{
"cells": [
{
"cell_type": "markdown",
"id": "70594b81",
"metadata": {},
"source": [
"# Data Inspection"
]
},
{
"cell_type": "markdown",
"id": "6fdaa7fa",
"metadata": {},
"source": [
"In this book, we'll be exploring the [NYC real estate dataset](https://www.kaggle.com/new-york-city/nyc-property-sales) provided by the City of New York and sourced from Kaggle. \n",
"\n",
"<img src=\"https://media.giphy.com/media/csxOkD7hISGWE0vQra/giphy.gif\"/>\n",
"\n",
"The dataset includes properties that were sold between September 2016 and September 2017. Each row represents the sale of a property, while the columns are features that describe the property. Here is a list of the columns:\n",
"\n",
"- Borough\n",
"- Neighborhood \n",
"- Building class category \n",
"- Tax class at present\n",
"- Block\n",
"- Lot \n",
"- Ease-ment \n",
"- Building class at present \n",
"- Address\n",
"- Apartment number \n",
"- Zip code \n",
"- Residential units\n",
"- Commercial units \n",
"- Total units \n",
"- Land square feet\n",
"- Gross square feet \n",
"- Year built \n",
"- Tax class at time of sale \n",
"- Building class at time of sale \n",
"- Sale price \n",
"- Sale date\n",
"\n",
"For the official definitions of each of these columns, refer to the [Data Defintions](AP_nyc_data_definitions.md) in the Appendix. "
]
},
{
"cell_type": "markdown",
"id": "a5418f7f",
"metadata": {},
"source": [
"## 1) Loading in the data\n",
"\n",
"Before we get started, the very first thing we need to do is import the `pandas` package into our environment so that it's readily available. Let's give it the alias of \"pd\" so that we don't have to write out \"pandas\" every time we call a function."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e0dad2f7",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "66fc1f31",
"metadata": {},
"source": [
"In order to explore the NYC real estate dataset, we need to load it into our environment. We can do this using the Pandas [`read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) function. Let's call our new dataframe `\"df\"` (short for dataframe)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b3e1b9c8",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"data/nyc_real_estate.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c2f39e2e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['BOROUGH', 'NEIGHBORHOOD', 'BUILDING CLASS CATEGORY',\n",
" 'TAX CLASS AT PRESENT', 'BLOCK', 'LOT', 'EASE-MENT',\n",
" 'BUILDING CLASS AT PRESENT', 'ADDRESS', 'APARTMENT NUMBER', 'ZIP CODE',\n",
" 'RESIDENTIAL UNITS', 'COMMERCIAL UNITS', 'TOTAL UNITS',\n",
" 'LAND SQUARE FEET', 'GROSS SQUARE FEET', 'YEAR BUILT',\n",
" 'TAX CLASS AT TIME OF SALE', 'BUILDING CLASS AT TIME OF SALE',\n",
" 'SALE PRICE', 'SALE DATE'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"id": "cf88c36e",
"metadata": {},
"source": [
"## 2) Checking the shape and size of our dataset \n",
"\n",
"As a data scientist, one of the first things we want to know about a dataset is how big it is. How many rows and columns are we dealing with? Let's figure out the \"shape\" of our dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e24658dc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(84548, 21)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "markdown",
"id": "5cbd0ce7",
"metadata": {},
"source": [
"`df.shape` returns the number of rows and number of columns of a dataframe:\n",
"\n",
"```\n",
"(n_rows, n_columns)\n",
"```\n",
"\n",
"Our dataset has ~84.5K rows, which means that we have data on ~84.5K property sales in NYC."
