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        "author": "Ragnar Lonn",
        "author_email": "hello@pushdata.io",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
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        "description": "# Pushdata Python client library\n\nThis library allows you to easily store and retrieve time series data using the online service [pushdata.io](https://pushdata.io).\n\n## Installation\n\n`pip install pushdata-io`\n\n## Getting started\n\nYou can start storing data on [pushdata.io](https://pushdata.io) immediately, without even registering an account there. Just install this package and write three lines of code, like this:\n\n```python\nimport pushdata\n\npd = pushdata.Client(email=\"youremail@yourdomain.com\", tsname=\"MyTimeseries\")\npd.send(12345)  # Stores the data point 12345, timestamped with the current date and time\n```\n\nAfter 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.\n\n## Usage\n\n```python\nimport pushdata\n\n# 1. Initialize with no authentication\n# Initialize with our account email and time series name we want to use\npd = pushdata.Client(email=\"myemail@example.com\", tsname=\"mytimeseries\")\n\n# 2. ...or initialize with authentication (for account with security=on)\npd = pushdata.Client(apikey=\"thd8JT73LsB8jah0F4d9\", tsname=\"mytimeseries\")\n\n# Send a data point to the time series\npd.send(4711)\n\n# Send to another time series by overriding tsname\npd.send(4711, tsname=\"myothertimeseries\")\n\n# Retrieve all data from the time series\nresponse = pd.recv()\n\n# Or from another time series\nresponse = pd.recv(tsname=\"anothertimeseries\")\n\n# Retrieve data timestamped during the last week\nimport datetime\none_week_ago = datetime.datetime.now() - datetime.timedelta(days=7)\nresponse = pd.recv(fromtime=one_week_ago)\n\n# Retrieve data for one 24-hour period, one week ago\nimport datetime\none_week_ago = datetime.datetime.now() - datetime.timedelta(days=7)\none_week_ago_plus_24h = one_week_ago + datetime.timedelta(days=1)\nresponse = pd.recv(fromtime=one_week_ago, totime=one_week_ago_plus_24h)\n\n#\n# Print time series data\n#\n# We get a Python Requests response object from recv(), which \n# includes response code, raw HTTP response body, and more.\n# We use the .json() method to parse the body text as JSON\n# and get a dictionary:\ntsdata = response.json()\n#\n# And then we print stuff:\nprint(\"Timeseries name: \" + tsdata[\"name\"])\nprint(\"First point recorded at   : \" + tsdata[\"first\"])    # timestamp of first point in time series\nprint(\"Last point recorded at    : \" + tsdata[\"last\"])     # timestamp of last point in time series\nprint(\"Total number of points    : \" + tsdata[\"total\"])    # total number of points in timeseries\nprint(\"Number of points returned : \" + tsdata[\"returned\"]) # number of points returned in this call\nprint(\"---- Points ----\")\nfor point in tsdata[\"points\"]:\n    print(\"Time=%s value=%f\" % (point[\"time\"], point[\"value\"]))\n\n#\n# tsdata (the decoded JSON response from pushdata.io) is \n# a dictionary that looks like this:\n#  {\n#     \"name\": \"mytimeseries\",\n#     \"first\": \"2019-02-15T07:43:31.546805Z\",\n#     \"last\": \"2019-03-05T11:21:06.20951Z\",\n#     \"total\": 482,\n#     \"returned: 482,\n#     \"offset\": 0,\n#     \"limit\": 10000,\n#     \"points\": [\n#        {\n#           \"time\": \"2019-02-15T07:43:31.546805Z\",\n#           \"value\": 4711.0\n#        },\n#        ...\n#     ]\n#  }\n#\n# See https://speca.io/ragnarlonn/pushdata-io#TimeSeriesData\n#\n```\n\n\n\n",
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        "name": "pushdata-io",
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