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gumdropsteve/sql_weekend_project_winston_solns.ipynb

Created November 30, 2020 08:48
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sql_weekend_project_winston_solns.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SQL Weekend Project\n",
"- [Requirements](https://docs.google.com/document/d/1_p0GndxoUw0MqNsIEiNVNNy-pBsaYiMPHDqwPznMZ_k/edit?usp=sharing)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Start up BlazingSQL "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BlazingContext ready\n"
]
}
],
"source": [
"from blazingsql import BlazingContext\n",
"bc = BlazingContext()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to S3 bucket"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(True,\n",
" '',\n",
" OrderedDict([('type', 's3'),\n",
" ('bucket_name', 'blazingsql-colab'),\n",
" ('access_key_id', ''),\n",
" ('secret_key', ''),\n",
" ('session_token', ''),\n",
" ('encryption_type', <S3EncryptionType.NONE: 1>),\n",
" ('kms_key_amazon_resource_name', ''),\n",
" ('endpoint_override', ''),\n",
" ('region', '')]))"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bc.s3('blazingsql-colab', bucket_name='blazingsql-colab')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# bc.s3('taxi', \n",
"# bucket_name='data',\n",
"# access_key_id='minioadmin', \n",
"# secret_key='minioadmin',\n",
"# endpoint_override=\"http://172.31.12.10:9000\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Table from S3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"bc.create_table('taxi', 's3://blazingsql-colab/yellow_taxi/taxi_data.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# bc.create_table('taxi', 's3://taxi/sample_taxi.csv', header=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Write 20+ Queries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. How many rows are in the dataset?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>count(*)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count(*)\n",
"0 1000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" select\n",
" count(*)\n",
" from\n",
" taxi\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. What are the column names of the dataset?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['VendorID', 'tpep_pickup_datetime', 'tpep_dropoff_datetime',\n",
" 'passenger_count', 'trip_distance', 'pickup_x', 'pickup_y',\n",
" 'RateCodeID', 'store_and_fwd_flag', 'dropoff_x', 'dropoff_y',\n",
" 'payment_type', 'fare_amount', 'extra', 'mta_tax', 'tip_amount',\n",
" 'tolls_amount', 'total_amount'],\n",
" dtype='object')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" select * from taxi limit 0\n",
" '''\n",
"bc.sql(query).columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. What do the first 5 rows look like?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>VendorID</th>\n",
" <th>tpep_pickup_datetime</th>\n",
" <th>tpep_dropoff_datetime</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>pickup_x</th>\n",
" <th>pickup_y</th>\n",
" <th>RateCodeID</th>\n",
" <th>store_and_fwd_flag</th>\n",
" <th>dropoff_x</th>\n",
" <th>dropoff_y</th>\n",
" <th>payment_type</th>\n",
" <th>fare_amount</th>\n",
" <th>extra</th>\n",
" <th>mta_tax</th>\n",
" <th>tip_amount</th>\n",
" <th>tolls_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>2015-03-05 07:38:36</td>\n",
" <td>2015-03-05 07:44:06</td>\n",
" <td>1.0</td>\n",
" <td>1.10</td>\n",
" <td>-8235279.565</td>\n",
" <td>4975191.631</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8235303.346</td>\n",
" <td>4973620.602</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>2.00</td>\n",
" <td>0.0</td>\n",
" <td>8.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>2015-02-03 05:41:55</td>\n",
" <td>2015-02-03 05:46:54</td>\n",
" <td>1.0</td>\n",
" <td>0.70</td>\n",
" <td>-8235656.655</td>\n",
" <td>4972873.569</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8236483.024</td>\n",
" <td>4973822.361</td>\n",
" <td>1.0</td>\n",
" <td>5.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>1.36</td>\n",
" <td>0.0</td>\n",
" <td>8.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-08 16:17:51</td>\n",
" <td>2015-02-08 16:37:45</td>\n",
" <td>1.0</td>\n",
" <td>3.65</td>\n",
" <td>-8238228.336</td>\n",
" <td>4974965.742</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8233503.678</td>\n",
" <td>4978004.758</td>\n",
" <td>2.0</td>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>16.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>2015-03-07 02:17:05</td>\n",
" <td>2015-03-07 02:22:18</td>\n",
" <td>3.0</td>\n",
" <td>0.77</td>\n",
" <td>-8232200.852</td>\n",
" <td>4980818.134</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8231245.389</td>\n",
" <td>4980848.420</td>\n",
" <td>2.0</td>\n",
" <td>5.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>6.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-05 21:05:35</td>\n",
" <td>2015-02-05 21:10:33</td>\n",
" <td>2.0</td>\n",
" <td>0.74</td>\n",
" <td>-8236124.619</td>\n",
" <td>4973741.097</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234966.173</td>\n",
" <td>4972970.518</td>\n",
" <td>1.0</td>\n",
" <td>5.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>1.00</td>\n",
" <td>0.0</td>\n",
" <td>7.80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
"0 2.0 2015-03-05 07:38:36 2015-03-05 07:44:06 1.0 \n",
"1 1.0 2015-02-03 05:41:55 2015-02-03 05:46:54 1.0 \n",
"2 2.0 2015-02-08 16:17:51 2015-02-08 16:37:45 1.0 \n",
"3 2.0 2015-03-07 02:17:05 2015-03-07 02:22:18 3.0 \n",
"4 2.0 2015-02-05 21:05:35 2015-02-05 21:10:33 2.0 \n",
"\n",
" trip_distance pickup_x pickup_y RateCodeID store_and_fwd_flag \\\n",
"0 1.10 -8235279.565 4975191.631 1.0 N \n",
"1 0.70 -8235656.655 4972873.569 1.0 N \n",
"2 3.65 -8238228.336 4974965.742 1.0 N \n",
"3 0.77 -8232200.852 4980818.134 1.0 N \n",
"4 0.74 -8236124.619 4973741.097 1.0 N \n",
"\n",
" dropoff_x dropoff_y payment_type fare_amount extra mta_tax \\\n",
"0 -8235303.346 4973620.602 1.0 6.0 0.0 0.5 \n",
"1 -8236483.024 4973822.361 1.0 5.5 0.5 0.5 \n",
"2 -8233503.678 4978004.758 2.0 16.0 0.0 0.5 \n",
"3 -8231245.389 4980848.420 2.0 5.5 0.5 0.5 \n",
"4 -8234966.173 4972970.518 1.0 5.5 0.5 0.5 \n",
"\n",
" tip_amount tolls_amount total_amount \n",
"0 2.00 0.0 8.80 \n",
"1 1.36 0.0 8.16 \n",
"2 0.00 0.0 16.80 \n",
"3 0.00 0.0 6.80 \n",
"4 1.00 0.0 7.80 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" select * from taxi limit 5\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Looks like all the data is from January 2015, let's look to see if any other of the first few months are obviously represented."
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>months</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999995</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999996</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999997</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999998</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999999</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000000 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" months\n",
"0 3\n",
"1 2\n",
"2 2\n",
"3 3\n",
"4 2\n",
"... ...\n",
"999995 2\n",
"999996 1\n",
"999997 2\n",
"999998 1\n",
"999999 3\n",
"\n",
"[1000000 rows x 1 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" MONTH(pickup_ds) AS months\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" YEAR(pickup_ds) = 2015\n",
" AND MONTH(pickup_ds) <= 3\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>months</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999995</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999996</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999997</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999998</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999999</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000000 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" months\n",
"0 3\n",
"1 2\n",
"2 2\n",
"3 3\n",
"4 2\n",
"... ...\n",
"999995 2\n",
"999996 1\n",
"999997 2\n",
"999998 1\n",
"999999 3\n",
"\n",
"[1000000 rows x 1 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" MONTH(pickup_ds) AS months\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" YEAR(pickup_ds) = 2015\n",
" AND MONTH(pickup_ds) <= 3\n",
" '''\n",
"bc.sql(query)#.months.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 364138\n",
"3 329813\n",
"2 305965\n",
"4 83\n",
"10 1\n",
"Name: months, dtype: int32"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" MONTH(dropoff_ds) AS months\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_dropoff_datetime AS timestamp) AS dropoff_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" YEAR(dropoff_ds) = 2015\n",
" '''\n",
"bc.sql(query).months.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>VendorID</th>\n",
" <th>tpep_pickup_datetime</th>\n",
" <th>tpep_dropoff_datetime</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>pickup_x</th>\n",
" <th>pickup_y</th>\n",
" <th>RateCodeID</th>\n",
" <th>store_and_fwd_flag</th>\n",
" <th>dropoff_x</th>\n",
" <th>dropoff_y</th>\n",
" <th>payment_type</th>\n",
" <th>fare_amount</th>\n",
" <th>extra</th>\n",
" <th>mta_tax</th>\n",
" <th>tip_amount</th>\n",
" <th>tolls_amount</th>\n",
" <th>total_amount</th>\n",
" <th>dropoff_ds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>2015-03-22 15:16:40</td>\n",
" <td>2015-10-30 04:35:01</td>\n",
" <td>1.0</td>\n",
" <td>1.1</td>\n",
" <td>-8236854.168</td>\n",
" <td>4975346.898</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8235262.579</td>\n",
" <td>4975923.146</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.3</td>\n",
" <td>2015-10-30 04:35:01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
"0 1.0 2015-03-22 15:16:40 2015-10-30 04:35:01 1.0 \n",
"\n",
" trip_distance pickup_x pickup_y RateCodeID store_and_fwd_flag \\\n",
"0 1.1 -8236854.168 4975346.898 1.0 N \n",
"\n",
" dropoff_x dropoff_y payment_type fare_amount extra mta_tax \\\n",
"0 -8235262.579 4975923.146 2.0 8.0 0.5 0.5 \n",
"\n",
" tip_amount tolls_amount total_amount dropoff_ds \n",
"0 0.0 0.0 9.3 2015-10-30 04:35:01 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" *\n",
" FROM\n",
" (\n",
" SELECT\n",
" *,\n",
" CAST(tpep_dropoff_datetime AS timestamp) AS dropoff_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" YEAR(dropoff_ds) = 2015\n",
" AND MONTH(dropoff_ds) = 10\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5. What are the the unique years seen in this data?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>unique_years</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_years\n",
"0 2015"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" DISTINCT YEAR(pickup_ds) AS unique_years\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6. What are the the unique months seen in this data?"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\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>unique_months</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_months\n",
"0 1\n",
"1 2\n",
"2 3"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" DISTINCT MONTH(pickup_ds) AS unique_months\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"7. I want to see all the data from January 15, 2015."
