diff --git a/dev_jeanvitola/Hadoop/Environment Setup.pdf b/dev_jeanvitola/Hadoop/Environment Setup.pdf
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diff --git a/dev_jeanvitola/Mapreduce_Opt/Mapreduce_meanTime.py b/dev_jeanvitola/Mapreduce_Opt/Mapreduce_meanTime.py
new file mode 100644
index 0000000..c3c2399
--- /dev/null
+++ b/dev_jeanvitola/Mapreduce_Opt/Mapreduce_meanTime.py
@@ -0,0 +1,125 @@
+import pandas as pd
+from functools import reduce
+import xml.etree.ElementTree as ET
+import os  
+from collections import Counter
+import datetime
+import numpy 
+import time
+
+
+#functions open files XML´s
+def read_xml(file):
+    read = ET.parse(file)
+    root = read.getroot()
+    return root
+
+#Files in to chunks
+def chunckify(file,chunks):
+    for i in range(0,len(file), chunks):
+        yield file[i:i + chunks]
+
+ 
+
+#  Get Score by "PostTypeId == 1" Question
+
+def score(file):
+    post_id_stack = file.attrib['PostTypeId']
+    if post_id_stack== '1':
+        post_id = file.attrib['Id']
+        post_score = int(file.attrib['Score'])
+        change_datetime = datetime.datetime.strptime(file.attrib["CreationDate"], '%Y-%m-%dT%H:%M:%S.%f')
+        return post_id, post_score, change_datetime
+    
+# Get score by "PostTypeId == 2" Answer
+
+def score_2(file):
+    post_id_stack = file.attrib['PostTypeId']
+    if post_id_stack== '2':
+        post_id_2 = file.attrib['ParentId']
+        change_datetime_2 = datetime.datetime.strptime(file.attrib["CreationDate"], '%Y-%m-%dT%H:%M:%S.%f')
+        return post_id_2, change_datetime_2
+    
+#Functions Map() ==1
+def map_post(data):
+    map_date_post = list(map(score, data))
+    date_count= Counter(map_date_post)
+    return date_count
+
+
+#Functions Map() ==2
+def map_post_2(data):
+    map_date_post = list(map(score_2, data))
+    date_count= Counter(map_date_post)
+    return date_count
+
+# Merge function
+def merge_date(D1,D2) :
+    D1.update(D2) 
+    return D1 
+
+#Reducer questions
+def reduce_date(iter):
+    reduce_post = reduce(merge_date,iter)
+    top100 =reduce_post.most_common(101)
+    df = pd.DataFrame(top100, columns=['date', 'count'])
+    #delete index 0
+    df.drop(df.index[0], inplace=True)
+    #create columns for tuple date
+    df['post_id'] = df['date'].apply(lambda x: x[0]) 
+    df['post_score'] = df['date'].apply(lambda x: x[1])
+    #order score to ASC
+    df = df.sort_values(by=['post_score'], ascending=False)
+    df['change_datetime'] = df['date'].apply(lambda x: x[2])
+    #delete column date
+    df.drop(['date'], axis=1, inplace=True)
+    return df
+
+#Reducer Answer
+def reduce_date_2(iter):
+    reduce_post = reduce(merge_date,iter)
+    top100 =reduce_post.most_common(101)
+    df2 = pd.DataFrame(top100, columns=['date', 'count'])
+    #delete index 0
+    df2.drop(df2.index[0], inplace=True)
+    #create columns for tuple date
+    df2['post_id'] = df2['date'].apply(lambda x: x[0])
+    df2['change_datetime'] = df2['date'].apply(lambda x: x[1])
+    #delete column date
+    df2.drop(['date'], axis=1, inplace=True)
+    #calculate unique valor post_id
+    return df2
+    
+# join df and df2
+def join_df(df,df2):
+    df_join = pd.merge(df, df2, on='post_id', how='inner')
+    df_join['time_diff'] = df_join['change_datetime_y'] - df_join['change_datetime_x']
+    df_join['time_diff'] = df_join['time_diff'].dt.total_seconds()
+    #calculate means time_diff
+    mean_time_diff = df_join['time_diff'].mean()
+    #converter mean_time_diff in hours
+    mean_time_diff_hours = mean_time_diff/3600
+    mean_time_diff_hours = round(mean_time_diff_hours )
+    print(f"from the ranking of the 0-100 data by score,\n the average response time is {mean_time_diff_hours}  hours")
+    return join_df
+    
+# create function main
+def main():
+    start = time.time()
