{ "cells": [ { "cell_type": "markdown", "id": "787e7a2c", "metadata": {}, "source": [ "# Data Exploration\n", "\n", "In order to generate meaningful insights from data, you need to have a good understanding of your data and what it represents. Exploratory data analysis (or \"EDA\" as it's known) is a very crucial step in the data science pipeline. It's also a very fun process that requires creativity and curiosity, by asking bold questions about the data and testing initial hypotheses. It's certainly *not* a formal process with a defined set of steps. \n", "\n", "<img src=\"https://media.giphy.com/media/pz7LNMYN2dj0hgKaHj/giphy.gif\"/>\n", "\n", "As you explore the data, you'll start to discover patterns, detect anomalies (outliers), and validate whether your initial assumptions are still valid. \n", "\n", "Now that we're equipped with clean data (Part 3: Data Cleaning) and data visualization tools (Part 4: Data Visualization), let's start exploring our data! π" ] }, { "cell_type": "markdown", "id": "6e0d8ce1", "metadata": {}, "source": [ "## Importing our dependencies\n", "\n", "The first thing we need to do is import our dependencies (the Python packages that we'll be using). For this analysis, we'll be using:\n", "\n", "- [pandas](https://pandas.pydata.org/) - data manipulation and analysis π οΈ\n", "- [seaborn](https://seaborn.pydata.org/) - data visualization π\n", "- [matplotlib](https://matplotlib.org/) - data visualization π" ] }, { "cell_type": "code", "execution_count": 1, "id": "526c7b71", "metadata": {}, "outputs": [], "source": [ "import pandas as pd \n", "import seaborn as sns\n", "import matplotlib.pyplot as plt " ] }, { "cell_type": "markdown", "id": "4d1efa98", "metadata": {}, "source": [ "Next, let's import our newly cleaned dataset:" ] }, { "cell_type": "code", "execution_count": 2, "id": "e10d3cee", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"data/nyc_real_estate_clean.csv\")" ] }, { "cell_type": "markdown", "id": "3b588614", "metadata": {}, "source": [ "## Descriptive Statistics \n", "\n", "[Descriptive statistics](https://en.wikipedia.org/wiki/Descriptive_statistics) is the process of describing and summarizing your data using statistical methods. It gives you a sense of the distribution of your data and whether there are any outliers. During EDA, you'll want to use descriptive statistics to investigate all variables* of your dataset. \n", "\n", "- **βNote:** A \"variable\" refers to a column in your dataframe.\n", "\n", "How you approach descriptive statistics will depend on the _type_ of variable you're dealing with.\n", "\n", "<img width=\"50%\" src=\"https://media.giphy.com/media/3orieVr84udUl4wbQs/giphy.gif\"/>\n" ] }, { "cell_type": "markdown", "id": "48a71add", "metadata": {}, "source": [ "### Types of Variables\n", "\n", "There are two main types of variables: \n", "\n", "**1. Categorical**\n", "\n", "A variable is \"categorical\" if it can be stored into categories. A categorical variable can be either nominal (no order) or ordinal (order matters). \n", "\n", "- **Nominal:** no logical order (e.g., NYC borough)\n", "- **Ordinal:** data can be sorted (e.g. building safety rating on a scale from 1 to 10)\n", "\n", "\n", "**2. Numerical**\n", "\n", "A variable is \"numerical\" if it represents the measurement of something. A numerical variable can be either discrete or continuous. \n", "\n", "- **Discrete:** countable elements (e.g., number of rental units) \n", "- **Continuous:** unlimited number of values (e.g., square footage)\n", "\n", "<img width=\"70%\" src=\"https://practicalpython.s3.us-east-2.amazonaws.com/assets/variable_types.png\"/>" ] }, { "cell_type": "markdown", "id": "e5cf964a", "metadata": {}, "source": [ "### Summarizing Categorical Data \n", "\n", "We have several categorical varialbes in our NYC dataframe, including:\n", "\n", "- `borough`\n", "- `neighborhood`\n", "- `building_class_category`\n", "\n", "#### NYC Boroughs\n", "\n", "Let's start exploring the \"borough\" column and count how many properties were sold in each borough. The easiest way to do this is with [`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html), which does exactly what it says: it counts the number of times each value appears in the data. \n", "\n", "In this case, we'll use `value_counts()` to count the number of times a given borough appears in our data.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "afbc195e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Brooklyn 21928\n", "Queens 17711\n", "Manhattan 12865\n", "Bronx 6328\n", "Staten Island 5849\n", "Name: borough, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['borough'].value_counts()" ] }, { "cell_type": "markdown", "id": "585a3393", "metadata": {}, "source": [ "The output of `value_counts()` will always be ordered from highest frequency to lowest frequency. We can see that Brooklyn has the highest number of property sales while Staten Island has the lowest. Interestingly, the number of property sales was almost 4 times higher in Brooklyn as compared to the Staten Island. π€―\n", "\n", "We can also get the percentage breakdown of property sales by borough by passing in `normalize=True` inside `value_counts()`. This will convert the output from counts to proportions which sum to 1. " ] }, { "cell_type": "code", "execution_count": 4, "id": "9556f6f3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Brooklyn 0.339018\n", "Queens 0.273821\n", "Manhattan 0.198899\n", "Bronx 0.097834\n", "Staten Island 0.090428\n", "Name: borough, dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['borough'].value_counts(normalize=True)" ] }, { "cell_type": "markdown", "id": "3fec9ad3", "metadata": {}, "source": [ "Brooklyn represents ~34% of property sales in NYC, followed by Queens at 27% and Manhattan at 20%. Meanwhile, only 10% of property sales occured in the Bronx and 9% in the Bronx." ] }, { "cell_type": "markdown", "id": "0a2e3e2f", "metadata": {}, "source": [ "Another way of interpreting borough data is to visualize it using a [countplot](https://seaborn.pydata.org/generated/seaborn.countplot.html)." ] }, { "cell_type": "code", "execution_count": 5, "id": "40b6a2fd", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='borough', data=df, palette='viridis');" ] }, { "cell_type": "markdown", "id": "d790b883", "metadata": {}, "source": [ "With Seaborn's `countplot()`, we simply need to configure:\n", "- `x`: the categorical variable that we're interested \n", "- `data`: the dataframe that we're using \n", "- `palette`: a color palette that we'd like to use (totally optional but fun!)