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>VendorID</th>\n",
" <th>tpep_pickup_datetime</th>\n",
" <th>tpep_dropoff_datetime</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>pickup_x</th>\n",
" <th>pickup_y</th>\n",
" <th>RateCodeID</th>\n",
" <th>store_and_fwd_flag</th>\n",
" <th>dropoff_x</th>\n",
" <th>dropoff_y</th>\n",
" <th>payment_type</th>\n",
" <th>fare_amount</th>\n",
" <th>extra</th>\n",
" <th>mta_tax</th>\n",
" <th>tip_amount</th>\n",
" <th>tolls_amount</th>\n",
" <th>total_amount</th>\n",
" <th>pickup_ds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-15 21:02:31</td>\n",
" <td>2015-01-15 21:20:46</td>\n",
" <td>2.0</td>\n",
" <td>3.26</td>\n",
" <td>-8234088.846</td>\n",
" <td>4977796.745</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8238124.721</td>\n",
" <td>4974404.684</td>\n",
" <td>1.0</td>\n",
" <td>14.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" <td>2015-01-15 21:02:31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-15 19:51:19</td>\n",
" <td>2015-01-15 19:54:39</td>\n",
" <td>1.0</td>\n",
" <td>0.79</td>\n",
" <td>-8234054.874</td>\n",
" <td>4976369.929</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234631.549</td>\n",
" <td>4975038.048</td>\n",
" <td>2.0</td>\n",
" <td>4.5</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" <td>2015-01-15 19:51:19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>2015-02-15 16:46:10</td>\n",
" <td>2015-02-15 17:00:07</td>\n",
" <td>1.0</td>\n",
" <td>1.70</td>\n",
" <td>-8235614.190</td>\n",
" <td>4978829.564</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8236789.621</td>\n",
" <td>4976089.636</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>3.5</td>\n",
" <td>0.0</td>\n",
" <td>15.3</td>\n",
" <td>2015-02-15 16:46:10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>2015-03-15 16:48:48</td>\n",
" <td>2015-03-15 16:52:25</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" <td>-8234979.762</td>\n",
" <td>4981349.273</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8235343.263</td>\n",
" <td>4980382.363</td>\n",
" <td>2.0</td>\n",
" <td>4.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.3</td>\n",
" <td>2015-03-15 16:48:48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" <td>2015-03-15 10:47:54</td>\n",
" <td>2015-03-15 10:52:02</td>\n",
" <td>1.0</td>\n",
" <td>0.50</td>\n",
" <td>-8233639.566</td>\n",
" <td>4978136.520</td>\n",
" <td>1.0</td>\n",
" <td>Y</td>\n",
" <td>-8232541.421</td>\n",
" <td>4977596.585</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.8</td>\n",
" <td>2015-03-15 10:47:54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33527</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-15 13:11:05</td>\n",
" <td>2015-01-15 13:20:27</td>\n",
" <td>1.0</td>\n",
" <td>1.40</td>\n",
" <td>-8237014.686</td>\n",
" <td>4974945.563</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8236667.322</td>\n",
" <td>4972624.197</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" <td>2015-01-15 13:11:05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33528</th>\n",
" <td>1.0</td>\n",
" <td>2015-03-15 15:19:30</td>\n",
" <td>2015-03-15 15:31:21</td>\n",
" <td>1.0</td>\n",
" <td>1.20</td>\n",
" <td>-8235072.336</td>\n",
" <td>4976164.754</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8236776.032</td>\n",
" <td>4975426.495</td>\n",
" <td>2.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.8</td>\n",
" <td>2015-03-15 15:19:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33529</th>\n",
" <td>1.0</td>\n",
" <td>2015-03-15 16:14:03</td>\n",
" <td>2015-03-15 16:31:48</td>\n",
" <td>1.0</td>\n",
" <td>2.10</td>\n",
" <td>-8236990.056</td>\n",
" <td>4975689.952</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234234.926</td>\n",
" <td>4976384.504</td>\n",
" <td>2.0</td>\n",
" <td>12.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>13.3</td>\n",
" <td>2015-03-15 16:14:03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33530</th>\n",
" <td>2.0</td>\n",
" <td>2015-03-15 10:58:35</td>\n",
" <td>2015-03-15 11:27:38</td>\n",
" <td>1.0</td>\n",
" <td>3.91</td>\n",
" <td>-8231906.994</td>\n",
" <td>4980676.240</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8235613.340</td>\n",
" <td>4979914.647</td>\n",
" <td>1.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>22.8</td>\n",
" <td>2015-03-15 10:58:35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33531</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-15 11:59:28</td>\n",
" <td>2015-01-15 12:18:51</td>\n",
" <td>1.0</td>\n",
" <td>3.94</td>\n",
" <td>-8234455.744</td>\n",
" <td>4977088.076</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234872.750</td>\n",
" <td>4970821.063</td>\n",
" <td>2.0</td>\n",
" <td>17.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" <td>2015-01-15 11:59:28</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>33532 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
"0 2.0 2015-01-15 21:02:31 2015-01-15 21:20:46 2.0 \n",
"1 2.0 2015-01-15 19:51:19 2015-01-15 19:54:39 1.0 \n",
"2 1.0 2015-02-15 16:46:10 2015-02-15 17:00:07 1.0 \n",
"3 1.0 2015-03-15 16:48:48 2015-03-15 16:52:25 1.0 \n",
"4 1.0 2015-03-15 10:47:54 2015-03-15 10:52:02 1.0 \n",
"... ... ... ... ... \n",
"33527 2.0 2015-01-15 13:11:05 2015-01-15 13:20:27 1.0 \n",
"33528 1.0 2015-03-15 15:19:30 2015-03-15 15:31:21 1.0 \n",
"33529 1.0 2015-03-15 16:14:03 2015-03-15 16:31:48 1.0 \n",
"33530 2.0 2015-03-15 10:58:35 2015-03-15 11:27:38 1.0 \n",
"33531 2.0 2015-01-15 11:59:28 2015-01-15 12:18:51 1.0 \n",
"\n",
" trip_distance pickup_x pickup_y RateCodeID store_and_fwd_flag \\\n",
"0 3.26 -8234088.846 4977796.745 1.0 N \n",
"1 0.79 -8234054.874 4976369.929 1.0 N \n",
"2 1.70 -8235614.190 4978829.564 1.0 N \n",
"3 0.50 -8234979.762 4981349.273 1.0 N \n",
"4 0.50 -8233639.566 4978136.520 1.0 Y \n",
"... ... ... ... ... ... \n",
"33527 1.40 -8237014.686 4974945.563 1.0 N \n",
"33528 1.20 -8235072.336 4976164.754 1.0 N \n",
"33529 2.10 -8236990.056 4975689.952 1.0 N \n",
"33530 3.91 -8231906.994 4980676.240 1.0 N \n",
"33531 3.94 -8234455.744 4977088.076 1.0 N \n",
"\n",
" dropoff_x dropoff_y payment_type fare_amount extra mta_tax \\\n",
"0 -8238124.721 4974404.684 1.0 14.5 0.5 0.5 \n",
"1 -8234631.549 4975038.048 2.0 4.5 1.0 0.5 \n",
"2 -8236789.621 4976089.636 1.0 11.0 0.0 0.5 \n",
"3 -8235343.263 4980382.363 2.0 4.5 0.0 0.5 \n",
"4 -8232541.421 4977596.585 2.0 5.0 0.0 0.5 \n",
"... ... ... ... ... ... ... \n",
"33527 -8236667.322 4972624.197 2.0 8.0 0.0 0.5 \n",
"33528 -8236776.032 4975426.495 2.0 9.0 0.0 0.5 \n",
"33529 -8234234.926 4976384.504 2.0 12.5 0.0 0.5 \n",
"33530 -8235613.340 4979914.647 1.0 20.0 0.0 0.5 \n",
"33531 -8234872.750 4970821.063 2.0 17.0 0.0 0.5 \n",
"\n",
" tip_amount tolls_amount total_amount pickup_ds \n",
"0 3.0 0.0 0.3 2015-01-15 21:02:31 \n",
"1 0.0 0.0 0.3 2015-01-15 19:51:19 \n",
"2 3.5 0.0 15.3 2015-02-15 16:46:10 \n",
"3 0.0 0.0 5.3 2015-03-15 16:48:48 \n",
"4 0.0 0.0 5.8 2015-03-15 10:47:54 \n",
"... ... ... ... ... \n",
"33527 0.0 0.0 0.3 2015-01-15 13:11:05 \n",
"33528 0.0 0.0 9.8 2015-03-15 15:19:30 \n",
"33529 0.0 0.0 13.3 2015-03-15 16:14:03 \n",
"33530 2.0 0.0 22.8 2015-03-15 10:58:35 \n",
"33531 0.0 0.0 0.3 2015-01-15 11:59:28 \n",
"\n",
"[33532 rows x 19 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" *\n",
" FROM\n",
" (\n",
" SELECT\n",
" *,\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" pickup_ds LIKE '%-15%'\n",
" AND pickup_ds LIKE '2015-%'\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"8. I want to see all the data from 4:00am - 10:00am for January 15, 2015."
