---
tags: Labs-F20, 2020
title: Lab 10
---

# Lab 10: Using Resources and Pandas

Cake Boss Buddy Valastro has decided to stay at Brown for another semester and often finds himself wanting to use a bit of code to handle a problem that's not covered in the course. To do this, it helps to know how to search effectively online for operations and information on programming languages. This week's lab is designed to help you and Buddy learn how to do this! In other words, getting stuck and unstuck is part of the point this week, so don't get frustrated.

We will be working on searching the Internet for useful and trustworthy sources to get information and debug your code:
- We'll start with a couple examples of scenarios and potential queries to get you familiarized with what an effective search query looks like.
- Then we'll apply these skills to write a program that reads from and writes to a file while utilizing Python packages, with a particular emphasis on [`pandas`](https://pandas.pydata.org/docs/getting_started/overview.html).

## Problem 1 - Googling for Python Questions

### Instructions

We'll go through a couple scenarios in which you might want to search online for information. Each scenario has several queries. For each of these scenarios:
1. Predict which example queries might be good and which might be bad.
2. Google the queries and look into a couple of the search results. Try to note whether the same sites are consistently helpful.
3. Write a ranking of the queries.

Then, after you've made a ranking for each scenario, think about the common elements of the good and bad queries.

### Scenario #1: Getting Help on Error Messages
Buddy decides to start out with some simple code, and writes the following: `2+"1"`, which throws an error: `TypeError: unsupported operand type(s) for +: 'int' and 'str'`. Help Sherlock and Watson search online for help understanding the error.

Example queries:
- `python 2+"1"`
- `TypeError: unsupported operand type(s) for +: 'int' and 'str'`
- `python add int and string`
- `python strings`

### Scenario #2: Getting Details on an Operation

Buddy has grown quite fond of [ASCII art](https://i.pinimg.com/originals/d9/83/1d/d9831d5626c42e481cd4d96b3938f6f2.jpg), and he wants to be able to use `print`, but without a new line (as is printed by default). In other words, he wants `print("(\")` and `print("(\")` to print:
`(\(\`
rather than:
`(\`
`(\`

Example queries:
- `print(“hello”) but without new line`
- `python print`
- `python print without new line`

### Scenario #3: Finding an Appropriate Operation

Buddy is doing list operations but wants to write a base case that checks if a list is empty, but he doesn't remember or know how to do that.

Example queries:
- `“[]” python`
- `check if list is empty`
- `python lists`
- `python check if list is empty`
- `python list length`

For scenarios 1, 2, and 3, which queries were the best? Which ones were the worst?
___

### CHECKPOINT:
**Call a TA over to discuss the questions above!**
___

## Problem 2 - Intro to Python Packages/`pandas`

### Instructions

Buddy is now equipped to navigate and effectively use the Internet to learn about programming! He wants to test his skills by tackling this topic that he's been hearing about a lot: file input and file output in Python.

Buddy learns that you can write a Python program that reads the contents of a file on your computer, makes calculations, and even writes data to a new file on your machine.

We'll start with a brief explanation of what a package is and how to install one in PyCharm. Then, the rest of the lab will consist of a number of explanations and practice problems meant to familiarize you with popular Python packages.

### What's a package?

First, some terminology:
- A **package** is a collection of files that make up a **module**.
- A **module** is a file containing Python definitions and statements which can be *imported* into your code. A more generalized word for a module is a *library*.

There are *hundreds of thousands* of Python packages available online. Some are so commonly used that you'll find them in almost every large-scale Python application; others serve highly specific purposes.

PyCharm makes it very easy to download and use packages in your projects.

### Installing Packages using PyCharm

**On a Mac computer:** go to PyCharm --> Preferences --> Project Interpreter and press the + button.

**On a Windows computer:** go to File --> Settings --> Project --> Project Interpreter and press the + button.

This will bring up a list of common packages, where you can search for the one that you want and press "Install Package".

---

### `pandas`
`pandas` is a really powerful and fun Python library for data manipulation/analysis, with easy syntax and fast operations. Because of this, it is the probably the most popular library for data analysis in Python programming language.

In this lab section, we're going to learn the basics of `pandas` and use its functionality to analyze some datasets.

To start using `pandas` in your code, include this line at the top of your Python file:

import pandas as pd

#### Understanding DataFrames
`pandas` is built around the concept of a `DataFrame`. Simply said, a `DataFrame` is a table. It has rows and columns. Each column in a `DataFrame` is a `Series` data structure, rows consist of elements inside `Series`.

A `DataFrame` can be constructed using built-in Python dictionaries:

>>> import pandas as pd
>>> df = pd.DataFrame({
...     'country': ['Kazakhstan', 'Russia', 'Belarus', 'Ukraine'],
...     'population': [17.04, 143.5, 9.5, 45.5],
...     'square': [2724902, 17125191, 207600, 603628]
... })

>>> df
   country  population    square
0  Kazakhstan       17.04   2724902
1      Russia      143.50  17125191
2     Belarus        9.50    207600
3     Ukraine       45.50    603628

#### Reading and Writing to Files
Reading and writing file data is incredibly easy using `pandas`, and `pandas` supports many file formats, including CSV, XML, HTML, Excel, JSON, and many more (check out the official `pandas` documentation).