\n", "\n", "You can check out all of the default palettes provided by Seaborn [here](AP_seaborn_palette.md).\n", "\n", "\n", "```{note}\n", "Why is there a semi-colon at the end of the seaborn code? This semi-colon is not necessary, but without it, you will get some text abovoe your plot that looks like this: `<matplotlib.axes._subplots.AxesSubplot at 0x1263354d0>`. The semi-colon removes that code so that plots looks cleaner. \n", "\n", "Another option is to add `plt.show()` before rendering your plot. Feel free to try it out!\n", "```" ] }, { "cell_type": "markdown", "id": "fdf02764", "metadata": {}, "source": [ "#### Building Class Category\n", "\n", "In additional to boroughs, we also have data on a property's building class category. This is another categorical variable that's worth investigating:\n", "\n", "Unlike `borough`, which only have 5 possible values, there are many possible values for `building_class_category`. We can count the number of unique values using [nunique()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.nunique.html):" ] }, { "cell_type": "code", "execution_count": 6, "id": "27fff2b6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "46" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['building_class_category'].nunique()" ] }, { "cell_type": "markdown", "id": "2fe82eb6", "metadata": {}, "source": [ "Wow! There are a lot of building classes. Are the NYC properties in our dataset evenly distributing across all 46 building classes or are they mostly concentrated in a handful of classes? Let's take a look at the 5 most common building classes:" ] }, { "cell_type": "code", "execution_count": 7, "id": "699be16d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "One family dwellings 0.223543\n", "Two family dwellings 0.202347\n", "Coops - elevator apartments 0.183253\n", "Condos - elevator apartments 0.129111\n", "Three family dwellings 0.056554\n", "Name: building_class_category, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['building_class_category'].value_counts(normalize=True).head()" ] }, { "cell_type": "markdown", "id": "955eee0d", "metadata": {}, "source": [ "It looks like a large proportion of properties fall under one of 5 building classes. We can figure out the exact percentage by apply `sum()` to the output above:" ] }, { "cell_type": "code", "execution_count": 8, "id": "bf142f89", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "79.48%\n" ] } ], "source": [ "df['building_class_category'].value_counts(normalize=True).head().sum()\n", "\n", "print(f\"{df['building_class_category'].value_counts(normalize=True).head().sum():.2%}\")" ] }, { "cell_type": "markdown", "id": "281d9f64", "metadata": {}, "source": [ "Around 79.5% of NYC properties belong to one of the top 5 building classes.\n", "\n", "If we take a look at the actual building class names, this makes sense:\n", "\n", "- **One family dwellings** = detached homes\n", "- **Two, Three family dwellings** = duplex and triplex buildings, respectively\n", "- **Coops/Condos** - rental apartments and condos" ] }, { "cell_type": "markdown", "id": "4366ace7", "metadata": {}, "source": [ "##### Visualizing Category Freuency with Word Clouds\n", "Another way to visualize building class category is to create a [word cloud](https://en.wikipedia.org/wiki/Tag_cloud) - a visual representation of text data. Using this [wordcloud](https://amueller.github.io/word_cloud/index.html) Python package, we can visualize building class categories by frequency. Large text represents more frequent buildling classes and small text represents less frequent building classes.\n", "\n", "To create a word cloud, you need to import the `WordCloud` class from `wordcloud`:" ] }, { "cell_type": "code", "execution_count": 9, "id": "1c3f76bc", "metadata": {}, "outputs": [], "source": [ "from wordcloud import WordCloud" ] }, { "cell_type": "markdown", "id": "bcf3f8c9", "metadata": {}, "source": [ "Then you have to initialize a `WordCloud` instance. This is where you define what background color the word cloud should have, what the maximum and minimum font size should be, and how many words it can hold. For a list of all possible settings to configure, check out the documentation [here](https://amueller.github.io/word_cloud/generated/wordcloud.WordCloud.html). " ] }, { "cell_type": "code", "execution_count": 10, "id": "21e79edc", "metadata": {}, "outputs": [], "source": [ "wc = WordCloud(\n", " background_color=\"white\",\n", " max_words=2000, \n", " max_font_size=300, \n", " width=1800, height=800\n", ")" ] }, { "cell_type": "markdown", "id": "6be1a72b", "metadata": {}, "source": [ "Once you have your world cloud initialized with all of the right settings, you can use the `generate_from_frequencies()` method to generate a word cloud based on the `building_class_category` column's value counts like this:" ] }, { "cell_type": "code", "execution_count": 11, "id": "aba40768", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<wordcloud.wordcloud.WordCloud at 0x15591f310>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wc.generate_from_frequencies(df['building_class_category'].value_counts())" ] }, { "cell_type": "markdown", "id": "f9404420", "metadata": {}, "source": [ "Lastly, and most importantly, you can render your word cloud plot using Matplotlib's `plt.imshow()` function. The extra `plt` lines are meant to customize the word cloud:\n", "- making it bigger β `plt.figsize(figsize=(width,height))`\n", "- removing the plot border β `plt.axis(\"off\")`" ] }, { "cell_type": "code", "execution_count": 12, "id": "8038b7fb", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x1080 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,15))\n", "plt.imshow(wc.recolor(colormap='PuBuGn', random_state=20), interpolation=\"bilinear\")\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e1a320f5", "metadata": {}, "source": [ "What an awesome word cloud π. It's a nice way to quickly scan the various building categories in our dataset.