]
},
{
"cell_type": "markdown",
"id": "ae9967cf",
"metadata": {},
"source": [
"### Number of rows"
]
},
{
"cell_type": "markdown",
"id": "d3fbbf81",
"metadata": {},
"source": [
"The length of a dataset represents the number of rows so if you're only interested in knowing how many rows we're dealing with, you can simply apply the `len()` function:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ca434b60",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"84548"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df)"
]
},
{
"cell_type": "markdown",
"id": "e997c146",
"metadata": {},
"source": [
"You can also get the number of rows from `df.shape` by accessing the first element of the `df.shape`'s output:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3224a0b3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"84548"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape[0]"
]
},
{
"cell_type": "markdown",
"id": "4b4ba838",
"metadata": {},
"source": [
"### Number of columns"
]
},
{
"cell_type": "markdown",
"id": "e0c0089c",
"metadata": {},
"source": [
"To get the number of columns of a dataset, you can first get the list of a dataset's columns:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8c9a760a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['BOROUGH', 'NEIGHBORHOOD', 'BUILDING CLASS CATEGORY',\n",
" 'TAX CLASS AT PRESENT', 'BLOCK', 'LOT', 'EASE-MENT',\n",
" 'BUILDING CLASS AT PRESENT', 'ADDRESS', 'APARTMENT NUMBER', 'ZIP CODE',\n",
" 'RESIDENTIAL UNITS', 'COMMERCIAL UNITS', 'TOTAL UNITS',\n",
" 'LAND SQUARE FEET', 'GROSS SQUARE FEET', 'YEAR BUILT',\n",
" 'TAX CLASS AT TIME OF SALE', 'BUILDING CLASS AT TIME OF SALE',\n",
" 'SALE PRICE', 'SALE DATE'],\n",
" dtype='object')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"id": "b953bf08",
"metadata": {},
"source": [
"Then, get the length of the column list:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7012387d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df.columns)"
]
},
{
"cell_type": "markdown",
"id": "1bac2d57",
"metadata": {},
"source": [
"Alternatively, you can also get the number of columns by getting the second element of `df.shape`'s output:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cec122cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape[1]"
]
},
{
"cell_type": "markdown",
"id": "2317a920",
"metadata": {},
"source": [
"### Size\n",
"\n",
"What if we want to how many data points (\"cells\") are in the dataset? This is considered the **size** of a dataframe. \n",
"\n",
"$\\text{number of data points} = \\text{size} = \\text{n_rows} \\times \\text{n_cols}$\n",
"\n",
"You can get a dataframe's size using `.size`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "94585197",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1775508"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.size"
]
},
{
"cell_type": "markdown",
"id": "af96bbab",
"metadata": {},
"source": [
"There are 1,775,508 data points in our dataset! "
]
},
{
"cell_type": "markdown",
"id": "2b19a8cf",
"metadata": {},
"source": [
"Let's check that `df.size` equal `n_rows` * `n_cols`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9b6398d9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.size == df.shape[0] * df.shape[1]"
]
},
{
"cell_type": "markdown",
"id": "cb313c1e",
"metadata": {},
"source": [
"```{note}\n",
"Since Pandas stores data in-memory, it is not optimized to work with very large datasets. The maximum amount of data that Pandas can handle depends on 2 things:\n",
"\n",
"- The type of data you’re working with (e.g., integers and floats are more space-efficient than strings)\n",
"- RAM of your machine. The larger the memory, the more data you’ll be able to work with. \n",
"\n",
"If your dataset is too big for Pandas to handle, you’ll get a `MemoryError`. There are several ways to get around this problem, as described in the [Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/scale.html#use-other-libraries), including splitting the dataset into smaller chunks or using [Dask](https://dask.org/), a parallel computing library instead. \n",
"```"
]
},
{
"cell_type": "markdown",
"id": "642159c4",
"metadata": {},
"source": [
"## 3) Viewing the data\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "2e9a9e32",
"metadata": {},
"source": [
"Now, let's take a peek at our data to get a sense of what it looks like 👀. There are 3 Pandas methods that you can use to inspect data:\n",
"\n",
"1. `.head()`: viewing first x rows \n",
"2. `.tail()`: viewing last x rows \n",
"3. `.sample()`: viewing random x rows \n",
"\n",
"If you don't mind viewing a lot of data at once, you can simply print `df` which will return the first 5 rows and last 5 rows of the dataframe."
