Python wrapper for Twitter Premium and Enterprise Search APIs
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README.rst

Python Twitter Search API

This project serves as a wrapper for the Twitter premium and enterprise search APIs, providing a command-line utility and a Python library. Pretty docs can be seen here.

Features

  • Supports 30-day Search and Full Archive Search (not the standard Search API at this time).
  • Command-line utility is pipeable to other tools (e.g., jq).
  • Automatically handles pagination of search results with specifiable limits
  • Delivers a stream of data to the user for low in-memory requirements
  • Handles enterprise and premium authentication methods
  • Flexible usage within a python program
  • Compatible with our group's Tweet Parser for rapid extraction of relevant data fields from each tweet payload
  • Supports the Search Counts endpoint, which can reduce API call usage and provide rapid insights if you only need Tweet volumes and not Tweet payloads

Installation

The searchtweets library is on Pypi:

pip install searchtweets

Or you can install the development version locally via

git clone https://github.com/twitterdev/search-tweets-python
cd search-tweets-python
pip install -e .

Credential Handling

The premium and enterprise Search APIs use different authentication methods and we attempt to provide a seamless way to handle authentication for all customers. We know credentials can be tricking or annoying - please read this in its entirety.

Premium clients will require the bearer_token and endpoint fields; Enterprise clients require username, password, and endpoint. If you do not specify the account_type, we attempt to discern the account type and declare a warning about this behavior.

For premium search products, we are using app-only authentication and the bearer tokens are not delivered with an expiration time. You can provide either: - your application key and secret (the library will handle bearer-token authentication) - a bearer token that you get yourself

Many developers might find providing your application key and secret more straightforward and letting this library manage your bearer token generation for you. Please see here for an overview of the premium authentication method.

We support both YAML-file based methods and environment variables for storing credentials, and provide flexible handling with sensible defaults.

YAML method

For premium customers, the simplest credential file should look like this:

search_tweets_api:
  account_type: premium
  endpoint: <FULL_URL_OF_ENDPOINT>
  consumer_key: <CONSUMER_KEY>
  consumer_secret: <CONSUMER_SECRET>

For enterprise customers, the simplest credential file should look like this:

search_tweets_api:
  account_type: enterprise
  endpoint: <FULL_URL_OF_ENDPOINT>
  username: <USERNAME>
  password: <PW>

By default, this library expects this file at "~/.twitter_keys.yaml", but you can pass the relevant location as needed, either with the --credential-file flag for the command-line app or as demonstrated below in a Python program.

Both above examples require no special command-line arguments or in-program arguments. The credential parsing methods, unless otherwise specified, will look for a YAML key called search_tweets_api.

For developers who have multiple endpoints and/or search products, you can keep all credentials in the same file and specify specific keys to use. --credential-file-key specifies this behavior in the command line app. An example:

search_tweets_30_day_dev:
  account_type: premium
  endpoint: <FULL_URL_OF_ENDPOINT>
  consumer_key: <KEY>
  consumer_secret: <SECRET>
  (optional) bearer_token: <TOKEN>


search_tweets_30_day_prod:
  account_type: premium
  endpoint: <FULL_URL_OF_ENDPOINT>
  bearer_token: <TOKEN>

search_tweets_fullarchive_dev:
  account_type: premium
  endpoint: <FULL_URL_OF_ENDPOINT>
  bearer_token: <TOKEN>

search_tweets_fullarchive_prod:
  account_type: premium
  endpoint: <FULL_URL_OF_ENDPOINT>
  bearer_token: <TOKEN>

Environment Variables

If you want or need to pass credentials via environment variables, you can set the appropriate variables for your product of the following:

export SEARCHTWEETS_ENDPOINT=
export SEARCHTWEETS_USERNAME=
export SEARCHTWEETS_PASSWORD=
export SEARCHTWEETS_BEARER_TOKEN=
export SEARCHTWEETS_ACCOUNT_TYPE=
export SEARCHTWEETS_CONSUMER_KEY=
export SEARCHTWEETS_CONSUMER_SECRET=

The load_credentials function will attempt to find these variables if it cannot load fields from the YAML file, and it will overwrite any credentials from the YAML file that are present as environment variables if they have been parsed. This behavior can be changed by setting the load_credentials parameter env_overwrite to False.