\n", "\n", "If you look closely, you'll notice that some of the properties in our dataset are not residential. For example, we have some properties that classify as \"hotels\", \"condo office buildlings\", \"warehouses\", and \"religious facilities\". It's important to keep this in mind as we continue our analysis." ] }, { "cell_type": "markdown", "id": "45f8e84d", "metadata": {}, "source": [ "### Summarizing Numerical Data \n", "\n", "There are several ways that you can summarize numerical data, including 1) measuring central tendency, and 2) measuring the spread of the data.\n", "\n", "#### Measuring Central Tendency \n", "\n", "We can find the central tendency (or βmiddleβ) of the data using mean and median. \n", "\n", "To calculate the **mean** (also known as average) of a numerical variable, you need to divide the sum of the value by the number of observations. \n", "\n", "For example, letβs say we have the sale price of 3 properties:\n", "1. \\$100\n", "2. \\$200\n", "3. \\$300\n", "\n", "\n", "The mean price would be:" ] }, { "cell_type": "code", "execution_count": 13, "id": "e2aaccfa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "200.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(100+200+300)/3" ] }, { "cell_type": "markdown", "id": "f76e8c15", "metadata": {}, "source": [ "The *median* is another way of calculating central tendency. It represents the *exact middle* value. To find out the median sale price, we need to sort them in order, from lowest to highest (or vice versa), then find the middle value:\n", "\n", "<img width=\"50%\" src=\"https://practicalpython.s3.us-east-2.amazonaws.com/assets/median_1.png\">\n", " \n", "The middle value is 200. In this particular example, $\\text{mean} = \\text{median}$. However, this isn't always the case - especially when there are outliers. \n", "\n", "#### When the mean and median are different \n", "\n", "Let's say there's a new property that gets added to the list and it's worth $10,000. The new mean price would be:" ] }, { "cell_type": "code", "execution_count": 14, "id": "12f50c54", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2650.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(100+200+300+10000)/4" ] }, { "cell_type": "markdown", "id": "eee213d3", "metadata": {}, "source": [ "The new median would be 2650 beacuse the \"middle\" falls between 300 and 10000. \n", "\n", "<img width=\"50%\" src=\"https://practicalpython.s3.us-east-2.amazonaws.com/assets/median_2.png\">" ] }, { "cell_type": "markdown", "id": "aa11b2be", "metadata": {}, "source": [ "In this example, there is a huge discrepancy between mean and median when we add a new value of 10,000 to our initial list of 100, 200, and 300. \n", "\n", "```{admonition} Rule of thumb\n", ":class: tip\n", "\n", "When the **mean is greater than the median**, the data is skewed to the right. This means that there are a large-value [outliers](https://en.wikipedia.org/wiki/Outlier) that are pulling the mean value up. \n", "\n", "When the median is greater than the mean, the data is skewed to the left. This means that there are small-value outliers that are pulling the mean value down.\n", "```\n", "\n", "* An \"outlier\" is a value that differs substantially from the rest of the data." ] }, { "cell_type": "markdown", "id": "cbdf0eb2", "metadata": {}, "source": [ "#### Measuring Spread \n", "\n", "Spread (also known as \"dispersion\") is another useful measure for numerical variables. Spread helps us understand how far apart the values are in our dataset. \n", "\n", "[Standard deviation](https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/variance-standard-deviation-population/a/introduction-to-standard-deviation) is a measure of _how far_ values are from the **mean**. The more disperse the data, the larger the standard deviation. Its mathematical formula looks like this:\n", "\n", "$\\sqrt{\\frac{1}{N-1} \\sum_{i=1}^N (x_i - \\overline{x})^2}$\n", "\n", "You can calculate the standard deviation of a list of numbers using NumPy's [`std()`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) method:" ] }, { "cell_type": "code", "execution_count": 15, "id": "30e09906", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "81.64965809277261" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "np.std([100,200,300])" ] }, { "cell_type": "markdown", "id": "9b8ac2d0", "metadata": {}, "source": [ "To calculate the standard deviation of a dataframe column, you can use the Pandas Series' `std()` method:" ] }, { "cell_type": "code", "execution_count": 16, "id": "8fc5820c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "11716293.54909658" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sale_price'].std()" ] }, { "cell_type": "markdown", "id": "2be0bce2", "metadata": {}, "source": [ "```{note}\n", "Standard deviation can't be negative. A standard deviation close to 0 indicates that the values tend to be close to the mean. A standard deviation equal to 0 indicates that all values equal the mean. \n", "```" ] }, { "cell_type": "markdown", "id": "78b0bd4a", "metadata": {}, "source": [ "#### Percentiles \n", "\n", "The standard deviation is a useful measure of spread when your data follows a normal distribution ($\\text{mean} = \\text{median}$). But if your data is *skewed* (i.e., *not* normally distributed), then quantiles (percentiles) are a better measure of spread. \n", "\n", "```{note}\n", "The $n^{th}$ percentile is a value such that $n$% of values fall at or below it.\n", "```\n", "\n", "For example: if you performed very well on a test and ranked in the 99th percentile, this means that 99% of the other test-takers had a score that was either the same as yours or below it. In other words, you scored in the top 1%. \n", "\n", "**Quartiles** are a common type of percentile that is used to measure spread. This involves dividing your data into quarters:\n", "\n", "<img width=\"50%\" src=\"https://practicalpython.s3.us-east-2.amazonaws.com/assets/quartiles.png\"/>\n", "\n", "The three quartiles are:\n", "- 1st quartile (**Q1**, 25th percentile): 25% of values are smaller than Q1 and 75% are larger \n", "- 2nd quartile (**Q2**, 50th percentile): 50% of values are smaller than Q2 and 50% are larger (equivalent to the median)\n", "- 3rd quartile (**Q3**, 75th percentile): 75% of values are smaller than Q3 and 25% are larger\n", "\n", "To get the quartiles of a numerical variable, you can use the `quantile()` method:" ] }, { "cell_type": "markdown", "id": "f440649b", "metadata": {}, "source": [ "**1st quartile (25th percentile)**" ] }, { "cell_type": "code", "execution_count": 17, "id": "2ffc399e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "235513.