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>VendorID</th>\n",
" <th>tpep_pickup_datetime</th>\n",
" <th>tpep_dropoff_datetime</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>pickup_x</th>\n",
" <th>pickup_y</th>\n",
" <th>RateCodeID</th>\n",
" <th>store_and_fwd_flag</th>\n",
" <th>dropoff_x</th>\n",
" <th>dropoff_y</th>\n",
" <th>payment_type</th>\n",
" <th>fare_amount</th>\n",
" <th>extra</th>\n",
" <th>mta_tax</th>\n",
" <th>tip_amount</th>\n",
" <th>tolls_amount</th>\n",
" <th>total_amount</th>\n",
" <th>pickup_ds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>2015-01-15 06:58:49</td>\n",
" <td>2015-01-15 07:13:10</td>\n",
" <td>1.0</td>\n",
" <td>3.20</td>\n",
" <td>-8235895.308</td>\n",
" <td>4972471.215</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234206.050</td>\n",
" <td>4978032.792</td>\n",
" <td>1.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>2.76</td>\n",
" <td>0.0</td>\n",
" <td>0.30</td>\n",
" <td>2015-01-15 06:58:49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>2015-03-15 09:36:53</td>\n",
" <td>2015-03-15 09:41:20</td>\n",
" <td>1.0</td>\n",
" <td>0.80</td>\n",
" <td>-8235876.623</td>\n",
" <td>4974535.277</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8235307.592</td>\n",
" <td>4975952.295</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>1.15</td>\n",
" <td>0.0</td>\n",
" <td>6.95</td>\n",
" <td>2015-03-15 09:36:53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-15 09:01:55</td>\n",
" <td>2015-02-15 09:10:03</td>\n",
" <td>5.0</td>\n",
" <td>1.72</td>\n",
" <td>-8237095.369</td>\n",
" <td>4976910.356</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234342.787</td>\n",
" <td>4974963.500</td>\n",
" <td>2.0</td>\n",
" <td>8.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>9.30</td>\n",
" <td>2015-02-15 09:01:55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-15 09:21:57</td>\n",
" <td>2015-01-15 09:35:10</td>\n",
" <td>1.0</td>\n",
" <td>1.76</td>\n",
" <td>-8235438.384</td>\n",
" <td>4977999.712</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8236012.511</td>\n",
" <td>4974879.423</td>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>2.00</td>\n",
" <td>0.0</td>\n",
" <td>0.30</td>\n",
" <td>2015-01-15 09:21:57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" <td>2015-01-15 04:06:06</td>\n",
" <td>2015-01-15 04:27:46</td>\n",
" <td>1.0</td>\n",
" <td>6.20</td>\n",
" <td>-8235668.545</td>\n",
" <td>4977524.821</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8233351.654</td>\n",
" <td>4970233.352</td>\n",
" <td>2.0</td>\n",
" <td>22.0</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.30</td>\n",
" <td>2015-01-15 04:06:06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4787</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-15 09:09:40</td>\n",
" <td>2015-01-15 09:28:28</td>\n",
" <td>6.0</td>\n",
" <td>1.55</td>\n",
" <td>-8236486.421</td>\n",
" <td>4977407.083</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234848.970</td>\n",
" <td>4975581.765</td>\n",
" <td>1.0</td>\n",
" <td>12.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>1.00</td>\n",
" <td>0.0</td>\n",
" <td>0.30</td>\n",
" <td>2015-01-15 09:09:40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4788</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-15 04:47:25</td>\n",
" <td>2015-02-15 04:55:05</td>\n",
" <td>2.0</td>\n",
" <td>1.62</td>\n",
" <td>-8232754.595</td>\n",
" <td>4984856.524</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8231020.324</td>\n",
" <td>4987293.605</td>\n",
" <td>2.0</td>\n",
" <td>7.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>8.80</td>\n",
" <td>2015-02-15 04:47:25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4789</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-15 07:40:27</td>\n",
" <td>2015-02-15 07:47:28</td>\n",
" <td>1.0</td>\n",
" <td>1.48</td>\n",
" <td>-8235532.657</td>\n",
" <td>4973696.822</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8237787.549</td>\n",
" <td>4973535.977</td>\n",
" <td>1.0</td>\n",
" <td>7.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>1.66</td>\n",
" <td>0.0</td>\n",
" <td>9.96</td>\n",
" <td>2015-02-15 07:40:27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4790</th>\n",
" <td>1.0</td>\n",
" <td>2015-01-15 06:16:10</td>\n",
" <td>2015-01-15 06:22:28</td>\n",
" <td>1.0</td>\n",
" <td>1.40</td>\n",
" <td>-8236668.171</td>\n",
" <td>4975564.949</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8235664.298</td>\n",
" <td>4977192.915</td>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.30</td>\n",
" <td>2015-01-15 06:16:10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4791</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-15 09:20:05</td>\n",
" <td>2015-01-15 09:50:34</td>\n",
" <td>5.0</td>\n",
" <td>7.23</td>\n",
" <td>-8233896.055</td>\n",
" <td>4976988.284</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8238613.918</td>\n",
" <td>4970943.764</td>\n",
" <td>1.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>2.00</td>\n",
" <td>0.0</td>\n",
" <td>0.30</td>\n",
" <td>2015-01-15 09:20:05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4792 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
"0 1.0 2015-01-15 06:58:49 2015-01-15 07:13:10 1.0 \n",
"1 1.0 2015-03-15 09:36:53 2015-03-15 09:41:20 1.0 \n",
"2 2.0 2015-02-15 09:01:55 2015-02-15 09:10:03 5.0 \n",
"3 2.0 2015-01-15 09:21:57 2015-01-15 09:35:10 1.0 \n",
"4 1.0 2015-01-15 04:06:06 2015-01-15 04:27:46 1.0 \n",
"... ... ... ... ... \n",
"4787 2.0 2015-01-15 09:09:40 2015-01-15 09:28:28 6.0 \n",
"4788 2.0 2015-02-15 04:47:25 2015-02-15 04:55:05 2.0 \n",
"4789 2.0 2015-02-15 07:40:27 2015-02-15 07:47:28 1.0 \n",
"4790 1.0 2015-01-15 06:16:10 2015-01-15 06:22:28 1.0 \n",
"4791 2.0 2015-01-15 09:20:05 2015-01-15 09:50:34 5.0 \n",
"\n",
" trip_distance pickup_x pickup_y RateCodeID store_and_fwd_flag \\\n",
"0 3.20 -8235895.308 4972471.215 1.0 N \n",
"1 0.80 -8235876.623 4974535.277 1.0 N \n",
"2 1.72 -8237095.369 4976910.356 1.0 N \n",
"3 1.76 -8235438.384 4977999.712 1.0 N \n",
"4 6.20 -8235668.545 4977524.821 1.0 N \n",
"... ... ... ... ... ... \n",
"4787 1.55 -8236486.421 4977407.083 1.0 N \n",
"4788 1.62 -8232754.595 4984856.524 1.0 N \n",
"4789 1.48 -8235532.657 4973696.822 1.0 N \n",
"4790 1.40 -8236668.171 4975564.949 1.0 N \n",
"4791 7.23 -8233896.055 4976988.284 1.0 N \n",
"\n",
" dropoff_x dropoff_y payment_type fare_amount extra mta_tax \\\n",
"0 -8234206.050 4978032.792 1.0 13.0 0.0 0.5 \n",
"1 -8235307.592 4975952.295 1.0 5.0 0.0 0.5 \n",
"2 -8234342.787 4974963.500 2.0 8.5 0.0 0.5 \n",
"3 -8236012.511 4974879.423 1.0 10.0 0.0 0.5 \n",
"4 -8233351.654 4970233.352 2.0 22.0 0.5 0.5 \n",
"... ... ... ... ... ... ... \n",
"4787 -8234848.970 4975581.765 1.0 12.5 0.0 0.5 \n",
"4788 -8231020.324 4987293.605 2.0 7.5 0.5 0.5 \n",
"4789 -8237787.549 4973535.977 1.0 7.5 0.0 0.5 \n",
"4790 -8235664.298 4977192.915 2.0 7.0 0.0 0.5 \n",
"4791 -8238613.918 4970943.764 1.0 28.0 0.0 0.5 \n",
"\n",
" tip_amount tolls_amount total_amount pickup_ds \n",
"0 2.76 0.0 0.30 2015-01-15 06:58:49 \n",
"1 1.15 0.0 6.95 2015-03-15 09:36:53 \n",
"2 0.00 0.0 9.30 2015-02-15 09:01:55 \n",
"3 2.00 0.0 0.30 2015-01-15 09:21:57 \n",
"4 0.00 0.0 0.30 2015-01-15 04:06:06 \n",
"... ... ... ... ... \n",
"4787 1.00 0.0 0.30 2015-01-15 09:09:40 \n",
"4788 0.00 0.0 8.80 2015-02-15 04:47:25 \n",
"4789 1.66 0.0 9.96 2015-02-15 07:40:27 \n",
"4790 0.00 0.0 0.30 2015-01-15 06:16:10 \n",
"4791 2.00 0.0 0.30 2015-01-15 09:20:05 \n",
"\n",
"[4792 rows x 19 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" *\n",
" FROM\n",
" (\n",
" SELECT\n",
" *,\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" pickup_ds LIKE '%-15%'\n",
" AND pickup_ds LIKE '2015-%'\n",
" AND HOUR(pickup_ds) < 10\n",
" AND HOUR(pickup_ds) >= 4\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"9. I want to see the average fare amount from 4:00am - 10:00am for January 15, 2015."