+    read_file = read_xml("posts.xml")
+    chunky_data = chunckify(read_file,100)
+    Map_data = list(map(map_post, chunky_data))
+    df = reduce_date(Map_data)
+    #Answer
+    read_file = read_xml("posts.xml")
+    chunky_data = chunckify(read_file,50)
+    Map_data_2 = list(map(map_post_2, chunky_data))
+    df2 =reduce_date_2(Map_data_2)
+    print(join_df(df,df2))
+    end = time.time()
+    print("Execution time: ", end - start)
+    
+if __name__ == '__main__':
+    main()
+    
+    
diff --git a/dev_jeanvitola/Mapreduce_Opt/Mapreduce_top10.py b/dev_jeanvitola/Mapreduce_Opt/Mapreduce_top10.py
new file mode 100644
index 0000000..f024241
--- /dev/null
+++ b/dev_jeanvitola/Mapreduce_Opt/Mapreduce_top10.py
@@ -0,0 +1,91 @@
+import pandas as pd
+import time
+from functools import reduce
+import xml.etree.ElementTree as ET
+import os
+from collections import Counter
+import datetime
+
+# functions open files XML´s
+
+start = time.time()
+def read_xml(file):
+    read = ET.parse(file)
+    root = read.getroot()
+    return root
+
+# Files in to chunks
+
+
+def chunckify(file, chunks):
+    for i in range(0, len(file), chunks):
+        yield file[i:i + chunks]
+
+# Convert to datetime and get attrib[date]
+
+
+def date_post(alkemy):
+    # date in %Y-%m-%dT%H:%M:%S.%f', convert to data in '%Y-%m-%d
+    change_datetime = datetime.datetime.strptime(
+        alkemy.attrib["CreationDate"], '%Y-%m-%dT%H:%M:%S.%f').strftime('%Y-%m-%d')
+    return change_datetime
+
+# Functions Map()
+
+
+def map_post(data):
+    map_date_post = list(map(date_post, data))
+    date_count = Counter(map_date_post)
+    return date_count
+
+
+# Reduce Function
+"""
+reduce(D1 --> D2 )"key" + sum "values"
+Retorna una lista de los n elementos mas comunes y sus conteos, del mas común al menos común
+"""
+
+
+def merge_date(D1, D2):
+    D1.update(D2)
+    return D1
+
+
+def reduce_date(iter):
+    reduce_post = reduce(merge_date, iter)
+    top10 = reduce_post.most_common(10)
+    df = pd.DataFrame(top10, columns=['date', 'count'])
+    longest = max(len(word) for word, count in top10)
+    for word, count in top10:
+        print('{word:<{len}}: {count:5}'.format(
+            len=longest + 1,
+            word=word,
+            count=count)
+        )
+    print("by : Jeanvitola")
+    # save df to csv
+    df.to_csv("top10_date.csv", index=False)
+
+#create main function
+
+
+def main():
+    # open files
+    root = read_xml("posts.xml")
+    # create chunks
+    chunks = chunckify(root,50)
+    # map function
+    Map_data = list(map(map_post, chunks))
+    # reduce function
+    reduce_date(Map_data )
+    # time execution
+    end = time.time()
+    print("Execution time: ", end - start)
+
+
+
+if __name__ == '__main__':
+    main()
+ 
+
+    
\ No newline at end of file
diff --git a/dev_jeanvitola/Mapreduce_Opt/Mapreduce_viewAnswer.py b/dev_jeanvitola/Mapreduce_Opt/Mapreduce_viewAnswer.py
new file mode 100644
index 0000000..87c5644
--- /dev/null
+++ b/dev_jeanvitola/Mapreduce_Opt/Mapreduce_viewAnswer.py
@@ -0,0 +1,116 @@
+import pandas as pd
+from functools import reduce
+import xml.etree.ElementTree as ET
+import os  
+from collections import Counter
+import datetime
+import numpy 
+#functions open files XML´s
+def read_xml(file):
+    read = ET.parse(file)
+    root = read.getroot()
+    return root
+
+#Files in to chunks
+def chunckify(file,chunks):
+    for i in range(0,len(file), chunks):
+        yield file[i:i + chunks]
+
+# relation  between in viewcount and answerscount
+
+#Viewcounts
+
+def view_count(file):
+
+    post_id_stack = file.attrib['PostTypeId']
+    if post_id_stack== '1':
+        try :
+            views = int(file.attrib['ViewCount'])
+        except:
+            None
+        return views
+    
+    
+#AnswerCount
+
+def get_post_views(data):
