Pandas Strings

Question 1

names.str.capitalize()

Answer 1

a single method that will capitalize all the entries, while skipping over any missing values

Question 2

Using tab completion on this str attribute

Answer 2

will list all the vectorized string methods available to Pandas.

Question 3

len() lower() translate() islower()

ljust() upper() startswith() isupper()

Answer 3

Methods similar to Python string methods

Question 4

rjust() find() endswith() isnumeric()

center() rfind() isalnum() isdecimal()

Answer 4

Methods similar to Python string methods

Question 5

zfill() index() isalpha() split()

strip() rindex() isdigit() rsplit()

Answer 5

Methods similar to Python string methods

Question 6

rstrip() capitalize() isspace() partition()

lstrip() swapcase() istitle() rpartition()

Answer 6

Methods similar to Python string methods

Question 7

match()

Answer 7

Call re.match() on each element, returning a boolean.

Question 8

extract()

Answer 8

Call re.match() on each element, returning matched groups as strings.

Question 9

findall()

Answer 9

Call re.findall() on each element

Question 10

replace()

Answer 10

Replace occurrences of pattern with some other string

Question 11

contains()

Answer 11

Call re.search() on each element, returning a boolean

Question 12

count()

Answer 12

Count occurrences of pattern

Question 13

split()

Answer 13

Count occurrences of pattern

Question 14

rsplit()

Answer 14

Equivalent to str.rsplit(), but accepts regexps

Question 15

get()

Answer 15

Index each element

Question 16

slice()

Answer 16

Slice each element

Question 17

slice_replace()

Answer 17

Replace slice in each element with passed value

Question 18

cat()

Answer 18

Concatenate strings

Question 19

repeat()

Answer 19

Repeat values

Question 20

normalize()

Answer 20

Return Unicode form of string

Question 21

pad()

Answer 21

Add whitespace to left, right, or both sides of strings

Question 22

wrap()

Answer 22

Split long strings into lines with length less than a given width

Question 23

join()

Answer 23

Join strings in each element of the Series with passed separator

Question 24

get_dummies()

Answer 24

extract dummy variables as a dataframe

Question 25

df.str.slice(0, 3) is equivalent to

Answer 25

df.str[0:3]

Question 26

monte.str.split().str.get(-1)

Answer 26

to extract the last name of each entry, we can combine split() and get():

Question 27

recipes.ingredients.str.len().describe()

Answer 27

sample info getting

Question 28

recipes.name[np.argmax(recipes.ingredients.str.len())]

Answer 28

sample info getting

which recipe has the longest ingredient list

Question 29

recipes.description.str.contains(‘[Bb]reakfast’).sum()

Answer 29

how many of the recipes are for breakfast food

Question 30

recipes.ingredients.str.contains(‘[Cc]innamon’).sum()

Answer 30

cinnamon as an ingredient

Question 31

spice_list = [‘salt’, ‘pepper’, ‘oregano’, ‘sage’, ‘parsley’,
‘rosemary’, ‘tarragon’, ‘thyme’, ‘paprika’, ‘cumin’]

import re
spice_df = pd.DataFrame(dict((spice, recipes.ingredients.str.contains(spice, re.IGNORECASE))
for spice in spice_list))
spice_df.head()

Answer 31

search to see whether they are in each recipe’s ingredient list

return boolean data frame