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>avg_fare_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10.264693</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_fare_amount\n",
"0 10.264693"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" AVG(fare_amount) AS avg_fare_amount\n",
" FROM\n",
" (\n",
" SELECT\n",
" *,\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" pickup_ds LIKE '%-15%'\n",
" AND pickup_ds LIKE '2015-%'\n",
" AND HOUR(pickup_ds) < 10\n",
" AND HOUR(pickup_ds) >= 4\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"10. I want to see the average, min, and max fare amounts from 4:00am - 10:00am for January 15, 2015."
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>avg_fare</th>\n",
" <th>max_fare</th>\n",
" <th>min_fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10.264693</td>\n",
" <td>55.0</td>\n",
" <td>-52.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_fare max_fare min_fare\n",
"0 10.264693 55.0 -52.0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" AVG(fare_amount) AS avg_fare,\n",
" MAX(fare_amount) AS max_fare,\n",
" MIN(fare_amount) AS min_fare\n",
" FROM\n",
" (\n",
" SELECT\n",
" *,\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" pickup_ds LIKE '%-15%'\n",
" AND pickup_ds LIKE '2015-%'\n",
" AND HOUR(pickup_ds) < 10\n",
" AND HOUR(pickup_ds) >= 4\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>VendorID</th>\n",
" <th>tpep_pickup_datetime</th>\n",
" <th>tpep_dropoff_datetime</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>pickup_x</th>\n",
" <th>pickup_y</th>\n",
" <th>RateCodeID</th>\n",
" <th>store_and_fwd_flag</th>\n",
" <th>dropoff_x</th>\n",
" <th>dropoff_y</th>\n",
" <th>payment_type</th>\n",
" <th>fare_amount</th>\n",
" <th>extra</th>\n",
" <th>mta_tax</th>\n",
" <th>tip_amount</th>\n",
" <th>tolls_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-25 16:30:34</td>\n",
" <td>2015-02-25 16:36:50</td>\n",
" <td>3.0</td>\n",
" <td>0.65</td>\n",
" <td>-8235271.922</td>\n",
" <td>4981072.202</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8235584.464</td>\n",
" <td>4979976.896</td>\n",
" <td>4.0</td>\n",
" <td>-6.0</td>\n",
" <td>-1.0</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-07 19:13:20</td>\n",
" <td>2015-02-07 19:14:03</td>\n",
" <td>5.0</td>\n",
" <td>0.05</td>\n",
" <td>-8237927.684</td>\n",
" <td>4973999.464</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8237818.124</td>\n",
" <td>4974169.845</td>\n",
" <td>3.0</td>\n",
" <td>-2.5</td>\n",
" <td>0.0</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-24 21:41:39</td>\n",
" <td>2015-01-24 21:42:12</td>\n",
" <td>1.0</td>\n",
" <td>0.02</td>\n",
" <td>-8238530.687</td>\n",
" <td>4974109.875</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8238523.043</td>\n",
" <td>4974082.412</td>\n",
" <td>4.0</td>\n",
" <td>-2.5</td>\n",
" <td>-0.5</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-18 01:33:51</td>\n",
" <td>2015-01-18 01:35:38</td>\n",
" <td>1.0</td>\n",
" <td>0.12</td>\n",
" <td>-8236496.613</td>\n",
" <td>4975864.286</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8236615.515</td>\n",
" <td>4975647.350</td>\n",
" <td>4.0</td>\n",
" <td>-3.0</td>\n",
" <td>-0.5</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-15 00:18:49</td>\n",
" <td>2015-02-15 00:19:59</td>\n",
" <td>1.0</td>\n",
" <td>0.12</td>\n",
" <td>-8239454.726</td>\n",
" <td>4970447.927</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8239463.219</td>\n",
" <td>4970392.462</td>\n",
" <td>4.0</td>\n",
" <td>-3.0</td>\n",
" <td>-0.5</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-4.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>2.0</td>\n",
" <td>2015-02-07 02:59:02</td>\n",
" <td>2015-02-07 03:01:16</td>\n",
" <td>2.0</td>\n",
" <td>0.37</td>\n",
" <td>-8238574.850</td>\n",
" <td>4971271.535</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8238548.522</td>\n",
" <td>4971512.468</td>\n",
" <td>4.0</td>\n",
" <td>-3.5</td>\n",
" <td>-0.5</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>2.0</td>\n",
" <td>2015-03-31 13:49:09</td>\n",
" <td>2015-03-31 13:51:01</td>\n",
" <td>1.0</td>\n",
" <td>0.90</td>\n",
" <td>-8220471.165</td>\n",
" <td>4974044.301</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8219644.796</td>\n",
" <td>4974519.583</td>\n",
" <td>4.0</td>\n",
" <td>-4.5</td>\n",
" <td>0.0</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-5.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-16 14:59:09</td>\n",
" <td>2015-01-16 15:05:06</td>\n",
" <td>1.0</td>\n",
" <td>0.98</td>\n",
" <td>-8234195.009</td>\n",
" <td>4976876.158</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8232722.322</td>\n",
" <td>4978185.862</td>\n",
" <td>4.0</td>\n",
" <td>-6.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>2.0</td>\n",
" <td>2015-03-20 05:21:46</td>\n",
" <td>2015-03-20 05:23:20</td>\n",
" <td>1.0</td>\n",
" <td>0.59</td>\n",
" <td>-8235522.465</td>\n",
" <td>4976896.901</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8234658.727</td>\n",
" <td>4977236.646</td>\n",
" <td>3.0</td>\n",
" <td>-3.5</td>\n",
" <td>-0.5</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>2.0</td>\n",
" <td>2015-01-14 16:46:29</td>\n",
" <td>2015-01-14 16:47:37</td>\n",
" <td>1.0</td>\n",
" <td>0.00</td>\n",
" <td>-8237351.858</td>\n",
" <td>4975138.381</td>\n",
" <td>2.0</td>\n",
" <td>N</td>\n",
" <td>-8237351.858</td>\n",
" <td>4975138.381</td>\n",
" <td>3.0</td>\n",
" <td>-52.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>273 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
"0 2.0 2015-02-25 16:30:34 2015-02-25 16:36:50 3.0 \n",
"1 2.0 2015-02-07 19:13:20 2015-02-07 19:14:03 5.0 \n",
"2 2.0 2015-01-24 21:41:39 2015-01-24 21:42:12 1.0 \n",
"3 2.0 2015-01-18 01:33:51 2015-01-18 01:35:38 1.0 \n",
"4 2.0 2015-02-15 00:18:49 2015-02-15 00:19:59 1.0 \n",
".. ... ... ... ... \n",
"268 2.0 2015-02-07 02:59:02 2015-02-07 03:01:16 2.0 \n",
"269 2.0 2015-03-31 13:49:09 2015-03-31 13:51:01 1.0 \n",
"270 2.0 2015-01-16 14:59:09 2015-01-16 15:05:06 1.0 \n",
"271 2.0 2015-03-20 05:21:46 2015-03-20 05:23:20 1.0 \n",
"272 2.0 2015-01-14 16:46:29 2015-01-14 16:47:37 1.0 \n",
"\n",
" trip_distance pickup_x pickup_y RateCodeID store_and_fwd_flag \\\n",
"0 0.65 -8235271.922 4981072.202 1.0 N \n",
"1 0.05 -8237927.684 4973999.464 1.0 N \n",
"2 0.02 -8238530.687 4974109.875 1.0 N \n",
"3 0.12 -8236496.613 4975864.286 1.0 N \n",
"4 0.12 -8239454.726 4970447.927 1.0 N \n",
".. ... ... ... ... ... \n",
"268 0.37 -8238574.850 4971271.535 1.0 N \n",
"269 0.90 -8220471.165 4974044.301 1.0 N \n",
"270 0.98 -8234195.009 4976876.158 1.0 N \n",
"271 0.59 -8235522.465 4976896.901 1.0 N \n",
"272 0.00 -8237351.858 4975138.381 2.0 N \n",
"\n",
" dropoff_x dropoff_y payment_type fare_amount extra mta_tax \\\n",
"0 -8235584.464 4979976.896 4.0 -6.0 -1.0 -0.5 \n",
"1 -8237818.124 4974169.845 3.0 -2.5 0.0 -0.5 \n",
"2 -8238523.043 4974082.412 4.0 -2.5 -0.5 -0.5 \n",
"3 -8236615.515 4975647.350 4.0 -3.0 -0.5 -0.5 \n",
"4 -8239463.219 4970392.462 4.0 -3.0 -0.5 -0.5 \n",
".. ... ... ... ... ... ... \n",
"268 -8238548.522 4971512.468 4.0 -3.5 -0.5 -0.5 \n",
"269 -8219644.796 4974519.583 4.0 -4.5 0.0 -0.5 \n",
"270 -8232722.322 4978185.862 4.0 -6.0 0.0 -0.5 \n",
"271 -8234658.727 4977236.646 3.0 -3.5 -0.5 -0.5 \n",
"272 -8237351.858 4975138.381 3.0 -52.0 0.0 -0.5 \n",
"\n",
" tip_amount tolls_amount total_amount \n",
"0 0.0 0.0 -7.8 \n",
"1 0.0 0.0 -3.3 \n",
"2 0.0 0.0 0.3 \n",
"3 0.0 0.0 0.3 \n",