+    # Get data views
+    try:
+        views = int(data.attrib['AnswerCount'])
+    except Exception as ex:
+        return None
+
+    return views
+        
+#Mappers
+#Functions Map() == Views
+def map_post(data):
+    map_date_post = list(map(view_count, data))
+    date_count= Counter(map_date_post)
+    return date_count
+
+
+#Functions Map() == Answers
+def mapper(data):
+    views = list(map(get_post_views, data))
+    date_count= Counter(views)
+    return date_count
+
+# Merge function
+def merge_date(D1,D2) :
+    D1.update(D2) 
+    return D1 
+
+#create function reduce for viewcount and answercount
+#ViewCounts
+def reduce_date(iter):
+    reduce_post = reduce(merge_date,iter)
+    #create dataframe to reduce_post
+    df = pd.DataFrame.from_dict(reduce_post, orient='index')
+    df.reset_index(inplace=True)  
+     #delete NaN and convert int
+    df.dropna(inplace=True)
+    df['index'] = df['index'].astype(int)
+    #rename index to viewcounts
+    df.rename(columns={'index':'ViewCounts'}, inplace=True)
+    #delete 0 counts
+    df = df[df.ViewCounts != 0]
+    return df
+
+def reduce_date_2(iter):
+    reduce_post = reduce(merge_date,iter)
+    #create dataframe to reduce_post
+    df2 = pd.DataFrame.from_dict(reduce_post, orient='index')
+    df2.reset_index(inplace=True)
+    # delete nan
+    df2.dropna(inplace=True)
+    #change name indx to counter_viwes
+    df2.rename(columns={'index':'counter_views',0:'views'},inplace=True)
+    #counter views as int
+    df2['counter_views']=df2['counter_views'].astype(int)
+    return df2
+
+#create function union df1 and df2
+def union_df(df1,df2):
+    df_union = pd.merge(df1,df2,on='ViewCounts',how='outer')
+    df_union.dropna(inplace=True)
+    df_union['counter_views']=df_union['counter_views'].astype(int)
+    return df_union
+
+#create function main
+def main ():
+    read_file = read_xml("posts.xml")
+    chunky_data = chunckify(read_file,50)
+    Map_data = list(map(map_post, chunky_data))
+    df1 = reduce_date(Map_data) 
+    #answercount
+    chunky_data = chunckify(read_file,50)
+    body_views = list(map(mapper,chunky_data))
+    df2 = reduce_date_2(body_views)
+    print(df2)
+
+
+#create function to create a new column with the relation between views and answers
+if __name__ == '__main__': 
+    main()
+    
\ No newline at end of file
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diff --git a/dev_jeanvitola/Test_function/Mapreduce_Walter102.py b/dev_jeanvitola/Test_function/Mapreduce_Walter102.py
new file mode 100644
index 0000000..928bc1f
--- /dev/null
+++ b/dev_jeanvitola/Test_function/Mapreduce_Walter102.py
@@ -0,0 +1,31 @@
+from wl_map_reduce import *
+
+import pytest
+
+
+data= "posts.xml"
+
+def test_mapper():
+     assert isinstance(mapper(data),list)
+
+
+def test_shuffle_sort():
+    lista = mapper(data)
+    assert isinstance(shuffle_sort(lista), list)
+
+
+def test_reduce():
+    lista = mapper(data)
+    lista2 = shuffle_sort(lista)
+    assert isinstance(reduce(lista2), list)
+    
+   
+def test_savetoCSV():
+    lista = mapper(data)
+    lista2 = shuffle_sort(lista)
+    lista3 = reduce(lista2)
+    data2 = lista3
+    filename = "top10tags.csv"
+    fields = ["TAG", "COUNT"]
+    type_data = 1
+    assert isinstance(savetoCSV(data2,filename,fields,type_data), data_frame.to_csv(index=False))
diff --git a/dev_jeanvitola/Test_function/Mapreduce_Walter102_Docstring.py b/dev_jeanvitola/Test_function/Mapreduce_Walter102_Docstring.py
new file mode 100644
index 0000000..1806f4f
--- /dev/null
+++ b/dev_jeanvitola/Test_function/Mapreduce_Walter102_Docstring.py
@@ -0,0 +1,85 @@
+from wl_map_reduce import *
+
+import pytest
+
+
+data= "posts.xml"
+
+"""
+La función mapper(), realiza el mapeo de los datos y como resultados retorna
+una lista para cada consigna a resolver.
