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140 lines
6.7 KiB
140 lines
6.7 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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"# Dane dotyczące taksówek w Nowym Jorku zimą i latem\n",
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"\n",
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"Zapoznaj się z [Słownikiem danych](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf), aby dowiedzieć się więcej o udostępnionych kolumnach.\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\n**Zastrzeżenie**: \nTen dokument został przetłumaczony za pomocą usługi tłumaczeniowej AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy wszelkich starań, aby zapewnić dokładność, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji krytycznych zaleca się skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia.\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.9.7 64-bit ('venv': venv)"
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"nbconvert_exporter": "python",
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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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"interpreter": {
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"hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
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"source_file": "4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb",
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