Chapter 7 - Pivot Table

Microsoft Excel introduced pivot tables in:

1994

To 'pivot' the data by re-organizing it by buyer (mean) we use which of the following code. A. print(pd.pivot_table(df, index=['Crop','Buyer'], aggfunc='count',)) B. print(pd.pivot_table(df, index=['Crop','Buyer'], aggfunc='mean',)) C. print(pd.pivot_table(df, index=['Crop','Season'], aggfunc='mean'))

B

To 'pivot' the data by re-organizing it by season of sales we use which of the following code. A. print(pd.pivot_table(df, index=['Crop','Buyer'], aggfunc='count',)) B. print(pd.pivot_table(df, index=['Crop','Buyer'], aggfunc='mean',)) C. print(pd.pivot_table(df, index=['Crop','Season'], aggfunc='mean'))

C

What is the first step to organize the crop prices in a data frame using panda?

Create a data frame with the data in simple numerical order

What order are sales of crops in?

The date order that our sales are made

A pivot table allows us to 'pivot' the data to:

Quickly realign the tabled data to address our research questions.

Pivot tables help you:

Slice, filter, and group data at high speed.

What crops does a Pivot table track?

Soybean, Wheat, Canola

Pivot table is a analysis tool to:

Summarizes the rows and columns of data

What are prices in?

"Per Bushel" for each sale

How is the pivot table organized for crop sales?

- Sale of crop - Price - Season (fall/spring) - Buyer

To use pivot tables with Python code we use which libraries?

- pandas - numpy

To 'pivot' the data by re-organizing it by buyer (count) we use which of the following code. A. print(pd.pivot_table(df, index=['Crop','Buyer'], aggfunc='count',)) B. print(pd.pivot_table(df, index=['Crop','Buyer'], aggfunc='mean',)) C. print(pd.pivot_table(df, index=['Crop','Season'], aggfunc='mean'))

A.