Transforming Data

Question 1

Function used to find missing data?

Answer 1

df.isna()

Question 2

Function used to compile all the missing data

Answer 2

df.isna().sum()

Question 3

Function used to drop all rows that are missing data?

Answer 3

df.dropna(inplace=True)

Using inplace=True makes the change stick

Question 4

Function used just to drop only rows in one column?

Answer 4

df. dropna(subset=[‘Embarked’], inplace=True)

df. isna().sum()

Question 5

Function used to drop column directly

Answer 5

df. drop(columns= ‘Cabinet, inplace=True)

df. isna().sum()

Question 6

Function used to drop all columns that are missing any data?

Answer 6

df.dropna(axis=1, inplace=True

Question 7

What argument can be used to decide how much incomplete data to drop in columns?

Answer 7

thresh= can be used to drop data with less than 45% of its data
df.dropna(axis=1, thresh=.45, inplace=True)

Question 8

How to fill missing data with new category? What code is used?

Answer 8

df[‘Gender’].fillna(‘Missing, inplace=True)

Replaces Gender column with Missing

Question 9

How to fill missing data with an average?

Answer 9

median_age = df[‘Age’].median()
df[‘Age’].fillna(median_age, inplace=True)
df.isna().sum()

Question 10

How to fill categorical data with the most common value in the column?

Answer 10

most_common_pet = df[‘Pet Type’].mode()

df[‘Pet Type].fillna(most_common_pet, inplace=True