# Creating Timelines

Before you can write TQL queries, you must import your raw time series data into TQL and construct timelines. A Timeline is simply a collection of events identified by a common join key, for example a logged in user_id or web cookie, and sorted by timestamp.  In this guide we will create a mappings from source data into TQL, inspect the data, then finally create a Project and Timelines that TQL queries can be executed against.

### Creating A TimeSeries

Your source data is typically be stored in your data lake in flat files, and/or in a SQL-like data warehouse such as Big Query or Snowflake.  The mapping between your data source and TQL is called a ***TimeSeries***.  It is important to note that a TimeSeries object in TQL is not the data itself, but merely a script on how to read the data out of storage into TQL.  We start by using the TQL function `create_timeseries()`, providing a source using a `from_files()` or `from_sql()` clause, and providing the names of the common id and timestamp fields in the source data.  


```python
from zeenk.tql import *
```

    -----------------------------------------------------------------
               Version: 20.1.17-SNAPSHOT
     Version Timestamp: 2021-08-27 20:56:17
           Version Age: 2 days, 19 hours, 33 minutes, 6 seconds
       Filesystem Root: /Users/zkozick/.tql/files
     Working Directory: /Users/zkozick/.tql
    Configuration File: /Users/zkozick/.tql/tql-conf.yml
           Api Gateway: http://localhost:9000
        Service Status: Icarus: ONLINE, Daedalus: ONLINE
        Service Uptime: 1 minutes, 5 seconds
    -----------------------------------------------------------------



```python
from zeenk.tql.demo_projects import get_project4_files_dir

# this directory contains the time series data files for demo project 4.
base_dir = get_project4_files_dir()

activities = create_timeseries('activity')
activities.identified_by('user_id', 'event_time')
activities.from_files(
    f'{base_dir}/activity*.csv',
     has_csv_header=True
  ) 
```




    TimeSeries(name=activity, 
    	timeline_id_col=user_id, 
    	timestamp_col=event_time, 
    	format=CSV, 
    	data_path=gs://noumena...ctivity*.csv, 
    	sql_statements=None)



### Validating A TimeSeries

the first step after your TimeSeries has been created is to validate it with `validate()`.  Validation checks to make sure your data can be loaded from the external source, the identity fields are not empty, and  the timestamp format is parsable.  


```python
activities.validate()
```

    just a moment, setting up a PySpark session...
    PySpark is now available via the function zeenk.tql.spark()
    or just spark() if you used from zeenk.tql import *
    validating 'activity'... no errors found.


### Loading A TimeSeries into Pandas or Spark
Often it is useful to load your timeseries data as a dataframe for inspection or external analysis.  For this we can use the convienence functions `pandas_dataframe()` and `spark_dataframe()`:


```python
activities.pandas_dataframe()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>activity_id</th>
      <th>event_time</th>
      <th>user_id</th>
      <th>conversion</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>a15784</td>
      <td>2021-01-11 00:00:03</td>
      <td>u196</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>1</th>
      <td>a15785</td>
      <td>2021-01-11 00:00:51</td>
      <td>u88</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>2</th>
      <td>a15786</td>
      <td>2021-01-11 00:01:18</td>
      <td>u668</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>3</th>
      <td>a15787</td>
      <td>2021-01-11 00:01:19</td>
      <td>u88</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>4</th>
      <td>a15788</td>
      <td>2021-01-11 00:01:22</td>
      <td>u116</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>25411</th>
      <td>a2924</td>
      <td>2021-01-02 23:54:36</td>
      <td>u718</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>25412</th>
      <td>a2925</td>
      <td>2021-01-02 23:55:02</td>
      <td>u80</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>25413</th>
      <td>a2926</td>
      <td>2021-01-02 23:59:07</td>
      <td>u819</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>25414</th>
      <td>a2927</td>
      <td>2021-01-02 23:59:35</td>
      <td>u264</td>
      <td>product_view</td>
    </tr>
    <tr>
      <th>25415</th>
      <td>a2928</td>
      <td>2021-01-02 23:59:59</td>
      <td>u169</td>
      <td>product_view</td>
    </tr>
  </tbody>
</table>
<p>25416 rows × 4 columns</p>
</div>




```python
activities.spark_dataframe().show(5)
```

    +-----------+-------------------+-------+------------+
    |activity_id|         event_time|user_id|  conversion|
    +-----------+-------------------+-------+------------+
    |     a15784|2021-01-11 00:00:03|   u196|product_view|
    |     a15785|2021-01-11 00:00:51|    u88|product_view|
    |     a15786|2021-01-11 00:01:18|   u668|product_view|
    |     a15787|2021-01-11 00:01:19|    u88|product_view|
    |     a15788|2021-01-11 00:01:22|   u116|product_view|
    +-----------+-------------------+-------+------------+
    only showing top 5 rows
    


### Getting an Example TimeSeries Row
You can get an example from your time series using `get_example()`:


```python
activities.get_example()
```




    {'activity_id': 'a15784',
     'event_time': '2021-01-11 00:00:03',
     'user_id': 'u196',
     'conversion': 'product_view'}



### Visualizing A TimeSeries
It is often helpful to inspect your timeseries visually.  For this we provide the function `visualize()`,
available as a Jupyter extension:


```python
activities.visualize()
```

    preparing data...



    Timeline(events='[{"_type": "activity", "_timeline_id": "u196", "_datetime": "2021-01-11 00:00:03", "activity_…


<img src="_static/jupyter_extensions/timeseries_visualizer.png" />

### Analyzing A TimeSeries
TQL also provides a quick way to understand the shape of your TimeSeries, via the function `analyze()`:


```python
activities.analyze()
```


Click to <a href="http://localhost:9000/v1/timeseries/analyze/135/abort" target="_blank">Abort Job</a>
                            <p><em>NOTE: Abort link will open the result of the call in a <B>new tab/window</B></em></p>


    SUCCESS:  224/224|██████████|, TOTAL ELAPSED=0:00:10, WAIT=0:00:00, PENDING=0:00:00, RUNNING=0:00:09





<div><b>TimeSeries Name</b>: activity<br/><b>Timeline Identity Column</b>: user_id<br/><b>Timestamp Column</b>: event_time<br/><b>File Format</b>: CSV<br/><b>Number of Events</b>:  25416<br/><b>Number of Timelines</b>:  876<br/><b>Data Path</b>: gs://noumena-public/data/lethe_demo_set_randomize_users_id4_v1.3/activity*.csv<br/><br/><style type="text/css">
    #T_9a876_ th {
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    }#T_9a876_row0_col0,#T_9a876_row0_col1,#T_9a876_row0_col2,#T_9a876_row0_col3,#T_9a876_row1_col0,#T_9a876_row1_col1,#T_9a876_row1_col2,#T_9a876_row1_col3,#T_9a876_row2_col0,#T_9a876_row2_col1,#T_9a876_row2_col2,#T_9a876_row2_col3{
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            white-space:  pre-wrap;
        }</style><table style="display:inline; padding-right: 20px" id="T_9a876_"><caption style="font-weight: bold; font-size:120%">TimeSeries Statistics</caption><thead>    <tr>        <th class="col_heading level0 col0">Column</th>        <th class="col_heading level0 col1">Cardinality</th>        <th class="col_heading level0 col2">Range</th>        <th class="col_heading level0 col3">Type</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_9a876_row0_col0" class="data row0 col0">user_id</td>
                        <td id="T_9a876_row0_col1" class="data row0 col1">876</td>
                        <td id="T_9a876_row0_col2" class="data row0 col2">u0 - u999</td>
                        <td id="T_9a876_row0_col3" class="data row0 col3">string</td>
            </tr>
            <tr>
                                <td id="T_9a876_row1_col0" class="data row1 col0">activity_id</td>
                        <td id="T_9a876_row1_col1" class="data row1 col1">25416</td>
                        <td id="T_9a876_row1_col2" class="data row1 col2">a0 - i999</td>
                        <td id="T_9a876_row1_col3" class="data row1 col3">string</td>
            </tr>
            <tr>
                                <td id="T_9a876_row2_col0" class="data row2 col0">conversion</td>
                        <td id="T_9a876_row2_col1" class="data row2 col1">3</td>
                        <td id="T_9a876_row2_col2" class="data row2 col2">add_to_cart - purchase</td>
                        <td id="T_9a876_row2_col3" class="data row2 col3">string</td>
            </tr>
    </tbody></table><br/><style type="text/css">
    #T_1f877_ th {
          text-align: left;
    }#T_1f877_row0_col0,#T_1f877_row0_col1,#T_1f877_row0_col2,#T_1f877_row1_col0,#T_1f877_row1_col1,#T_1f877_row1_col2,#T_1f877_row2_col0,#T_1f877_row2_col1,#T_1f877_row2_col2,#T_1f877_row3_col0,#T_1f877_row3_col1,#T_1f877_row3_col2,#T_1f877_row4_col0,#T_1f877_row4_col1,#T_1f877_row4_col2{
            text-align:  left;
            white-space:  pre-wrap;
        }</style><table style="display:inline; padding-right: 20px" id="T_1f877_"><caption style="font-weight: bold; font-size:120%">Top 5 Values of activity_id:</caption><thead>    <tr>        <th class="col_heading level0 col0">Value</th>        <th class="col_heading level0 col1">Number of Occurrences</th>        <th class="col_heading level0 col2">Pct of All Events</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_1f877_row0_col0" class="data row0 col0">a15822</td>
                        <td id="T_1f877_row0_col1" class="data row0 col1">1</td>
                        <td id="T_1f877_row0_col2" class="data row0 col2">0.0%</td>
            </tr>
            <tr>
                                <td id="T_1f877_row1_col0" class="data row1 col0">a15890</td>
                        <td id="T_1f877_row1_col1" class="data row1 col1">1</td>
                        <td id="T_1f877_row1_col2" class="data row1 col2">0.0%</td>
            </tr>
            <tr>
                                <td id="T_1f877_row2_col0" class="data row2 col0">a16415</td>
                        <td id="T_1f877_row2_col1" class="data row2 col1">1</td>
                        <td id="T_1f877_row2_col2" class="data row2 col2">0.0%</td>
            </tr>
            <tr>
                                <td id="T_1f877_row3_col0" class="data row3 col0">a16450</td>
                        <td id="T_1f877_row3_col1" class="data row3 col1">1</td>
                        <td id="T_1f877_row3_col2" class="data row3 col2">0.0%</td>
            </tr>
            <tr>
                                <td id="T_1f877_row4_col0" class="data row4 col0">a16462</td>
                        <td id="T_1f877_row4_col1" class="data row4 col1">1</td>
                        <td id="T_1f877_row4_col2" class="data row4 col2">0.0%</td>
            </tr>
    </tbody></table><style type="text/css">
    #T_16b68_ th {
          text-align: left;
    }#T_16b68_row0_col0,#T_16b68_row0_col1,#T_16b68_row0_col2,#T_16b68_row1_col0,#T_16b68_row1_col1,#T_16b68_row1_col2,#T_16b68_row2_col0,#T_16b68_row2_col1,#T_16b68_row2_col2{
            text-align:  left;
            white-space:  pre-wrap;
        }</style><table style="display:inline; padding-right: 20px" id="T_16b68_"><caption style="font-weight: bold; font-size:120%">Top 5 Values of conversion:</caption><thead>    <tr>        <th class="col_heading level0 col0">Value</th>        <th class="col_heading level0 col1">Number of Occurrences</th>        <th class="col_heading level0 col2">Pct of All Events</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_16b68_row0_col0" class="data row0 col0">product_view</td>
                        <td id="T_16b68_row0_col1" class="data row0 col1">22251</td>
                        <td id="T_16b68_row0_col2" class="data row0 col2">87.55%</td>
            </tr>
            <tr>
                                <td id="T_16b68_row1_col0" class="data row1 col0">add_to_cart</td>
                        <td id="T_16b68_row1_col1" class="data row1 col1">2165</td>
                        <td id="T_16b68_row1_col2" class="data row1 col2">8.52%</td>
            </tr>
            <tr>
                                <td id="T_16b68_row2_col0" class="data row2 col0">purchase</td>
                        <td id="T_16b68_row2_col1" class="data row2 col1">1000</td>
                        <td id="T_16b68_row2_col2" class="data row2 col2">3.93%</td>
            </tr>
    </tbody></table><style type="text/css">
    #T_d1b00_ th {
          text-align: left;
    }#T_d1b00_row0_col0,#T_d1b00_row0_col1,#T_d1b00_row0_col2,#T_d1b00_row1_col0,#T_d1b00_row1_col1,#T_d1b00_row1_col2,#T_d1b00_row2_col0,#T_d1b00_row2_col1,#T_d1b00_row2_col2,#T_d1b00_row3_col0,#T_d1b00_row3_col1,#T_d1b00_row3_col2,#T_d1b00_row4_col0,#T_d1b00_row4_col1,#T_d1b00_row4_col2{
            text-align:  left;
            white-space:  pre-wrap;
        }</style><table style="display:inline; padding-right: 20px" id="T_d1b00_"><caption style="font-weight: bold; font-size:120%">Top 5 Values of user_id:</caption><thead>    <tr>        <th class="col_heading level0 col0">Value</th>        <th class="col_heading level0 col1">Number of Occurrences</th>        <th class="col_heading level0 col2">Pct of All Events</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_d1b00_row0_col0" class="data row0 col0">u134</td>
                        <td id="T_d1b00_row0_col1" class="data row0 col1">171</td>
                        <td id="T_d1b00_row0_col2" class="data row0 col2">0.67%</td>
            </tr>
            <tr>
                                <td id="T_d1b00_row1_col0" class="data row1 col0">u634</td>
                        <td id="T_d1b00_row1_col1" class="data row1 col1">156</td>
                        <td id="T_d1b00_row1_col2" class="data row1 col2">0.61%</td>
            </tr>
            <tr>
                                <td id="T_d1b00_row2_col0" class="data row2 col0">u482</td>
                        <td id="T_d1b00_row2_col1" class="data row2 col1">151</td>
                        <td id="T_d1b00_row2_col2" class="data row2 col2">0.59%</td>
            </tr>
            <tr>
                                <td id="T_d1b00_row3_col0" class="data row3 col0">u677</td>
                        <td id="T_d1b00_row3_col1" class="data row3 col1">149</td>
                        <td id="T_d1b00_row3_col2" class="data row3 col2">0.59%</td>
            </tr>
            <tr>
                                <td id="T_d1b00_row4_col0" class="data row4 col0">u963</td>
                        <td id="T_d1b00_row4_col1" class="data row4 col1">148</td>
                        <td id="T_d1b00_row4_col2" class="data row4 col2">0.58%</td>
            </tr>
    </tbody></table></div>




### Creating Another TimeSeries 
Usually, a set of timelines will consist of more than a single time series.  For example, we may wish to augment
our site activity with a set of bids we made to show ads to the same set of users.  For this we simply create another
`TimeSeries` object, which we will eventually add to our project to build timelines.


```python
bids = create_timeseries('bid')
bids.identified_by('user_id', 'event_time')
bids.from_files(
    f'{base_dir}/bid*.csv',
     has_csv_header=True
  ) 
```




    TimeSeries(name=bid, 
    	timeline_id_col=user_id, 
    	timestamp_col=event_time, 
    	format=CSV, 
    	data_path=gs://noumena...1.3/bid*.csv, 
    	sql_statements=None)



### Creating a TimeSeries from an External SQL Store

It is common to already have your time series data loaded into a data warehouse engine for analytics.
TQL supports directly ingesting data from an external source with the function `from_sql()`  Flat files,
Big Query, and Snowflake (with the appropriate credentials) are supported out of the box.  Using SQL
to map your data into TQL has the added benefit of being able to manipulate your data upon ingestion. Other common uses for SQL-style ingestion would be to filter out bot, duplicate, or garbage data, or to augment your timeseries with additional attributes from another table using a JOIN statement.  In the following example, we assign a proxy `timestamp` to all our user records, as well as add a `duration` field:


```python
users = create_timeseries('user')
users.identified_by('user_id', 'timestamp', duration_col='duration')
users.from_sql(f'''
  CREATE OR REPLACE TEMPORARY VIEW users 
  USING CSV options (
    path="{base_dir}/user.csv",
    inferSchema="true",
    header="true"
  )''',
#   '''create or replace temporary view users_filtered as select u.* from users where <fancy bot filter statement here>''',
  'SELECT u.*, "2021-01-01" timestamp, "1" duration FROM users u'
)
```




    TimeSeries(name=user, 
    	timeline_id_col=user_id, 
    	timestamp_col=timestamp, 
    	format=SQL, 
    	data_path=None, 
    	sql_statements=['\n  CREATE OR...r="true"\n  )', 'SELECT u.*, ...FROM users u'])




```python
project = create_project('advertising_funnel_data', or_update=True)
project.from_timeseries(bids, activities, users)
timelines = project.build_timelines()
```


Click to <a href="http://localhost:9000/v1/projects/timelines/149/abort" target="_blank">Abort Job</a>
                            <p><em>NOTE: Abort link will open the result of the call in a <B>new tab/window</B></em></p>


    SUCCESS:  234/234|██████████|, TOTAL ELAPSED=0:00:22, WAIT=0:00:00, PENDING=0:00:04, COMPILING=0:00:18  


### Listing All Available Projects
You can list all available projects by using `show_projects()`.  Note that TQL ships with four demo
projects to help you learn the basics of TQL, without having to ingest any time series data:


```python
show_projects()
```




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      <thead>    <tr>        <th class="col_heading level0 col0">id</th>        <th class="col_heading level0 col1">name</th>        <th class="col_heading level0 col2">description</th>        <th class="col_heading level0 col3">timeseries</th>        <th class="col_heading level0 col4">timelines</th>        <th class="col_heading level0 col5">events</th>        <th class="col_heading level0 col6">status</th>    </tr></thead>
      <tbody>
                <tr>
                                <td id="T_8ce13_row0_col0" class="data row0 col0">1</td>
                        <td id="T_8ce13_row0_col1" class="data row0 col1">acheron</td>
                        <td id="T_8ce13_row0_col2" class="data row0 col2">Acheron synthetic data, 2000 users, 5 requests per user, v1.1</td>
                        <td id="T_8ce13_row0_col3" class="data row0 col3">['request', 'conversion', 'user']</td>
                        <td id="T_8ce13_row0_col4" class="data row0 col4">2000.000000</td>
                        <td id="T_8ce13_row0_col5" class="data row0 col5">14220.000000</td>
                        <td id="T_8ce13_row0_col6" class="data row0 col6">ready for queries</td>
            </tr>
            <tr>
                                <td id="T_8ce13_row1_col0" class="data row1 col0">2</td>
                        <td id="T_8ce13_row1_col1" class="data row1 col1">acheron2</td>
                        <td id="T_8ce13_row1_col2" class="data row1 col2">Acheron 2 synthetic data, reasonably_rich, 200 users, 10 days, v1.1</td>
                        <td id="T_8ce13_row1_col3" class="data row1 col3">['request', 'conversion', 'user']</td>
                        <td id="T_8ce13_row1_col4" class="data row1 col4">200.000000</td>
                        <td id="T_8ce13_row1_col5" class="data row1 col5">13464.000000</td>
                        <td id="T_8ce13_row1_col6" class="data row1 col6">ready for queries</td>
            </tr>
            <tr>
                                <td id="T_8ce13_row2_col0" class="data row2 col0">3</td>
                        <td id="T_8ce13_row2_col1" class="data row2 col1">lethe3</td>
                        <td id="T_8ce13_row2_col2" class="data row2 col2">Lethe synthetic data, demo_set, random treatment, 1000 users, 14 days, v1.3</td>
                        <td id="T_8ce13_row2_col3" class="data row2 col3">['bid', 'activity', 'user']</td>
                        <td id="T_8ce13_row2_col4" class="data row2 col4">1000.000000</td>
                        <td id="T_8ce13_row2_col5" class="data row2 col5">198341.000000</td>
                        <td id="T_8ce13_row2_col6" class="data row2 col6">ready for queries</td>
            </tr>
            <tr>
                                <td id="T_8ce13_row3_col0" class="data row3 col0">4</td>
                        <td id="T_8ce13_row3_col1" class="data row3 col1">lethe4</td>
                        <td id="T_8ce13_row3_col2" class="data row3 col2">Lethe synthetic data, demo_set, random users, 1000 users, 14 days, v1.3</td>
                        <td id="T_8ce13_row3_col3" class="data row3 col3">['bid', 'activity', 'user']</td>
                        <td id="T_8ce13_row3_col4" class="data row3 col4">1000.000000</td>
                        <td id="T_8ce13_row3_col5" class="data row3 col5">198393.000000</td>
                        <td id="T_8ce13_row3_col6" class="data row3 col6">ready for queries</td>
            </tr>
            <tr>
                                <td id="T_8ce13_row4_col0" class="data row4 col0">10</td>
                        <td id="T_8ce13_row4_col1" class="data row4 col1">unittest</td>
                        <td id="T_8ce13_row4_col2" class="data row4 col2">this is a unit test project</td>
                        <td id="T_8ce13_row4_col3" class="data row4 col3">['request']</td>
                        <td id="T_8ce13_row4_col4" class="data row4 col4">1990.000000</td>
                        <td id="T_8ce13_row4_col5" class="data row4 col5">12220.000000</td>
                        <td id="T_8ce13_row4_col6" class="data row4 col6">ready for queries</td>
            </tr>
            <tr>
                                <td id="T_8ce13_row5_col0" class="data row5 col0">101</td>
                        <td id="T_8ce13_row5_col1" class="data row5 col1">None</td>
                        <td id="T_8ce13_row5_col2" class="data row5 col2">unittest</td>
                        <td id="T_8ce13_row5_col3" class="data row5 col3">['unittest']</td>
                        <td id="T_8ce13_row5_col4" class="data row5 col4">nan</td>
                        <td id="T_8ce13_row5_col5" class="data row5 col5">nan</td>
                        <td id="T_8ce13_row5_col6" class="data row5 col6">timelines not yet created</td>
            </tr>
            <tr>
                                <td id="T_8ce13_row6_col0" class="data row6 col0">102</td>
                        <td id="T_8ce13_row6_col1" class="data row6 col1">advertising_funnel_data</td>
                        <td id="T_8ce13_row6_col2" class="data row6 col2">None</td>
                        <td id="T_8ce13_row6_col3" class="data row6 col3">['bid', 'activity', 'user']</td>
                        <td id="T_8ce13_row6_col4" class="data row6 col4">1000.000000</td>
                        <td id="T_8ce13_row6_col5" class="data row6 col5">198393.000000</td>
                        <td id="T_8ce13_row6_col6" class="data row6 col6">ready for queries</td>
            </tr>
            <tr>
                                <td id="T_8ce13_row7_col0" class="data row7 col0">103</td>
                        <td id="T_8ce13_row7_col1" class="data row7 col1">demo_project</td>
                        <td id="T_8ce13_row7_col2" class="data row7 col2">None</td>
                        <td id="T_8ce13_row7_col3" class="data row7 col3">['activity', 'bid', 'user']</td>
                        <td id="T_8ce13_row7_col4" class="data row7 col4">1000.000000</td>
                        <td id="T_8ce13_row7_col5" class="data row7 col5">198393.000000</td>
                        <td id="T_8ce13_row7_col6" class="data row7 col6">ready for queries</td>
            </tr>
    </tbody>
    </table>
  </body>
</html>




### Loading a Project Definition

Previously built projects can be recalled using `load_project()`.  This is often useful to inspect the
TQL expression attributes that are adressable in queries.


```python
load_project('advertising_funnel_data')
```




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  <head>
    <style type="text/css">
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            text-align:  left;
            white-space:  pre-wrap;
        }</style>
  </head>
  <body>
    <table style="display:inline; padding-right: 20px" id="T_7ea7f_">
      <caption style="font-weight: bold; font-size:120%">Table Overview</caption>
      <thead>    <tr>        <th class="col_heading level0 col0">id</th>        <th class="col_heading level0 col1">name</th>        <th class="col_heading level0 col2">description</th>        <th class="col_heading level0 col3">timeseries</th>        <th class="col_heading level0 col4">status</th>    </tr></thead>
      <tbody>
                <tr>
                                <td id="T_7ea7f_row0_col0" class="data row0 col0">102</td>
                        <td id="T_7ea7f_row0_col1" class="data row0 col1">advertising_funnel_data</td>
                        <td id="T_7ea7f_row0_col2" class="data row0 col2">No description yet added</td>
                        <td id="T_7ea7f_row0_col3" class="data row0 col3">['bid', 'activity', 'user']</td>
                        <td id="T_7ea7f_row0_col4" class="data row0 col4">ready for queries</td>
            </tr>
    </tbody>
    </table>
    <br/>
    <style type="text/css">
    #T_1fd0b_ th {
          text-align: left;
    }#T_1fd0b_row0_col0,#T_1fd0b_row0_col1,#T_1fd0b_row1_col0,#T_1fd0b_row1_col1,#T_1fd0b_row2_col0,#T_1fd0b_row2_col1,#T_1fd0b_row3_col0,#T_1fd0b_row3_col1,#T_1fd0b_row4_col0,#T_1fd0b_row4_col1,#T_1fd0b_row5_col0,#T_1fd0b_row5_col1,#T_1fd0b_row6_col0,#T_1fd0b_row6_col1,#T_1fd0b_row7_col0,#T_1fd0b_row7_col1,#T_1fd0b_row8_col0,#T_1fd0b_row8_col1{
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        }</style>
    <table style="display:inline; padding-right: 20px" id="T_1fd0b_">
      <caption style="font-weight: bold; font-size:120%">Timeline Statistics</caption>
      <thead>    <tr>        <th class="col_heading level0 col0">0</th>        <th class="col_heading level0 col1">1</th>    </tr></thead>
      <tbody>
                <tr>
                                <td id="T_1fd0b_row0_col0" class="data row0 col0">Total Timelines</td>
                        <td id="T_1fd0b_row0_col1" class="data row0 col1">1000</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row1_col0" class="data row1 col0">Smallest Timeline</td>
                        <td id="T_1fd0b_row1_col1" class="data row1 col1">1 events</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row2_col0" class="data row2 col0">Largest Timeline</td>
                        <td id="T_1fd0b_row2_col1" class="data row2 col1">904 events</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row3_col0" class="data row3 col0">Min Timestamp</td>
                        <td id="T_1fd0b_row3_col1" class="data row3 col1">2021-01-01 00:00:00</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row4_col0" class="data row4 col0">Max Timestamp</td>
                        <td id="T_1fd0b_row4_col1" class="data row4 col1">2021-01-14 23:59:59</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row5_col0" class="data row5 col0">Total Events</td>
                        <td id="T_1fd0b_row5_col1" class="data row5 col1">198393</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row6_col0" class="data row6 col0">Activity Events</td>
                        <td id="T_1fd0b_row6_col1" class="data row6 col1">25416</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row7_col0" class="data row7 col0">Bid Events</td>
                        <td id="T_1fd0b_row7_col1" class="data row7 col1">171977</td>
            </tr>
            <tr>
                                <td id="T_1fd0b_row8_col0" class="data row8 col0">User Events</td>
                        <td id="T_1fd0b_row8_col1" class="data row8 col1">1000</td>
            </tr>
    </tbody>
    </table>
    <br/>
    <style type="text/css">
    #T_9514b_ th {
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    }#T_9514b_row0_col0,#T_9514b_row0_col1,#T_9514b_row0_col2,#T_9514b_row1_col0,#T_9514b_row1_col1,#T_9514b_row1_col2,#T_9514b_row2_col0,#T_9514b_row2_col1,#T_9514b_row2_col2,#T_9514b_row3_col0,#T_9514b_row3_col1,#T_9514b_row3_col2,#T_9514b_row4_col0,#T_9514b_row4_col1,#T_9514b_row4_col2,#T_9514b_row5_col0,#T_9514b_row5_col1,#T_9514b_row5_col2,#T_9514b_row6_col0,#T_9514b_row6_col1,#T_9514b_row6_col2,#T_9514b_row7_col0,#T_9514b_row7_col1,#T_9514b_row7_col2,#T_9514b_row8_col0,#T_9514b_row8_col1,#T_9514b_row8_col2,#T_9514b_row9_col0,#T_9514b_row9_col1,#T_9514b_row9_col2,#T_9514b_row10_col0,#T_9514b_row10_col1,#T_9514b_row10_col2,#T_9514b_row11_col0,#T_9514b_row11_col1,#T_9514b_row11_col2,#T_9514b_row12_col0,#T_9514b_row12_col1,#T_9514b_row12_col2,#T_9514b_row13_col0,#T_9514b_row13_col1,#T_9514b_row13_col2,#T_9514b_row14_col0,#T_9514b_row14_col1,#T_9514b_row14_col2,#T_9514b_row15_col0,#T_9514b_row15_col1,#T_9514b_row15_col2,#T_9514b_row16_col0,#T_9514b_row16_col1,#T_9514b_row16_col2,#T_9514b_row17_col0,#T_9514b_row17_col1,#T_9514b_row17_col2,#T_9514b_row18_col0,#T_9514b_row18_col1,#T_9514b_row18_col2,#T_9514b_row19_col0,#T_9514b_row19_col1,#T_9514b_row19_col2,#T_9514b_row20_col0,#T_9514b_row20_col1,#T_9514b_row20_col2,#T_9514b_row21_col0,#T_9514b_row21_col1,#T_9514b_row21_col2,#T_9514b_row22_col0,#T_9514b_row22_col1,#T_9514b_row22_col2,#T_9514b_row23_col0,#T_9514b_row23_col1,#T_9514b_row23_col2,#T_9514b_row24_col0,#T_9514b_row24_col1,#T_9514b_row24_col2,#T_9514b_row25_col0,#T_9514b_row25_col1,#T_9514b_row25_col2,#T_9514b_row26_col0,#T_9514b_row26_col1,#T_9514b_row26_col2,#T_9514b_row27_col0,#T_9514b_row27_col1,#T_9514b_row27_col2,#T_9514b_row28_col0,#T_9514b_row28_col1,#T_9514b_row28_col2{
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            white-space:  pre-wrap;
        }</style>
    <table style="display:inline; padding-right: 20px" id="T_9514b_">
      <caption style="font-weight: bold; font-size:120%">Attributes</caption>
      <thead>    <tr>        <th class="col_heading level0 col0">TQL Expression</th>        <th class="col_heading level0 col1">Description</th>        <th class="col_heading level0 col2">Type</th>    </tr></thead>
      <tbody>
                <tr>
                                <td id="T_9514b_row0_col0" class="data row0 col0">activity.activity_id</td>
                        <td id="T_9514b_row0_col1" class="data row0 col1">None</td>
                        <td id="T_9514b_row0_col2" class="data row0 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row1_col0" class="data row1 col0">activity.conversion</td>
                        <td id="T_9514b_row1_col1" class="data row1 col1">None</td>
                        <td id="T_9514b_row1_col2" class="data row1 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row2_col0" class="data row2 col0">activity.event_time</td>
                        <td id="T_9514b_row2_col1" class="data row2 col1">None</td>
                        <td id="T_9514b_row2_col2" class="data row2 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row3_col0" class="data row3 col0">activity.user_id</td>
                        <td id="T_9514b_row3_col1" class="data row3 col1">None</td>
                        <td id="T_9514b_row3_col2" class="data row3 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row4_col0" class="data row4 col0">bid.ad_size</td>
                        <td id="T_9514b_row4_col1" class="data row4 col1">None</td>
                        <td id="T_9514b_row4_col2" class="data row4 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row5_col0" class="data row5 col0">bid.bid</td>
                        <td id="T_9514b_row5_col1" class="data row5 col1">None</td>
                        <td id="T_9514b_row5_col2" class="data row5 col2">double</td>
            </tr>
            <tr>
                                <td id="T_9514b_row6_col0" class="data row6 col0">bid.event_time</td>
                        <td id="T_9514b_row6_col1" class="data row6 col1">None</td>
                        <td id="T_9514b_row6_col2" class="data row6 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row7_col0" class="data row7 col0">bid.ghosted</td>
                        <td id="T_9514b_row7_col1" class="data row7 col1">None</td>
                        <td id="T_9514b_row7_col2" class="data row7 col2">boolean</td>
            </tr>
            <tr>
                                <td id="T_9514b_row8_col0" class="data row8 col0">bid.request_id</td>
                        <td id="T_9514b_row8_col1" class="data row8 col1">None</td>
                        <td id="T_9514b_row8_col2" class="data row8 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row9_col0" class="data row9 col0">bid.user_id</td>
                        <td id="T_9514b_row9_col1" class="data row9 col1">None</td>
                        <td id="T_9514b_row9_col2" class="data row9 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row10_col0" class="data row10 col0">bid.won</td>
                        <td id="T_9514b_row10_col1" class="data row10 col1">None</td>
                        <td id="T_9514b_row10_col2" class="data row10 col2">boolean</td>
            </tr>
            <tr>
                                <td id="T_9514b_row11_col0" class="data row11 col0">duration</td>
                        <td id="T_9514b_row11_col1" class="data row11 col1">The duration of the event. Typically used with generated events.</td>
                        <td id="T_9514b_row11_col2" class="data row11 col2">long</td>
            </tr>
            <tr>
                                <td id="T_9514b_row12_col0" class="data row12 col0">event</td>
                        <td id="T_9514b_row12_col1" class="data row12 col1">The current event object</td>
                        <td id="T_9514b_row12_col2" class="data row12 col2">event</td>
            </tr>
            <tr>
                                <td id="T_9514b_row13_col0" class="data row13 col0">generated</td>
                        <td id="T_9514b_row13_col1" class="data row13 col1">This event was created by generate_events()</td>
                        <td id="T_9514b_row13_col2" class="data row13 col2">boolean</td>
            </tr>
            <tr>
                                <td id="T_9514b_row14_col0" class="data row14 col0">generated_weight</td>
                        <td id="T_9514b_row14_col1" class="data row14 col1">The weight to apply to the the sample</td>
                        <td id="T_9514b_row14_col2" class="data row14 col2">double</td>
            </tr>
            <tr>
                                <td id="T_9514b_row15_col0" class="data row15 col0">id</td>
                        <td id="T_9514b_row15_col1" class="data row15 col1">The auto-generated event id</td>
                        <td id="T_9514b_row15_col2" class="data row15 col2">long</td>
            </tr>
            <tr>
                                <td id="T_9514b_row16_col0" class="data row16 col0">index</td>
                        <td id="T_9514b_row16_col1" class="data row16 col1">The index of the current event in the timeline</td>
                        <td id="T_9514b_row16_col2" class="data row16 col2">integer</td>
            </tr>
            <tr>
                                <td id="T_9514b_row17_col0" class="data row17 col0">outcome</td>
                        <td id="T_9514b_row17_col1" class="data row17 col1">The outcome event.  Only used in conjunction with SUM_OPPORTUNITIES() and CAUSAL_EFFFCT</td>
                        <td id="T_9514b_row17_col2" class="data row17 col2">event</td>
            </tr>
            <tr>
                                <td id="T_9514b_row18_col0" class="data row18 col0">timeline.events</td>
                        <td id="T_9514b_row18_col1" class="data row18 col1">Time-ordered array of events</td>
                        <td id="T_9514b_row18_col2" class="data row18 col2">array</td>
            </tr>
            <tr>
                                <td id="T_9514b_row19_col0" class="data row19 col0">timeline.id</td>
                        <td id="T_9514b_row19_col1" class="data row19 col1">Shared timeline id</td>
                        <td id="T_9514b_row19_col2" class="data row19 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row20_col0" class="data row20 col0">timestamp</td>
                        <td id="T_9514b_row20_col1" class="data row20 col1">The timestamp of the event</td>
                        <td id="T_9514b_row20_col2" class="data row20 col2">long</td>
            </tr>
            <tr>
                                <td id="T_9514b_row21_col0" class="data row21 col0">type</td>
                        <td id="T_9514b_row21_col1" class="data row21 col1">The event type of this event</td>
                        <td id="T_9514b_row21_col2" class="data row21 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row22_col0" class="data row22 col0">user.age</td>
                        <td id="T_9514b_row22_col1" class="data row22 col1">None</td>
                        <td id="T_9514b_row22_col2" class="data row22 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row23_col0" class="data row23 col0">user.duration</td>
                        <td id="T_9514b_row23_col1" class="data row23 col1">None</td>
                        <td id="T_9514b_row23_col2" class="data row23 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row24_col0" class="data row24 col0">user.gender</td>
                        <td id="T_9514b_row24_col1" class="data row24 col1">None</td>
                        <td id="T_9514b_row24_col2" class="data row24 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row25_col0" class="data row25 col0">user.group</td>
                        <td id="T_9514b_row25_col1" class="data row25 col1">None</td>
                        <td id="T_9514b_row25_col2" class="data row25 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row26_col0" class="data row26 col0">user.region</td>
                        <td id="T_9514b_row26_col1" class="data row26 col1">None</td>
                        <td id="T_9514b_row26_col2" class="data row26 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row27_col0" class="data row27 col0">user.timestamp</td>
                        <td id="T_9514b_row27_col1" class="data row27 col1">None</td>
                        <td id="T_9514b_row27_col2" class="data row27 col2">string</td>
            </tr>
            <tr>
                                <td id="T_9514b_row28_col0" class="data row28 col0">user.user_id</td>
                        <td id="T_9514b_row28_col1" class="data row28 col1">None</td>
                        <td id="T_9514b_row28_col2" class="data row28 col2">string</td>
            </tr>
    </tbody>
    </table>
  </body>
</html>




### Using A Project In A TQL Query

use `select(...).from_events('project_name')` to write Queries that produce ResultSets from your timelines.


```python
select('*').from_events('advertising_funnel_data').limit(10)
```




<h4><em>Query results:</em></h4>partition "_default"
<div>
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        vertical-align: middle;
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        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>activity_activity_id</th>
      <th>activity_conversion</th>
      <th>activity_event_time</th>
      <th>activity_user_id</th>
      <th>bid_ad_size</th>
      <th>bid_bid</th>
      <th>bid_event_time</th>
      <th>bid_ghosted</th>
      <th>bid_request_id</th>
      <th>bid_user_id</th>
      <th>...</th>
      <th>timeline_id</th>
      <th>timestamp</th>
      <th>type</th>
      <th>user_age</th>
      <th>user_duration</th>
      <th>user_gender</th>
      <th>user_group</th>
      <th>user_region</th>
      <th>user_timestamp</th>
      <th>user_user_id</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>...</td>
      <td>u252</td>
      <td>1609459200000</td>
      <td>user</td>
      <td>55plus</td>
      <td>None</td>
      <td>male</td>
      <td>influenced</td>
      <td>south</td>
      <td>2021-01-01</td>
      <td>u252</td>
    </tr>
    <tr>
      <th>1</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>big</td>
      <td>1.0</td>
      <td>2021-01-02 00:02:36</td>
      <td>false</td>
      <td>r13260</td>
      <td>u252</td>
      <td>...</td>
      <td>u252</td>
      <td>1609545756000</td>
      <td>bid</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>2</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>big</td>
      <td>1.0</td>
      <td>2021-01-02 00:16:41</td>
      <td>false</td>
      <td>r13376</td>
      <td>u252</td>
      <td>...</td>
      <td>u252</td>
      <td>1609546601000</td>
      <td>bid</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>3</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>small</td>
      <td>1.0</td>
      <td>2021-01-02 00:30:55</td>
      <td>false</td>
      <td>r13509</td>
      <td>u252</td>
      <td>...</td>
      <td>u252</td>
      <td>1609547455000</td>
      <td>bid</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>4</th>
      <td>i176</td>
      <td>product_view</td>
      <td>2021-01-02 00:57:47</td>
      <td>u252</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>...</td>
      <td>u252</td>
      <td>1609549067000</td>
      <td>activity</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>5</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>small</td>
      <td>1.0</td>
      <td>2021-01-02 01:04:01</td>
      <td>false</td>
      <td>r13737</td>
      <td>u252</td>
      <td>...</td>
      <td>u252</td>
      <td>1609549441000</td>
      <td>bid</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>6</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>big</td>
      <td>1.0</td>
      <td>2021-01-02 01:37:26</td>
      <td>false</td>
      <td>r13985</td>
      <td>u252</td>
      <td>...</td>
      <td>u252</td>
      <td>1609551446000</td>
      <td>bid</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>7</th>
      <td>i187</td>
      <td>product_view</td>
      <td>2021-01-02 02:15:43</td>
      <td>u252</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>...</td>
      <td>u252</td>
      <td>1609553743000</td>
      <td>activity</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>8</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>big</td>
      <td>1.0</td>
      <td>2021-01-02 02:18:30</td>
      <td>false</td>
      <td>r14310</td>
      <td>u252</td>
      <td>...</td>
      <td>u252</td>
      <td>1609553910000</td>
      <td>bid</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
    <tr>
      <th>9</th>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>big</td>
      <td>1.0</td>
      <td>2021-01-02 03:01:48</td>
      <td>false</td>
      <td>r14607</td>
      <td>u252</td>
      <td>...</td>
      <td>u252</td>
      <td>1609556508000</td>
      <td>bid</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
      <td>None</td>
    </tr>
  </tbody>
</table>
<p>10 rows × 27 columns</p>
</div><i>query produced 10 rows x 27 columns in 1.12 seconds</i><br><br>



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