Merge pull request #556 from ironmussa/develop

Road to 2.2.6

.gitignore

@@ -47,3 +47,5 @@ handyspark \.ipynb
 handyspark1\.ipynb
 
 examples/new-api-sandbox\.ipynb
+
+examples/new-api-optimus-jars\.ipynb

MANIFEST.in

@@ -1,3 +1,4 @@
 include optimus/templates/*
 include optimus/profiler/templates/*
 include optimus/css/*
+include optimus/jars/*

README.md

@@ -1,3 +1,4 @@
+
 [![Logo Optimus](images/logoOptimus.png)](https://hioptimus.com) 
 
 
@@ -225,21 +226,25 @@ Let's load a "big" dataset
 df = op.load.csv("https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/Meteorite_Landings.csv").h_repartition()
 ```
 
+### Numeric
+
 ```python
-op.profiler.run(df, "name", infer=False)
+op.profiler.run(df, "mass (g)", infer=False)
 ```
 
+![](images/profiler_numeric.png)
+
 ```python
+op.profiler.run(df, "name", infer=False)
 ```
+
 ![](images/profiler.png)
 
 For dates data types Optimus can give you extra data
 ```python
 op.profiler.run(df, "year", infer=True)
 ```
 
-```python
-```
 ![](images/profiler1.png)
 
 ## Plots
@@ -251,15 +256,29 @@ df = df.rows.drop_na(["age","fare"])
 ```
 
 ```python
-df.plot.scatter(["fare", "age"], buckets=30)
+df.plot.hist("fare", output="image", path="images/hist.png")
+```
+
+```python
+df.plot.frequency("age", output="image", path="images/frequency.png")
 ```
 
 ```python
-df.plot.box("age")
+df.plot.scatter(["fare", "age"], buckets=30, output="image", path="images/scatter.png")
+```
+
+```python
+df.plot.box("age", output="image", path="images/box.png")
 ```
 ```python
 df.plot.correlation(["age","fare","survived"])
 ```
+### Using other plotting libraries
+
+
+Optimus has a tiny API so you can use any plotting library. For example, you can use df.cols.scatter(), df.cols.frequency(), df.cols.boxplot() or df.cols.hist() to output a JSON that you can process to adapt the data to any plotting library.
+
+
 ## Outliers
 
 
@@ -282,9 +301,9 @@ df.outliers.iqr("age").drop().table()
 
 
 
-```
+```python
 df.outliers.z_score("age", threshold=2).drop()
-df.outliers.modified_z_score("age", threshold = 2 ).drop()
+df.outliers.modified_z_score("age", threshold = 2).drop()
 df.outliers.mad("age", threshold = 2).drop()
 ```
 

docs/source/conf.py

@@ -60,7 +60,7 @@
 # The short X.Y version.
 version = '2.2'
 # The full version, including alpha/beta/rc tags.
-release = '2.2.51'
+release = '2.2.6'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.