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sale_price'].quantile(0.25)" ] }, { "cell_type": "markdown", "id": "ac78a367", "metadata": {}, "source": [ "**2nd quartile (50th percentile, median)**" ] }, { "cell_type": "code", "execution_count": 18, "id": "68422642", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "530000.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sale_price'].quantile(0.5)" ] }, { "cell_type": "markdown", "id": "8c5b447c", "metadata": {}, "source": [ "**3rd quartile (75th percentile)**" ] }, { "cell_type": "code", "execution_count": 19, "id": "23aeb595", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "937000.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sale_price'].quantile(0.75)" ] }, { "cell_type": "markdown", "id": "a8b6e6ad", "metadata": {}, "source": [ "Alternatively, if you wanted to summarize all quartiles, along with measuring mean, standard deviation, minimum value and maximum value, you can use [`describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html):" ] }, { "cell_type": "code", "execution_count": 20, "id": "e3279a2a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 6.468100e+04\n", "mean 1.279183e+06\n", "std 1.171629e+07\n", "min 0.000000e+00\n", "25% 2.355130e+05\n", "50% 5.300000e+05\n", "75% 9.370000e+05\n", "max 2.210000e+09\n", "Name: sale_price, dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sale_price'].describe()" ] }, { "cell_type": "markdown", "id": "a22e16ab", "metadata": {}, "source": [ "```{note}\n", "The descriptive statistics generated from `describe()` exclude missing values. \n", "```" ] }, { "cell_type": "markdown", "id": "3794cec0", "metadata": {}, "source": [ "Interestingly, the minimum sale price is $0. How many properties were sold for \"free\"? Let's take a look:" ] }, { "cell_type": "code", "execution_count": 21, "id": "51c0bf90", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 0.858057\n", "True 0.141943\n", "Name: sale_price, dtype: float64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(df['sale_price'] == 0).value_counts(normalize=True)" ] }, { "cell_type": "markdown", "id": "0b0382b5", "metadata": {}, "source": [ "15% of NYC property sales were sold at $0. These sales are actually transfers of deeds between parties: for example, parents transferring ownership to their home to a child after moving out for retirement.\n", "\n", "Where are these property transfers (inheritance) happening? Let's take a look at the borough distribution:" ] }, { "cell_type": "code", "execution_count": 22, "id": "fa8f0e64", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Brooklyn 0.828123\n", "Bronx 0.171877\n", "Name: borough, dtype: float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['sale_price'] == 0]['borough'].value_counts(normalize=True)" ] }, { "cell_type": "markdown", "id": "27c121e0", "metadata": {}, "source": [ "83% of the $0 property sales are happening in Brooklyn, while 17% are happening in the Bronx." ] }, { "cell_type": "markdown", "id": "b6816f9b", "metadata": {}, "source": [ "For the purpose of this analysis, let's assume we only care about property sales over $1000. We can remove anything below this threshold. " ] }, { "cell_type": "code", "execution_count": 23, "id": "1ca65b0e", "metadata": {}, "outputs": [], "source": [ "df = df[df['sale_price'] > 1000]" ] }, { "cell_type": "markdown", "id": "42cbae13", "metadata": {}, "source": [ "What's the new mean and median price after removing these low-dollar value property sales?" ] }, { "cell_type": "code", "execution_count": 24, "id": "9b405c01", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "630000.0" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sale_price'].median()" ] }, { "cell_type": "markdown", "id": "358d8c2c", "metadata": {}, "source": [ "The median sale price increased by \\\\$100K - from \\\\$530,000 to \\\\$630,000 - after removing suspicious property sales below \\\\$1000." ] }, { "cell_type": "markdown", "id": "1a695a02", "metadata": {}, "source": [ "### Segmenting Numerical Data by Category\n", "\n", "Sometimes, it can be useful to segment your data in different ways to extract new insights. So far, we've analyzed the NYC property sale data as a whole. But what if we segmented this data by borough, property age (old vs. new), or building type (one storey house vs. multi-storey apartment)? Would we see any interesting patterns in median sale price across these segments? \n", "\n", "<img width=\"60%\" src=\"https://practicalpython.s3.us-east-2.amazonaws.com/assets/data_segmentation.png\"/>\n", "\n", "Let's practice our segmentation skills by analyzing sale price segemented by borough. We saw that the overall median price was \\$630,000 across all boroughs. However, it's possible that the median sale price could be very different from borough to borough. We can investigate this by segmenting sale price by borough. The [groupby()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html) function is useful for segmentation like this:" ] }, { "cell_type": "code", "execution_count": 25, "id": "14be4137", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>borough</th>\n", " <th>median</th>\n", " <th>mean</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Bronx</td>\n", " <td>425000.0</td>\n", " <td>8.656155e+05</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Brooklyn</td>\n", " <td>775000.0</td>\n", " <td>1.326239e+06</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Manhattan</td>\n", " <td>1125000.0</td>\n", " <td>3.472688e+06</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Queens</td>\n", " <td>500000.0</td>\n", " <td>7.420868e+05</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Staten Island</td>\n", " <td>470000.0</td>\n", " <td>5.159014e+05</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " borough median mean\n", "0 Bronx 425000.0 8.656155e+05\n", "1 Brooklyn 775000.0 1.326239e+06\n", "2 Manhattan 1125000.0 3.472688e+06\n", "3 Queens 500000.0 7.420868e+05\n", "4 Staten Island 470000.0 5.159014e+05" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sale_price_borough = df.groupby('borough')['sale_price'].agg(['median', 'mean']).reset_index()\n", "sale_price_borough" ] }, { "cell_type": "markdown", "id": "b9d1f9ab", "metadata": {}, "source": [ "We see that the median sale price varies a lot across boroughs. Let's re-order the dataframe from highest to lowest median sale