1.2.2 - Ingesting NY Taxi Data to Postgres

Last updated Jan 19, 2025

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1.2.2 - Ingesting NY Taxi Data to Postgres

Last updated Jan 19, 2025

Youtube Video | ~29min

I recommend not pausing your workflow and going through this entire video. You may run into a number of issues related to pgcli, so allow for extra time spent here (maybe hours). Search Slack, Search FAQ, try a new virtual environment, ask for help.

The Taxi now provides data in .parquet format instead of .csv. The website gives directions on how to read .parquet files and convert it to Pandas data frame. For this course, we want to use the .csvbackup located :

Datasets

Running Postgres w/ Docker (Mac)

docker run -it \
  -e POSTGRES_USER="root" \
  -e POSTGRES_PASSWORD="root" \
  -e POSTGRES_DB="ny_taxi" \
  -v $(pwd)/ny_taxi_postgres_data:/var/lib/postgresql/data \
  -p 5432:5432 \
  postgres:13

Be sure to run this from your terminal (base) and that you run it in the correct directory to point to your ny_taxi_postgres_data folder correctly

PGCLI

pip install pgcli

Be sure to run pip install pgcli on (base) not your environment.

If you are having issues with the above command, try: conda install -c conda-forge pgcli pip install -U mycli

Using pgcli to connect to Postgres

pgcli -h localhost -p 5432 -u root -d ny_taxi

hhostname p port u username d database name

Jupyter Notebook

Be sure jupyter is installed in your environment. Use pip install jupyter if not. Be sure your .csv taxi data is downloaded locally.

jupyter notebook

This should open a jupyter notebook web browser tab. Follow along with the youtube video to finalize your jupyter notebook.

import pandas as pd
from sqlalchemy import create_engine
from time import time

df = pd.read_csv('yellow_tripdata_2021-01.csv', nrows=100)
print(pd.io.sql.get_schema(df, name="yellow_taxi_data")) #ddl
pd.to_datetime(df.tpep_pickup_datetime)
pd.to_datetime(df.tpep_dropoff_datetime)

conn_string = "postgresql+psycopg2://root:root@localhost:5432/ny_taxi"
engine = create_engine(conn_string)
engine.connect()
print(pd.io.sql.get_schema(df, name="yellow_taxi_data", con=engine)) #ddl

df_iter = pd.read_csv('yellow_tripdata_2021-01.csv', iterator=True, chunksize=100000)
df = next(df_iter)
len(df)

df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime)
df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime)

df.head(n=0).to_sql(name='yellow_taxi_data', con=engine, if_exists='replace')
%time df.to_sql(name='yellow_taxi_data', con=engine, if_exists='append')


while True:
    t_start = time()
    df=next(df_iter)
    df.tpep_pickup_datetime = pd.to_datetime(df.tpep_pickup_datetime)
    df.tpep_dropoff_datetime = pd.to_datetime(df.tpep_dropoff_datetime)
    df.to_sql(name='yellow_taxi_data', con=engine, if_exists='append')
    t_end = time()
    print('inserted another chunk...took %.3f seconds' % (t_end - t_start))
    
# my addition for zones    
df_zones = pd.read_csv('taxi_zone_lookup.csv')
df_zones.head()
df_zones.to_sql(name='zones', con=engine, if_exists='replace')

Resources

My repo for this video can be found here

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