Readme fix (#82)

* fixed quotes

* added local python readme

* committing python readme

* commiting latest

README.md

@@ -15,13 +15,13 @@ The purpose of this project is to make time series manipulation with Spark simpl
 Python - pip install in Databricks notebooks using:
 
 ```
-%pip install dbl-tempo"
+%pip install dbl-tempo
 ```
 
 Install locally using: 
 
 ```
-pip install dbl-tempo"
+pip install dbl-tempo
 ```
 
 Scala installation: 

python/README.md

@@ -0,0 +1,168 @@
+# tempo - Time Series Utilities for Data Teams Using Databricks
+
+<p align="center">
+  <img src="tempo - light background.svg" width="300px"/>
+</p>
+
+
+## Project Description
+The purpose of this project is to make time series manipulation with Spark simpler. Operations covered under this package include AS OF joins, rolling statistics with user-specified window lengths, featurization of time series using lagged values, and Delta Lake optimization on time and partition fields.
+
+[![codecov](https://codecov.io/gh/databrickslabs/tempo/branch/master/graph/badge.svg)](https://codecov.io/gh/databrickslabs/tempo)
+
+## Using the Project
+
+Python - pip install in Databricks notebooks using:
+
+```
+%pip install dbl-tempo
+```
+
+Install locally using: 
+
+```
+pip install dbl-tempo
+```
+
+
+### Starting Point: TSDF object, a wrapper over a Spark data frame
+The entry point into all features for time series analysis in tempo is a TSDF object which wraps the Spark data frame. At a high level, a TSDF contains a data frame which contains many smaller time series, one per partition key. In order to create a TSDF object, a distinguished timestamp column much be provided in order for sorting purposes for public methods. Optionally, a sequence number and partition columns can be provided as the assumptive columns on which to create new features from. Below are the public methods available for TSDF transformation and enrichment.
+
+#### Sample Reference Architecture for Capital Markets
+
+<p align="center">
+  <img src="ts_in_fs.png" width="700px"/>
+</p>
+
+## Quickstart - Python 
+
+Data source is UCI public accelerometer data available at this URL https://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition
+
+#### 0. Read in Data 
+
+```
+from pyspark.sql.functions import * 
+
+phone_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/home/tempo/Phones_accelerometer").withColumn("event_ts", (col("Arrival_Time").cast("double")/1000).cast("timestamp")).withColumn("x", col("x").cast("double")).withColumn("y", col("y").cast("double")).withColumn("z", col("z").cast("double")).withColumn("event_ts_dbl", col("event_ts").cast("double"))
+
+from tempo import * 
+
+phone_accel_tsdf = TSDF(phone_accel_df, ts_col="event_ts", partition_cols = ["User"])
+```
+
+#### 1. Resample and Visualize
+
+###### Sample usage: 
+Possible values for frequency include patterns such as 1 minute, 4 hours, 2 days or simply sec, min, day. For the accepted functions to aggregate data, options are 'floor', 'ceil', 'min', 'max', 'mean'. Custom functions will be available in a future release.
+
+Note: You can upsample any missing values by using an option in the resample interface (fill = True)
+
+```
+# ts_col = timestamp column on which to sort fact and source table
+# partition_cols - columns to use for partitioning the TSDF into more granular time series for windowing and sorting
+
+resampled_sdf = phone_accel_tsdf.resample(freq='min', func='floor')
+resampled_pdf = resampled_sdf.df.filter(col('event_ts').cast("date") == "2015-02-23").toPandas()
+
+import plotly.graph_objs as go
+import plotly.express as px
+import pandas as pd
+
+# Plotly figure 1
+fig = px.line(resampled_pdf, x='event_ts', y='z',
+              color="User",
+              line_group="User", hover_name="User")
+fig.update_layout(title='Phone Accelerometer Usage' , showlegend=False)
+
+fig.show()
+```
+
+#### 2. AS OF Join 
+##### This join uses windowing in order to select the latest record from a source table and merges this onto the base Fact table
+
+
+<p align="center">
+  <img src="AS_OF_Join.png" width="700px"/>
+</p>
+
+
+```
+from pyspark.sql.functions import * 
+
+watch_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/home/tempo/Watch_accelerometer").withColumn("event_ts", (col("Arrival_Time").cast("double")/1000).cast("timestamp")).withColumn("x", col("x").cast("double")).withColumn("y", col("y").cast("double")).withColumn("z", col("z").cast("double")).withColumn("event_ts_dbl", col("event_ts").cast("double"))
+
+watch_accel_tsdf = TSDF(watch_accel_df, ts_col="event_ts", partition_cols = ["User"])
+
+# Applying AS OF join to TSDF datasets
+joined_df = watch_accel_tsdf.asofJoin(phone_accel_tsdf, right_prefix="phone_accel").df
+
+joined_df.show(10, False)
+```
+
+#### 3. Skew Join Optimized AS OF Join
+
+The purpose of the skew optimized as of join is to bucket each set of `partition_cols` to get the latest source record merged onto the fact table
+
+Parameters: 
+
+ts_col = timestamp column for sorting 
+partition_cols = partition columns for defining granular time series for windowing and sorting
+tsPartitionVal = value to break up each partition into time brackets
+fraction = overlap fraction
+right_prefix = prefix used for source columns when merged into fact table
+
+```
+joined_df = watch_accel_tsdf.asofJoin(phone_accel_tsdf, right_prefix="watch_accel", tsPartitionVal = 10, fraction = 0.1).df
+joined_df.show(10, False)
+```
+
+#### 4 - Approximate Exponential Moving Average
+
+The approximate exponential moving average uses an approximation of the form `EMA = e * lag(col,0) + e * (1 - e) * lag(col, 1) + e * (1 - e)^2 * lag(col, 2) ` to define a rolling moving average based on exponential decay.
+
+Parameters: 
+
+window = number of lagged values to compute for moving average
+
+```
+ema_trades = watch_accel_tsdf.EMA("x", window = 50).df
+ema_trades.show(10, False)
+```
+
+#### 5 - Simple Moving Average
+
+Method for computing rolling statistics based on the distinguished timestamp column 
+
+Parameters: 
+
+rangeBackWindowSecs = number of seconds to look back
+
+```
+moving_avg = watch_accel_tsdf.withRangeStats("y", rangeBackWindowSecs=600).df
+moving_avg.select('event_ts', 'x', 'y', 'z', 'mean_y').show(10, False)
+```
+
+
+
+## Project Support
+Please note that all projects in the /databrickslabs github account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs).  They are provided AS-IS and we do not make any guarantees of any kind.  Please do not submit a support ticket relating to any issues arising from the use of these projects.
+
+Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo.  They will be reviewed as time permits, but there are no formal SLAs for support.
+
+## Project Setup
+After cloning the repo, it is highly advised that you create a [virtual environment](https://docs.python.org/3/library/venv.html) to isolate and manage
+packages for this project, like so:
+
+`python -m venv <path to project root>/venv`
+
+You can then install the required modules via pip:
+
+`pip install requirements.txt`
+
+## Building the Project
+Once in the main project folder, build into a wheel using the following command: 
+
+`python setup.py bdist_wheel`
+
+## Releasing the Project
+Instructions for how to release a version of the project

