Skip to main content

Python client library for pushdata.io

Project description

Pushdata Python client library

This library allows you to easily store and retrieve time series data using the online service pushdata.io.

Installation

pip install pushdata-io

Getting started

You can start storing data on pushdata.io immediately, without even registering an account there. Just install this package and write three lines of code, like this:

import pushdata

pd = pushdata.Client(email="youremail@yourdomain.com", tsname="MyTimeseries")
pd.send(12345)  # Stores the data point 12345, timestamped with the current date and time

After you've run the code and stored at least one data point, go to https://pushdata.io/youremail@yourdomain.com where you will be able to view your timeseries.

Usage

import pushdata

# 1. Initialize with no authentication
# Initialize with our account email and time series name we want to use
pd = pushdata.Client(email="myemail@example.com", tsname="mytimeseries")

# 2. ...or initialize with authentication (for account with security=on)
pd = pushdata.Client(apikey="thd8JT73LsB8jah0F4d9", tsname="mytimeseries")

# Send a data point to the time series
pd.send(4711)

# Send to another time series by overriding tsname
pd.send(4711, tsname="myothertimeseries")

# Retrieve all data from the time series
response = pd.recv()

# Or from another time series
response = pd.recv(tsname="anothertimeseries")

# Retrieve data timestamped during the last week
import datetime
one_week_ago = datetime.datetime.now() - datetime.timedelta(days=7)
response = pd.recv(fromtime=one_week_ago)

# Retrieve data for one 24-hour period, one week ago
import datetime
one_week_ago = datetime.datetime.now() - datetime.timedelta(days=7)
one_week_ago_plus_24h = one_week_ago + datetime.timedelta(days=1)
response = pd.recv(fromtime=one_week_ago, totime=one_week_ago_plus_24h)

#
# Print time series data
#
# We get a Python Requests response object from recv(), which 
# includes response code, raw HTTP response body, and more.
# We use the .json() method to parse the body text as JSON
# and get a dictionary:
tsdata = response.json()
#
# And then we print stuff:
print("Timeseries name: " + tsdata["name"])
print("First point recorded at   : " + tsdata["first"])    # timestamp of first point in time series
print("Last point recorded at    : " + tsdata["last"])     # timestamp of last point in time series
print("Total number of points    : " + tsdata["total"])    # total number of points in timeseries
print("Number of points returned : " + tsdata["returned"]) # number of points returned in this call
print("---- Points ----")
for point in tsdata["points"]:
    print("Time=%s value=%f" % (point["time"], point["value"]))

#
# tsdata (the decoded JSON response from pushdata.io) is 
# a dictionary that looks like this:
#  {
#     "name": "mytimeseries",
#     "first": "2019-02-15T07:43:31.546805Z",
#     "last": "2019-03-05T11:21:06.20951Z",
#     "total": 482,
#     "returned: 482,
#     "offset": 0,
#     "limit": 10000,
#     "points": [
#        {
#           "time": "2019-02-15T07:43:31.546805Z",
#           "value": 4711.0
#        },
#        ...
#     ]
#  }
#
# See https://speca.io/ragnarlonn/pushdata-io#TimeSeriesData
#

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
pushdata_io-0.1.0-py3-none-any.whl (3.6 kB) Copy SHA256 hash SHA256 Wheel py3
pushdata-io-0.1.0.tar.gz (3.2 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page