Pandas

Question 1

Pandas

Answer 1

Pandas, and in particular its Series and DataFrame objects, builds on the NumPy array structure and provides efficient access to these sorts of “data munging” tasks that occupy much of a data scientist’s time.”

Question 2

Pandas Series

Answer 2

• A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a Series is a structure that maps typed keys to a set of typed values.
• It is like a column in a table. It is a one-dimensional array holding data of any type.

Question 3

Dataframe

Answer 3

• this is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns.

Question 4

CSV

Answer 4

• A simple way to store big data sets it to use CSV files.

* Csv files contain plain text and is a well know format that can be read by everyone including pandas.

Question 5

Concat

Answer 5

• This combines all dataframes into one. Rows are added to create a bigger table.

Question 6

Merge

Answer 6

• Merge is a way to combine dtaFrames column-wise, result is wider and with more columns. This is similar to a join in SQL.

Question 7

Inner join

Answer 7

• get matching values only from both dataframes. Non-matching values will be dropped.

Question 8

Outer Join

Answer 8

• this will combine everything from both dataframes.Will contain matching and non-matching values.

Question 9

Left/Right Join

Answer 9

• This will include every value from the left/right dataframe and pull in matching items from the other dataframe

Question 10

Groupby

Answer 10

• This can be used to run computations against different groups of rows.

Question 11

Pivot

Answer 11

• This can help collapse data spread across multiple rows into a single row with data now spread across columns.