diff --git a/.gitignore b/.gitignore index 5422084..19203ba 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ .DS_Store -notebooks/EDA_tut01.ipynb \ No newline at end of file +notebooks/EDA_tut01.ipynb +notebooks/James Review.ipynb diff --git a/P01-Checking-Out-Our-Data/content.md b/P01-Checking-Out-Our-Data/content.md index aa1159f..6f3b2e4 100644 --- a/P01-Checking-Out-Our-Data/content.md +++ b/P01-Checking-Out-Our-Data/content.md @@ -1,8 +1,8 @@ -### Welcome to Part 1 of the tutorial series on basic exploratory data science! +### Welcome to Part 1 of the tutorial series on basic exploratory data science! -> NOTE: Add realistic scenario to connect to students. +> NOTE: Add realistic scenario to connect to students. -This tutorial is geared towards introductory data science students at the Make School Product College. +This tutorial is geared towards introductory data science students at the Make School Product College. You should have the following prerequisite skills: @@ -12,21 +12,24 @@ You should have the following prerequisite skills: You may realize as this course goes on that it does not have the same continuous project-based approach as many other Product College courses. -That's because this course is best taught by working through dataset after dataset. +That's because this course is best taught by working through dataset after dataset. -While you'll be able to apply data science skills into any app you build, it is not directly project-focused. +While you'll be able to apply data science skills into any app you build, it is not directly project-focused. --- For this tutorial, navigate to the `notebooks/ ` folder and create a new Jupyter notebook file. -In this course, you'll be working primarily with **Jupyter Notebooks**, a type of interactive Python simulations where you'll be able to easily manipulate, visualize, and model data. +> NOTE: How does one make a new jupyter notebook file? + + +In this course, you'll be working primarily with **Jupyter Notebooks**, a type of interactive Python simulations where you'll be able to easily manipulate, visualize, and model data.
### **EDA: Mobile App Data** -In this tutorial, we'll start you off with a nod towards our mobile developers and roots as a mobile game academy by exploring a **curated dataset on over 10k mobile apps** on the app store. +In this tutorial, we'll start you off with a nod towards our mobile developers and roots as a mobile game academy by exploring a **curated dataset on over 10k mobile apps** on the app store. Consider the following questions as you work through the tutorial: - What's the average byte size of mobile apps? @@ -34,21 +37,21 @@ Consider the following questions as you work through the tutorial: - How many mobile apps are games and what is the general categorical distribution of apps by genre? - What are the most popular mobile games by total popular rating? -As you complete the tutorial, **write down answers you find to these questions** and be sure to ask your own! +As you complete the tutorial, **write down answers you find to these questions** and be sure to ask your own! -A strong part of data science is being able to formulate critical questions based off of observations you see in unknown data. +A strong part of data science is being able to formulate critical questions based off of observations you see in unknown data. --- ##
Introduction
-Welcome to the first tutorial on Exploratory Data Analysis! +Welcome to the first tutorial on Exploratory Data Analysis! -In this tutorial, we'll quickly explore a simple dataset designed to cultivate your interest in extracting unique insights from data. +In this tutorial, we'll quickly explore a simple dataset designed to cultivate your interest in extracting unique insights from data. -As many of you are mobile developers, we'll be exploring the [App Store Mobile Apps](https://www.kaggle.com/ramamet4/app-store-apple-data-set-10k-apps/version/2) dataset from Kaggle! No need to download or set anything up; if you followed the setup instructions correctly, you should have the dataset already downloaded in your local directory! +As many of you are mobile developers, we'll be exploring the [App Store Mobile Apps](https://www.kaggle.com/ramamet4/app-store-apple-data-set-10k-apps/version/2) dataset from Kaggle! No need to download or set anything up; if you followed the setup instructions correctly, you should have the dataset already downloaded in your local directory! -We'll explore trends among mobile app data, including **most popular mobile apps/games**, **top rated games**, and **free vs. paid mobile apps**. +We'll explore trends among mobile app data, including **most popular mobile apps/games**, **top rated games**, and **free vs. paid mobile apps**. ### Before moving any further, ensure you have the following prerequisites met: - Anaconda Navigator installed and running successfully. @@ -58,6 +61,10 @@ We'll explore trends among mobile app data, including **most popular mobile apps Open up a Jupyter Notebook (See Tutorial 0 (link here) for setup instructions.) and enter the following into the first cell to set up our dependencies needed for basic data analysis and visualization. +> NOTE: I already set up anaconda, I don't want to look at that link again. Can't you just tell me how to open it? And also what notebook am I opening up? A picture or gif showing how