 | \n", + "1025 | \n", + "2840 | \n", + "0 | \n", + "NaN | \n", + "0 | \n", + "3 | \n", + "... | \n", + "0.8 | \n", + "1994.0 | \n", + "5.0 | \n", + "120.0 | \n", + "82.0 | \n", + "428.0 | \n", + "85.0 | \n", + "95.0 | \n", + "140.0 | \n", + "541.0 | \n", + "
301 | \n", + "White Pass | \n", + "Washington | \n", + "Washington | \n", + "6550 | \n", + "2050 | \n", + "4500 | \n", + "0 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "... | \n", + "2.5 | \n", + "1402.0 | \n", + "30.0 | \n", + "148.0 | \n", + "67.0 | \n", + "400.0 | \n", + "69.0 | \n", + "69.0 | \n", + "144.0 | \n", + "90.0 | \n", + "
322 | \n", + "Grand Targhee Resort | \n", + "Wyoming | \n", + "Wyoming | \n", + "9920 | \n", + "2270 | \n", + "7851 | \n", + "0 | \n", + "0.0 | \n", + "0 | \n", + "2 | \n", + "... | \n", + "2.7 | \n", + "2602.0 | \n", + "NaN | \n", + "152.0 | \n", + "50.0 | \n", + "500.0 | \n", + "90.0 | \n", + "90.0 | \n", + "152.0 | \n", + "NaN | \n", + "
324 | \n", + "Jackson Hole | \n", + "Wyoming | \n", + "Wyoming | \n", + "10450 | \n", + "4139 | \n", + "6311 | \n", + "3 | \n", + "0.0 | \n", + "0 | \n", + "4 | \n", + "... | \n", + "4.5 | \n", + "2500.0 | \n", + "195.0 | \n", + "130.0 | \n", + "54.0 | \n", + "459.0 | \n", + "NaN | \n", + "NaN | \n", + "133.0 | \n", + "NaN | \n", + "
66 rows × 27 columns
\n", + "\n", + " | 11 | \n", + "27 | \n", + "39 | \n", + "45 | \n", + "140 | \n", + "231 | \n", + "266 | \n", + "267 | \n", + "
---|---|---|---|---|---|---|---|---|
Name | \n", + "Heavenly Mountain Resort | \n", + "Aspen / Snowmass | \n", + "Silverton Mountain | \n", + "Vail | \n", + "Big Sky Resort | \n", + "Mt. Bachelor | \n", + "Park City | \n", + "Powder Mountain | \n", + "
Region | \n", + "Sierra Nevada | \n", + "Colorado | \n", + "Colorado | \n", + "Colorado | \n", + "Montana | \n", + "Oregon | \n", + "Salt Lake City | \n", + "Utah | \n", + "
state | \n", + "California | \n", + "Colorado | \n", + "Colorado | \n", + "Colorado | \n", + "Montana | \n", + "Oregon | \n", + "Utah | \n", + "Utah | \n", + "
summit_elev | \n", + "10067 | \n", + "12510 | \n", + "13487 | \n", + "11570 | \n", + "11166 | \n", + "9065 | \n", + "10000 | \n", + "9422 | \n", + "
vertical_drop | \n", + "3500 | \n", + "4406 | \n", + "3087 | \n", + "3450 | \n", + "4350 | \n", + "3365 | \n", + "3200 | \n", + "2522 | \n", + "
base_elev | \n", + "7170 | \n", + "8104 | \n", + "10400 | \n", + "8120 | \n", + "7500 | \n", + "5700 | \n", + "6800 | \n", + "6900 | \n", + "
trams | \n", + "2 | \n", + "3 | \n", + "0 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "4 | \n", + "0 | \n", + "
fastEight | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "0.0 | \n", + "
fastSixes | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "2 | \n", + "0 | \n", + "6 | \n", + "0 | \n", + "
fastQuads | \n", + "7 | \n", + "15 | \n", + "0 | \n", + "15 | \n", + "5 | \n", + "8 | \n", + "10 | \n", + "1 | \n", + "
quad | \n", + "1 | \n", + "4 | \n", + "0 | \n", + "1 | \n", + "3 | \n", + "0 | \n", + "4 | \n", + "4 | \n", + "
triple | \n", + "5 | \n", + "3 | \n", + "0 | \n", + "1 | \n", + "7 | \n", + "3 | \n", + "7 | \n", + "1 | \n", + "
double | \n", + "3 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "4 | \n", + "0 | \n", + "
surface | \n", + "8 | \n", + "9 | \n", + "0 | \n", + "9 | \n", + "12 | \n", + "0 | \n", + "6 | \n", + "3 | \n", + "
total_chairs | \n", + "28 | \n", + "40 | \n", + "1 | \n", + "31 | \n", + "36 | \n", + "11 | \n", + "41 | \n", + "9 | \n", + "
Runs | \n", + "97.0 | \n", + "336.0 | \n", + "NaN | \n", + "195.0 | \n", + "317.0 | \n", + "101.0 | \n", + "341.0 | \n", + "167.0 | \n", + "
TerrainParks | \n", + "3.0 | \n", + "10.0 | \n", + "NaN | \n", + "3.0 | \n", + "8.0 | \n", + "5.0 | \n", + "8.0 | \n", + "2.0 | \n", + "
LongestRun_mi | \n", + "5.5 | \n", + "5.3 | \n", + "1.5 | \n", + "4.0 | \n", + "6.0 | \n", + "4.0 | \n", + "3.5 | \n", + "3.5 | \n", + "
SkiableTerrain_ac | \n", + "4800.0 | \n", + "5517.0 | \n", + "26819.0 | \n", + "5289.0 | \n", + "5800.0 | \n", + "4318.0 | \n", + "7300.0 | \n", + "8464.0 | \n", + "
Snow Making_ac | \n", + "3379.0 | \n", + "658.0 | \n", + "NaN | \n", + "461.0 | \n", + "400.0 | \n", + "20.0 | \n", + "750.0 | \n", + "NaN | \n", + "
daysOpenLastYear | \n", + "155.0 | \n", + "138.0 | \n", + "175.0 | \n", + "149.0 | \n", + "144.0 | \n", + "185.0 | \n", + "142.0 | \n", + "120.0 | \n", + "
yearsOpen | \n", + "64.0 | \n", + "72.0 | \n", + "17.0 | \n", + "57.0 | \n", + "46.0 | \n", + "61.0 | \n", + "56.0 | \n", + "47.0 | \n", + "
averageSnowfall | \n", + "360.0 | \n", + "300.0 | \n", + "400.0 | \n", + "354.0 | \n", + "400.0 | \n", + "462.0 | \n", + "355.0 | \n", + "500.0 | \n", + "
AdultWeekday | \n", + "NaN | \n", + "179.0 | \n", + "79.0 | \n", + "NaN | \n", + "NaN | \n", + "99.0 | \n", + "NaN | \n", + "88.0 | \n", + "
AdultWeekend | \n", + "NaN | \n", + "179.0 | \n", + "79.0 | \n", + "NaN | \n", + "NaN | \n", + "99.0 | \n", + "NaN | \n", + "88.0 | \n", + "
projectedDaysOpen | \n", + "157.0 | \n", + "138.0 | \n", + "181.0 | \n", + "142.0 | \n", + "144.0 | \n", + "185.0 | \n", + "143.0 | \n", + "146.0 | \n", + "
NightSkiing_ac | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "300.0 | \n", + "
\n", + " | Name | \n", + "Region | \n", + "state | \n", + "summit_elev | \n", + "vertical_drop | \n", + "base_elev | \n", + "trams | \n", + "fastSixes | \n", + "fastQuads | \n", + "quad | \n", + "... | \n", + "LongestRun_mi | \n", + "SkiableTerrain_ac | \n", + "Snow Making_ac | \n", + "daysOpenLastYear | \n", + "yearsOpen | \n", + "averageSnowfall | \n", + "AdultWeekday | \n", + "AdultWeekend | \n", + "projectedDaysOpen | \n", + "NightSkiing_ac | \n", + "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34 | \n", + "Howelsen Hill | \n", + "Colorado | \n", + "Colorado | \n", + "7136 | \n", + "440 | \n", + "6696 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "6.0 | \n", + "50.0 | \n", + "25.0 | \n", + "100.0 | \n", + "104.0 | \n", + "150.0 | \n", + "25.0 | \n", + "25.0 | \n", + "100.0 | \n", + "10.0 | \n", + "
115 | \n", + "Pine Knob Ski Resort | \n", + "Michigan | \n", + "Michigan | \n", + "1308 | \n", + "300 | \n", + "1009 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "1.0 | \n", + "80.0 | \n", + "80.0 | \n", + "NaN | \n", + "2019.0 | \n", + "NaN | \n", + "49.0 | \n", + "57.0 | \n", + "NaN | \n", + "NaN | \n", + "
2 rows × 26 columns
\n", + "\n", + " | state | \n", + "resorts_per_state | \n", + "state_total_skiable_area_ac | \n", + "state_total_days_open | \n", + "state_total_terrain_parks | \n", + "state_total_nightskiing_ac | \n", + "
---|---|---|---|---|---|---|
0 | \n", + "Alaska | \n", + "3 | \n", + "2280.0 | \n", + "345.0 | \n", + "4.0 | \n", + "580.0 | \n", + "
1 | \n", + "Arizona | \n", + "2 | \n", + "1577.0 | \n", + "237.0 | \n", + "6.0 | \n", + "80.0 | \n", + "
2 | \n", + "California | \n", + "21 | \n", + "25948.0 | \n", + "2738.0 | \n", + "81.0 | \n", + "587.0 | \n", + "
3 | \n", + "Colorado | \n", + "22 | \n", + "43682.0 | \n", + "3258.0 | \n", + "74.0 | \n", + "428.0 | \n", + "
4 | \n", + "Connecticut | \n", + "5 | \n", + "358.0 | \n", + "353.0 | \n", + "10.0 | \n", + "256.0 | \n", + "
\n", + " | state | \n", + "state_population | \n", + "state_area_sq_miles | \n", + "
---|---|---|---|
0 | \n", + "Alabama | \n", + "4903185 | \n", + "52420 | \n", + "
1 | \n", + "Alaska | \n", + "731545 | \n", + "665384 | \n", + "
2 | \n", + "Arizona | \n", + "7278717 | \n", + "113990 | \n", + "
3 | \n", + "Arkansas | \n", + "3017804 | \n", + "53179 | \n", + "
4 | \n", + "California | \n", + "39512223 | \n", + "163695 | \n", + "
\n", + " | state | \n", + "resorts_per_state | \n", + "state_total_skiable_area_ac | \n", + "state_total_days_open | \n", + "state_total_terrain_parks | \n", + "state_total_nightskiing_ac | \n", + "state_population | \n", + "state_area_sq_miles | \n", + "
---|---|---|---|---|---|---|---|---|
0 | \n", + "Alaska | \n", + "3 | \n", + "2280.0 | \n", + "345.0 | \n", + "4.0 | \n", + "580.0 | \n", + "731545 | \n", + "665384 | \n", + "
1 | \n", + "Arizona | \n", + "2 | \n", + "1577.0 | \n", + "237.0 | \n", + "6.0 | \n", + "80.0 | \n", + "7278717 | \n", + "113990 | \n", + "
2 | \n", + "California | \n", + "21 | \n", + "25948.0 | \n", + "2738.0 | \n", + "81.0 | \n", + "587.0 | \n", + "39512223 | \n", + "163695 | \n", + "
3 | \n", + "Colorado | \n", + "22 | \n", + "43682.0 | \n", + "3258.0 | \n", + "74.0 | \n", + "428.0 | \n", + "5758736 | \n", + "104094 | \n", + "
4 | \n", + "Connecticut | \n", + "5 | \n", + "358.0 | \n", + "353.0 | \n", + "10.0 | \n", + "256.0 | \n", + "3565278 | \n", + "5543 | \n", + "
\n", + " | AdultWeekend | \n", + "AdultWeekday | \n", + "
---|---|---|
141 | \n", + "42.0 | \n", + "42.0 | \n", + "
142 | \n", + "63.0 | \n", + "63.0 | \n", + "
143 | \n", + "49.0 | \n", + "49.0 | \n", + "
144 | \n", + "48.0 | \n", + "48.0 | \n", + "
145 | \n", + "46.0 | \n", + "46.0 | \n", + "
146 | \n", + "39.0 | \n", + "39.0 | \n", + "
147 | \n", + "50.0 | \n", + "50.0 | \n", + "
148 | \n", + "67.0 | \n", + "67.0 | \n", + "
149 | \n", + "47.0 | \n", + "47.0 | \n", + "
150 | \n", + "39.0 | \n", + "39.0 | \n", + "
151 | \n", + "81.0 | \n", + "81.0 | \n", + "