+
+input : La entrada de la función sera un archivo de formato .xml, donde
+se creará un objecto Tree utilizando la libreria xml.etree
+
+output: Lo que se espera de la función es que identifique diversos tag y
+busque de forma iterativa los diversos caracteres de interes que se iran guardando en unas listas
+vacias, luego se creará una lista de lista, el resultado esperado es un lista de lista
+
+
+"""
+def test_mapper():
+     assert isinstance(mapper(data),list)
+
+
+
+"""  
+La función shuffle_sort acomoda y ordena las listas que reciben como parámetro los datos mapeados,
+
+
+input: la entrada que se espera es una lista de listas con los parámetros mapeados en la anterior
+función.
+
+output : Se espera que el resultado sea una lista que contenga un Dataframe, este objeto 
+contiene el merge de los caracteres de interes.
+
+
+"""
+
+
+def test_shuffle_sort():
+    lista = mapper(data)
+    assert isinstance(shuffle_sort(lista), list)
+
+
+
+"""  
+La fución reduce, reduce la cual El método reduce() ejecuta una función reductora sobre cada elemento de un array,
+ devolviendo como resultado un único valor
+
+input : La entrada será una lista con 3 listas, en este caso sería la lista que retorna el mapper
+
+Output : lo que se espera es una lista que contiene dos listas y una seria de pandas
+"""
+
+
+def test_reduce():
+    lista = mapper(data)
+    lista2 = shuffle_sort(lista)
+    assert isinstance(reduce(lista2), list)
+    
+   
+"""
+La función SavetoCSV fuarda la salida de la función
+
+   input:
+        data (list | pandas.Serie): los datos reducidos a grabar en el reporte
+        filename (string):
+        fields (list): es una lista con los nombres de las columnas del reporte
+        type_data (int): si es 1, los datos son una lista, si es 2, los datos son una serie de pandas
+
+    output : La salida será un archivo .CSV con las casacteristicas menciondas en el input
+    
+
+"""
+
+
+def test_savetoCSV():
+    lista = mapper(data)
+    lista2 = shuffle_sort(lista)
+    lista3 = reduce(lista2)
+    data2 = lista3
+    filename = "top10tags.csv"
+    fields = ["TAG", "COUNT"]
+    type_data = 1
+    assert isinstance(savetoCSV(data2,filename,fields,type_data), data_frame.to_csv(index=False))
diff --git a/dev_jeanvitola/Test_function/Mapreduce_walter_110.py b/dev_jeanvitola/Test_function/Mapreduce_walter_110.py
new file mode 100644
index 0000000..eb9ebd7
--- /dev/null
+++ b/dev_jeanvitola/Test_function/Mapreduce_walter_110.py
@@ -0,0 +1,38 @@
+from wl_mapreduce_op import *
+import pytest 
+
+
+
+post = "posts.xml"
+
+
+
+def test_chunkify():
+    assert isinstance(chunkify(xmlfile = post,number_of_chunks =16),list)
+
+
+def test_mapper() :
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    assert isinstance(mapper(chunk=lista),list)
+
+
+def test_shuffler():
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    lista2 = mapper(chunk=lista)
+    assert isinstance(shuffler(mapper = lista2),list)
+
+
+def test_reduce():
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    lista2 = mapper(chunk=lista)
+    lista3 = shuffler(mapper =lista2)
+    assert isinstance(reduce(lista =lista3),object)
+
+
+def test_savetoCSV():
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    lista2 = mapper(chunk=lista)
