Question

Use Python

Step 1: Create a DataFrame of Aggregate Statistics Create a DataFrame ipCountDF that uses logDF to create a count of each time a given IP address appears in the logs, with the counts sorted in descending order. The result should have two columns: ip and count Cnd 25 I #TO 3ipCountDF GAttributeError: DataFrameReader' object has no attribute 'select' Command took θ.06 seconds-by jshavice@ksu.edu at 5/11/2019, 7:54:11 AM on JeremieHavice Cnd 26 TEST-Run this cell to test your solution lpi, countl : İpCountDF.fi 4 colsset (ipCountDF.columns) 6 dbTest("ET1-P-02-01-01", "213.152.28.bhe", ip1) 7 dbTest("ET1-P-02-01-02", True, countl> 500000 and countl 55000e) 8 dbTest("ET1-P-02-01-03", 'count','ip'), cols) 1 print("Tests passed")

Answer 1

As you are not given any where in the question what are the columns present in the logDf dataframe so i am assuming the logdf has a column ipaddress. which is more relevant to us now.

Code:

import pandas as pd
# create the empty ip address set to store unique ip address
ipadd = set()
# given data frame logDf get the unique ipaddress colums values
ipadd = set(logDf["ipaddress"])
# creating the list of all zeros initally to store count
count = [0 for i in ipadd]
# loop through each values in the logdf data frame ip address column and count th
# number of occurance
index=0
for j in ipadd:
   index = index+1
   for i in logDf["ipaddress"]:
       if i == j:
           count[index] = count[index]+1
       else:
           continue
# creating the dataframe ipCountDf with columns ip and count
# using ipadd set and count list
# create the dictonary initially
dct = {"ip":ipadd,"count":count}
ipCountDf = pd.DataFrame(dct)

# sorting the dataframe based on count in descending order
ipCountDf.sort_values(by="count",ascending=False,inplace=True)
# output the dataframe
print(ipCountDf)