]
},
{
"cell_type": "markdown",
"id": "f18e6426",
"metadata": {},
"source": [
"### First $n$ rows\n",
"\n",
"By default, `df.head()` returns the first 5 rows but you can customize this by defining `n` rows that you want to return inside of the parentheses. For example, `df.head(n=3)` would return 3 rows and `df.head(n=7)` would return 7 rows. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f050b32c",
"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>NEIGHBORHOOD</th>\n",
" <th>BUILDING CLASS CATEGORY</th>\n",
" <th>TAX CLASS AT PRESENT</th>\n",
" <th>BLOCK</th>\n",
" <th>LOT</th>\n",
" <th>EASE-MENT</th>\n",
" <th>BUILDING CLASS AT PRESENT</th>\n",
" <th>ADDRESS</th>\n",
" <th>APARTMENT NUMBER</th>\n",
" <th>...</th>\n",
" <th>RESIDENTIAL UNITS</th>\n",
" <th>COMMERCIAL UNITS</th>\n",
" <th>TOTAL UNITS</th>\n",
" <th>LAND SQUARE FEET</th>\n",
" <th>GROSS SQUARE FEET</th>\n",
" <th>YEAR BUILT</th>\n",
" <th>TAX CLASS AT TIME OF SALE</th>\n",
" <th>BUILDING CLASS AT TIME OF SALE</th>\n",
" <th>SALE PRICE</th>\n",
" <th>SALE DATE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>ALPHABET CITY</td>\n",
" <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
" <td>2A</td>\n",
" <td>392</td>\n",
" <td>6</td>\n",
" <td></td>\n",
" <td>C2</td>\n",
" <td>153 AVENUE B</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1633</td>\n",
" <td>6440</td>\n",
" <td>1900</td>\n",
" <td>2</td>\n",
" <td>C2</td>\n",
" <td>6625000</td>\n",
" <td>2017-07-19 00:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>ALPHABET CITY</td>\n",
" <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
" <td>2</td>\n",
" <td>399</td>\n",
" <td>26</td>\n",
" <td></td>\n",
" <td>C7</td>\n",
" <td>234 EAST 4TH STREET</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>28</td>\n",
" <td>3</td>\n",
" <td>31</td>\n",
" <td>4616</td>\n",
" <td>18690</td>\n",
" <td>1900</td>\n",
" <td>2</td>\n",
" <td>C7</td>\n",
" <td>-</td>\n",
" <td>2016-12-14 00:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>ALPHABET CITY</td>\n",
" <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
" <td>2</td>\n",
" <td>399</td>\n",
" <td>39</td>\n",
" <td></td>\n",
" <td>C7</td>\n",
" <td>197 EAST 3RD STREET</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>17</td>\n",
" <td>2212</td>\n",
" <td>7803</td>\n",
" <td>1900</td>\n",
" <td>2</td>\n",
" <td>C7</td>\n",
" <td>-</td>\n",
" <td>2016-12-09 00:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>ALPHABET CITY</td>\n",
" <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
" <td>2B</td>\n",
" <td>402</td>\n",
" <td>21</td>\n",
" <td></td>\n",
" <td>C4</td>\n",
" <td>154 EAST 7TH STREET</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2272</td>\n",
" <td>6794</td>\n",
" <td>1913</td>\n",
" <td>2</td>\n",
" <td>C4</td>\n",
" <td>3936272</td>\n",
" <td>2016-09-23 00:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>ALPHABET CITY</td>\n",
" <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
" <td>2A</td>\n",
" <td>404</td>\n",
" <td>55</td>\n",
" <td></td>\n",
" <td>C2</td>\n",
" <td>301 EAST 10TH STREET</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