+    lista3 = shuffler(mapper =lista2)
+    lista4 = reduce(lista=lista3)
+    assert isinstance(savetoCSV(data =lista4 ,filename="OP_top10tags.csv",fields=["TAG", "COUNT"]), list)
+    
diff --git a/dev_jeanvitola/Test_function/Mapreduce_walter_110_Docstring.py b/dev_jeanvitola/Test_function/Mapreduce_walter_110_Docstring.py
new file mode 100644
index 0000000..a98d7f7
--- /dev/null
+++ b/dev_jeanvitola/Test_function/Mapreduce_walter_110_Docstring.py
@@ -0,0 +1,91 @@
+from wl_mapreduce_op import *
+import pytest 
+
+
+post = "posts.xml"
+
+
+"""  
+Está función llama un archivo de tipo .xml y lo convierte en un objeto para acceder a sus atributos,
+la información del archivo .xml se itera con un condicional ==1 para que cumpla la condición de buscar 
+el elemento  "PostType1"  con respuesta no aceptada "AcceptedAnswerId" == None,se dividen en 16 chunks.
+
+
+input = la entradas son xmlfile = que sera igual a un archivo .xml en este caso posts.xml,
+        number_of_chunks = hace referencia a las particiones del archivo que por defecto es de 16
+
+output =  La salida sera un objeto chunks contenido en una lista
+
+  """
+
+
+def test_chunkify():
+    assert isinstance(chunkify(xmlfile = post,number_of_chunks =16),list)
+
+
+
+""" 
+La función mapper(), realiza el mapeo de los datos y como resultados retorna
+una lista para cada consigna a resolver.
+
+input = La entrada sera una lista chunk con el listado de tags provenientes de la función anterior.
+
+Output = La salida será una lista que contiene un listado con los tagas, es decir una lista de listas
+
+"""
+
+def test_mapper() :
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    assert isinstance(mapper(chunk=lista),list)
+
+
+""" 
+La función shuffle pasa la salida de la función mapper(Lista que contine un listado de los tagas)
+
+Output : La salida sera una lista que agrupa los elementos de los tags en un solo listado
+
+"""
+
+def test_shuffler():
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    lista2 = mapper(chunk=lista)
+    assert isinstance(shuffler(mapper = lista2),list)
+
+
+
+"""  
+La fución reduce ejecuta una función reductora sobre cada elemento de un array,
+ devolviendo como resultado un único valor
+
+input : Listado  de las ocurrencias de los tags
+
+Output : La salida sera un objeto que contiene la cantidad del top 10 de los tags
+"""
+
+def test_reduce():
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    lista2 = mapper(chunk=lista)
+    lista3 = shuffler(mapper =lista2)
+    assert isinstance(reduce(lista =lista3),object)
+
+"""
+La función SavetoCSV fuarda la salida de la función
+
+   input:
+        data (list | pandas.Serie): los datos reducidos a grabar en el reporte
+        filename (string): nombre del archivo csv
+        fields (list): es una lista con los nombres de las columnas del reporte
+
+        
+    output : La salida será un archivo .CSV con las casacteristicas menciondas en el input
+    
+"""
+
+
+def test_savetoCSV():
+    lista =chunkify(xmlfile = post,number_of_chunks =16)
+    lista2 = mapper(chunk=lista)
+    lista3 = shuffler(mapper =lista2)
+    lista4 = reduce(lista=lista3)
+    assert isinstance(savetoCSV(data =lista4 ,filename="OP_top10tags.csv",fields=["TAG", "COUNT"]), list)
+    
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