The following cells demonstrates credential handling in the Python library.

from searchtweets import load_credentials
load_credentials(filename="./search_tweets_creds_example.yaml",
                 yaml_key="search_tweets_ent_example",
                 env_overwrite=False)
{'username': '<MY_USERNAME>',
 'password': '<MY_PASSWORD>',
 'endpoint': '<MY_ENDPOINT>'}
load_credentials(filename="./search_tweets_creds_example.yaml",
                 yaml_key="search_tweets_premium_example",
                 env_overwrite=False)
{'bearer_token': '<A_VERY_LONG_MAGIC_STRING>',
 'endpoint': 'https://api.twitter.com/1.1/tweets/search/30day/dev.json',
 'extra_headers_dict': None}

Environment Variable Overrides

If we set our environment variables, the program will look for them regardless of a YAML file's validity or existence.

import os
os.environ["SEARCHTWEETS_USERNAME"] = "<ENV_USERNAME>"
os.environ["SEARCHTWEETS_PASSWORD"] = "<ENV_PW>"
os.environ["SEARCHTWEETS_ENDPOINT"] = "<https://endpoint>"

load_credentials(filename="nothing_here.yaml", yaml_key="no_key_here")
cannot read file nothing_here.yaml
Error parsing YAML file; searching for valid environment variables
{'username': '<ENV_USERNAME>',
 'password': '<ENV_PW>',
 'endpoint': '<https://endpoint>'}

Command-line app

the flags:

  • --credential-file <FILENAME>
  • --credential-file-key <KEY>
  • --env-overwrite

are used to control credential behavior from the command-line app.


Using the Comand Line Application

The library includes an application, search_tweets.py, that provides rapid access to Tweets. When you use pip to install this package, search_tweets.py is installed globally. The file is located in the tools/ directory for those who want to run it locally.

Note that the --results-per-call flag specifies an argument to the API ( maxResults, results returned per CALL), not as a hard max to number of results returned from this program. The argument --max-results defines the maximum number of results to return from a given call. All examples assume that your credentials are set up correctly in the default location - .twitter_keys.yaml or in environment variables.

Stream json results to stdout without saving

search_tweets.py \
  --max-results 1000 \
  --results-per-call 100 \
  --filter-rule "beyonce has:hashtags" \
  --print-stream

Stream json results to stdout and save to a file

search_tweets.py \
  --max-results 1000 \
  --results-per-call 100 \
  --filter-rule "beyonce has:hashtags" \
  --filename-prefix beyonce_geo \
  --print-stream

Save to file without output

search_tweets.py \
  --max-results 100 \
  --results-per-call 100 \
  --filter-rule "beyonce has:hashtags" \
  --filename-prefix beyonce_geo \
  --no-print-stream

One or more custom headers can be specified from the command line, using the --extra-headers argument and a JSON-formatted string representing a dictionary of extra headers:

search_tweets.py \
  --filter-rule "beyonce has:hashtags" \
  --extra-headers '{"<MY_HEADER_KEY>":"<MY_HEADER_VALUE>"}'

Options can be passed via a configuration file (either ini or YAML). Example files can be found in the tools/api_config_example.config or ./tools/api_yaml_example.yaml files, which might look like this:

[search_rules]
from_date = 2017-06-01
to_date = 2017-09-01
pt_rule = beyonce has:geo

[search_params]
results_per_call = 500
max_results = 500

[output_params]
save_file = True
filename_prefix = beyonce
results_per_file = 10000000

Or this:

search_rules:
    from-date: 2017-06-01
    to-date: 2017-09-01 01:01
    pt-rule: kanye

search_params:
    results-per-call: 500
    max-results: 500

output_params:
    save_file: True
    filename_prefix: kanye
    results_per_file: 10000000

Custom headers can be specified in a config file, under a specific credentials key:

search_tweets_api:
  account_type: premium
  endpoint: <FULL_URL_OF_ENDPOINT>
  username: <USERNAME>
  password: <PW>
  extra_headers:
    <MY_HEADER_KEY>: <MY_HEADER_VALUE>

When using a config file in conjunction with the command-line utility, you need to specify your config file via the --config-file parameter. Additional command-line arguments will either be added to the config file args or overwrite the config file args if both are specified and present.