\n", + " | resorts_per_state | \n", + "state_total_skiable_area_ac | \n", + "state_total_days_open | \n", + "state_total_terrain_parks | \n", + "state_total_nightskiing_ac | \n", + "resorts_per_100kcapita | \n", + "resorts_per_100ksq_mile | \n", + "
---|---|---|---|---|---|---|---|
state | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
Alaska | \n", + "3 | \n", + "2280.0 | \n", + "345.0 | \n", + "4.0 | \n", + "580.0 | \n", + "0.410091 | \n", + "0.450867 | \n", + "
Arizona | \n", + "2 | \n", + "1577.0 | \n", + "237.0 | \n", + "6.0 | \n", + "80.0 | \n", + "0.027477 | \n", + "1.754540 | \n", + "
California | \n", + "21 | \n", + "25948.0 | \n", + "2738.0 | \n", + "81.0 | \n", + "587.0 | \n", + "0.053148 | \n", + "12.828736 | \n", + "
Colorado | \n", + "22 | \n", + "43682.0 | \n", + "3258.0 | \n", + "74.0 | \n", + "428.0 | \n", + "0.382028 | \n", + "21.134744 | \n", + "
Connecticut | \n", + "5 | \n", + "358.0 | \n", + "353.0 | \n", + "10.0 | \n", + "256.0 | \n", + "0.140242 | \n", + "90.203861 | \n", + "
\n", + " | resorts_per_state | \n", + "state_total_skiable_area_ac | \n", + "state_total_days_open | \n", + "state_total_terrain_parks | \n", + "state_total_nightskiing_ac | \n", + "resorts_per_100kcapita | \n", + "resorts_per_100ksq_mile | \n", + "
---|---|---|---|---|---|---|---|
0 | \n", + "-0.806912 | \n", + "-0.392012 | \n", + "-0.689059 | \n", + "-0.816118 | \n", + "0.069410 | \n", + "0.139593 | \n", + "-0.689999 | \n", + "
1 | \n", + "-0.933558 | \n", + "-0.462424 | \n", + "-0.819038 | \n", + "-0.726994 | \n", + "-0.701326 | \n", + "-0.644706 | \n", + "-0.658125 | \n", + "
2 | \n", + "1.472706 | \n", + "1.978574 | \n", + "2.190933 | \n", + "2.615141 | \n", + "0.080201 | \n", + "-0.592085 | \n", + "-0.387368 | \n", + "
3 | \n", + "1.599351 | \n", + "3.754811 | \n", + "2.816757 | \n", + "2.303209 | \n", + "-0.164893 | \n", + "0.082069 | \n", + "-0.184291 | \n", + "
4 | \n", + "-0.553622 | \n", + "-0.584519 | \n", + "-0.679431 | \n", + "-0.548747 | \n", + "-0.430027 | \n", + "-0.413557 | \n", + "1.504408 | \n", + "
\n", + " | PC1 | \n", + "PC2 | \n", + "
---|---|---|
state | \n", + "\n", + " | \n", + " |
Alaska | \n", + "-1.336533 | \n", + "-0.182208 | \n", + "
Arizona | \n", + "-1.839049 | \n", + "-0.387959 | \n", + "
California | \n", + "3.537857 | \n", + "-1.282509 | \n", + "
Colorado | \n", + "4.402210 | \n", + "-0.898855 | \n", + "
Connecticut | \n", + "-0.988027 | \n", + "1.020218 | \n", + "
\n", + " | PC1 | \n", + "PC2 | \n", + "AdultWeekend | \n", + "
---|---|---|---|
state | \n", + "\n", + " | \n", + " | \n", + " |
Alaska | \n", + "-1.336533 | \n", + "-0.182208 | \n", + "57.333333 | \n", + "
Arizona | \n", + "-1.839049 | \n", + "-0.387959 | \n", + "83.500000 | \n", + "
California | \n", + "3.537857 | \n", + "-1.282509 | \n", + "81.416667 | \n", + "
Colorado | \n", + "4.402210 | \n", + "-0.898855 | \n", + "90.714286 | \n", + "
Connecticut | \n", + "-0.988027 | \n", + "1.020218 | \n", + "56.800000 | \n", + "
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('linearregression', LinearRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('linearregression', LinearRegression())])
SimpleImputer(strategy='median')
