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140 lines
6.6 KiB
140 lines
6.6 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# NYC Taxi data for Winter and Summer\n",
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"\n",
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"Check di [Data dictionary](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) to sabi more about di columns wey dem provide.\n"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"#Install the pandas library\r\n",
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"!pip install pandas"
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],
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"outputs": [],
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"metadata": {
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"scrolled": true
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}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"source": [
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"import pandas as pd\r\n",
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"\r\n",
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"path = '../../data/taxi.csv'\r\n",
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"\r\n",
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"#Load the csv file into a dataframe\r\n",
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"df = pd.read_csv(path)\r\n",
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"\r\n",
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"#Print the dataframe\r\n",
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"print(df)\r\n"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
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"0 2.0 2019-07-15 16:27:53 2019-07-15 16:44:21 3.0 \n",
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"1 2.0 2019-07-17 20:26:35 2019-07-17 20:40:09 6.0 \n",
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"2 2.0 2019-07-06 16:01:08 2019-07-06 16:10:25 1.0 \n",
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"3 1.0 2019-07-18 22:32:23 2019-07-18 22:35:08 1.0 \n",
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"4 2.0 2019-07-19 14:54:29 2019-07-19 15:19:08 1.0 \n",
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".. ... ... ... ... \n",
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"195 2.0 2019-01-18 08:42:15 2019-01-18 08:56:57 1.0 \n",
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"196 1.0 2019-01-19 04:34:45 2019-01-19 04:43:44 1.0 \n",
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"197 2.0 2019-01-05 10:37:39 2019-01-05 10:42:03 1.0 \n",
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"198 2.0 2019-01-23 10:36:29 2019-01-23 10:44:34 2.0 \n",
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"199 2.0 2019-01-30 06:55:58 2019-01-30 07:07:02 5.0 \n",
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"\n",
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" trip_distance RatecodeID store_and_fwd_flag PULocationID DOLocationID \\\n",
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"0 2.02 1.0 N 186 233 \n",
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"1 1.59 1.0 N 141 161 \n",
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"2 1.69 1.0 N 246 249 \n",
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"3 0.90 1.0 N 229 141 \n",
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"4 4.79 1.0 N 237 107 \n",
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".. ... ... ... ... ... \n",
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"195 1.18 1.0 N 43 237 \n",
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"196 2.30 1.0 N 148 234 \n",
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"197 0.83 1.0 N 237 263 \n",
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"198 1.12 1.0 N 144 113 \n",
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"199 2.41 1.0 N 209 107 \n",
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"\n",
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" payment_type fare_amount extra mta_tax tip_amount tolls_amount \\\n",
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"0 1.0 12.0 1.0 0.5 4.08 0.0 \n",
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"1 2.0 10.0 0.5 0.5 0.00 0.0 \n",
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"2 2.0 8.5 0.0 0.5 0.00 0.0 \n",
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"3 1.0 4.5 3.0 0.5 1.65 0.0 \n",
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"4 1.0 19.5 0.0 0.5 5.70 0.0 \n",
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".. ... ... ... ... ... ... \n",
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"195 1.0 10.0 0.0 0.5 2.16 0.0 \n",
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"196 1.0 9.5 0.5 0.5 2.15 0.0 \n",
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"197 1.0 5.0 0.0 0.5 1.16 0.0 \n",
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"198 2.0 7.0 0.0 0.5 0.00 0.0 \n",
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"199 1.0 10.5 0.0 0.5 1.00 0.0 \n",
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"\n",
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" improvement_surcharge total_amount congestion_surcharge \n",
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"0 0.3 20.38 2.5 \n",
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"1 0.3 13.80 2.5 \n",
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"2 0.3 11.80 2.5 \n",
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"3 0.3 9.95 2.5 \n",
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"4 0.3 28.50 2.5 \n",
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".. ... ... ... \n",
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"195 0.3 12.96 0.0 \n",
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"196 0.3 12.95 0.0 \n",
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"197 0.3 6.96 0.0 \n",
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"198 0.3 7.80 0.0 \n",
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"199 0.3 12.30 0.0 \n",
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"\n",
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"[200 rows x 18 columns]\n"
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]
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}
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Disclaimer**: \nDis dokyument don translate wit AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). Even as we dey try make am accurate, abeg sabi say machine translation fit get mistake or no dey correct well. Di original dokyument for im native language na di main source wey you go trust. For important information, e better make professional human translation dey use. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because you use dis translation.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
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]
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}
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],
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"name": "04-nyc-taxi-join-weather-in-pandas",
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