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Attacking Passphrases •Combinator attacks -dictionary combined with a dictionary •Google top 10,000 words https://github com/first20hours/google-10000-
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computed using the Tesla K80 GPU hosted on Google Colaboratory C Natural Language Processing The natural language processing portion of our model depends on determining the probabilities of arbitrary n -grams in English An n -gram is a group of n letters For example, in order to determine the
More with pandas - Fabrice Rossi
Toaccessthelowerleveloftheindex,onecanusesuccessivelocatorssuchasdemo[:,2] whichis even 4 Exercise 1 (Hierarchical Index) Thisexerciseusestheworddataset'google-10000
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ADVANCED English WORDS- Improve your vocabulary #advancedenglish 10000 Most Common English Words With Examples and Meanings — 1-500 Words 10000 most common english words - part 1 Learn 100 most common ENGLISH words * American English Pronunciation * + examples Learn 8000
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More with pandas
Fabrice RossiExamples in this series of exercises are based on the data sets available on the course web
page.Lecture notes Pandas indexes can have severallevels. This can be used to implement complex data transformations.The main idea is to have several lists of values of the same size, each of which providing a level of
the index. For instance the program import pandas as pd demo pdSeries([x
2 for x in range 1 11 index [["odd","even"]*5, [z for z i n rang e 1 11 print (demo) prints odd 1 1 even 2 4 odd 3 9 even 4 16 odd 5 25 even 6 36 odd 7 49 even 8 64 odd 9 81 even 10 100 dtype: int64 The display shows the content of theSeries(squares of integers) as well as the two levels of the index, a string level and an integer level. TheSeriescan then be accessed using the higher level in a standard way. For instance demo["odd"]corresponds to 1 1 3 9 5 25 7 49 9 81 dtype: int64Page 1 /5 To access the lower level of the index, one can use successive locators such asdemo[:,2]which is even 4Exercise 1(Hierarchical Index) This exercise uses the word data set"google-10000-english.txt"1. Load it with import pandas as pd words pd read_csv("google-10000-english.txt", names ["word"], header None )["word"] The use ofwords.strto access to the words with a string interface is recommended. Question 1Remove words with a single letter from theSeries.Question 2
Replace the index ofwordsby a hierarchical index with the first letter of the word as the high level index and the second as the low level one. Question 3Change the name of the index levels to"fl"and"sl", usingwords.index.names. Question 4Study the effect of thesort_index()method (this is a method ofSeries).Question 5
Print for each letter the number of words starting with this letter and the number ofwords with this letter as their second letter. Hint: access to the letters withwords.index.levels[0].
Question 6Study the results ofwords.count(level="fl")andwords.count(level="sl"). In par- ticular, what is printed by for l, n in word s count(level ="fl").items(): print (l, n)Lecture notes As experimented in the first exercise, numerousSeriesmethods have alevelparameter that can be set to the name of a level of the index. The method is applied to all the subSeriesobtained by selecting one by one all the possible values of the selected level. The result is aSeriesmade of the obtained values and indexed by the selected level. For instance import pandas as pd ms pdSeries([
2 x for x in range 10 index [[t for t in "aaabbccccc"], [x 5 for x in ra nge 10 ms index names [ "letter","digit"] print (ms) print (ms sum(level ="letter")) print (ms sum(level ="digit")) prints letter digit1Page 2 /
5 a 0 0 1 2 2 4 b 3 6 4 8 c 0 10 1 12 2 14 3 16 4 18 dtype: int64 letter a 6 b 14 c 70 dtype: int64 digit 0 10 1 14 2 18 3 22 4 26 dtype: int64Exercise 2(Hierarchical index and aggregates) This exercise uses the french population data set. Load it with import pandas as pd population pd read_csv("population-2014.csv") Question 1Frompopulationcreate aSeries nb_comfrom theNombre de communes2column using columnsRégionandDépartementas the levels of theSeriesindex. Hint: use thevaluesattribute of the column to extract the values without the index. Question 2Usingnb_com, count the number ofdépartementsperRégion. Question 3Usingnb_com, count the total number of cities perRégion.Question 4
Obtain the sames results as in the two previous questions using agroupbystrategy directly on thepopulation DataFrame.Lecture notes A convenient way to build a meaningful hierarchical index in aDataFrameis to use some of its variables. For instance thepopulation DataFramecan be given a global hierarchical index onRégionandDépartementusing
population populationNumber of cities.
Page 3 /
5Then theDataFramecan begroupbyusing alevelparameter.Question 5Add a variable containing the average population per city for eachdépartement(using
the columnPopulation municipale3). Question 6Compute the average of the average population per city for eachRégion.Exercise 3(Joins)
This exercise uses the financial relational data set4. Load the tables into data frames named after the
tables, e.g. import pandas as pd client pd read_csv("client.csv") account pd read_csv("account.csv") Question 1Create a joined table integrating client information into the disposition table.Question 2
Using the previous table compute the distribution of the account type conditionally onthe gender of the user of the account. Hint: see exercise 6 from the "First steps with Pandas" exercise
list.Question 3
Create aDataFrame both_districtswhich lists client ids together with the district id oftheir residence (from the client table) and the district id of their bank (from the account table). Hint:
use two joins and take care of identical column names.