StandardScaler()
LinearRegression()
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('selectkbest',\n", + " SelectKBest(score_func=<function f_regression at 0x00000295311C3A60>)),\n", + " ('linearregression', LinearRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('selectkbest',\n", + " SelectKBest(score_func=<function f_regression at 0x00000295311C3A60>)),\n", + " ('linearregression', LinearRegression())])
SimpleImputer(strategy='median')
StandardScaler()
SelectKBest(score_func=<function f_regression at 0x00000295311C3A60>)
LinearRegression()
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('selectkbest',\n", + " SelectKBest(k=15,\n", + " score_func=<function f_regression at 0x00000295311C3A60>)),\n", + " ('linearregression', LinearRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('selectkbest',\n", + " SelectKBest(k=15,\n", + " score_func=<function f_regression at 0x00000295311C3A60>)),\n", + " ('linearregression', LinearRegression())])
SimpleImputer(strategy='median')
StandardScaler()
SelectKBest(k=15, score_func=<function f_regression at 0x00000295311C3A60>)
LinearRegression()
GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('simpleimputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('selectkbest',\n", + " SelectKBest(score_func=<function f_regression at 0x00000295311C3A60>)),\n", + " ('linearregression',\n", + " LinearRegression())]),\n", + " n_jobs=-1,\n", + " param_grid={'selectkbest__k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", + " 12, 13, 14, 15, 16, 17, 18, 19, 20,\n", + " 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, ...]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('simpleimputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('selectkbest',\n", + " SelectKBest(score_func=<function f_regression at 0x00000295311C3A60>)),\n", + " ('linearregression',\n", + " LinearRegression())]),\n", + " n_jobs=-1,\n", + " param_grid={'selectkbest__k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", + " 12, 13, 14, 15, 16, 17, 18, 19, 20,\n", + " 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, ...]})
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('selectkbest',\n", + " SelectKBest(k=8,\n", + " score_func=<function f_regression at 0x00000295311C3A60>)),\n", + " ('linearregression', LinearRegression())])
SimpleImputer(strategy='median')
StandardScaler()
SelectKBest(k=8, score_func=<function f_regression at 0x00000295311C3A60>)
LinearRegression()
GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('simpleimputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(random_state=47))]),\n", + " n_jobs=-1,\n", + " param_grid={'randomforestregressor__n_estimators': [10, 12, 16, 20,\n", + " 26, 33, 42, 54,\n", + " 69, 88, 112,\n", + " 143, 183, 233,\n", + " 297, 379, 483,\n", + " 615, 784,\n", + " 1000],\n", + " 'simpleimputer__strategy': ['mean', 'median'],\n", + " 'standardscaler': [StandardScaler(), None]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('simpleimputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(random_state=47))]),\n", + " n_jobs=-1,\n", + " param_grid={'randomforestregressor__n_estimators': [10, 12, 16, 20,\n", + " 26, 33, 42, 54,\n", + " 69, 88, 112,\n", + " 143, 183, 233,\n", + " 297, 379, 483,\n", + " 615, 784,\n", + " 1000],\n", + " 'simpleimputer__strategy': ['mean', 'median'],\n", + " 'standardscaler': [StandardScaler(), None]})