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" <td>8000000</td>\n",
" <td>2016-11-17 00:00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" BOROUGH NEIGHBORHOOD BUILDING CLASS CATEGORY \\\n",
"0 1 ALPHABET CITY 07 RENTALS - WALKUP APARTMENTS \n",
"1 1 ALPHABET CITY 07 RENTALS - WALKUP APARTMENTS \n",
"2 1 ALPHABET CITY 07 RENTALS - WALKUP APARTMENTS \n",
"3 1 ALPHABET CITY 07 RENTALS - WALKUP APARTMENTS \n",
"4 1 ALPHABET CITY 07 RENTALS - WALKUP APARTMENTS \n",
"\n",
" TAX CLASS AT PRESENT BLOCK LOT EASE-MENT BUILDING CLASS AT PRESENT \\\n",
"0 2A 392 6 C2 \n",
"1 2 399 26 C7 \n",
"2 2 399 39 C7 \n",
"3 2B 402 21 C4 \n",
"4 2A 404 55 C2 \n",
"\n",
" ADDRESS APARTMENT NUMBER ... RESIDENTIAL UNITS \\\n",
"0 153 AVENUE B ... 5 \n",
"1 234 EAST 4TH STREET ... 28 \n",
"2 197 EAST 3RD STREET ... 16 \n",
"3 154 EAST 7TH STREET ... 10 \n",
"4 301 EAST 10TH STREET ... 6 \n",
"\n",
" COMMERCIAL UNITS TOTAL UNITS LAND SQUARE FEET GROSS SQUARE FEET \\\n",
"0 0 5 1633 6440 \n",
"1 3 31 4616 18690 \n",
"2 1 17 2212 7803 \n",
"3 0 10 2272 6794 \n",
"4 0 6 2369 4615 \n",
"\n",
" YEAR BUILT TAX CLASS AT TIME OF SALE BUILDING CLASS AT TIME OF SALE \\\n",
"0 1900 2 C2 \n",
"1 1900 2 C7 \n",
"2 1900 2 C7 \n",
"3 1913 2 C4 \n",
"4 1900 2 C2 \n",
"\n",
" SALE PRICE SALE DATE \n",
"0 6625000 2017-07-19 00:00:00 \n",
"1 - 2016-12-14 00:00:00 \n",
"2 - 2016-12-09 00:00:00 \n",
"3 3936272 2016-09-23 00:00:00 \n",
"4 8000000 2016-11-17 00:00:00 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "be072519",
"metadata": {},
"source": [
"### Last $n$ rows\n",
"\n",
"`df.tail()` does the opposite of `df.head()` - it returns the last 5 rows of a dataframe rather than the first 5. Similarly, you can specify how many rows you want to return by passing in the number inside the parentheses:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7a48981d",
"metadata": {},
"outputs": [
{
"data": {
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" <td></td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>10796</td>\n",
" <td>2400</td>\n",
" <td>2006</td>\n",
" <td>4</td>\n",
" <td>P9</td>\n",
" <td>69300</td>\n",
" <td>2016-10-27 00:00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" BOROUGH NEIGHBORHOOD BUILDING CLASS CATEGORY \\\n",
"84546 5 WOODROW 22 STORE BUILDINGS \n",
"84547 5 WOODROW 35 INDOOR PUBLIC AND CULTURAL FACILITIES \n",
"\n",
" TAX CLASS AT PRESENT BLOCK LOT EASE-MENT BUILDING CLASS AT PRESENT \\\n",
"84546 4 7100 28 K6 \n",
"84547 4 7105 679 P9 \n",
"\n",
" ADDRESS APARTMENT NUMBER ... RESIDENTIAL UNITS \\\n",
"84546 2730 ARTHUR KILL ROAD ... 0 \n",
"84547 155 CLAY PIT ROAD ... 0 \n",
"\n",
" COMMERCIAL UNITS TOTAL UNITS LAND SQUARE FEET GROSS SQUARE FEET \\\n",
"84546 7 7 208033 64117 \n",
"84547 1 1 10796 2400 \n",
"\n",
" YEAR BUILT TAX CLASS AT TIME OF SALE BUILDING CLASS AT TIME OF SALE \\\n",
"84546 2001 4 K6 \n",
"84547 2006 4 P9 \n",
"\n",
" SALE PRICE SALE DATE \n",
"84546 11693337 2016-12-21 00:00:00 \n",
"84547 69300 2016-10-27 00:00:00 \n",
"\n",
"[2 rows x 21 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail(n=2)"
