old_contents: """
  @author: efourrier
  
  Purpose : This module is designed to provide custom test assertion return in True or
  False so you can quickly include them into tests against your DataFrame
  
  """
  
  #########################################################
  # Import Packages and Constants
  #########################################################
  
  import pandas as pd 
  from pandas_patch import * 
  import numpy as np 
  
  
  
  
  #########################################################
  # Custom assert function 
  #########################################################
  
  def isna(df,index,axis = 1):
  		""" This function will return True if there is na values in the index of the DataFrame
  
  		Parameters
  		-----------
  
  		index : index can be a list of columns or a rows index
  
  		 """
  		return pd.isnull(df[index]).any().any()
  
  def is_nacolumns(df,columns):
  	""" This function will return True if at least one of the columns is composed only of missing values """
  	return pd.isnull(test[columns]).all().any()
  
  def is_positive(df,columns):
  	""" Return True if all columns are positive """
  	return (df[columns] > 0).all().all()
  
  def is_compact(df,column,inf = None,sup = None ):
  	""" Return True if the column meet the inf and sup criteria """
  
  	if inf is None :
  		 (df[column] <= sup).all().all()
  	elif sup is None:
  		 (df[column] <= sup).all().all()
  	else : 
  		return ((df[column] >= inf) & (df[column] <= sup)).all().all()
  
  def is_many_missing 
  def is_key(df,columns):
  	return df[columns].apply(lambda col : len(pd.is_unique(col)) == len(df.index)).all()
  
  def is_constant(df,columns):
  	return df[columns].apply(lambda col : len(pd.is_unique(col)) == 1).all()
  
  def is_numeric(df,columns):
  
  def is_mixed_uni_str(df):
  	""" Return True if there is str type (byte in python 2.7) and unicode """
  	
  	types = set(df.apply(lambda x :pd.lib.infer_dtype(x.values)))
  	if 'unicode' in types and 'string' in types:
  		return True
  
  def is_unicode(df,columns):
  	return df[columns].apply(lambda x :pd.lib.infer_dtype(x.values) == unicode).any()
  
new_contents: """
  @author: efourrier
  
  Purpose : This module is designed to provide custom test assertion return in True or
  False so you can quickly include them into tests against your DataFrame
  
  """
  
  #########################################################
  # Import Packages and Constants
  #########################################################
  
  import pandas as pd 
  import numpy as np 
  
  #########################################################
  # Custom assert function 
  #########################################################
  
  def isna(df,index,axis = 1):
  		""" This function will return True if there is na values in the index of the DataFrame
  
  		Parameters
  		-----------
  
  		index : index can be a list of columns or a rows index
  
  		 """
  		return pd.isnull(df[index]).any().any()
  
  def is_nacolumns(df,columns):
  	""" This function will return True if at least one of the columns is composed only of missing values """
  	return pd.isnull(test[columns]).all().any()
  
  def is_positive(df,columns):
  	""" Return True if all columns are positive """
  	return (df[columns] > 0).all().all()
  
  def is_compact(df,column,inf = None,sup = None ):
  	""" Return True if the column meet the inf and sup criteria """
  
  	if inf is None :
  		 (df[column] <= sup).all().all()
  	elif sup is None:
  		 (df[column] <= sup).all().all()
  	else : 
  		return ((df[column] >= inf) & (df[column] <= sup)).all().all()
  
  def is_many_missing 
  def is_key(df,columns):
  	return df[columns].apply(lambda col : len(pd.is_unique(col)) == len(df.index)).all()
  
  def is_constant(df,columns):
  	return df[columns].apply(lambda col : len(pd.is_unique(col)) == 1).all()
  
  def is_numeric(df,columns):
  
  def is_mixed_uni_str(df):
  	""" Return True if there is str type (byte in python 2.7) and unicode """
  	
  	types = set(df.apply(lambda x :pd.lib.infer_dtype(x.values)))
  	if 'unicode' in types and 'string' in types:
  		return True
  
  def is_unicode(df,columns):
  	return df[columns].apply(lambda x :pd.lib.infer_dtype(x.values) == unicode).any()
  
subject: Correct import