These functions are exactly equivalent
Reference | ||
---|---|---|
filter | where | pyspark.sql.DataFrame.filter |
drop_duplicates | dropDuplicates | pyspark.sql.DataFrame.drop_duplicates |
avg | mean | pyspark.sql.GroupedData.avg |
import numpy as np | |
EPSILON = 1e-10 | |
def _error(actual: np.ndarray, predicted: np.ndarray): | |
""" Simple error """ | |
return actual - predicted |
These functions are exactly equivalent
Reference | ||
---|---|---|
filter | where | pyspark.sql.DataFrame.filter |
drop_duplicates | dropDuplicates | pyspark.sql.DataFrame.drop_duplicates |
avg | mean | pyspark.sql.GroupedData.avg |
""" | |
Python script for batch geocoding of addresses using the Google Geocoding API. | |
This script allows for massive lists of addresses to be geocoded for free by pausing when the | |
geocoder hits the free rate limit set by Google (2500 per day). If you have an API key for paid | |
geocoding from Google, set it in the API key section. | |
Addresses for geocoding can be specified in a list of strings "addresses". In this script, addresses | |
come from a csv file with a column "Address". Adjust the code to your own requirements as needed. | |
After every 500 successul geocode operations, a temporary file with results is recorded in case of | |
script failure / loss of connection later. | |
Addresses and data are held in memory, so this script may need to be adjusted to process files line |