]
},
{
"cell_type": "markdown",
"id": "46eafa61",
"metadata": {},
"source": [
"```{note}\n",
"`df.tail(2)` and `df.tail(n=2)` return the same thing. The latter is more explicit which can be easier for other people reading your code to understand.\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "bcb929f3",
"metadata": {},
"source": [
"### Sampling $n$ rows\n",
"\n",
"If you need to randomly sample rows from your dataframe, you can use `df.sample()`. Unlike `df.head()` and `df.tail()`, `df.sample()` will return 1 row by default instead of 5. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "3e38ef7c",
"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>NEIGHBORHOOD</th>\n",
" <th>BUILDING CLASS CATEGORY</th>\n",
" <th>TAX CLASS AT PRESENT</th>\n",
" <th>BLOCK</th>\n",
" <th>LOT</th>\n",
" <th>EASE-MENT</th>\n",
" <th>BUILDING CLASS AT PRESENT</th>\n",
" <th>ADDRESS</th>\n",
" <th>APARTMENT NUMBER</th>\n",
" <th>...</th>\n",
" <th>RESIDENTIAL UNITS</th>\n",
" <th>COMMERCIAL UNITS</th>\n",
" <th>TOTAL UNITS</th>\n",
" <th>LAND SQUARE FEET</th>\n",
" <th>GROSS SQUARE FEET</th>\n",
" <th>YEAR BUILT</th>\n",
" <th>TAX CLASS AT TIME OF SALE</th>\n",
" <th>BUILDING CLASS AT TIME OF SALE</th>\n",
" <th>SALE PRICE</th>\n",
" <th>SALE DATE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19932</th>\n",
" <td>2</td>\n",
" <td>CROTONA PARK</td>\n",
" <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
" <td>2A</td>\n",
" <td>2952</td>\n",
" <td>28</td>\n",
" <td></td>\n",
" <td>C3</td>\n",
" <td>1790 PROSPECT AVENUE</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1760</td>\n",
" <td>4620</td>\n",
" <td>1908</td>\n",
" <td>2</td>\n",
" <td>C3</td>\n",
" <td>422573</td>\n",
" <td>2016-09-27 00:00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" BOROUGH NEIGHBORHOOD BUILDING CLASS CATEGORY \\\n",
"19932 2 CROTONA PARK 07 RENTALS - WALKUP APARTMENTS \n",
"\n",
" TAX CLASS AT PRESENT BLOCK LOT EASE-MENT BUILDING CLASS AT PRESENT \\\n",
"19932 2A 2952 28 C3 \n",
"\n",
" ADDRESS APARTMENT NUMBER ... RESIDENTIAL UNITS \\\n",
"19932 1790 PROSPECT AVENUE ... 4 \n",
"\n",
" COMMERCIAL UNITS TOTAL UNITS LAND SQUARE FEET GROSS SQUARE FEET \\\n",
"19932 0 4 1760 4620 \n",
"\n",
" YEAR BUILT TAX CLASS AT TIME OF SALE BUILDING CLASS AT TIME OF SALE \\\n",
"19932 1908 2 C3 \n",
"\n",
" SALE PRICE SALE DATE \n",
"19932 422573 2016-09-27 00:00:00 \n",
"\n",
"[1 rows x 21 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sample()"
]
},
{
"cell_type": "markdown",
"id": "8f788f2b",
"metadata": {},
"source": [
"## 4) Assessing missing values\n",
"\n",
"The fastest way to assess missing values of a given dataset is to use `df.info()`. It gives a good overview of the columns, datatypes, and \"non-null count\" (a crpytic way of describing how many values are not missing).\n",
"\n",
"Let's test it out on our dataset."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "186e45a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 84548 entries, 0 to 84547\n",
"Data columns (total 21 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 BOROUGH 84548 non-null int64 \n",
