#!/usr/bin/env python
# coding: utf-8

# # Introduction to Jupyter Notebooks and Pandas
# 
# ## What is a Jupyter Notebook? 📓
# 
# - A [Jupyter](https://jupyter.org/) notebook is a document that can contain live code w/ results, visualizations, and rich text.
# - It is widely used in data science and analytics.
# - A Jupyter notebook has a `.ipynb` file extension (e.g., `my_notebook.ipynb`).
# - A Jupyter notebook is a list of cells.
# 
# ### How do you run a Jupyter notebook?
# 
# A Jupyter notebook can be run in one of the following Jupyter environments.
# 
# 1. Jupyter Notebook - original web application for creating and sharing computational documents
# 2. JupyterLab - a web-based development environment for notebooks (considered a newer version of the Jupyter Notebook)
# 3. [Google Colab](https://colab.research.google.com/) - Google's cloud notebook platform built on top of [Jupyter](https://jupyter.org/) environment
# 
# The first two environments require installations on your local machine or a server. We will use Google Colab as you can run it inside a cloud environment.
# 
# ### Types of cells
# 
# Every cell in a Jupyter notebook is of a specific type. The list of supported types vary by Jupyter environment.
# 
# Google Colab supports two types of cells.
# 
# 1. Code cell
# 2. Text cell (also known as a Markdown cell)
# 
# The cell below is a *code* cell. It contains a block of executable code.
# 
# Run the code below by clicking on the cell below and clicking the "Run" icon (▶).

# In[ ]:


print(10 + 20)


# ▶️ Run the code cell below to import `unittest`, a module used for **🧭 Check Your Work** sections and the autograder.

# In[2]:


import unittest
tc = unittest.TestCase()


# ---
# 
# ### 🎯 Challenge 1: Find the sum of a list
# 
# #### 👇 Tasks
# 
# - ✔️ Complete the code cell below to find the sum of all values in `my_list`.
# - ✔️ Store the result in a new variable named `result`.

# In[3]:


my_list = [11, 20, 52, 91, 90, 75, 74, 20, 21, 10, 14]

### BEGIN SOLUTION
result = 0

for num in my_list:
    result = result + num
### END SOLUTION

print(result)


# #### 🧭 Check Your Work
# 
# - Once you're done, run the code cell below to test correctness.
# - ✔️ If the code cell runs without an error, you're good to move on.
# - ❌ If the code cell throws an error, go back and fix any incorrect parts.

# In[4]:


# Challenge 1 Autograder
import unittest

tc = unittest.TestCase()

tc.assertEqual(result, 478)


# ---
# 
# ## Introduction to Pandas
# 
# Pandas is a Python *library* for data manipulation and analysis. Although it's used universally in data-related programming applications, it was initially developed for financial analysis by [AQR Capital Management](https://www.aqr.com/).
# 
# Note: A *library* in the context of programming is a collection of functions (and other data) that others have already written for you.
# 
# Pandas is popular for many reasons:
# 
# 1. 🏃🏿‍♀️ It's fast (for most cases where the dataset can be loaded to your memory).
# 2. 🪒 It supports most of the features required for data manipulation.
# 3. 💡 Write less code. Get more done.

# ---
# 
# ### 🎯 Challenge 2: Import packages
# 
# #### 👇 Tasks
# 
# - ✔️ Import the following Python packages.
#     1. `pandas`: Use alias `pd`.
#     2. `numpy`: Use alias `np`.

# In[5]:


### BEGIN SOLUTION
import pandas as pd
import numpy as np
### END SOLUTION


# #### 🧭 Check Your Work
# 
# - Once you're done, run the code cell below to test correctness.
# - ✔️ If the code cell runs without an error, you're good to move on.
# - ❌ If the code cell throws an error, go back and fix incorrect parts.

# In[6]:


# Challenge 2 Autograder
import sys
tc.assertTrue("pd" in globals(), "Check whether you have correctly import Pandas with an alias.")
tc.assertTrue("np" in globals(), "Check whether you have correctly import NumPy with an alias.")


# ---
# 
# ### It all starts with a `Series`...
# 
# The basic building block of Pandas is a `Series`. A `Series` is like a list, but with many more features.
# 
# You can create a `Series` by passing a list of values to `pd.Series()`.

# In[7]:


s = pd.Series([1, 2, 3, np.nan, 5, 6])

s


# ### Few things to note here
# 
# 1. These look similar to a Python `list`.
# 2. The last line of the printed output tells us the data type of values in the `Series` (`dtype: float64`).
# - What the heck is `np.nan`?
#     - It is used to indicate a "missing value".
#     - `np.nan` is NOT the same as `0`.
#     
# ### Differences between a list and a Series

# In[8]:


my_list = [1, 2, 3, 4]

print(type(my_list))
display(my_list * 2)


# In[9]:


my_series = pd.Series([1, 2, 3, 4])

print(type(my_series))
display(my_series * 2)


# What happens when you multiply a Python `list` by number `2`? It repeats the elements.
# 
# How about a `Series`? It multiples each element by `2`!

# ---
# 
# ### 🎯 Challenge 3: Create new `Series`
# 
# #### 👇 Tasks
# 
# - ✔️ Create a new Pandas `Series` named `my_series` with the following three values: `10`, `20`, `30`.
# 
# #### 🚀 Hint
# 
# The code below creates a new Pandas `Series` with the values `1` and `2`.
# 
# ```python
# my_new_series = pd.Series([1, 2])
# ```

# In[10]:


### BEGIN SOLUTION
my_series = pd.Series([10, 20, 30])
### END SOLUTION

my_series


# #### 🧭 Check Your Work
# 
# - Once you're done, run the code cell below to test correctness.
# - ✔️ If the code cell runs without an error, you're good to move on.
# - ❌ If the code cell throws an error, go back and fix any incorrect parts.

# In[11]:


# Challenge 2 Autograder
pd.testing.assert_series_equal(my_series, pd.Series([1, 2, 3]) * 10)


# ---
# 
# ### Using `Series` methods
# 
# A pandas `Series` is similar to a Python `list`. However, a `Series` provides many methods (equivalent to functions) for you to use.
# 
# As an example, `num_reviews.mean()` will return the average number of reviews.

# In[12]:


reviews_count = [12715, 2274, 2771, 3952, 528, 2766, 724]
num_reviews = pd.Series(reviews_count)

print(num_reviews)