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.