" 1 NEIGHBORHOOD 84548 non-null object\n",
" 2 BUILDING CLASS CATEGORY 84548 non-null object\n",
" 3 TAX CLASS AT PRESENT 84548 non-null object\n",
" 4 BLOCK 84548 non-null int64 \n",
" 5 LOT 84548 non-null int64 \n",
" 6 EASE-MENT 84548 non-null object\n",
" 7 BUILDING CLASS AT PRESENT 84548 non-null object\n",
" 8 ADDRESS 84548 non-null object\n",
" 9 APARTMENT NUMBER 84548 non-null object\n",
" 10 ZIP CODE 84548 non-null int64 \n",
" 11 RESIDENTIAL UNITS 84548 non-null int64 \n",
" 12 COMMERCIAL UNITS 84548 non-null int64 \n",
" 13 TOTAL UNITS 84548 non-null int64 \n",
" 14 LAND SQUARE FEET 84548 non-null object\n",
" 15 GROSS SQUARE FEET 84548 non-null object\n",
" 16 YEAR BUILT 84548 non-null int64 \n",
" 17 TAX CLASS AT TIME OF SALE 84548 non-null int64 \n",
" 18 BUILDING CLASS AT TIME OF SALE 84548 non-null object\n",
" 19 SALE PRICE 84548 non-null object\n",
" 20 SALE DATE 84548 non-null object\n",
"dtypes: int64(9), object(12)\n",
"memory usage: 13.5+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"id": "72b1e06c",
"metadata": {},
"source": [
"At the very bottom of the output, we see:\n",
"\n",
"```\n",
"dtypes: int64(9), object(12)\n",
"```\n",
"\n",
"There are 9 columns that `int64`, which store integer (numerical) values. There are 12 columns that are \"objects\" which typically describes columns storing string values.\n",
"\n",
"There are 84,548 rows and across all 21 columns, there are 84,548 non-null values which indicates that we don't have any missing values in our dataset. 🎉"
]
},
{
"cell_type": "markdown",
"id": "2b74f4dd",
"metadata": {},
"source": [
"### Using `.isnull()` and `.notnull()`\n",
"\n",
"You can use [`.isnul()`](https://pandas.pydata.org/docs/reference/api/pandas.isnull.html) to generate a boolean mask that returns `True` when the value is missing and `False` when it is not.\n",
"\n",
"```{note}\n",
"To reverse the boolean mask, you can use [`df.notnull()`](https://pandas.pydata.org/docs/reference/api/pandas.notnull.html) which returns True when the value is not null (i.e., not missing).\n",
"```\n",
"\n",
"Let's try it out on our dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e64f278b",
"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>NEIGHBORHOOD</th>\n",
" <th>BUILDING CLASS CATEGORY</th>\n",
" <th>TAX CLASS AT PRESENT</th>\n",
" <th>BLOCK</th>\n",
" <th>LOT</th>\n",
" <th>EASE-MENT</th>\n",
" <th>BUILDING CLASS AT PRESENT</th>\n",
" <th>ADDRESS</th>\n",
" <th>APARTMENT NUMBER</th>\n",
" <th>...</th>\n",
" <th>RESIDENTIAL UNITS</th>\n",
" <th>COMMERCIAL UNITS</th>\n",
" <th>TOTAL UNITS</th>\n",
" <th>LAND SQUARE FEET</th>\n",
" <th>GROSS SQUARE FEET</th>\n",
" <th>YEAR BUILT</th>\n",
" <th>TAX CLASS AT TIME OF SALE</th>\n",
" <th>BUILDING CLASS AT TIME OF SALE</th>\n",
" <th>SALE PRICE</th>\n",
" <th>SALE DATE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" BOROUGH NEIGHBORHOOD BUILDING CLASS CATEGORY TAX CLASS AT PRESENT \\\n",
"0 False False False False \n",
"1 False False False False \n",
"2 False False False False \n",
"3 False False False False \n",
"4 False False False False \n",
"\n",
" BLOCK LOT EASE-MENT BUILDING CLASS AT PRESENT ADDRESS \\\n",
"0 False False False False False \n",
"1 False False False False False \n",
"2 False False False False False \n",
"3 False False False False False \n",
"4 False False False False False \n",
"\n",
" APARTMENT NUMBER ... RESIDENTIAL UNITS COMMERCIAL UNITS TOTAL UNITS \\\n",
"0 False ... False False False \n",
"1 False ... False False False \n",
"2 False ... False False False \n",
"3 False ... False False False \n",
"4 False ... False False False \n",
"\n",
" LAND SQUARE FEET GROSS SQUARE FEET YEAR BUILT TAX CLASS AT TIME OF SALE \\\n",
"0 False False False False \n",
"1 False False False False \n",
"2 False False False False \n",
"3 False False False False \n",
"4 False False False False \n",
"\n",
" BUILDING CLASS AT TIME OF SALE SALE PRICE SALE DATE \n",
"0 False False False \n",
"1 False False False \n",
"2 False False False \n",
"3 False False False \n",
"4 False False False \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().head()"
]
},
{
"cell_type": "markdown",
"id": "033b6c61",
"metadata": {},
"source": [
"We can count the number of missing values in each column like this:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f40bc9f6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BOROUGH 0\n",
"NEIGHBORHOOD 0\n",
"BUILDING CLASS CATEGORY 0\n",
"TAX CLASS AT PRESENT 0\n",
"BLOCK 0\n",
"LOT 0\n",
"EASE-MENT 0\n",
"BUILDING CLASS AT PRESENT 0\n",
"ADDRESS 0\n",
"APARTMENT NUMBER 0\n",
"ZIP CODE 0\n",
"RESIDENTIAL UNITS 0\n",
"COMMERCIAL UNITS 0\n",
"TOTAL UNITS 0\n",
"LAND SQUARE FEET 0\n",
"GROSS SQUARE FEET 0\n",
"YEAR BUILT 0\n",
"TAX CLASS AT TIME OF SALE 0\n",
"BUILDING CLASS AT TIME OF SALE 0\n",
"SALE PRICE 0\n",
"SALE DATE 0\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"id": "105e3404",
"metadata": {},
"source": [
"As expected, there are 0 missing values in our dataset. 👍"
]
},
{
"cell_type": "markdown",
"id": "080c250b",
"metadata": {},
"source": [
"### Using the missingno package\n",
"\n",
"Another way to visualize missing values in your dataset is to use the [missingno](https://github.com/ResidentMario/missingno) package. It's a great way to assess which rows have missing values and how sparse or dense your dataset is. Let's import it and give it the alias \"msno\"."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c226c92f",
"metadata": {},
"outputs": [],
"source": [
"import missingno as msno"
]
},
{
"cell_type": "markdown",
"id": "20c328d1",
"metadata": {},
"source": [
"Let's use the matrix() function to visualize patterns of missing values in our dataset:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7ba5c0c2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(df)"
]
},
{
"cell_type": "markdown",
"id": "c9803bfc",
"metadata": {},
"source": [
"From the plot above, we can see that our data is very dense. There are no missing values. For examples of what the matrix would look like with missing data, check out the [missingno docs](https://github.com/ResidentMario/missingno#matrix). "
]
}
],
"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
}