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154 lines
6.8 KiB
154 lines
6.8 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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"source": [
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"# Use di cells wey dey below to do your own Exploratory Data Analysis\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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"outputs": [],
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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 docu don dey translate wit AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). Even though we dey try make am accurate, abeg sabi say automatic translation fit get mistake or no correct well. Di original docu for im native language na di main correct source. For important information, e good make una use professional human translation. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because of 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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"file_extension": ".py"
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"name": "04-nyc-taxi-join-weather-in-pandas",
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"notebookId": 1709144033725344,
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