"4 0.0 0.0 -4.3 \n",
".. ... ... ... \n",
"268 0.0 0.0 -4.8 \n",
"269 0.0 0.0 -5.3 \n",
"270 0.0 0.0 0.3 \n",
"271 0.0 0.0 -4.8 \n",
"272 0.0 0.0 0.3 \n",
"\n",
"[273 rows x 18 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" *\n",
" FROM\n",
" taxi\n",
" WHERE\n",
" fare_amount < 0\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"11. I want to see the average, min, and max fare amounts and trip distances from 4:00am - 10:00am for January 15, 2015."
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>avg_fare</th>\n",
" <th>max_fare</th>\n",
" <th>min_fare</th>\n",
" <th>avg_trip_distance</th>\n",
" <th>max_trip_distance</th>\n",
" <th>min_trip_distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10.264693</td>\n",
" <td>55.0</td>\n",
" <td>-52.0</td>\n",
" <td>2.309875</td>\n",
" <td>18.29</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_fare max_fare min_fare avg_trip_distance max_trip_distance \\\n",
"0 10.264693 55.0 -52.0 2.309875 18.29 \n",
"\n",
" min_trip_distance \n",
"0 0.0 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" AVG(fare_amount) AS avg_fare,\n",
" MAX(fare_amount) AS max_fare,\n",
" MIN(fare_amount) AS min_fare,\n",
" \n",
" AVG(trip_distance) AS avg_trip_distance,\n",
" MAX(trip_distance) AS max_trip_distance,\n",
" MIN(trip_distance) AS min_trip_distance\n",
" FROM\n",
" (\n",
" SELECT\n",
" *,\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" pickup_ds LIKE '%-15%'\n",
" AND pickup_ds LIKE '2015-%'\n",
" AND HOUR(pickup_ds) < 10\n",
" AND HOUR(pickup_ds) >= 4\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"12. I want to see the differences in hours, minutes, and seconds from the start of each ride to the end of that ride."
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>pickup_date</th>\n",
" <th>hours_diff</th>\n",
" <th>minutes_diff</th>\n",
" <th>seconds_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-3-5</td>\n",
" <td>0</td>\n",
" <td>-6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-2-3</td>\n",
" <td>0</td>\n",
" <td>-5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-2-8</td>\n",
" <td>0</td>\n",
" <td>-20</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-3-7</td>\n",
" <td>0</td>\n",
" <td>-5</td>\n",
" <td>-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-2-5</td>\n",
" <td>0</td>\n",
" <td>-5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999995</th>\n",
" <td>2015-2-23</td>\n",
" <td>0</td>\n",
" <td>-3</td>\n",
" <td>-8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999996</th>\n",
" <td>2015-1-22</td>\n",
" <td>0</td>\n",
" <td>-3</td>\n",
" <td>-48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999997</th>\n",
" <td>2015-2-5</td>\n",
" <td>0</td>\n",
" <td>-19</td>\n",
" <td>-38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999998</th>\n",
" <td>2015-1-27</td>\n",
" <td>0</td>\n",
" <td>-11</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999999</th>\n",
" <td>2015-3-1</td>\n",
" <td>0</td>\n",
" <td>-5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000000 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" pickup_date hours_diff minutes_diff seconds_diff\n",
"0 2015-3-5 0 -6 30\n",
"1 2015-2-3 0 -5 1\n",
"2 2015-2-8 0 -20 6\n",
"3 2015-3-7 0 -5 -13\n",
"4 2015-2-5 0 -5 2\n",
"... ... ... ... ...\n",
"999995 2015-2-23 0 -3 -8\n",
"999996 2015-1-22 0 -3 -48\n",
"999997 2015-2-5 0 -19 -38\n",
"999998 2015-1-27 0 -11 30\n",
"999999 2015-3-1 0 -5 0\n",
"\n",
"[1000000 rows x 4 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" YEAR(pickup_ds) || '-' || MONTH(pickup_ds) || '-' || DAYOFMONTH(pickup_ds) AS pickup_date,\n",
" HOUR(pickup_ds) - HOUR(dropoff_ds) AS hours_diff,\n",
" MINUTE(pickup_ds) - MINUTE(dropoff_ds) AS minutes_diff,\n",
" SECOND(pickup_ds) - SECOND(dropoff_ds) AS seconds_diff\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds,\n",
" CAST(tpep_dropoff_datetime AS timestamp) AS dropoff_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" YEAR(pickup_ds) = 2015\n",
" AND MONTH(pickup_ds) <= 3\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"13. I want to see what the average hours, minutes, and seconds of a ride are."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>avg_hours_diff</th>\n",
" <th>avg_minutes_diff</th>\n",
" <th>avg_seconds_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_hours_diff avg_minutes_diff avg_seconds_diff\n",
"0 0 0 0"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" AVG(hours_diff) AS avg_hours_diff,\n",
" AVG(minutes_diff) AS avg_minutes_diff,\n",
" AVG(seconds_diff) AS avg_seconds_diff\n",
" FROM\n",
" (\n",
" SELECT\n",
" YEAR(pickup_ds) || '-' || MONTH(pickup_ds) || '-' || DAYOFMONTH(pickup_ds) AS pickup_date,\n",
" HOUR(pickup_ds) - HOUR(dropoff_ds) AS hours_diff,\n",
" MINUTE(pickup_ds) - MINUTE(dropoff_ds) AS minutes_diff,\n",
" SECOND(pickup_ds) - SECOND(dropoff_ds) AS seconds_diff\n",
"\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds,\n",
" CAST(tpep_dropoff_datetime AS timestamp) AS dropoff_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" YEAR(pickup_ds) = 2015\n",
" AND MONTH(pickup_ds) <= 3\n",
" )\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"14. I want to see what the average hours, minutes, and seconds of a ride are by day."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"\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>pickup_date</th>\n",
" <th>avg_hours_diff</th>\n",
" <th>avg_minutes_diff</th>\n",
" <th>avg_seconds_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-1-16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-3-9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-2-25</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-3-16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-2-13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>2015-3-5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2015-1-29</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2015-3-2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2015-1-30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2015-3-28</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" pickup_date avg_hours_diff avg_minutes_diff avg_seconds_diff\n",
"0 2015-1-16 0 0 0\n",
"1 2015-3-9 0 0 0\n",
"2 2015-2-25 0 0 0\n",
"3 2015-3-16 0 0 0\n",
"4 2015-2-13 0 0 0\n",
".. ... ... ... ...\n",
"85 2015-3-5 0 0 0\n",
"86 2015-1-29 0 0 0\n",
"87 2015-3-2 0 0 0\n",
"88 2015-1-30 0 0 0\n",
"89 2015-3-28 0 0 0\n",
"\n",
"[90 rows x 4 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" pickup_date,\n",
" AVG(hours_diff) AS avg_hours_diff,\n",
" AVG(minutes_diff) AS avg_minutes_diff,\n",
" AVG(seconds_diff) AS avg_seconds_diff\n",
" FROM\n",
" (\n",
" SELECT\n",
" YEAR(pickup_ds) || '-' || MONTH(pickup_ds) || '-' || DAYOFMONTH(pickup_ds) AS pickup_date,\n",
" HOUR(pickup_ds) - HOUR(dropoff_ds) AS hours_diff,\n",
" MINUTE(pickup_ds) - MINUTE(dropoff_ds) AS minutes_diff,\n",
" SECOND(pickup_ds) - SECOND(dropoff_ds) AS seconds_diff\n",
"\n",
" FROM\n",
" (\n",
" SELECT\n",
" CAST(tpep_pickup_datetime AS timestamp) AS pickup_ds,\n",
" CAST(tpep_dropoff_datetime AS timestamp) AS dropoff_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" WHERE \n",
" YEAR(pickup_ds) = 2015\n",
" AND MONTH(pickup_ds) <= 3\n",
" )\n",
" GROUP BY\n",
" pickup_date\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"15. I want to see the different tip amounts based on how long the trip's distance is, and am going to cluster them by the nearest mile (round trip distances to nearest int)."
]
},
{
"cell_type": "code",
"execution_count": 44,
"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>trip_distance_int</th>\n",
" <th>tip_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999995</th>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999996</th>\n",
" <td>1</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999997</th>\n",
" <td>5</td>\n",
" <td>5.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999998</th>\n",
" <td>1</td>\n",
" <td>2.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999999</th>\n",
" <td>1</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000000 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" trip_distance_int tip_amount\n",
"0 1 2.00\n",
"1 0 1.36\n",
"2 3 0.00\n",
"3 0 0.00\n",
"4 0 1.00\n",
"... ... ...\n",
"999995 0 0.00\n",
"999996 1 0.00\n",
"999997 5 5.32\n",
"999998 1 2.26\n",
"999999 1 1.00\n",
"\n",
"[1000000 rows x 2 columns]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT \n",
" cast(trip_distance as int) trip_distance_int, tip_amount \n",
" FROM \n",
" taxi\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"16. I want to see the different tip amounts (up to $40) based on how long the trip's distance is (up to 20 miles), and am going to cluster them by the nearest mile (round trip distances to nearest int)."
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>trip_distance_int</th>\n",
" <th>tip_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999834</th>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999835</th>\n",
" <td>1</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999836</th>\n",
" <td>5</td>\n",
" <td>5.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999837</th>\n",
" <td>1</td>\n",
" <td>2.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999838</th>\n",
" <td>1</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>999839 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" trip_distance_int tip_amount\n",
"0 1 2.00\n",
"1 0 1.36\n",
"2 3 0.00\n",
"3 0 0.00\n",
"4 0 1.00\n",
"... ... ...\n",
"999834 0 0.00\n",
"999835 1 0.00\n",
"999836 5 5.32\n",
"999837 1 2.26\n",
"999838 1 1.00\n",
"\n",
"[999839 rows x 2 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT \n",
" cast(trip_distance as int) trip_distance_int, tip_amount \n",
" FROM \n",
" taxi\n",
" WHERE\n",
" trip_distance <= 20\n",
" AND tip_amount BETWEEN 0 AND 40\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"17. I want to see the average tip amounts based on how long the trip's distance is, and am going to cluster them by the nearest mile (round trip distances to nearest int)."
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>trip_distance_int</th>\n",
" <th>avg_tip_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7</td>\n",
" <td>3.343613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>1.913886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>2.628681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20</td>\n",
" <td>11.620000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18</td>\n",
" <td>5.892222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>15</td>\n",
" <td>7.115785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>10</td>\n",
" <td>5.482508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>13</td>\n",
" <td>6.399573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>0.728329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>19</td>\n",
" <td>5.976190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>12</td>\n",
" <td>6.079384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>16</td>\n",
" <td>7.807522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2</td>\n",
" <td>1.520703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>11</td>\n",
" <td>5.877420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>6</td>\n",
" <td>2.986567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>17</td>\n",
" <td>6.759189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>4</td>\n",
" <td>2.299710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>9</td>\n",
" <td>5.058977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1</td>\n",
" <td>1.087722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>8</td>\n",
" <td>4.134524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>14</td>\n",
" <td>6.764825</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" trip_distance_int avg_tip_amount\n",
"0 7 3.343613\n",
"1 3 1.913886\n",
"2 5 2.628681\n",
"3 20 11.620000\n",
"4 18 5.892222\n",
"5 15 7.115785\n",
"6 10 5.482508\n",
"7 13 6.399573\n",
"8 0 0.728329\n",
"9 19 5.976190\n",
"10 12 6.079384\n",
"11 16 7.807522\n",
"12 2 1.520703\n",
"13 11 5.877420\n",
"14 6 2.986567\n",
"15 17 6.759189\n",
"16 4 2.299710\n",
"17 9 5.058977\n",
"18 1 1.087722\n",
"19 8 4.134524\n",
"20 14 6.764825"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT \n",
" cast(trip_distance as int) trip_distance_int, AVG(tip_amount) AS avg_tip_amount\n",
" FROM \n",
" taxi\n",
" WHERE\n",
" trip_distance <= 20\n",
" AND tip_amount BETWEEN 0 AND 40\n",
" GROUP BY\n",
" cast(trip_distance as int)\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"18. I want to see the average fare amount (cost of a trip) by passenger count."
]
},
{
"cell_type": "code",
"execution_count": 48,
"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>passenger_count</th>\n",
" <th>average_fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7.0</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6.0</td>\n",
" <td>10.414002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.0</td>\n",
" <td>10.487478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.0</td>\n",
" <td>10.702679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>10.562942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.0</td>\n",
" <td>10.648845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.0</td>\n",
" <td>10.374170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.0</td>\n",
" <td>10.451082</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" passenger_count average_fare\n",
"0 7.0 8.000000\n",
"1 6.0 10.414002\n",
"2 5.0 10.487478\n",
"3 4.0 10.702679\n",
"4 3.0 10.562942\n",
"5 2.0 10.648845\n",
"6 1.0 10.374170\n",
"7 0.0 10.451082"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" passenger_count, AVG(fare_amount) AS average_fare\n",
" FROM\n",
" taxi\n",
" GROUP BY passenger_count\n",
" ORDER BY passenger_count DESC\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"19. I want to see the trips more than 6 passengers."
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>VendorID</th>\n",
" <th>tpep_pickup_datetime</th>\n",
" <th>tpep_dropoff_datetime</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>pickup_x</th>\n",
" <th>pickup_y</th>\n",
" <th>RateCodeID</th>\n",
" <th>store_and_fwd_flag</th>\n",
" <th>dropoff_x</th>\n",
" <th>dropoff_y</th>\n",
" <th>payment_type</th>\n",
" <th>fare_amount</th>\n",
" <th>extra</th>\n",
" <th>mta_tax</th>\n",
" <th>tip_amount</th>\n",
" <th>tolls_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>2015-03-09 18:12:28</td>\n",
" <td>2015-03-09 18:21:00</td>\n",
" <td>7.0</td>\n",
" <td>1.6</td>\n",
" <td>-8238754.902</td>\n",
" <td>4971225.03</td>\n",
" <td>1.0</td>\n",
" <td>N</td>\n",
" <td>-8237894.561</td>\n",
" <td>4973638.536</td>\n",
" <td>1.0</td>\n",
" <td>8.0</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>1.95</td>\n",
" <td>0.0</td>\n",
" <td>11.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
"0 1.0 2015-03-09 18:12:28 2015-03-09 18:21:00 7.0 \n",
"\n",
" trip_distance pickup_x pickup_y RateCodeID store_and_fwd_flag \\\n",
"0 1.6 -8238754.902 4971225.03 1.0 N \n",
"\n",
" dropoff_x dropoff_y payment_type fare_amount extra mta_tax \\\n",
"0 -8237894.561 4973638.536 1.0 8.0 1.0 0.5 \n",
"\n",
" tip_amount tolls_amount total_amount \n",
"0 1.95 0.0 11.75 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" *\n",
" FROM\n",
" taxi\n",
" WHERE\n",
" passenger_count > 6\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"20. How many unique trip distances are there?"
]
},
{
"cell_type": "code",
"execution_count": 50,
"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>n_unique_distances</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1863</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_unique_distances\n",
"0 1863"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" COUNT(DISTINCT trip_distance) AS n_unique_distances\n",
" FROM\n",
" taxi\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"21. I want to see the unique trip distances."
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>unique_distances</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1858</th>\n",
" <td>202.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1859</th>\n",
" <td>293.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1860</th>\n",
" <td>803.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1861</th>\n",
" <td>40000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1862</th>\n",
" <td>6420001.60</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1863 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" unique_distances\n",
"0 0.00\n",
"1 0.01\n",
"2 0.02\n",
"3 0.03\n",
"4 0.04\n",
"... ...\n",
"1858 202.00\n",
"1859 293.68\n",
"1860 803.80\n",
"1861 40000.00\n",
"1862 6420001.60\n",
"\n",
"[1863 rows x 1 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" DISTINCT trip_distance AS unique_distances\n",
" FROM\n",
" taxi\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"22. What are the overall max, min, and average trip distances? "
]
},
{
"cell_type": "code",
"execution_count": 52,
"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>avg_distance</th>\n",
" <th>max_distance</th>\n",
" <th>min_distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.635469</td>\n",
" <td>6420001.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_distance max_distance min_distance\n",
"0 8.635469 6420001.6 0.0"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" AVG(trip_distance) AS avg_distance,\n",
" MAX(trip_distance) AS max_distance,\n",
" MIN(trip_distance) AS min_distance\n",
" FROM\n",
" taxi\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize 3+ Queries"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Fare Amount by Passenger Count'}, ylabel='passenger_count'>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" passenger_count, AVG(fare_amount) AS average_fare\n",
" FROM\n",
" taxi\n",
" GROUP BY passenger_count\n",
" ORDER BY passenger_count DESC\n",
" '''\n",
"bc.sql(query).to_pandas().plot(kind='barh', x='passenger_count', y='average_fare', title='Fare Amount by Passenger Count', figsize=(16, 4))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='trip_distance_int', ylabel='tip_amount'>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"query = '''\n",
" SELECT \n",
" cast(trip_distance as int) trip_distance_int, tip_amount \n",
" FROM \n",
" taxi\n",
" WHERE\n",
" trip_distance <= 20\n",
" AND tip_amount BETWEEN 0 AND 40\n",
" '''\n",
"bc.sql(query).to_pandas().plot(kind='scatter', x='trip_distance_int', y='tip_amount', figsize=(12, 8))"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['VendorID', 'tpep_pickup_datetime', 'tpep_dropoff_datetime',\n",
" 'passenger_count', 'trip_distance', 'pickup_x', 'pickup_y',\n",
" 'RateCodeID', 'store_and_fwd_flag', 'dropoff_x', 'dropoff_y',\n",
" 'payment_type', 'fare_amount', 'extra', 'mta_tax', 'tip_amount',\n",
" 'tolls_amount', 'total_amount'],\n",
" dtype='object')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bc.sql('select * from taxi').columns"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 63,
"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": [
"# REVISIT THIS\n",
"query = '''\n",
" SELECT\n",
" VendorID, payment_type, COUNT(*)\n",
" FROM\n",
" taxi\n",
" GROUP BY \n",
" VendorID, payment_type\n",
" '''\n",
"bc.sql(query).to_pandas().plot(kind='bar', stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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'/>"
],
"text/plain": [
"<xarray.Image (dropoff_y: 600, dropoff_x: 600)>\n",
"array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
" 4278190080],\n",
" [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
" 4278190080],\n",
" [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
" 4278190080],\n",
" ...,\n",
" [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
" 4278190080],\n",
" [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
" 4278190080],\n",
" [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
" 4278190080]], dtype=uint32)\n",
"Coordinates:\n",
" * dropoff_x (dropoff_x) float64 -8.254e+06 -8.254e+06 ... -8.21e+06 -8.21e+06\n",
" * dropoff_y (dropoff_y) float64 4.965e+06 4.965e+06 ... 4.989e+06 4.989e+06"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from colorcet import fire\n",
"from datashader import Canvas, transfer_functions as tf\n",
"\n",
"agg = Canvas().points(bc.sql('SELECT dropoff_x, dropoff_y FROM taxi'), 'dropoff_x', 'dropoff_y')\n",
"\n",
"tf.set_background(tf.shade(agg, cmap=fire), \"black\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Write 2+ Queries to the Bucket"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"query = '''\n",
" SELECT\n",
" y || '-' || m || '-' || dom AS ds,\n",
" AVG(passenger_count) as avg_passenger_count,\n",
" MAX(passenger_count) as max_passenger_count,\n",
" MIN(passenger_count) as min_passenger_count\n",
" FROM\n",
" (\n",
" SELECT\n",
" passenger_count,\n",
" YEAR(pickup_ds) AS y,\n",
" MONTH(pickup_ds) AS m,\n",
" DAYOFMONTH(pickup_ds) AS dom\n",
" FROM\n",
" (\n",
" SELECT\n",
" passenger_count,\n",
" CAST(tpep_pickup_datetime AS TIMESTAMP) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" )\n",
" GROUP BY\n",
" y, m, dom\n",
" '''\n",
"bc.sql(query).to_csv('q1.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"query = '''\n",
" SELECT\n",
" y || '-' || m || '-' || dom AS ds,\n",
" \n",
" AVG(tip_amount) AS avg_fare_amount,\n",
" MAX(tip_amount) AS max_fare_amount,\n",
" MIN(tip_amount) AS min_fare_count,\n",
" \n",
" AVG(tip_amount) AS avg_tip_amount,\n",
" MAX(tip_amount) AS max_tip_amount,\n",
" MIN(tip_amount) AS min_tip_amount,\n",
" \n",
" AVG(trip_distance) AS avg_trip_distance,\n",
" MAX(trip_distance) AS max_trip_distance,\n",
" MIN(trip_distance) AS min_trip_distance\n",
" FROM\n",
" (\n",
" SELECT\n",
" fare_amount,\n",
" tip_amount,\n",
" trip_distance,\n",
" YEAR(pickup_ds) AS y,\n",
" MONTH(pickup_ds) AS m,\n",
" DAYOFMONTH(pickup_ds) AS dom\n",
" FROM\n",
" (\n",
" SELECT\n",
" *,\n",
" CAST(tpep_pickup_datetime AS TIMESTAMP) AS pickup_ds\n",
" FROM\n",
" taxi\n",
" )\n",
" )\n",
" GROUP BY\n",
" y, m, dom\n",
" '''\n",
"bc.sql(query).to_csv('q2.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create New Tables from the Files You just Wrote"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"bc.create_table('passenger_stats', 'q1.csv')"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"bc.create_table('fare_stats', 'q2.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Query those Tables (5+ Queries)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many rows are in the passenger_stats table?"
]
},
{
"cell_type": "code",
"execution_count": 69,
"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>count(*)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>91</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count(*)\n",
"0 91"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" COUNT(*)\n",
" FROM\n",
" passenger_stats\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many unique values are in the fare_stats table's max_fare_amount column?"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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>count(max_fare_amount)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count(max_fare_amount)\n",
"0 59"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" COUNT(DISTINCT max_fare_amount)\n",
" FROM\n",
" fare_stats\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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>ds</th>\n",
" <th>avg_passenger_count</th>\n",
" <th>avg_fare_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-2-14</td>\n",
" <td>1.761718</td>\n",
" <td>1.361413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-3-31</td>\n",
" <td>1.649613</td>\n",
" <td>1.523232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-1-20</td>\n",
" <td>1.620054</td>\n",
" <td>1.281566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-1-15</td>\n",
" <td>1.658889</td>\n",
" <td>1.320653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-3-23</td>\n",
" <td>1.660204</td>\n",
" <td>1.492662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2015-3-11</td>\n",
" <td>1.638266</td>\n",
" <td>1.651525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2015-2-25</td>\n",
" <td>1.628918</td>\n",
" <td>1.638296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2015-3-15</td>\n",
" <td>1.713193</td>\n",
" <td>1.394633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2015-2-4</td>\n",
" <td>1.634705</td>\n",
" <td>1.629997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2015-1-27</td>\n",
" <td>1.704728</td>\n",
" <td>1.129892</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>91 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" ds avg_passenger_count avg_fare_amount\n",
"0 2015-2-14 1.761718 1.361413\n",
"1 2015-3-31 1.649613 1.523232\n",
"2 2015-1-20 1.620054 1.281566\n",
"3 2015-1-15 1.658889 1.320653\n",
"4 2015-3-23 1.660204 1.492662\n",
".. ... ... ...\n",
"86 2015-3-11 1.638266 1.651525\n",
"87 2015-2-25 1.628918 1.638296\n",
"88 2015-3-15 1.713193 1.394633\n",
"89 2015-2-4 1.634705 1.629997\n",
"90 2015-1-27 1.704728 1.129892\n",
"\n",
"[91 rows x 3 columns]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" p.ds,\n",
" p.avg_passenger_count,\n",
" f.avg_fare_amount\n",
" FROM\n",
" passenger_stats AS p\n",
" JOIN\n",
" fare_stats AS f\n",
" ON p.ds = f.ds\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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>ds</th>\n",
" <th>avg_trip_distance</th>\n",
" <th>avg_passenger_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-2-14</td>\n",
" <td>2.326926</td>\n",
" <td>1.761718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-3-31</td>\n",
" <td>2.354940</td>\n",
" <td>1.649613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-1-20</td>\n",
" <td>1.870509</td>\n",
" <td>1.620054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-1-15</td>\n",
" <td>1.883465</td>\n",
" <td>1.658889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-3-23</td>\n",
" <td>2.387445</td>\n",
" <td>1.660204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2015-3-11</td>\n",
" <td>607.511626</td>\n",
" <td>1.638266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2015-2-25</td>\n",
" <td>2.356953</td>\n",
" <td>1.628918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2015-3-15</td>\n",
" <td>2.541118</td>\n",
" <td>1.713193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2015-2-4</td>\n",
" <td>2.297154</td>\n",
" <td>1.634705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2015-1-27</td>\n",
" <td>1.782684</td>\n",
" <td>1.704728</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>91 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" ds avg_trip_distance avg_passenger_count\n",
"0 2015-2-14 2.326926 1.761718\n",
"1 2015-3-31 2.354940 1.649613\n",
"2 2015-1-20 1.870509 1.620054\n",
"3 2015-1-15 1.883465 1.658889\n",
"4 2015-3-23 2.387445 1.660204\n",
".. ... ... ...\n",
"86 2015-3-11 607.511626 1.638266\n",
"87 2015-2-25 2.356953 1.628918\n",
"88 2015-3-15 2.541118 1.713193\n",
"89 2015-2-4 2.297154 1.634705\n",
"90 2015-1-27 1.782684 1.704728\n",
"\n",
"[91 rows x 3 columns]"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" p.ds,\n",
" f.avg_trip_distance,\n",
" p.avg_passenger_count\n",
" FROM\n",
" passenger_stats AS p\n",
" FULL JOIN\n",
" fare_stats AS f\n",
" ON p.ds = f.ds\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"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>ds</th>\n",
" <th>avg_passenger_count</th>\n",
" <th>max_passenger_count</th>\n",
" <th>min_passenger_count</th>\n",
" <th>ds0</th>\n",
" <th>avg_fare_amount</th>\n",
" <th>max_fare_amount</th>\n",
" <th>min_fare_count</th>\n",
" <th>avg_tip_amount</th>\n",
" <th>max_tip_amount</th>\n",
" <th>min_tip_amount</th>\n",
" <th>avg_trip_distance</th>\n",
" <th>max_trip_distance</th>\n",
" <th>min_trip_distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-2-14</td>\n",
" <td>1.761718</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-2-14</td>\n",
" <td>1.361413</td>\n",
" <td>26.00</td>\n",
" <td>0.0</td>\n",
" <td>1.361413</td>\n",
" <td>26.00</td>\n",
" <td>0.0</td>\n",
" <td>2.326926</td>\n",
" <td>202.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-3-31</td>\n",
" <td>1.649613</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-3-31</td>\n",
" <td>1.523232</td>\n",
" <td>17.50</td>\n",
" <td>0.0</td>\n",
" <td>1.523232</td>\n",
" <td>17.50</td>\n",
" <td>0.0</td>\n",
" <td>2.354940</td>\n",
" <td>803.80</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-1-20</td>\n",
" <td>1.620054</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-1-20</td>\n",
" <td>1.281566</td>\n",
" <td>22.00</td>\n",
" <td>0.0</td>\n",
" <td>1.281566</td>\n",
" <td>22.00</td>\n",
" <td>0.0</td>\n",
" <td>1.870509</td>\n",
" <td>17.14</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-1-15</td>\n",
" <td>1.658889</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-1-15</td>\n",
" <td>1.320653</td>\n",
" <td>100.00</td>\n",
" <td>0.0</td>\n",
" <td>1.320653</td>\n",
" <td>100.00</td>\n",
" <td>0.0</td>\n",
" <td>1.883465</td>\n",
" <td>14.30</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-3-23</td>\n",
" <td>1.660204</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-3-23</td>\n",
" <td>1.492662</td>\n",
" <td>53.00</td>\n",
" <td>0.0</td>\n",
" <td>1.492662</td>\n",
" <td>53.00</td>\n",
" <td>0.0</td>\n",
" <td>2.387445</td>\n",
" <td>30.40</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2015-3-11</td>\n",
" <td>1.638266</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-3-11</td>\n",
" <td>1.651525</td>\n",
" <td>69.00</td>\n",
" <td>0.0</td>\n",
" <td>1.651525</td>\n",
" <td>69.00</td>\n",
" <td>0.0</td>\n",
" <td>607.511626</td>\n",
" <td>6420001.60</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2015-2-25</td>\n",
" <td>1.628918</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-2-25</td>\n",
" <td>1.638296</td>\n",
" <td>55.55</td>\n",
" <td>0.0</td>\n",
" <td>1.638296</td>\n",
" <td>55.55</td>\n",
" <td>0.0</td>\n",
" <td>2.356953</td>\n",
" <td>21.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2015-3-15</td>\n",
" <td>1.713193</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-3-15</td>\n",
" <td>1.394633</td>\n",
" <td>30.00</td>\n",
" <td>0.0</td>\n",
" <td>1.394633</td>\n",
" <td>30.00</td>\n",
" <td>0.0</td>\n",
" <td>2.541118</td>\n",
" <td>43.90</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2015-2-4</td>\n",
" <td>1.634705</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-2-4</td>\n",
" <td>1.629997</td>\n",
" <td>22.00</td>\n",
" <td>0.0</td>\n",
" <td>1.629997</td>\n",
" <td>22.00</td>\n",
" <td>0.0</td>\n",
" <td>2.297154</td>\n",
" <td>19.24</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2015-1-27</td>\n",
" <td>1.704728</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>2015-1-27</td>\n",
" <td>1.129892</td>\n",
" <td>20.00</td>\n",
" <td>0.0</td>\n",
" <td>1.129892</td>\n",
" <td>20.00</td>\n",
" <td>0.0</td>\n",
" <td>1.782684</td>\n",
" <td>17.70</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>91 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" ds avg_passenger_count max_passenger_count min_passenger_count \\\n",
"0 2015-2-14 1.761718 6.0 0.0 \n",
"1 2015-3-31 1.649613 6.0 0.0 \n",
"2 2015-1-20 1.620054 6.0 0.0 \n",
"3 2015-1-15 1.658889 6.0 0.0 \n",
"4 2015-3-23 1.660204 6.0 0.0 \n",
".. ... ... ... ... \n",
"86 2015-3-11 1.638266 6.0 0.0 \n",
"87 2015-2-25 1.628918 6.0 0.0 \n",
"88 2015-3-15 1.713193 6.0 0.0 \n",
"89 2015-2-4 1.634705 6.0 0.0 \n",
"90 2015-1-27 1.704728 6.0 0.0 \n",
"\n",
" ds0 avg_fare_amount max_fare_amount min_fare_count \\\n",
"0 2015-2-14 1.361413 26.00 0.0 \n",
"1 2015-3-31 1.523232 17.50 0.0 \n",
"2 2015-1-20 1.281566 22.00 0.0 \n",
"3 2015-1-15 1.320653 100.00 0.0 \n",
"4 2015-3-23 1.492662 53.00 0.0 \n",
".. ... ... ... ... \n",
"86 2015-3-11 1.651525 69.00 0.0 \n",
"87 2015-2-25 1.638296 55.55 0.0 \n",
"88 2015-3-15 1.394633 30.00 0.0 \n",
"89 2015-2-4 1.629997 22.00 0.0 \n",
"90 2015-1-27 1.129892 20.00 0.0 \n",
"\n",
" avg_tip_amount max_tip_amount min_tip_amount avg_trip_distance \\\n",
"0 1.361413 26.00 0.0 2.326926 \n",
"1 1.523232 17.50 0.0 2.354940 \n",
"2 1.281566 22.00 0.0 1.870509 \n",
"3 1.320653 100.00 0.0 1.883465 \n",
"4 1.492662 53.00 0.0 2.387445 \n",
".. ... ... ... ... \n",
"86 1.651525 69.00 0.0 607.511626 \n",
"87 1.638296 55.55 0.0 2.356953 \n",
"88 1.394633 30.00 0.0 2.541118 \n",
"89 1.629997 22.00 0.0 2.297154 \n",
"90 1.129892 20.00 0.0 1.782684 \n",
"\n",
" max_trip_distance min_trip_distance \n",
"0 202.00 0.0 \n",
"1 803.80 0.0 \n",
"2 17.14 0.0 \n",
"3 14.30 0.0 \n",
"4 30.40 0.0 \n",
".. ... ... \n",
"86 6420001.60 0.0 \n",
"87 21.00 0.0 \n",
"88 43.90 0.0 \n",
"89 19.24 0.0 \n",
"90 17.70 0.0 \n",
"\n",
"[91 rows x 14 columns]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = '''\n",
" SELECT\n",
" p.*,\n",
" f.*\n",
" FROM\n",
" passenger_stats AS p\n",
" FULL OUTER JOIN\n",
" fare_stats AS f\n",
" ON p.ds = f.ds\n",
" '''\n",
"bc.sql(query)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "RAPIDS Stable",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
@gumdropsteve
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Recording: https://drive.google.com/file/d/1c8gh1V6WP3_IovBct7q7WN4QMW0LH-gg/view?usp=sharing

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