python/dbl_tempo.egg-info/PKG-INFO

@@ -0,0 +1,183 @@
+Metadata-Version: 2.1
+Name: dbl-tempo
+Version: 0.1.1
+Summary: Spark Time Series Utility Package
+Home-page: https://github.com/databrickslabs/tempo
+Author: Ricardo Portilla, Tristan Nixon, Max Thone, Sonali Guleria
+Author-email: [email protected]
+License: UNKNOWN
+Description: # tempo - Time Series Utilities for Data Teams Using Databricks
+
+        <p align="center">
+          <img src="tempo - light background.svg" width="300px"/>
+        </p>
+
+
+        ## Project Description
+        The purpose of this project is to make time series manipulation with Spark simpler. Operations covered under this package include AS OF joins, rolling statistics with user-specified window lengths, featurization of time series using lagged values, and Delta Lake optimization on time and partition fields.
+
+        [![codecov](https://codecov.io/gh/databrickslabs/tempo/branch/master/graph/badge.svg)](https://codecov.io/gh/databrickslabs/tempo)
+
+        ## Using the Project
+
+        Python - pip install in Databricks notebooks using:
+
+        ```
+        %pip install dbl-tempo
+        ```
+
+        Install locally using: 
+
+        ```
+        pip install dbl-tempo
+        ```
+
+
+        ### Starting Point: TSDF object, a wrapper over a Spark data frame
+        The entry point into all features for time series analysis in tempo is a TSDF object which wraps the Spark data frame. At a high level, a TSDF contains a data frame which contains many smaller time series, one per partition key. In order to create a TSDF object, a distinguished timestamp column much be provided in order for sorting purposes for public methods. Optionally, a sequence number and partition columns can be provided as the assumptive columns on which to create new features from. Below are the public methods available for TSDF transformation and enrichment.
+
+        #### Sample Reference Architecture for Capital Markets
+
+        <p align="center">
+          <img src="ts_in_fs.png" width="700px"/>
+        </p>
+
+        ## Quickstart - Python 
+
+        Data source is UCI public accelerometer data available at this URL https://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition
+
+        #### 0. Read in Data 
+
+        ```
+        from pyspark.sql.functions import * 
+
+        phone_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/home/tempo/Phones_accelerometer").withColumn("event_ts", (col("Arrival_Time").cast("double")/1000).cast("timestamp")).withColumn("x", col("x").cast("double")).withColumn("y", col("y").cast("double")).withColumn("z", col("z").cast("double")).withColumn("event_ts_dbl", col("event_ts").cast("double"))
+
+        from tempo import * 
+
+        phone_accel_tsdf = TSDF(phone_accel_df, ts_col="event_ts", partition_cols = ["User"])
+        ```
+
+        #### 1. Resample and Visualize
+
+        ###### Sample usage: 
+        Possible values for frequency include patterns such as 1 minute, 4 hours, 2 days or simply sec, min, day. For the accepted functions to aggregate data, options are 'floor', 'ceil', 'min', 'max', 'mean'. Custom functions will be available in a future release.
+
+        Note: You can upsample any missing values by using an option in the resample interface (fill = True)
+
+        ```
+        # ts_col = timestamp column on which to sort fact and source table
+        # partition_cols - columns to use for partitioning the TSDF into more granular time series for windowing and sorting
+
+        resampled_sdf = phone_accel_tsdf.resample(freq='min', func='floor')
+        resampled_pdf = resampled_sdf.df.filter(col('event_ts').cast("date") == "2015-02-23").toPandas()
+
+        import plotly.graph_objs as go
+        import plotly.express as px
+        import pandas as pd
+
+        # Plotly figure 1
+        fig = px.line(resampled_pdf, x='event_ts', y='z',
+                      color="User",
+                      line_group="User", hover_name="User")
+        fig.update_layout(title='Phone Accelerometer Usage' , showlegend=False)
+
+        fig.show()
+        ```
+
+        #### 2. AS OF Join 
+        ##### This join uses windowing in order to select the latest record from a source table and merges this onto the base Fact table
+
+
+        <p align="center">
+          <img src="AS_OF_Join.png" width="700px"/>