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(random_state=47))])
SimpleImputer(strategy='median')
StandardScaler()
RandomForestRegressor(random_state=47)
GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('simpleimputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(random_state=47))]),\n", + " n_jobs=-1,\n", + " param_grid={'randomforestregressor__n_estimators': [10, 12, 16, 20,\n", + " 26, 33, 42, 54,\n", + " 69, 88, 112,\n", + " 143, 183, 233,\n", + " 297, 379, 483,\n", + " 615, 784,\n", + " 1000],\n", + " 'simpleimputer__strategy': ['mean', 'median'],\n", + " 'standardscaler': [StandardScaler(), None]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('simpleimputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(random_state=47))]),\n", + " n_jobs=-1,\n", + " param_grid={'randomforestregressor__n_estimators': [10, 12, 16, 20,\n", + " 26, 33, 42, 54,\n", + " 69, 88, 112,\n", + " 143, 183, 233,\n", + " 297, 379, 483,\n", + " 615, 784,\n", + " 1000],\n", + " 'simpleimputer__strategy': ['mean', 'median'],\n", + " 'standardscaler': [StandardScaler(), None]})
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', None),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(n_estimators=69, random_state=47))])
SimpleImputer(strategy='median')
None
RandomForestRegressor(n_estimators=69, random_state=47)
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', None),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(n_estimators=69, random_state=47))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', None),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(n_estimators=69, random_state=47))])
SimpleImputer(strategy='median')
None
RandomForestRegressor(n_estimators=69, random_state=47)
\n", + " | summit_elev | \n", + "vertical_drop | \n", + "base_elev | \n", + "trams | \n", + "fastSixes | \n", + "fastQuads | \n", + "quad | \n", + "triple | \n", + "double | \n", + "surface | \n", + "... | \n", + "resorts_per_100kcapita | \n", + "resorts_per_100ksq_mile | \n", + "resort_skiable_area_ac_state_ratio | \n", + "resort_days_open_state_ratio | \n", + "resort_terrain_park_state_ratio | \n", + "resort_night_skiing_state_ratio | \n", + "total_chairs_runs_ratio | \n", + "total_chairs_skiable_ratio | \n", + "fastQuads_runs_ratio | \n", + "fastQuads_skiable_ratio | \n", + "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
124 | \n", + "6817 | \n", + "2353 | \n", + "4464 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "2 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "... | \n", + "1.122778 | \n", + "8.161045 | \n", + "0.140121 | \n", + "0.129338 | \n", + "0.148148 | \n", + "0.84507 | \n", + "0.133333 | \n", + "0.004667 | \n", + "0.028571 | \n", + "0.001 | \n", + "
1 rows × 32 columns
\n", + "