Example:

search_tweets.py \
  --config-file myapiconfig.config \
  --no-print-stream

Full options are listed below:

$ search_tweets.py -h
usage: search_tweets.py [-h] [--credential-file CREDENTIAL_FILE]
                      [--credential-file-key CREDENTIAL_YAML_KEY]
                      [--env-overwrite ENV_OVERWRITE]
                      [--config-file CONFIG_FILENAME]
                      [--account-type {premium,enterprise}]
                      [--count-bucket COUNT_BUCKET]
                      [--start-datetime FROM_DATE] [--end-datetime TO_DATE]
                      [--filter-rule PT_RULE]
                      [--results-per-call RESULTS_PER_CALL]
                      [--max-results MAX_RESULTS] [--max-pages MAX_PAGES]
                      [--results-per-file RESULTS_PER_FILE]
                      [--filename-prefix FILENAME_PREFIX]
                      [--no-print-stream] [--print-stream]
                      [--extra-headers EXTRA_HEADERS] [--debug]

optional arguments:
  -h, --help            show this help message and exit
  --credential-file CREDENTIAL_FILE
                        Location of the yaml file used to hold your
                        credentials.
  --credential-file-key CREDENTIAL_YAML_KEY
                        the key in the credential file used for this session's
                        credentials. Defaults to search_tweets_api
  --env-overwrite ENV_OVERWRITE
                        Overwrite YAML-parsed credentials with any set
                        environment variables. See API docs or readme for
                        details.
  --config-file CONFIG_FILENAME
                        configuration file with all parameters. Far, easier to
                        use than the command-line args version., If a valid
                        file is found, all args will be populated, from there.
                        Remaining command-line args, will overrule args found
                        in the config, file.
  --account-type {premium,enterprise}
                        The account type you are using
  --count-bucket COUNT_BUCKET
                        Bucket size for counts API. Options:, day, hour,
                        minute (default is 'day').
  --start-datetime FROM_DATE
                        Start of datetime window, format 'YYYY-mm-DDTHH:MM'
                        (default: -30 days)
  --end-datetime TO_DATE
                        End of datetime window, format 'YYYY-mm-DDTHH:MM'
                        (default: most recent date)
  --filter-rule PT_RULE
                        PowerTrack filter rule (See: http://support.gnip.com/c
                        ustomer/portal/articles/901152-powertrack-operators)
  --results-per-call RESULTS_PER_CALL
                        Number of results to return per call (default 100; max
                        500) - corresponds to 'maxResults' in the API
  --max-results MAX_RESULTS
                        Maximum number of Tweets or Counts to return for this
                        session (defaults to 500)
  --max-pages MAX_PAGES
                        Maximum number of pages/API calls to use for this
                        session.
  --results-per-file RESULTS_PER_FILE
                        Maximum tweets to save per file.
  --filename-prefix FILENAME_PREFIX
                        prefix for the filename where tweet json data will be
                        stored.
  --no-print-stream     disable print streaming
  --print-stream        Print tweet stream to stdout
  --extra-headers EXTRA_HEADERS
                        JSON-formatted str representing a dict of additional
                        request headers
  --debug               print all info and warning messages

Using the Twitter Search APIs' Python Wrapper

Working with the API within a Python program is straightforward both for Premium and Enterprise clients.

We'll assume that credentials are in the default location, ~/.twitter_keys.yaml.

from searchtweets import ResultStream, gen_rule_payload, load_credentials

Enterprise setup

enterprise_search_args = load_credentials("~/.twitter_keys.yaml",
                                          yaml_key="search_tweets_enterprise",
                                          env_overwrite=False)

Premium Setup

premium_search_args = load_credentials("~/.twitter_keys.yaml",
                                       yaml_key="search_tweets_premium",
                                       env_overwrite=False)

There is a function that formats search API rules into valid json queries called gen_rule_payload. It has sensible defaults, such as pulling more Tweets per call than the default 100 (but note that a sandbox environment can only have a max of 100 here, so if you get errors, please check this) not including dates, and defaulting to hourly counts when using the counts api. Discussing the finer points of generating search rules is out of scope for these examples; I encourage you to see the docs to learn the nuances within, but for now let's see what a rule looks like.

rule = gen_rule_payload("beyonce", results_per_call=100) # testing with a sandbox account
print(rule)
{"query":"beyonce","maxResults":100}

This rule will match tweets that have the text beyonce in them.

From this point, there are two ways to interact with the API. There is a quick method to collect smaller amounts of Tweets to memory that requires less thought and knowledge, and interaction with the ResultStream object which will be introduced later.

Fast Way

We'll use the search_args variable to power the configuration point for the API. The object also takes a valid PowerTrack rule and has options to cutoff search when hitting limits on both number of Tweets and API calls.

We'll be using the collect_results function, which has three parameters.

  • rule: a valid PowerTrack rule, referenced earlier
  • max_results: as the API handles pagination, it will stop collecting when we get to this number
  • result_stream_args: configuration args that we've already specified.

For the remaining examples, please change the args to either premium or enterprise depending on your usage.

Let's see how it goes:

from searchtweets import collect_results
tweets = collect_results(rule,
                         max_results=100,
                         result_stream_args=enterprise_search_args) # change this if you need to

By default, Tweet payloads are lazily parsed into a Tweet object. An overwhelming number of Tweet attributes are made available directly, as such:

[print(tweet.all_text, end='\n\n') for tweet in tweets[0:10]];
Jay-Z &amp; Beyoncé sat across from us at dinner tonight and, at one point, I made eye contact with Beyoncé. My limbs turned to jello and I can no longer form a coherent sentence. I have seen the eyes of the lord.

Beyoncé and it isn't close. https://t.co/UdOU9oUtuW

As you could guess.. Signs by Beyoncé will always be my shit.

When Beyoncé adopts a dog 🙌🏾 https://t.co/U571HyLG4F

Hold up, you can't just do that to Beyoncé
https://t.co/3p14DocGqA

Why y'all keep using Rihanna and Beyoncé gifs to promote the show when y'all let Bey lose the same award she deserved 3 times and let Rihanna leave with nothing but the clothes on her back? https://t.co/w38QpH0wma

30) anybody tell you that you look like Beyoncé https://t.co/Vo4Z7bfSCi

Mi Beyoncé favorita https://t.co/f9Jp600l2B
Beyoncé necesita ver esto. Que diosa @TiniStoessel 🔥🔥🔥 https://t.co/gadVJbehQZ

Joanne Pearce Is now playing IF I WAS A BOY - BEYONCE.mp3 by !

I'm trynna see beyoncé's finsta before I die
[print(tweet.created_at_datetime) for tweet in tweets[0:10]];
2018-01-17 00:08:50
2018-01-17 00:08:49
2018-01-17 00:08:44
2018-01-17 00:08:42
2018-01-17 00:08:42
2018-01-17 00:08:42
2018-01-17 00:08:40
2018-01-17 00:08:38
2018-01-17 00:08:37
2018-01-17 00:08:37
[print(tweet.generator.get("name")) for tweet in tweets[0:10]];
Twitter for iPhone
Twitter for iPhone
Twitter for iPhone
Twitter for iPhone
Twitter for iPhone
Twitter for iPhone
Twitter for Android
Twitter for iPhone
Airtime Pro
Twitter for iPhone

Voila, we have some Tweets. For interactive environments and other cases where you don't care about collecting your data in a single load or don't need to operate on the stream of Tweets or counts directly, I recommend using this convenience function.

Working with the ResultStream

The ResultStream object will be powered by the search_args, and takes the rules and other configuration parameters, including a hard stop on number of pages to limit your API call usage.

rs = ResultStream(rule_payload=rule,
                  max_results=500,
                  max_pages=1,
                  **premium_search_args)

print(rs)
ResultStream:
     {
    "username":null,
    "endpoint":"https:\/\/api.twitter.com\/1.1\/tweets\/search\/30day\/dev.json",
    "rule_payload":{
        "query":"beyonce",
        "maxResults":100
    },
    "tweetify":true,
    "max_results":500
}

There is a function, .stream, that seamlessly handles requests and pagination for a given query. It returns a generator, and to grab our 500 Tweets that mention beyonce we can do this:

tweets = list(rs.stream())

Tweets are lazily parsed using our Tweet Parser, so tweet data is very easily extractable.

# using unidecode to prevent emoji/accents printing
[print(tweet.all_text) for tweet in tweets[0:10]];
gente socorro kkkkkkkkkk BEYONCE https://t.co/kJ9zubvKuf
Jay-Z &amp; Beyoncé sat across from us at dinner tonight and, at one point, I made eye contact with Beyoncé. My limbs turned to jello and I can no longer form a coherent sentence. I have seen the eyes of the lord.
Beyoncé and it isn't close. https://t.co/UdOU9oUtuW
As you could guess.. Signs by Beyoncé will always be my shit.
When Beyoncé adopts a dog 🙌🏾 https://t.co/U571HyLG4F
Hold up, you can't just do that to Beyoncé
https://t.co/3p14DocGqA
Why y'all keep using Rihanna and Beyoncé gifs to promote the show when y'all let Bey lose the same award she deserved 3 times and let Rihanna leave with nothing but the clothes on her back? https://t.co/w38QpH0wma
30) anybody tell you that you look like Beyoncé https://t.co/Vo4Z7bfSCi
Mi Beyoncé favorita https://t.co/f9Jp600l2B
Beyoncé necesita ver esto. Que diosa @TiniStoessel 🔥🔥🔥 https://t.co/gadVJbehQZ
Joanne Pearce Is now playing IF I WAS A BOY - BEYONCE.mp3 by !

Counts Endpoint

We can also use the Search API Counts endpoint to get counts of Tweets that match our rule. Each request will return up to 30 results, and each count request can be done on a minutely, hourly, or daily basis. The underlying ResultStream object will handle converting your endpoint to the count endpoint, and you have to specify the count_bucket argument when making a rule to use it.

The process is very similar to grabbing Tweets, but has some minor differences.

Caveat - premium sandbox environments do NOT have access to the Search API counts endpoint.

count_rule = gen_rule_payload("beyonce", count_bucket="day")

counts = collect_results(count_rule, result_stream_args=enterprise_search_args)

Our results are pretty straightforward and can be rapidly used.

counts
[{'count': 366, 'timePeriod': '201801170000'},
 {'count': 44580, 'timePeriod': '201801160000'},
 {'count': 61932, 'timePeriod': '201801150000'},
 {'count': 59678, 'timePeriod': '201801140000'},
 {'count': 44014, 'timePeriod': '201801130000'},
 {'count': 46607, 'timePeriod': '201801120000'},
 {'count': 41523, 'timePeriod': '201801110000'},
 {'count': 47056, 'timePeriod': '201801100000'},
 {'count': 65506, 'timePeriod': '201801090000'},
 {'count': 95251, 'timePeriod': '201801080000'},
 {'count': 162883, 'timePeriod': '201801070000'},
 {'count': 106344, 'timePeriod': '201801060000'},
 {'count': 93542, 'timePeriod': '201801050000'},
 {'count': 110415, 'timePeriod': '201801040000'},
 {'count': 127523, 'timePeriod': '201801030000'},
 {'count': 131952, 'timePeriod': '201801020000'},
 {'count': 176157, 'timePeriod': '201801010000'},
 {'count': 57229, 'timePeriod': '201712310000'},
 {'count': 72277, 'timePeriod': '201712300000'},
 {'count': 72051, 'timePeriod': '201712290000'},
 {'count': 76371, 'timePeriod': '201712280000'},
 {'count': 61578, 'timePeriod': '201712270000'},
 {'count': 55118, 'timePeriod': '201712260000'},
 {'count': 59115, 'timePeriod': '201712250000'},
 {'count': 106219, 'timePeriod': '201712240000'},
 {'count': 114732, 'timePeriod': '201712230000'},
 {'count': 73327, 'timePeriod': '201712220000'},
 {'count': 89171, 'timePeriod': '201712210000'},
 {'count': 192381, 'timePeriod': '201712200000'},
 {'count': 85554, 'timePeriod': '201712190000'},
 {'count': 57829, 'timePeriod': '201712180000'}]

Dated searches / Full Archive Search

Note that this will only work with the full archive search option, which is available to my account only via the enterprise options. Full archive search will likely require a different endpoint or access method; please see your developer console for details.

Let's make a new rule and pass it dates this time.

gen_rule_payload takes timestamps of the following forms:

  • YYYYmmDDHHMM
  • YYYY-mm-DD (which will convert to midnight UTC (00:00)
  • YYYY-mm-DD HH:MM
  • YYYY-mm-DDTHH:MM

Note - all Tweets are stored in UTC time.

rule = gen_rule_payload("from:jack",
                        from_date="2017-09-01", #UTC 2017-09-01 00:00
                        to_date="2017-10-30",#UTC 2017-10-30 00:00
                        results_per_call=500)
print(rule)
{"query":"from:jack","maxResults":500,"toDate":"201710300000","fromDate":"201709010000"}
tweets = collect_results(rule, max_results=500, result_stream_args=enterprise_search_args)
[print(tweet.all_text) for tweet in tweets[0:10]];
More clarity on our private information policy and enforcement. Working to build as much direct context into the product too https://t.co/IrwBexPrBA
To provide more clarity on our private information policy, we’ve added specific examples of what is/is not a violation and insight into what we need to remove this type of content from the service. https://t.co/NGx5hh2tTQ
Launching violent groups and hateful images/symbols policy on November 22nd https://t.co/NaWuBPxyO5
We will now launch our policies on violent groups and hateful imagery and hate symbols on Nov 22. During the development process, we received valuable feedback that we’re implementing before these are published and enforced. See more on our policy development process here 👇 https://t.co/wx3EeH39BI
@WillStick @lizkelley Happy birthday Liz!
Off-boarding advertising from all accounts owned by Russia Today (RT) and Sputnik.

We’re donating all projected earnings ($1.9mm) to support external research into the use of Twitter in elections, including use of malicious automation and misinformation. https://t.co/zIxfqqXCZr
@TMFJMo @anthonynoto Thank you
@gasca @stratechery @Lefsetz letter
@gasca @stratechery Bridgewater’s Daily Observations
Yup!!!! ❤️❤️❤️❤️ #davechappelle https://t.co/ybSGNrQpYF
@ndimichino Sometimes
Setting up at @CampFlogGnaw https://t.co/nVq8QjkKsf
rule = gen_rule_payload("from:jack",
                        from_date="2017-09-20",
                        to_date="2017-10-30",
                        count_bucket="day",
                        results_per_call=500)
print(rule)
{"query":"from:jack","toDate":"201710300000","fromDate":"201709200000","bucket":"day"}
counts = collect_results(rule, max_results=500, result_stream_args=enterprise_search_args)
[print(c) for c in counts];
{'timePeriod': '201710290000', 'count': 0}
{'timePeriod': '201710280000', 'count': 0}
{'timePeriod': '201710270000', 'count': 3}
{'timePeriod': '201710260000', 'count': 6}
{'timePeriod': '201710250000', 'count': 4}
{'timePeriod': '201710240000', 'count': 4}
{'timePeriod': '201710230000', 'count': 0}
{'timePeriod': '201710220000', 'count': 0}
{'timePeriod': '201710210000', 'count': 3}
{'timePeriod': '201710200000', 'count': 2}
{'timePeriod': '201710190000', 'count': 1}
{'timePeriod': '201710180000', 'count': 6}
{'timePeriod': '201710170000', 'count': 2}
{'timePeriod': '201710160000', 'count': 2}
{'timePeriod': '201710150000', 'count': 1}
{'timePeriod': '201710140000', 'count': 64}
{'timePeriod': '201710130000', 'count': 3}
{'timePeriod': '201710120000', 'count': 4}
{'timePeriod': '201710110000', 'count': 8}
{'timePeriod': '201710100000', 'count': 4}
{'timePeriod': '201710090000', 'count': 1}
{'timePeriod': '201710080000', 'count': 0}
{'timePeriod': '201710070000', 'count': 0}
{'timePeriod': '201710060000', 'count': 1}
{'timePeriod': '201710050000', 'count': 3}
{'timePeriod': '201710040000', 'count': 5}
{'timePeriod': '201710030000', 'count': 8}
{'timePeriod': '201710020000', 'count': 5}
{'timePeriod': '201710010000', 'count': 0}
{'timePeriod': '201709300000', 'count': 0}
{'timePeriod': '201709290000', 'count': 0}
{'timePeriod': '201709280000', 'count': 9}
{'timePeriod': '201709270000', 'count': 41}
{'timePeriod': '201709260000', 'count': 13}
{'timePeriod': '201709250000', 'count': 6}
{'timePeriod': '201709240000', 'count': 7}
{'timePeriod': '201709230000', 'count': 3}
{'timePeriod': '201709220000', 'count': 0}
{'timePeriod': '201709210000', 'count': 1}
{'timePeriod': '201709200000', 'count': 7}

Contributing

Any contributions should follow the following pattern:

  1. Make a feature or bugfix branch, e.g., git checkout -b my_new_feature
  2. Make your changes in that branch
  3. Ensure you bump the version number in searchtweets/_version.py to reflect your changes. We use Semantic Versioning, so non-breaking enhancements should increment the minor version, e.g., 1.5.0 -> 1.6.0, and bugfixes will increment the last version, 1.6.0 -> 1.6.1.
  4. Create a pull request

After the pull request process is accepted, package maintainers will handle building documentation and distribution to Pypi.

For reference, distributing to Pypi is accomplished by the following commands, ran from the root directory in the repo:

python setup.py bdist_wheel
python setup.py sdist
twine upload dist/*

How to build the documentation:

Building the documentation requires a few Sphinx packages to build the webpages:

pip install sphinx
pip install sphinx_bootstrap_theme
pip install sphinxcontrib-napoleon

Then (once your changes are committed to master) you should be able to run the documentation-generating bash script and follow the instructions:

bash build_sphinx_docs.sh master searchtweets

Note that this README is also generated, and so after any README changes you'll need to re-build the README (you need pandoc version 2.1+ for this) and commit the result:

bash make_readme.sh