MDA Lab + recap (40 no choke)

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Why would you excecute this?

• Name column, you'll realize that titles like Mr., Mrs, ... are not homogeneous across the dataset

• Replacing the values of a column by a lower number of equivalent values meaning the same

• Deriving new columns from existing ones in order to have more meaningful columns and, overall, values

• To extract the Name, Title and Age to later on concatenate them

What does this function do?

• Count how many People have the same Title

• Count how many Titles have the same Name

• Count how many Names are in the Dataset

• Count how many Titles are in the Dataset

What are we doing here?

• Counting Null Values in Every Column

• Counting Null Values in Every Row

• Counting Variables without Null Values in Every Column

• Counting Variables without Null Values in Every Row

What are you doing here?

• Removing an aleatory column from the same dataframe

• Removing the Cabin column from the same dataframe

• Adding the Cabin column from the same dataframe

• Creating a New Dataframe without Cabin

What are ytou doing here?

• We'll create a DataFrame with the average age per title

• We'll create a DataFrame with the average title per age

• We'll create a DataFrame with the averages of every age per title with 10 year buckets

• We'll create a DataFrame with the average age per name

Well Derive a dataframe ....

• containing rows with null age and we'll remove the Age column

• containing rows with null age and we'll aggregate the Age column

• containing rows with non- null age and we'll remove the Age column

• containing rows with non-null age and we'll aggregate the Age column

We'll add a new column to the previous DataFrame containing the average age for the corresponding title; this new column will be added by joining the two previous DataFrames together

• True

• False