For example, if we wanted to save our previous DataFrame `df` to a [CSV file](https://en.wikipedia.org/wiki/Comma-separated_values) (spreadsheet), we only need a single line of code:

>>> df.to_csv('filename.csv')

We have saved our DataFrame, but what about reading data? No problem:

>>> df = pd.read_csv('filename.csv', sep=',')

Now that we know the basics of `pandas`, let's go ahead and analyze some datasets! Here are some links to the official documentation and a cheat sheet if you get stuck. And don't forget about our good friend Stack Overflow!
* [Official Documentation](http://pandas.pydata.org/pandas-docs/stable/index.html)
* [Pandas Cheat Sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)

---

### Candy Data

Buddy has recently been [craving candy](https://gifimage.net/wp-content/uploads/2017/07/candy-gif-17.gif) a lot, so we've been requested to revisit the candy dataset from [Lab 3](https://hackmd.io/@cs111/lab3).

Unlike Pyret, Python has no built-in table functionality (like reading a table directly from Google Sheets, table functions, etc). To complete this lab, we're going to have to take advantage of Python's ability to mutate data, iterate through data, and read and write data to and from input/output files, specifically using `pandas`.

### Setup
1. Navigate to [this GitHub file](https://github.com/fivethirtyeight/data/blob/master/candy-power-ranking/candy-data.csv). In the top right of the dataset, click 'Raw'. You should see a wall of text on your browser.
2. Go ahead and right click the page of text and click "Save as..." to save the file as `candy-data.csv` inside your current directory.
3. If trying this doesn't work (it might say it can only be saved as a `.webarchive`, not a `.csv`), instead create a new file in your PyCharm project called `candy-data.csv`. This will open a blank file and you can copy and paste all the 'Raw' data into it.
4. Create a Python file called `lab10.py`. This is where you'll implement the functions below.

### Task 1: Read Candy Data using `pandas`

You and Buddy should be experts on surfing the web for relevant information and answers now, so let's put those skills to the test. We aren't going to give you much guidance about how to complete these tasks; remember the takeaways from Part 1, and try to use online resources (but if you get stuck, the TAs are still here to help).

1. Write a function that reads from `candy-data.csv` and calculates the name of the candy with the highest win percentage.

:::info
**HINT:** If you're not sure where to start, try following the steps below:
- Figure out how to read a CSV file into Python using `pandas`
- Try to print out a few win percentages for different candies (figure out how to access them from a `DataFrame`).
- Design your function! Calculating the highest win percentage shouldn't require looking up new approaches. Instead, use things that we've already learned in class.
- Test your function to ensure that it works properly.

2. Write a second function that writes the results of your answer to Question 1 to a file named `result-1.csv`.

:::info
**HINT:** This time, start by writing a function that just writes the string `"Hello, World"` onto the first line of a file. Once you've done so, integrate data from the CSV.
:::

3. Write a function that reads from `candy-data.csv` and writes the names of the candies with chocolate to a file named `result-2.csv`, such that each name is on a separate line.
:::info
**HINT:** Consider which parts of the code your wrote for problems 1 and 2 help you solve this problem.
:::

:::warning
**NOTE:**
- Your solutions should read directly from the file. Make sure not to copy the contents of the file into your code.
:::
___

### CHECKPOINT:
**Call a TA over to go over your work from above!**
___

### Task 2: More Candy Data Manipulation
1. Use `pandas` to get the candy with the highest sugar percentage.
2. Use `pandas` to get candy that contains both chocolate and caramel.
3. Save the `DataFrame` with candy containing chocolate and caramel as a csv file called `chocolate_and_caramel.csv`.
4. Use `pandas` to find the top 5 most "boujee" candies, aka the ones with the highest price percents.
5. Use `pandas` to find the top 3 most liked and popular **non-chocolate** candies (highest win percents).
6. Now, use `pandas` to add a column to the candy data called `too-sugary`, which will store a Boolean value (`True` and `False`, rather than 1s and 0s) for each candy depending on if it's too sugary. In this case, if sugar-percent is 0.50 or higher, then it's too sugary.
___

### CHECKPOINT:
**Call a TA over to go over your work from above!**
___

## Key Takeaways + Resources

### Googling for Python Questions

Things to consider when googling in order to debug your code:

- [Stack Overflow](https://stackoverflow.com/) is a website useful for answering specific coding questions (but try to find posts with lots of upvotes)
- Websites with tutorials such as [GeeksForGeeks](https://www.geeksforgeeks.org/) are more useful for explaining a particular concept or algorithm
- If an answer contains concepts that you haven't seen before, keep searching -- there are often many ways to implement the same feature, and a different one might be more familiar
- If you're not sure why an answer isn't working, double check that it uses Python 3.7 or higher (and not Python 2)

### Python Packages

More information about python packages (you can browse this whole site for more specific info):
- [An Overview of Packaging for Python](https://packaging.python.org/overview/)

CSV files:
- CSV files are plain text files that arrange tabular data, with each piece of data separated by a comma
- CSV files makes it easy to import/ export large chunks of data from spreadsheets or databases
- You can use `pandas` to manipulate CSVs
- You can also utilize Python CSV's package since Python already has a built-in CSV library, which we can import (not covered in this lab)

### `pandas`

If you want to learn more about the power of `pandas`, below are a few resources that you can explore:
- [12 Useful Pandas Techniques in Python for Data Manipulation](https://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/)
- [Pandas Tutorial](https://www.python-course.eu/pandas.php)