price. We can use [sort_values()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html) to do this:" ] }, { "cell_type": "code", "execution_count": 26, "id": "886b776d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>borough</th>\n", " <th>median</th>\n", " <th>mean</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>Manhattan</td>\n", " <td>1125000.0</td>\n", " <td>3.472688e+06</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Brooklyn</td>\n", " <td>775000.0</td>\n", " <td>1.326239e+06</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Queens</td>\n", " <td>500000.0</td>\n", " <td>7.420868e+05</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Staten Island</td>\n", " <td>470000.0</td>\n", " <td>5.159014e+05</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>Bronx</td>\n", " <td>425000.0</td>\n", " <td>8.656155e+05</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " borough median mean\n", "2 Manhattan 1125000.0 3.472688e+06\n", "1 Brooklyn 775000.0 1.326239e+06\n", "3 Queens 500000.0 7.420868e+05\n", "4 Staten Island 470000.0 5.159014e+05\n", "0 Bronx 425000.0 8.656155e+05" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sale_price_borough.sort_values(by='median', ascending=False)" ] }, { "cell_type": "markdown", "id": "62f0719a", "metadata": {}, "source": [ "By default, `sort_values()` sorts rows in ascending order. That's why we had to specify `ascending=False` to make sure the dataframe was sorted in descending (highest to lowest) order. \n", "\n", "We can see from the dataframe above that Manhattan has by far the highest median sale price, while the Bronx has the lowest median price. \n", "\n", "Let's create a Seaborn [barplot()](https://seaborn.pydata.org/generated/seaborn.barplot.html) to visualize the differences in price by borough:" ] }, { "cell_type": "code", "execution_count": 27, "id": "ebf71f1c", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(x='borough', y='median', data=sale_price_borough);" ] }, { "cell_type": "markdown", "id": "460ff5be", "metadata": {}, "source": [ "It's not so easy to visualize the price discrepancies when the bars are unsorted. Let's update our plot so that we show the bars in descending order. We can simply pass in the sorted dataframe we created earlier using `sort_values()`:" ] }, { "cell_type": "code", "execution_count": 28, "id": "77d101dc", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(\n", " x='borough', \n", " y='median', \n", " data=sale_price_borough.sort_values(by='median', ascending=False)\n", ")\n", "plt.title('Median Property Sales Price in NYC by Borough')\n", "plt.ylabel('median sales price')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "0adc56b3", "metadata": {}, "source": [ "Much better! When the bars are sorted, it's easier to understand which boroughs have the highest and lowest median sales prices.\n", "\n", "<img src=\"https://media.giphy.com/media/srg19CG0cKMuI/giphy.gif\"/>\n", "\n", "In our newly created dataframe `sale_price_borough`, we calculated the median and mean sales price by borough. Let's compare the two measures side by side to see if there are any differences. We can use [plt.subplot()](../book/04_data_visualization.ipynb#Combining-Multiple-Plots) to do that:" ] }, { "cell_type": "code", "execution_count": 29, "id": "3ad78f37", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Mean')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,3))\n", "plt.subplot(1,2,1)\n", "sns.barplot(\n", " x='borough', \n", " y='median', \n", " data=sale_price_borough.sort_values(by='median', ascending=False),\n", " palette='Blues_d'\n", ")\n", "plt.ylabel('median sales price')\n", "plt.title('Median')\n", "\n", "plt.subplot(1,2,2)\n", "sns.barplot(\n", " x='borough', \n", " y='mean', \n", " data=sale_price_borough.sort_values(by='mean', ascending=False),\n", " palette='Blues_d'\n", ")\n", "plt.ylabel('mean sales price')\n", "plt.title('Mean')" ] }, { "cell_type": "markdown", "id": "5bdb453d", "metadata": {}, "source": [ "Interesting! Based on the mean and median barplots above, we can see that Manhattan has had some very expensive property sales that pull the mean sales price up. While the Bronx had the lowest median sales price, it actually has the third highest average sales price, meaning that the Bronx has a lot of cheap properties but also has some big outliers which make the average property value not so cheap." ] }, { "cell_type": "markdown", "id": "0435103f", "metadata": {}, "source": [ "#### Segmenting Year Built by Borough\n", "\n", "We can continue to use the borough segmentation and explore other property sale attributes. For example, it would be interesting to understand the average age of homes across boroughs. Does the Bronx have older homes than Staten Island? We can find out by grouping the data by `borough` and calculating the descriptive statistics about `year_built` like this:" ] }, { "cell_type": "code", "execution_count": 30, "id": "1e301c56", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mean</th>\n", " <th>median</th>\n", " <th>min</th>\n", " <th>max</th>\n", " </tr>\n", " <tr>\n", " <th>borough</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Bronx</th>\n", " <td>1943.872133</td>\n", " <td>1940.0</td>\n", " <td>1883</td>\n", " <td>2016</td>\n", " </tr>\n", " <tr>\n", " <th>Brooklyn</th>\n", " <td>1945.771769</td>\n", " <td>1931.0</td>\n", " <td>1800</td>\n", " <td>2016</td>\n", " </tr>\n", " <tr>\n", " <th>Manhattan</th>\n", " <td>1952.582028</td>\n", " <td>1955.0</td>\n", " <td>1800</td>\n", " <td>2016</td>\n", " </tr>\n", " <tr>\n", " <th>Queens</th>\n", " <td>1949.426402</td>\n", " <td>1949.0</td>\n", " <td>1870</td>\n", " <td>2017</td>\n", " </tr>\n", " <tr>\n", " <th>Staten Island</th>\n", " <td>1970.160273</td>\n", " <td>1975.0</td>\n", " <td>1800</td>\n", " <td>2016</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mean median min max\n", "borough \n", "Bronx 1943.872133 1940.0 1883 2016\n", "Brooklyn 1945.771769 1931.0 1800 2016\n", "Manhattan 1952.582028 1955.0 1800 2016\n", "Queens 1949.426402 1949.0 1870 2017\n", "Staten Island 1970.160273 1975.0 1800 2016" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('borough')['year_built'].agg(['mean', 'median', 'min', 'max'])" ] }, { "cell_type": "markdown", "id": "7db06d3c", "metadata": {}, "source": [ "In the code above, we created a custom aggregation and specified four statistics to inspect: `mean`, `median`, `min` and `max`. We can see that the median age of homes in Brooklyn is older than in Staten Island. \n", "\n", "Let's say we wanted to plot the distribution of the \"year_built\" attribute for each borough. This would invovle creating a grid of plots where each plot represents the distribution of home age for a given borough. The first thing we need to do is create a list of boroughs so that we can loop over each borough to create a separate distribution plot. You can get all unique borough names using the [unique()](https://pandas.pydata.org/docs/reference/api/pandas.unique.html) function from Pandas." ] }, { "cell_type": "code", "execution_count": 31, "id": "0b58909d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Manhattan', 'Bronx', 'Brooklyn', 'Queens', 'Staten Island']" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boroughs = df['borough'].unique().tolist()\n", "boroughs" ] }, { "cell_type": "markdown", "id": "97058804", "metadata": {}, "source": [ "With 5 boroughs, there are several options for plot layouts: \n", "- 1 rows with 5 columns plots \n", "- 1 column with 5 rows of plots\n", "- 2 rows with 3 and 2 columns plots\n", "- 2 columns with 3 and 2 rows of plots \n", "\n", "I find the multi-row plot to be easier to read so let's use that:" ] }, { "cell_type": "code", "execution_count": 32, "id": "c76d7803", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "for i, borough in enumerate(boroughs):\n", " plt.subplot(2,3,i+1)\n", " sns.histplot(x='year_built', data=df[df['borough']==borough])\n", " plt.ylabel('count')\n", " plt.xlabel('year built')\n", " plt.title(borough)\n", " plt.xlim([1850,2020])\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "1be4f7cc", "metadata": {}, "source": [ "In the code above, we use a for loop to iterate over each borough. In each iteration, we:\n", "- created a subset of the dataframe that filtered for that particular borough\n", "- used the index of the borough to specify the plot location\n", "- customized the plot title to be the borough name \n", "- applied limits to the x-axis so that the range of years was standardized across all plots\n", "\n", "[plt.tight_layout()](https://matplotlib.org/stable/tutorials/intermediate/tight_layout_guide.html) is a helpful function to prevent the plots from overlapping ontop of each other. " ] }, { "cell_type": "markdown", "id": "5ae74863", "metadata": {}, "source": [ "#### Segmenting with a Custom Column\n", "\n", "Is there a discrepancy in sale price between old and new houses? We could try to answer this question by visualizing the relationship between \"year_built\" and \"sale_price\". However, if you take a look at the plot below, you can see that the relationship is not so clear due to outliers." ] }, { "cell_type": "code", "execution_count": 33, "id": "58bd0ca7", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(x='year_built', y='sale_price', data=df);" ] }, { "cell_type": "markdown", "id": "1f822c91", "metadata": {}, "source": [ "Another approach is to convert \"year_built\" to a categorical variable. For example, we could segment the year a property was built into the following categories:\n", "- pre-1940\n", "- post-1940\n", "\n", "Let's create a function that captures this logic:" ] }, { "cell_type": "code", "execution_count": 34, "id": "be6eb377", "metadata": {}, "outputs": [], "source": [ "def year_category(year):\n", " if year < 1940:\n", " return 'pre 1940'\n", " else:\n", " return 'post 1940'" ] }, { "cell_type": "markdown", "id": "f9e07e5e", "metadata": {}, "source": [ "Let's test out this custom function with a couple of sample years to make sure it's working as expected:" ] }, { "cell_type": "code", "execution_count": 35, "id": "f851f300", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'pre 1940'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year_category(1860)" ] }, { "cell_type": "code", "execution_count": 36, "id": "f53702b0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'post 1940'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year_category(1956)" ] }, { "cell_type": "markdown", "id": "8852ecb0", "metadata": {}, "source": [ "Great! Our custom category function works as expected. Let's apply it to the entire \"year_built\" column. To do this, we can use the [apply()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html) functions which allows you to apply a custom functions to your Pandas columns. " ] }, { "cell_type": "code", "execution_count": 37, "id": "6653cf20", "metadata": {}, "outputs": [], "source": [ "df['year_category'] = df['year_built'].apply(year_category)" ] }, { "cell_type": "markdown", "id": "8f1fb6da", "metadata": {}, "source": [ "Let's visualize the distribution of sale prices by \"year_category\" using a [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html). We'll limit sale price to anything below $10M since there are some outliers in our dataset that can skew the results." ] }, { "cell_type": "code", "execution_count": 38, "id": "6e15e803", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEDCAYAAAA4FgP0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWVklEQVR4nO3dfbRddX3n8feHYHgwipVkKAQ0mARttID2DlV0EFE7qC20VhQc29phxLEFbRmdMjMaLRZWHcc61OJDliK2nYpoZ6ZZGkXqwzjjU7kJGkkQvEWFpFRjBASCaMh3/jg7cnJzc3MC2efce/f7tdZZ9/z2w9nfm7VzP+e3f/shVYUkqbsOGHUBkqTRMggkqeMMAknqOINAkjrOIJCkjjMIJKnjZmUQJLkiyfeT3DDAsu9M8rXmdXOSO4dQoiTNGpmN1xEkOQW4B/jLqnrKPqx3AfDUqvq3rRUnSbPMrOwRVNUXgB/2T0uyNMmnkqxN8n+TPGmKVc8BPjyUIiVpljhw1AXsR6uAf19V30ryy8C7gdN2zkzyeOBY4LMjqk+SZqQ5EQRJFgAnAx9NsnPyQZMWOxv4WFU9MMzaJGmmmxNBQO8Q151VdeI0y5wN/P5wypGk2WNWjhFMVlU/Ar6d5CyA9Jywc34zXvBzwJdHVKIkzVizMgiSfJjeH/UnJtmU5Fzg3wDnJvk6sAE4s2+Vs4GrajaeIiVJLZuVp49KkvafWdkjkCTtP7NusHjhwoW1ZMmSUZchSbPK2rVrf1BVi6aaN+uCYMmSJYyPj4+6DEmaVZJ8d0/zPDQkSR1nEEhSxxkEktRxBoEkdZxB0GFbt27lta99LVu3bh11KZJGqLUg2NvDY5rbQPx5kokk65M8ra1aNLVLLrmE9evXc+mll466FGkX4+PjnHbaaaxdu3bUpXRCmz2CK4HTp5n/AmB58zoPeE+LtWiSrVu3sm7dOgDWrl1rr0AzysqVK9mxYwdvetObRl1KJ7QWBFM9PGaSM+k9Yayq6ivAY5Ic2VY92tUll1yyS9tegWaK8fFxtm3bBsC2bdvsFQzBKMcIFgO39bU3NdN2k+S8JONJxrds2TKU4ua6nb2BnfzPppli5cqVu7TtFbRvVgwWV9WqqhqrqrFFi6a8QlrSHLGzN7Cntva/UQbBZuCYvvbRzTRJ0hCNMghWA7/dnD30dOCuqrp9hPVIUie1dtO55uExpwILk2wC3gw8AqCq3gusAV4ITADbgN9tqxZJ0p61FgRVdc5e5hc+Q1iSRm5WDBZL6o4DDjhg2rb2P/+FJc0oO3bsmLat/c8gkDSjHHnkrteVHnXUUSOqpDsMAkkzyn333TdtW/ufQSBpRrnzzjt3ad9xxx2jKaRDDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6rtUgSHJ6kpuSTCS5aIr5j0vyuSTXJ1mf5IVt1iNJ2l1rQZBkHnA58AJgBXBOkhWTFnsjcHVVPRU4G3h3W/VIkqbWZo/gJGCiqm6pqp8AVwFnTlqmgEc37w8D/qnFeiRJU2gzCBYDt/W1NzXT+r0FeEWSTcAa4IKpPijJeUnGk4xv2bKljVolqbNGPVh8DnBlVR0NvBD4qyS71VRVq6pqrKrGFi1aNPQiJWkuazMINgPH9LWPbqb1Oxe4GqCqvgwcDCxssSZJ0iRtBsF1wPIkxyaZT28wePWkZW4FnguQ5BfoBYHHfiRpiFoLgqraDpwPXAPcSO/soA1JLk5yRrPYfwBeleTrwIeBV1ZVtVWTJGl3B7b54VW1ht4gcP+0lX3vNwLPbLMGSdL0Rj1YLEkaMYNAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOO3C6mUmeAbwC+FfAkcB9wA3AJ4C/rqq7Wq9QktSqPfYIknwS+HfANcDp9IJgBfBG4GDg75KcMYwiJUntma5H8FtV9YNJ0+4B1jWvdyRZ2FplkqSh2GOPYIoQeEjLSJJmtmkHi5O8LMkTmvfHJ5lI8k9JfnM45UmS2ra3s4beAGxu3r8VeB3wS8Cb2yxKkjQ8exwjSPJm4Cjgj5LMA54FXA+MAYclWQl8vqq+MJRKJUmtmG6M4I+Bm4FvA3cBn6qqtzTTN1fVxXsLgSSnJ7mpOaR00R6WeWmSjUk2JPmbh/G7SJIegmmvIwBeA7wFuB94PUCSFfSuI5hW04u4HHg+sAm4LsnqqtrYt8xy4D8Bz6yqO5L8i4fyS0iSHrppg6CqbgReNmnaRmDj1Gvs4iRgoqpuAUhyFXDmpHVfBVxeVXc0n/39wUuXJO0P011Q9ook081fmuRZ03z2YuC2vvamZlq/44DjknwxyVeSnL6HbZ2XZDzJ+JYtW6bZpCRpX03XIzgcuD7JWmAtsIXeFcXLgGcDPwCmPO6/j9tfDpwKHA18IckvVtWd/QtV1SpgFcDY2Fg9zG1KkvrsMQiq6rIkfwGcBjwTOJ7evYZupHfV8a17+ezNwDF97aN58FTUnTYBX62qnwLfTnIzvWC4bp9+C0nSQ7a3MYIHgGub1766Dlie5Fh6AXA28PJJy/xv4Bzgg83tKo4DbnkI25IkPUSt3Ya6qrYD59O7ad2NwNVVtSHJxX03q7sG2JpkI/A54A1VtbWtmiRJu9vb6aMPS1WtAdZMmray730BFzYvSdII+GAaSeq4vQZBkiOSfKB5PgFJViQ5t/3SJEnDMEiP4Ep6x/KPato3A3/QUj2SpCEbJAgWVtXVwA742SDwA61WJUkamkGC4N4khwMFkOTp9G5CJ0maAwY5a+hCYDWwNMkXgUXAS1qtSpI0NHsNgqpal+TZwBOBADc1VwJLkuaAQc4a+n1gQVVtqKobgAVJfq/90iRJwzDIGMGr+m8C19wy+lWtVSRJGqpBgmBekuxsNA+cmd9eSZKkYRpksPhTwEeSvK9pv7qZJkmaAwYJgj+i98f/NU37WuD9rVUkSRqqQc4a2gG8p3lJkuaYPQZBkqur6qVJvkFzMVm/qjq+1cokSUMxXY/gdc3PXx1GIZKk0ZjuUZW3N2cIXVlVzxliTZKkIZr29NHmUZU7khw2pHokSUM2yFlD9wDfSHItcO/OiVX12taqkiQNzSBB8D+blyRpDhrk9NEPJZkPPIne2UM3VdVPWq9MkjQUew2CJC8E3gf8I727jx6b5NVV9cm2i5MktW+QQ0N/BjynqiYAkiwFPgEYBJI0Bwxy07m7d4ZA4xbg7pbqkSQN2SA9gvEka4Cr6Y0RnAVcl+TFAFXlQLIkzWKDBMHBwPeAZzftLcAhwK/RCwaDQJJmsUHOGvrdYRQiSRqNQcYIJElzmEEgSR1nEEhSx+01CJIckeQDST7ZtFckObf90iRJwzBIj+BK4BrgqKZ9M/AHLdUjSRqyQYJgYVVdDewAqKrtwAODfHiS05PclGQiyUXTLPebSSrJ2EBVS5L2m0GC4N4kh9M8rjLJ04G79rZS81Cby4EXACuAc5KsmGK5R9F7GtpX96FuSdJ+MkgQXAisBpYm+SLwl8AFA6x3EjBRVbc0dyu9CjhziuXeCrwN+PFgJUuS9qe9BkFVraN3VfHJwKuBJ1fV+gE+ezFwW197UzPtZ5I8DTimqj4x3QclOS/JeJLxLVu2DLBpSdKg9nhl8c57CU3huCQP+x5DSQ6gd2fTV+5t2apaBawCGBsbq4ezXUnSrqa7xcSvTTNvkHsMbQaO6Wsf3Uzb6VHAU4DPJwH4eWB1kjOqanwvny1J2k/2GAT74R5D1wHLkxxLLwDOBl7e9/l3AQt3tpN8Hni9ISBJwzXI3UdJ8iLgyfTuRApAVV083TpVtT3J+fSuQZgHXFFVG5JcDIxX1eqHXrYkaX8Z5FGV7wUOBZ4DvB94CfAPg3x4Va0B1kyatnIPy546yGdKkvavQU4fPbmqfhu4o6r+GHgGcFy7ZUmShmWQILiv+bktyVHAT4Ej2ytJkjRMg4wRfDzJY4C3A+vonTH0/jaLkiQNzyBPKHtr8/Zvk3wcOLg540eSNAcMchvqs5r7AQG8Afhgkqe2W5YkaVgGGSN4U1XdneRZwPOADwDvbbcsSdKwDBIEO285/SJgVXNfoPntlSRJGqZBgmBzkvcBLwPWJDlowPUkSbPAIH/QX0rv6uB/XVV3Ao+lN1YgSZoDBjlraBt9N5irqtuB29ssSpI0PB7ikaSOMwgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknquL0+j0D737ve9S4mJiZGXcZuXve6141ku8uWLeOCCy4YybYl2SOQpM6zRzACM+Hb76mnnrrbtMsuu2z4hUgaOXsEHfWIRzxil/b8+fNHVImkUTMIOuraa6/dpf3pT396RJVIGrVWgyDJ6UluSjKR5KIp5l+YZGOS9Uk+k+TxbdajqdkbkLqttSBIMg+4HHgBsAI4J8mKSYtdD4xV1fHAx4D/2lY92t0JJ5zACSecYG9A6rg2ewQnARNVdUtV/QS4Cjizf4Gq+lxVbWuaXwGObrEeSdIU2gyCxcBtfe1NzbQ9ORf45FQzkpyXZDzJ+JYtW/ZjiZKkGTFYnOQVwBjw9qnmV9WqqhqrqrFFixYNtzhJmuPavI5gM3BMX/voZtoukjwP+C/As6vq/hbrkSRNoc0ewXXA8iTHJpkPnA2s7l8gyVOB9wFnVNX3W6xFkrQHrfUIqmp7kvOBa4B5wBVVtSHJxcB4Va2mdyhoAfDRJAC3VtUZbdUkae9m4r2wRnUfLOjGvbBavcVEVa0B1kyatrLv/fPa3L4kae+815CkXYz626/3wRq+GXHWkCTtdPLJJ+/SPuWUU0ZUSXcYBJJmlEsvvXSX9sUXXzyiSrrDIJA04yxYsACwNzAsjhFImnGWLl0K2BsYFnsEktRxBoEkdZxBIEkdZxBIUsd1brB4Jl4+Pyo7/x1Gefn+TNKFWwlIU+lcEExMTPC1G27kgUMfO+pSRu6AnxQAa2/53ogrGb1523446hKkkelcEAA8cOhjue9JLxx1GZpBDvnmmr0vJM1RjhFIUscZBJLUcQaBJHVcJ8cIpJnIM9oe5Bltu2r7jDaDQJohJiYm+NaG63ncggdGXcrIzf9p72DF/d8dH3Elo3frPfNa34ZBIM0gj1vwAP/5aT8adRmaQS5d9+jWt+EYgSR1XOd6BJs3b2betrs8b1y7mLdtK5s3bx91GdJI2COQpI7rXI9g8eLF/PP9B3plsXZxyDfXsHjxESOtYfPmzdx797yhHBPW7PHdu+fxyM2bW92GPQJJ6rjO9QikmWrx4sXcv/12zxrSLi5d92gOWry41W3YI5Ckjutkj2Deth961hBwwI973zx3HOwx6d5tqEc7RiCNSueCYNmyZaMuYcaYmLgbgGVP8A8gHDEj9o1b73GwGOB723oHK444dMeIKxm9W++Zx/KWt9G5IPAJVA/aeR+Xyy67bMSVCPyS0u8nzb2GDnq8/ybLaX/f6FwQSDOVX1Ie5JeU4XKwWJI6ziCQpI5rNQiSnJ7kpiQTSS6aYv5BST7SzP9qkiVt1iNJ2l1rQZBkHnA58AJgBXBOkhWTFjsXuKOqlgHvBN7WVj2SpKm1OVh8EjBRVbcAJLkKOBPY2LfMmcBbmvcfA/4iSaqqWqxr5GbKk6hmylOg2n76kvbNTNg/Z8q+Cd3YP9s8NLQYuK2vvamZNuUyVbUduAs4fPIHJTkvyXiS8S1btrRUbvcccsghHHLIIaMuQ9qN++ZwzYrTR6tqFbAKYGxsbNb3Fub6twvNbu6f3dNmj2AzcExf++hm2pTLJDkQOAzY2mJNkqRJ2gyC64DlSY5NMh84G1g9aZnVwO80718CfHaujw9I0kzT2qGhqtqe5HzgGmAecEVVbUhyMTBeVauBDwB/lWQC+CG9sJAkDVGrYwRVtQZYM2nayr73PwbOarMGSdL0vLJYkjrOIJCkjjMIJKnjDAJJ6rjMtrM1k2wBvjvqOuaQhcAPRl2ENAX3zf3r8VW1aKoZsy4ItH8lGa+qsVHXIU3mvjk8HhqSpI4zCCSp4wwCrRp1AdIeuG8OiWMEktRx9ggkqeMMAknqOIOgg5KclWRDkh1Jxvqmz0/ywSTfSPL1JKdOse7qJDf0tR+b5Nok32p+/txwfgvNBUl+fYpnme+cd0qSdUm2J3nJpHlvS3JD83rZFOv+eZJ7+toHJflIkokkX02yZL//MrOYQTCHJJk34KI3AC8GvjBp+qsAquoXgecD70jys30kyYuBeyatcxHwmapaDnymaUuD+nVgyiAAbgVeCfxN/8QkLwKeBpwI/DLw+iSP7ps/Bkz+QnIucEdVLQPeCbzt4Zc+dxgEs0CSJUm+meR/JLkxyceSHNrM+07z7WgdcFaSX0ny5eab1EeTLJj8eVV1Y1XdNMWmVgCfbZb5PnAnMNZsZwFwIfAnk9Y5E/hQ8/5D9P5ja47byz753CTXNz3LK5Ic1Ez/0yQbk6xP8t+SnAycAbw9ydeSLO3fRlV9p6rWAzsmbX4F8IWq2l5V9wLrgdObbcwD3g78x0nr9O+nHwOemyT77R9kljMIZo8nAu+uql8AfgT8Xt+8rVX1NODvgTcCz2va4/T+eA/q68AZSQ5McizwSzz4uNG3Au8Atk1a54iqur15/8/AEfuwPc1uu+2TSQ4GrgRe1vQsDwRek+Rw4DeAJ1fV8cCfVNWX6D2l8A1VdWJV/eOA2/06cHqSQ5MsBJ7Dg/vp+cDqvn1yp8XAbdB7aBZwF3D4Q/qt5yCDYPa4raq+2Lz/a+BZffM+0vx8Or1vS19M8jV6jwF9/D5s4wpgE70A+e/Al4AHkpwILK2q/zXdys1jRj0fuTum2iefCHy7qm5upn8IOIXeH94fAx9oDjFO/kIxsKr6NL0HXn0J+DDwZXr76VH0HnT1rof62V1lEMwek//A9rfvbX4GuLb5dnViVa2oqnMH3kCvq/2HzbpnAo8BbgaeAYwl+Q7w/4Djkny+We17SY4EaH5+fx9/L81e0+2Tu87ofQs/id5hmV8FPvWwNlx1SbOfPp/efn8z8FRgGTDR7KuHNo/BBdhM02tIciBwGLD14dQwlxgEs8fjkjyjef9yen+QJ/sK8MwkywCSPDLJcYNuoOlqP7J5/3xge1VtrKr3VNVRVbWE3re+m6vq1Ga11fR6HjQ//24ffy/NXlPtkzcBS3bug8BvAf+nGWM6rHl87R8CJzTz7wYetS8bTTKvOdREkuOB44FPV9Unqurnq2pJs69uawaHYdf99CXAZ8uraR9UVb5m+AtYAnyTXvf7RuBvgUObed8BFvYtexpwHb0BtPXAGVN83m/QOwR0P/A94Jq+7dzUbOPv6d22dqpabuhrH07vbKFvNes8dtT/Xr5Gvk8+F7ge+Aa9w40HAUcC/9Dsk98AfqdZ9pnAxmb5pZO28S+b/fReet/eNzTTD27W2Ujvy8+Je6jxnr73BwMfBSaaOp4w6n/DmfTyFhOzQHPO88er6imjrkUC98m5xkNDktRx9ggkqePsEUhSxxkEktRxBoEkdZxBIEkdZxBIUsf9f/gm79zySNFJAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='year_category', y='sale_price', data=df[df['sale_price']<10**7])\n", "plt.xlabel('')\n", "plt.ylabel('sale price ($)')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "37379c24", "metadata": {}, "source": [ "The plot above shows that older homes, built before 1940, sell for higher than those built after 1940. " ] }, { "cell_type": "markdown", "id": "a20e35d0", "metadata": {}, "source": [ "### Working with Datetime Data\n", "\n", "In Python, there is a datetime datatype that can be used to represent time-series data. When you first load in your Pandas DataFrame, your datetime columns will be represented as strings. You can convert them to datetime dataype using the Pandas [to_datetime()](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html) function:" ] }, { "cell_type": "code", "execution_count": 39, "id": "c65d5cbf", "metadata": {}, "outputs": [], "source": [ "df['sale_date'] = pd.to_datetime(df['sale_date'])" ] }, { "cell_type": "markdown", "id": "a8cfa704", "metadata": {}, "source": [ "Once your column is convereted to datetime, you can pull information like month, year, day of year, or whether it's a weekday or weekend. Let's go ahead and create three columns:\n", "- \"sale_month\"\n", "- \"sale_year\"\n", "- \"sale_weekday\"" ] }, { "cell_type": "code", "execution_count": 40, "id": "fe893751", "metadata": {}, "outputs": [], "source": [ "df['sale_month'] = df['sale_date'].dt.month\n", "df['sale_year'] = df['sale_date'].dt.year\n", "df['sale_weekday'] = df['sale_date'].dt.weekday" ] }, { "cell_type": "markdown", "id": "540733ca", "metadata": {}, "source": [ "Let's first figure out which year had the most property sales. We can use `value_counts()` to easily calculate this:" ] }, { "cell_type": "code", "execution_count": 41, "id": "fbd7e184", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2017 0.657428\n", "2016 0.342572\n", "Name: sale_year, dtype: float64" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sale_year'].value_counts(normalize=True)" ] }, { "cell_type": "markdown", "id": "76b7943d", "metadata": {}, "source": [ "We see that 66% of property sales took place in 2017. " ] }, { "cell_type": "markdown", "id": "a0e0d6c3", "metadata": {}, "source": [ "Let's next plot the number of property sales per month. We can do this by combining a scatterplot and lineplot together. The scatterplot is a nice touch to make the data points very visible." ] }, { "cell_type": "code", "execution_count": 42, "id": "3f4b45cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 6000.0)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "agg_sale_month = df.groupby(['sale_month']).size().reset_index(name='count')\n", "\n", "sns.lineplot(x='sale_month', y='count', data=agg_sale_month)\n", "sns.scatterplot(x='sale_month', y='count', data=agg_sale_month, color='red')\n", "plt.ylim([0,6000])" ] }, { "cell_type": "markdown", "id": "cec72447", "metadata": {}, "source": [ "June is the most popular month to sell a property in New York City while August is the least popular. Looking at the trends in the plot above, there isn't too much variation in property sale count across months.\n", "\n", "Let's take a look at property sale trends segmented by day of week. " ] }, { "cell_type": "code", "execution_count": 43, "id": "87a3c343", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sale_weekday</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>8817</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>10448</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>11199</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>13000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>10863</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5</td>\n", " <td>101</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6</td>\n", " <td>51</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sale_weekday count\n", "0 0 8817\n", "1 1 10448\n", "2 2 11199\n", "3 3 13000\n", "4 4 10863\n", "5 5 101\n", "6 6 51" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agg_sale_weekday = df.groupby(['sale_weekday']).size().reset_index(name='count')\n", "agg_sale_weekday" ] }, { "cell_type": "markdown", "id": "1d1becdc", "metadata": {}, "source": [ "To make the weekday column more human readable, we can conert the numerical values into proper week names. 0 is Monday and 6 is Sunday. Let's create a dictionary that maps weekday number to weekday abbreviation:" ] }, { "cell_type": "code", "execution_count": 44, "id": "f3cfadeb", "metadata": {}, "outputs": [], "source": [ "weekday_values = {\n", " 0: 'Mon',\n", " 1: 'Tues',\n", " 2: 'Wed',\n", " 3: 'Thurs',\n", " 4: 'Fri',\n", " 5: 'Sat',\n", " 6: 'Sun'\n", "}" ] }, { "cell_type": "markdown", "id": "22c2cb44", "metadata": {}, "source": [ "We can use the [map()](https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html) function to apply the new values to the weekday column. " ] }, { "cell_type": "code", "execution_count": 45, "id": "9ec1eb8e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sale_weekday</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Mon</td>\n", " <td>8817</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Tues</td>\n", " <td>10448</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Wed</td>\n", " <td>11199</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Thurs</td>\n", " <td>13000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Fri</td>\n", " <td>10863</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Sat</td>\n", " <td>101</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Sun</td>\n", " <td>51</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sale_weekday count\n", "0 Mon 8817\n", "1 Tues 10448\n", "2 Wed 11199\n", "3 Thurs 13000\n", "4 Fri 10863\n", "5 Sat 101\n", "6 Sun 51" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agg_sale_weekday['sale_weekday'] = agg_sale_weekday['sale_weekday'].map(weekday_values)\n", "agg_sale_weekday" ] }, { "cell_type": "markdown", "id": "37d4bcfd", "metadata": {}, "source": [ "The `sale_weekday` column is much easier to read. Now, let's plot this using Seaborn barplot:" ] }, { "cell_type": "code", "execution_count": 46, "id": "6127f597", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(x='sale_weekday', y='count', data=agg_sale_weekday, palette='Blues_d')\n", "plt.xlabel('weekday')\n", "plt.title('NYC Property Sales by Day of Week')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "5671328d", "metadata": {}, "source": [ "Interesting! Based on the plot above, we can see that Thursday was the most popular day to sell a property. It was quite rare to sell over the weekend, on Saturday and Sunday." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }