Purpose: Produce a new column-based (e.g. dict[str, list[str]]) table with only a specific subset of the original columns. Why: Many data tables will contain many columns that are not related to the analysis you are trying to perform. Selecting only the columns you care about makes it easier to focus your attention on the problem at hand. Function Name: select • Parameters: 1. dict[str, list[str]]-a column-based table of data that will not be mutate 2. list[str]-the names of the columns to copy to the new, returned dictionary • Return type: dict[str, list[str]]
Purpose: Produce a new column-based (e.g. dict[str, list[str]]) table with only a specific subset of the original columns. Why: Many data tables will contain many columns that are not related to the analysis you are trying to perform. Selecting only the columns you care about makes it easier to focus your attention on the problem at hand. Function Name: select • Parameters: 1. dict[str, list[str]]-a column-based table of data that will not be mutate 2. list[str]-the names of the columns to copy to the new, returned dictionary • Return type: dict[str, list[str]]
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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In python don't import libraries
![**Purpose:** Produce a new column-based (e.g. `dict[str, list[str]]`) table with only a specific subset of the original columns.
**Why:** Many data tables will contain many columns that are not related to the analysis you are trying to perform. Selecting only the columns you care about makes it easier to focus your attention on the problem at hand.
- **Function Name:** `select`
- **Parameters:**
1. `dict[str, list[str]]` - a column-based table of data that *will not be mutated*
2. `list[str]` - the names of the columns to copy to the new, returned dictionary
- **Return type:** `dict[str, list[str]]`
**Implementation Strategy:**
1. Establish an empty dictionary that will serve as the returned dictionary this function is building up.
2. Loop through each of the columns *in the second parameter of the function*
1. Assign to the column key of the result dictionary the list of values stored in the input dictionary at the same column
3. Return the dictionary produced](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff7a3ae14-42ec-4f5f-a07a-1d8ec0198df6%2Fcfec0cac-83de-4da1-af8d-3a449a00d16f%2Ftg2g8zq_processed.png&w=3840&q=75)
Transcribed Image Text:**Purpose:** Produce a new column-based (e.g. `dict[str, list[str]]`) table with only a specific subset of the original columns.
**Why:** Many data tables will contain many columns that are not related to the analysis you are trying to perform. Selecting only the columns you care about makes it easier to focus your attention on the problem at hand.
- **Function Name:** `select`
- **Parameters:**
1. `dict[str, list[str]]` - a column-based table of data that *will not be mutated*
2. `list[str]` - the names of the columns to copy to the new, returned dictionary
- **Return type:** `dict[str, list[str]]`
**Implementation Strategy:**
1. Establish an empty dictionary that will serve as the returned dictionary this function is building up.
2. Loop through each of the columns *in the second parameter of the function*
1. Assign to the column key of the result dictionary the list of values stored in the input dictionary at the same column
3. Return the dictionary produced
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