You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
141 lines
6.5 KiB
141 lines
6.5 KiB
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# NYC Taxi data in Winter and Summer\r\n",
|
|
"\r\n",
|
|
"Refer to the [Data dictionary](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) to learn more about the columns that have been provided.\r\n"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"source": [
|
|
"#Install the pandas library\r\n",
|
|
"!pip install pandas"
|
|
],
|
|
"outputs": [],
|
|
"metadata": {
|
|
"scrolled": true
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"source": [
|
|
"import pandas as pd\r\n",
|
|
"\r\n",
|
|
"path = '../../data/taxi.csv'\r\n",
|
|
"\r\n",
|
|
"#Load the csv file into a dataframe\r\n",
|
|
"df = pd.read_csv(path)\r\n",
|
|
"\r\n",
|
|
"#Print the dataframe\r\n",
|
|
"print(df)\r\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
" VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n",
|
|
"0 2.0 2019-07-15 16:27:53 2019-07-15 16:44:21 3.0 \n",
|
|
"1 2.0 2019-07-17 20:26:35 2019-07-17 20:40:09 6.0 \n",
|
|
"2 2.0 2019-07-06 16:01:08 2019-07-06 16:10:25 1.0 \n",
|
|
"3 1.0 2019-07-18 22:32:23 2019-07-18 22:35:08 1.0 \n",
|
|
"4 2.0 2019-07-19 14:54:29 2019-07-19 15:19:08 1.0 \n",
|
|
".. ... ... ... ... \n",
|
|
"195 2.0 2019-01-18 08:42:15 2019-01-18 08:56:57 1.0 \n",
|
|
"196 1.0 2019-01-19 04:34:45 2019-01-19 04:43:44 1.0 \n",
|
|
"197 2.0 2019-01-05 10:37:39 2019-01-05 10:42:03 1.0 \n",
|
|
"198 2.0 2019-01-23 10:36:29 2019-01-23 10:44:34 2.0 \n",
|
|
"199 2.0 2019-01-30 06:55:58 2019-01-30 07:07:02 5.0 \n",
|
|
"\n",
|
|
" trip_distance RatecodeID store_and_fwd_flag PULocationID DOLocationID \\\n",
|
|
"0 2.02 1.0 N 186 233 \n",
|
|
"1 1.59 1.0 N 141 161 \n",
|
|
"2 1.69 1.0 N 246 249 \n",
|
|
"3 0.90 1.0 N 229 141 \n",
|
|
"4 4.79 1.0 N 237 107 \n",
|
|
".. ... ... ... ... ... \n",
|
|
"195 1.18 1.0 N 43 237 \n",
|
|
"196 2.30 1.0 N 148 234 \n",
|
|
"197 0.83 1.0 N 237 263 \n",
|
|
"198 1.12 1.0 N 144 113 \n",
|
|
"199 2.41 1.0 N 209 107 \n",
|
|
"\n",
|
|
" payment_type fare_amount extra mta_tax tip_amount tolls_amount \\\n",
|
|
"0 1.0 12.0 1.0 0.5 4.08 0.0 \n",
|
|
"1 2.0 10.0 0.5 0.5 0.00 0.0 \n",
|
|
"2 2.0 8.5 0.0 0.5 0.00 0.0 \n",
|
|
"3 1.0 4.5 3.0 0.5 1.65 0.0 \n",
|
|
"4 1.0 19.5 0.0 0.5 5.70 0.0 \n",
|
|
".. ... ... ... ... ... ... \n",
|
|
"195 1.0 10.0 0.0 0.5 2.16 0.0 \n",
|
|
"196 1.0 9.5 0.5 0.5 2.15 0.0 \n",
|
|
"197 1.0 5.0 0.0 0.5 1.16 0.0 \n",
|
|
"198 2.0 7.0 0.0 0.5 0.00 0.0 \n",
|
|
"199 1.0 10.5 0.0 0.5 1.00 0.0 \n",
|
|
"\n",
|
|
" improvement_surcharge total_amount congestion_surcharge \n",
|
|
"0 0.3 20.38 2.5 \n",
|
|
"1 0.3 13.80 2.5 \n",
|
|
"2 0.3 11.80 2.5 \n",
|
|
"3 0.3 9.95 2.5 \n",
|
|
"4 0.3 28.50 2.5 \n",
|
|
".. ... ... ... \n",
|
|
"195 0.3 12.96 0.0 \n",
|
|
"196 0.3 12.95 0.0 \n",
|
|
"197 0.3 6.96 0.0 \n",
|
|
"198 0.3 7.80 0.0 \n",
|
|
"199 0.3 12.30 0.0 \n",
|
|
"\n",
|
|
"[200 rows x 18 columns]\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Use the cells below to do your own Exploratory Data Analysis"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"source": [],
|
|
"outputs": [],
|
|
"metadata": {}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"name": "python3",
|
|
"display_name": "Python 3.9.7 64-bit ('venv': venv)"
|
|
},
|
|
"language_info": {
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"version": "3.9.7",
|
|
"nbconvert_exporter": "python",
|
|
"file_extension": ".py"
|
|
},
|
|
"name": "04-nyc-taxi-join-weather-in-pandas",
|
|
"notebookId": 1709144033725344,
|
|
"interpreter": {
|
|
"hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
} |