to do this would be amazing. + +> NOTE: Also what is a cell anyway? + ```py # Pandas is a library for basic data analysis import pandas as pd @@ -76,6 +83,9 @@ Here, we run some basic setup for some visualization work in SeaBorn and MatPlot Don't worry about what this all means now: simply plug this into your next cell for some data visualization setup. +> NOTE: Tell the students about shift + enter to make a new cell + + ```py sns.set(style="white", context="notebook", palette="deep") @@ -84,6 +94,9 @@ COLOR_COLUMNS = ["#66C2FF", "#5CD6D6", "#00CC99", "#85E085", "#FFD966", "#FFB366 sns.set_palette(palette=COLOR_COLUMNS, n_colors=4) ``` +> NOTE: jupyter output says "name 'sns' is not defined" I thought seaborn was imported as sb + + ## Setting up our DataFrame ### NOTE: We'll be quickly reviewing some basic Bash commands in Jupyter. @@ -94,9 +107,9 @@ Here, we can use Bash commands preceded by a percentage sign (%) to check our cu % ls ``` -Next, we can run the following Bash command preceded by '%' to navigate to our raw data file. +Next, we can run the following Bash command preceded by '%' to navigate to our raw data file. -After running the next cell, run the previous cell to see reflected changes in the current directory location! +After running the next cell, run the previous cell to see reflected changes in the current directory location! ```py # NOTE: BASH COMMANDS IN JUPYTER (cont'd) @@ -106,7 +119,7 @@ After running the next cell, run the previous cell to see reflected changes i Sweet! Let's reset now. -Run the following command to navigate to the last location: the starting directory from where this notebook was launched. +Run the following command to navigate to the last location: the starting directory from where this notebook was launched. ```py % cd - @@ -127,9 +140,9 @@ Your output in Jupyter should be the following: ![screenshot01-dfhead](../media/screenshot01_dfhead.png?raw=true) -Look over the data as a whole and see if you can detect any interesting trends or patterns across the data. +Look over the data as a whole and see if you can detect any interesting trends or patterns across the data. -Some good questions to keep in mind: +Some good questions to keep in mind: - Any *convoluted* data? @@ -139,9 +152,9 @@ Some good questions to keep in mind: - Any data that's *difficult to understand*? -All these realizations from a quick glance at the information is crucial to establish a good relationship with your data at the start. +All these realizations from a quick glance at the information is crucial to establish a good relationship with your data at the start. -Your intuition will lead you to deeper questions and important revelations as you sift through more and more data in attempts to answer said questions. +Your intuition will lead you to deeper questions and important revelations as you sift through more and more data in attempts to answer said questions. That being said, time for a quick side note... @@ -149,25 +162,25 @@ That being said, time for a quick side note... ### **Quick Note on Data Cleaning**: -Before we go any further, it's important to make one very important assumption regarding the cleanliness of data in this practice tutorial. +Before we go any further, it's important to make one very important assumption regarding the cleanliness of data in this practice tutorial. -Right now, the majority of your data science work focuses on *data analysis* and *data visualization* - not easy work, especially if your data is **unclean**. +Right now, the majority of your data science work focuses on *data analysis* and *data visualization* - not easy work, especially if your data is **unclean**. -Unclean data is indicative of an important factor now at play: important trends or patterns that exist among your data may be *obscured* by lack of data clarity. +Unclean data is indicative of an important factor now at play: important trends or patterns that exist among your data may be *obscured* by lack of data clarity. This could be for a multitude of reasons, including irregularities in data types, inconsistencies in how data was recorded, or the presence of inappropriate data values such as *null values*. (Remember that term - it'll come up again next tutorial!) -It is crucial as a data scientist to always **question the data in front of you** from as many angles possible, including its validity and cleanliness. +It is crucial as a data scientist to always **question the data in front of you** from as many angles possible, including its validity and cleanliness. -In this tutorial, we simply want to instill that sense of critical thinking in you, but do not wish to throw some complex data cleaning challenges at you just yet. +In this tutorial, we simply want to instill that sense of critical thinking in you, but do not wish to throw some complex data cleaning challenges at you just yet. -However, keep in mind ways that the data in this tutorial could be potentially obscured to make your job tougher; you'll find that industry data science often spends a lot of time tackling the issue of data cleaning and processing. +However, keep in mind ways that the data in this tutorial could be potentially obscured to make your job tougher; you'll find that industry data science often spends a lot of time tackling the issue of data cleaning and processing. --- -Now that we're done rambling, let's get back into checking out the values in our data! +Now that we're done rambling, let's get back into checking out the values in our data! -Let's take a look at the very first app in our dataset and see some of its information. +Let's take a look at the very first app in our dataset and see some of its information. We can see the information from the very first app using either of the following two commands in new Jupyter cells: @@ -179,29 +192,29 @@ df.iloc[0] df.loc[1] ``` -Both commands should return data for the app *PAC-MAN Premium*. +Both commands should return data for the app *PAC-MAN Premium*. -`.iloc[]` and `.loc[]` are powerful selector tools in Pandas that allow you to view either a single or multiple rows, columns, and/or cells in a dataset. +`.iloc[]` and `.loc[]` are powerful selector tools in Pandas that allow you to view either a single or multiple rows, columns, and/or cells in a dataset. -You may be wondering what the difference is between `.iloc[]` and `.loc[]`? +You may be wondering what the difference is between `.iloc[]` and `.loc[]`? -Let's explore that using the example of the number **3**. +Let's explore that using the example of the number **3**. -When I run the following command in a new cell: +When I run the following command in a new cell: ```py df.iloc[3] ``` -Pandas returns the data for the app *eBay: Best App to Buy, Sell, Save! Online Shopping*. +Pandas returns the data for the app *eBay: Best App to Buy, Sell, Save! Online Shopping*. -However, if you notice carefully, the *eBay* app appears in the **fourth position** in the original dataframe. +However, if you notice carefully, the *eBay* app appears in the **fourth position** in the original dataframe. **Why is this the case?** -Well, it turns out that `.iloc[]` is useful for selecting *data by index*. +Well, it turns out that `.iloc[]` is useful for selecting *data by index*. -Since Python (like most languages), has zero-indexed arrays, the input value **3** refers to the **fourth** row of the dataset! +Since Python (like most languages), has zero-indexed arrays, the input value **3** refers to the **fourth** row of the dataset! Let's contrast that with the following command: @@ -209,21 +222,21 @@ Let's contrast that with the following command: df.loc[3] ``` -In this case, Pandas returns the data for the app *WeatherBug - Local Weather, Radar, Apps, Alerts*. +In this case, Pandas returns the data for the app *WeatherBug - Local Weather, Radar, Apps, Alerts*. **Why is this the case?** -Simply put, `.loc[]` is useful for selecting *data by label*. +Simply put, `.loc[]` is useful for selecting *data by label*. -This means that the original label of the data as defined by its leftmost column becomes the selection value. +This means that the original label of the data as defined by its leftmost column becomes the selection value. Therefore, when we ran `df.loc[3]`, rather than selecting the row defined by the third index, it selected the row defined by the number **3** occurring in the leftmost column, which happens to be the second index! If this seems tricky, don't worry. -Even the most advanced Python developers and data scientists can trip up these commands. Practice makes perfect! +Even the most advanced Python developers and data scientists can trip up these commands. Practice makes perfect! -These selector methods also support *slicing*. +These selector methods also support *slicing*. Try running either of the following: @@ -235,11 +248,11 @@ df.iloc[:3] df.loc[:3] ``` -Either one should return you the first three rows appearing in the dataset - accurately sliced from the first data value, as your slice parameter indicates. +Either one should return you the first three rows appearing in the dataset - accurately sliced from the first data value, as your slice parameter indicates. -Ask yourself the following: *why would both slicing index and label selectors return the same output*? +Ask yourself the following: *why would both slicing index and label selectors return the same output*? -Before we go any further, let's take a quick peek at the top of our DataFrame to check the data for any obvious red flags. +Before we go any further, let's take a quick peek at the top of our DataFrame to check the data for any obvious red flags. You can peek at the top of a DataFrame with the following line: @@ -261,13 +274,15 @@ You should see the following: ![screenshot07_dftail](../media/screenshot07_dftail.png?raw=true) -Doesn't this look familiar to some of the original commands we played with, including `.describe()`? +Doesn't this look familiar to some of the original commands we played with, including `.describe()`? + +>NOTE: I don't remember playing with .describe() --- -Let's try something new - let's use our Pythonic powers and attempt to calculate the average price across all apps. +Let's try something new - let's use our Pythonic powers and attempt to calculate the average price across all apps. -In this example, we don't care about which apps are free vs. paid or the general distribution of apps by payment; we only want to calculate the exact average price. +In this example, we don't care about which apps are free vs. paid or the general distribution of apps by payment; we only want to calculate the exact average price. > (For those of you who are more statistically inclined out there, you may be raising an eyebrow.

