String Operations

Question 1

Filter on a single string value

strings

2 alternatives

Answer 1

df_filtered = data[data.col1 == "A"]
df_filtered = data.query("col1 == 'A'")

Question 2

Filter on multiple string values

strings

3 alternatives

Answer 2

df_filtered = data[(data.col1 == "A") | (data.col1 == "B")]
df_filtered = data[data.col1.isin(["A", "B"])]
df_filtered = data.query("col1 == 'A' | col1 == 'B'")

Question 3

Filter on the length of a string

strings

Answer 3

df_filtered = data[data.col1.str.len() > 4]

Question 4

Filter on a substring

string

3 alternatives

Answer 4

df_filtered = data[data.col1.str.startswith("Jo")]
df_filtered = data[data.col1.str.endswith("n")]
df_filtered = data[data.col1.str.contains("ak")]

Question 5

Make the string filtering case-insensitive

strings

Answer 5

df_filtered = data[data.col1.str.contains("ak", case = False)]

Question 6

Make the string filtering ignore missing values

strings

Answer 6

df_filtered = data[data.col1.str.startswith("Jo", na = False)]

Question 7

Filter on the type of character in the string

strings

Answer 7

df_filtered = data[data.col1.str.isalnum()]

Other keywords:
- all characters are upper-case : isupper()
- all characters are lower-case : islower()
- all characters are alphabetic : isalpha()
- all characters are numeric : isnumeric()
- all characters are digits : isdigit()
- all characters are decimal : isdecimal()
- all characters are whitespace : isspace()
- all characters are titlecase : istitle()
- all characters are alphanumeric : isalnum()

Question 8

Change case of a string

Answer 8

myname = "Michael Lemay"
str.upper(myname)
str.title(myname)
str.lower(myname)

Question 9

String formatting

Answer 9

Traditional formatting

name = “John”
age = 30

message = “My name is {} and I’m {} years old.”.format(name, age)

message = f”My name is {name} and I’m {age} years old.”python

Question 10

Split string

Answer 10

text = “This is a sample text.”
words = text.split()