+        </p>
+
+
+        ```
+        from pyspark.sql.functions import * 
+
+        watch_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/home/tempo/Watch_accelerometer").withColumn("event_ts", (col("Arrival_Time").cast("double")/1000).cast("timestamp")).withColumn("x", col("x").cast("double")).withColumn("y", col("y").cast("double")).withColumn("z", col("z").cast("double")).withColumn("event_ts_dbl", col("event_ts").cast("double"))
+
+        watch_accel_tsdf = TSDF(watch_accel_df, ts_col="event_ts", partition_cols = ["User"])
+
+        # Applying AS OF join to TSDF datasets
+        joined_df = watch_accel_tsdf.asofJoin(phone_accel_tsdf, right_prefix="phone_accel").df
+
+        joined_df.show(10, False)
+        ```
+
+        #### 3. Skew Join Optimized AS OF Join
+
+        The purpose of the skew optimized as of join is to bucket each set of `partition_cols` to get the latest source record merged onto the fact table
+
+        Parameters: 
+
+        ts_col = timestamp column for sorting 
+        partition_cols = partition columns for defining granular time series for windowing and sorting
+        tsPartitionVal = value to break up each partition into time brackets
+        fraction = overlap fraction
+        right_prefix = prefix used for source columns when merged into fact table
+
+        ```
+        joined_df = watch_accel_tsdf.asofJoin(phone_accel_tsdf, right_prefix="watch_accel", tsPartitionVal = 10, fraction = 0.1).df
+        joined_df.show(10, False)
+        ```
+
+        #### 4 - Approximate Exponential Moving Average
+
+        The approximate exponential moving average uses an approximation of the form `EMA = e * lag(col,0) + e * (1 - e) * lag(col, 1) + e * (1 - e)^2 * lag(col, 2) ` to define a rolling moving average based on exponential decay.
+
+        Parameters: 
+
+        window = number of lagged values to compute for moving average
+
+        ```
+        ema_trades = watch_accel_tsdf.EMA("x", window = 50).df
+        ema_trades.show(10, False)
+        ```
+
+        #### 5 - Simple Moving Average
+
+        Method for computing rolling statistics based on the distinguished timestamp column 
+
+        Parameters: 
+
+        rangeBackWindowSecs = number of seconds to look back
+
+        ```
+        moving_avg = watch_accel_tsdf.withRangeStats("y", rangeBackWindowSecs=600).df
+        moving_avg.select('event_ts', 'x', 'y', 'z', 'mean_y').show(10, False)
+        ```
+
+
+
+        ## Project Support
+        Please note that all projects in the /databrickslabs github account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs).  They are provided AS-IS and we do not make any guarantees of any kind.  Please do not submit a support ticket relating to any issues arising from the use of these projects.
+
+        Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo.  They will be reviewed as time permits, but there are no formal SLAs for support.
+
+        ## Project Setup
+        After cloning the repo, it is highly advised that you create a [virtual environment](https://docs.python.org/3/library/venv.html) to isolate and manage
+        packages for this project, like so:
+
+        `python -m venv <path to project root>/venv`
+
+        You can then install the required modules via pip:
+
+        `pip install requirements.txt`
+
+        ## Building the Project
+        Once in the main project folder, build into a wheel using the following command: 
+
+        `python setup.py bdist_wheel`
+
+        ## Releasing the Project
+        Instructions for how to release a version of the project
+
+Platform: UNKNOWN
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Description-Content-Type: text/markdown
+Provides-Extra: tests

python/dbl_tempo.egg-info/SOURCES.txt

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+README.md
+setup.py
+dbl_tempo.egg-info/PKG-INFO
+dbl_tempo.egg-info/SOURCES.txt
+dbl_tempo.egg-info/dependency_links.txt
+dbl_tempo.egg-info/requires.txt
+dbl_tempo.egg-info/top_level.txt
+tempo/__init__.py
+tempo/io.py
+tempo/resample.py
+tempo/tsdf.py

python/dbl_tempo.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

python/dbl_tempo.egg-info/requires.txt

@@ -0,0 +1,3 @@
+
+[tests]
+pytest

python/dbl_tempo.egg-info/top_level.txt

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+tempo