Good!

That intuition is important for answering more realistic data science questions involving descriptive statistics like the population average.

For now however, it is important to focus on the example at hand and understand the data science process.) @@ -277,15 +292,15 @@ To calculate the exact average price for all apps, we can run the following comm df.price.mean() ``` -This should return the value ``` 1.7262178685562626 ```, suggesting that the average price of an app is about $1.73. +This should return the value ``` 1.7262178685562626 ```, suggesting that the average price of an app is about $1.73. --- -By the way, those selectors seem great, but imagine trying to get general information about **your entire dataset** that way. +By the way, those selectors seem great, but imagine trying to get general information about **your entire dataset** that way. Seems impractical, right? -Pandas agrees - that's why it offers a far better way to grab immediate descriptive statistics on *all columns* across your dataset in a highly visual manner. +Pandas agrees - that's why it offers a far better way to grab immediate descriptive statistics on *all columns* across your dataset in a highly visual manner. Run the following in a new cell to quickly show general column-formatted information on the dataset. @@ -309,15 +324,17 @@ df.describe(include="O") ![screenshot05_advdfdescribe](../media/screenshot05_advdfdescribe.png?raw=true) -### Notice anything interesting? +>NOTE: Or this result: "TypeError: include and exclude must both be non-string sequences" + +### Notice anything interesting? -If you look carefully, the CURRENCY column has a 'count' value of 7000 but also has a 'unique' value of 1. +If you look carefully, the CURRENCY column has a 'count' value of 7000 but also has a 'unique' value of 1. -This means that every value in the CURRENCY column **is the same**! +This means that every value in the CURRENCY column **is the same**! -Looking at the data shows that the value under observation is the type of currency of all data: "USD". +Looking at the data shows that the value under observation is the type of currency of all data: "USD". -This isn't that important to us, so we can make the executive judgment to **drop that column** to reduce the size of the data. +This isn't that important to us, so we can make the executive judgment to **drop that column** to reduce the size of the data. ```py # NOTE: Running this multiple times will throw errors. @@ -326,15 +343,15 @@ df = df.drop("currency", axis="columns") --- -Now imagine we want to know how large our apps are. +Now imagine we want to know how large our apps are. -How can we go about solving that question? +How can we go about solving that question? -In the last few cells, ever take a look at that 'size_bytes' column? +In the last few cells, ever take a look at that 'size_bytes' column? -Pretty nasty, huh? +Pretty nasty, huh? -In fact, on our `.describe( )` cell above, we see that most data contains 'size_bytes' attributes ranging in the millions. +In fact, on our `.describe( )` cell above, we see that most data contains 'size_bytes' attributes ranging in the millions. Well, in the real world, we call those Megabytes and can do some quick reformatting to our DataFrame to clean that up a little. @@ -346,19 +363,19 @@ df["size_Mb"] = df["size_bytes"].apply(_byte_resizer) df.drop("size_bytes", axis="columns", inplace=True) ``` -Notice how in the previous cell, we wrote a custom helper function ```_byte_resizer()``` that contained our logic for grabbing ```size_bytes``` data and dividing it effectively to give us our new ```size_Mb``` column. +Notice how in the previous cell, we wrote a custom helper function ```_byte_resizer()``` that contained our logic for grabbing ```size_bytes``` data and dividing it effectively to give us our new ```size_Mb``` column. -Pandas is full of little gems: tools and tricks that optimize certain tasks, like looking at the beginning few elements of a dataset. +Pandas is full of little gems: tools and tricks that optimize certain tasks, like looking at the beginning few elements of a dataset. -However, the best Python developers and data science experts maintain their flexibility with Python to be able to write custom functions and commands at will based on what they're doing. +However, the