Remove duplicates and display in a column the total number of times the entire row was repeated

I've been dragging queries from this problem:

Join two dataFrame pandas

Using this code:

import random
import pandas as pd
from datetime import datetime

inicio = datetime(2019, 1, 1)
final =  datetime(2019, 3, 21)

datos = []

for i in range (0, 10000):
    fechaRandom = inicio + (final - inicio) * random.random() 
    datos.append(fechaRandom.strftime('%Y-%m-%d'))

df = pd.DataFrame(datos)

df.rename(columns={0: "Fecha"}, inplace=True)

procesos = []

for a in range (1, 11):
    procesos.append('Proceso' + str(a))

total = 0

proceso = []

for i in range (0, 10):
    for j in range ( 0, 1000):
        proceso.append(procesos[total])
    total += 1

datosProceso = pd.DataFrame(proceso)

datosProceso.rename(index=str, columns={0: "Proceso"}, inplace=True)

result = pd.merge(datosProceso.reset_index(),
              df.reset_index(),
              left_index=True, 
              right_index=True)

result = result.drop(columns={'index_x', 'index_y'})

I get the following:

A Df with 10,000 random data in two columns, processes and dates (With 10,000 random dates grouped into 10 random processes of 1000 records each)

What I need now is to be able to remove the duplicates, and leave the process plus the date and in a column the total number of times that date was repeated for that process. In summary it is this:

(result
.groupby(["Proceso", "Fecha"]) 
["Fecha"].count())

The problem I have is that I need everything to be in a new Dataframe that has this format: