wip

Author: asmirnov69

README.md

@@ -1,6 +1,6 @@
 # pyjviz
 
-`pyjviz` is Python package to visual support of programmers and data engeneers efforts using `pyjanitor` package.
+`pyjviz` is Python package to deliver some level of visual support to help programmers and data engeneers to use `pyjanitor` package.
 `pyjviz` provides simple way to see method call chains flow and intermidiate results.
 
 ## Quick start
@@ -22,6 +22,8 @@ Resulting logs are in ~/.pyjviz/rdflog - visualized output stored in .svg files.
 Consider pyjanitor example why-janitor.py. Modified version is given below (also avaliable here):
 
 ```python
+# using example from https://pyjanitor-devs.github.io/pyjanitor/#why-janitor as base
+
 import numpy as np
 import pandas as pd
 import janitor
@@ -30,39 +32,25 @@ import pyjviz
 # Sample Data curated for this example
 company_sales = {
         'SalesMonth': ['Jan', 'Feb', 'Mar', 'April'],
-	'Company1': [150.0, 200.0, 300.0, 400.0],
-	'Company2': [180.0, 250.0, np.nan, 500.0],
-	'Company3': [400.0, 500.0, 600.0, 675.0]
-   }
+        'Company1': [150.0, 200.0, 300.0, 400.0],
+        'Company2': [180.0, 250.0, np.nan, 500.0],
+        'Company3': [400.0, 500.0, 600.0, 675.0]
+    }
 
 print(pd.DataFrame.from_dict(company_sales))
-#  SalesMonth  Company1  Company2  Company3
-#  0        Jan     150.0     180.0     400.0
-#  1        Feb     200.0     250.0     500.0
-#  2        Mar     300.0       NaN     600.0
-#  3      April     400.0     500.0     675.0
 
-with pyjviz.CB() as sg:
+with pyjviz.CB("WHY JANITOR?") as sg:
     df = (
-          pd.DataFrame.from_dict(company_sales)
-		      .remove_columns(["Company1"])
-		      .dropna(subset=["Company2", "Company3"])
-		      .rename_column("Company2", "Amazon")
-		      .rename_column("Company3", "Facebook")
-		      .add_column("Google", [450.0, 550.0, 800.0])
-         )
-
-# Output looks like this:
-# Out[15]:
-#   SalesMonth  Amazon  Facebook  Google
-# 0        Jan   180.0     400.0   450.0
-# 1        Feb   250.0     500.0   550.0
-# 3      April   500.0     675.0   800.0
-
-# comment line below to fix spurious apply calls caused by pandas printing implementation
-print(df)
-
-pyjviz.save_dot(vertical = True)
+        pd.DataFrame.from_dict(company_sales)
+        .remove_columns(["Company1"])
+        .dropna(subset=["Company2", "Company3"])
+        .rename_column("Company2", "Amazon")
+        .rename_column("Company3", "Facebook")
+        .add_column("Google", [450.0, 550.0, 800.0])
+    )
+    print(df)
+
+pyjviz.save_dot(vertical = True, popup_output = True)
 ```
 
 The [`result`][res] of run is SVG file with clickable nodes to provide the way to see some details of program behaviour and generated data objects.

examples/scripts/why-janitor.py

@@ -14,13 +14,8 @@
     }
 
 print(pd.DataFrame.from_dict(company_sales))
-#  SalesMonth  Company1  Company2  Company3
-#  0        Jan     150.0     180.0     400.0
-#  1        Feb     200.0     250.0     500.0
-#  2        Mar     300.0       NaN     600.0
-#  3      April     400.0     500.0     675.0
 
-with pyjviz.CB() as sg:
+with pyjviz.CB("WHY JANITOR?") as sg:
     df = (
         pd.DataFrame.from_dict(company_sales)
         .remove_columns(["Company1"])
@@ -29,15 +24,6 @@
         .rename_column("Company3", "Facebook")
         .add_column("Google", [450.0, 550.0, 800.0])
     )
-    
-    # Output looks like this:
-    # Out[15]:
-    #   SalesMonth  Amazon  Facebook  Google
-    # 0        Jan   180.0     400.0   450.0
-    # 1        Feb   250.0     500.0   550.0
-    # 3      April   500.0     675.0   800.0
-
-    # comment line below to fix spurious apply calls caused by pandas printing implementation
     print(df)
 
 pyjviz.save_dot(vertical = True, popup_output = True)