best Python developers and data science experts maintain their flexibility with Python to be able to write custom functions and commands at will based on what they're doing. -For instance, take a look at the following command. It does the exact same thing as the previous cell, but is written quite differently using more advanced Python syntax that may not be as nice for readability. +For instance, take a look at the following command. It does the exact same thing as the previous cell, but is written quite differently using more advanced Python syntax that may not be as nice for readability. ```py df["size_Mb"] = df["size_bytes"].apply(lambda num: np.around(num / 1000000, decimals=2)) df.drop("size_bytes", axis="columns", inplace=True) ``` -Which looks cleaner to you? +Which looks cleaner to you? -Use your skills wisely! \ No newline at end of file +Use your skills wisely! diff --git a/P02-Creating-Basic-Visualizations/content.md b/P02-Creating-Basic-Visualizations/content.md index c0ef236..712c8c5 100644 --- a/P02-Creating-Basic-Visualizations/content.md +++ b/P02-Creating-Basic-Visualizations/content.md @@ -1,32 +1,32 @@ -## Part 2: Exploratory Data Science (cont'd) +## Part 2: Exploratory Data Science (cont'd) ## Basic Visualizations -Now that we've spent enough time playing with our data and assuring ourselves that it's relatively clean, let's get to the fun part! +Now that we've spent enough time playing with our data and assuring ourselves that it's relatively clean, let's get to the fun part! -We're going to rely primarily on SeaBorn visualizations to illustrate its authenticity and aptitude for beautiful and simple visualizations. +We're going to rely primarily on SeaBorn visualizations to illustrate its authenticity and aptitude for beautiful and simple visualizations. -Each of these can also be replicated through MatPlotLib, though it would take much longer. +Each of these can also be replicated through MatPlotLib, though it would take much longer. ### Let's explore the distribution of apps based on file size. -Remember that reformat we did to change 'size_bytes' data to 'size_Mb' data? +Remember that reformat we did to change 'size_bytes' data to 'size_Mb' data? -That's going to come in handy, as we're going to generate a histogram of frequency occurrences (counts) based on general data byte size. +That's going to come in handy, as we're going to generate a histogram of frequency occurrences (counts) based on general data byte size. -First things first: let's set up the plotting space in a new cell. +First things first: let's set up the plotting space in a new cell. -This should be continued in the same Jupyter notebook as the previous tutorial section. +This should be continued in the same Jupyter notebook as the previous tutorial section. ```py plt.subplots(figsize=(10, 8)) ``` -Nice! +Nice! -Next, let's initialize the bins by which our histogram data will fall. +Next, let's initialize the bins by which our histogram data will fall. -Take note that for this example, the bins are not evenly sized but grow exponentially. +Take note that for this example, the bins are not evenly sized but grow exponentially. Why do you think this is the case? @@ -37,18 +37,21 @@ LABELS = ["<10m", "10-20m", "20-50m", "50-100m", "100-200m", "200-500m", "500-10 Finally, let's produce our plotting object of frequency counts and use SeaBorn to visualize it for us. -Enter the following into a new cell: +Enter the following into a new cell: ```py freqs = pd.cut(df["size_Mb"], BINS, include_lowest=True, labels=LABELS) sns.barplot(y=freqs.value_counts().values, x=freqs.value_counts().index) ``` -If all is well, you should see your very first tutorial visualization with SeaBorn here: +If all is well, you should see your very first tutorial visualization with SeaBorn here: ![screenshot08_bargraph](../media/screenshot08_bargraph.png?raw=true) -### Looks great! Let's take a further look into priced vs. unpriced apps on the mobile store. +>NOTE: I had to do plt.show() afterwards to see the visualization. +>NOTE: If you put "%matplotlib inline" after importing matplotlib it works fine. + +### Looks great! Let's take a further look into priced vs. unpriced apps on the mobile store. We'll generate a doughnut plot using basic MatPlotLib to get a basic sense as to the distribution of paid vs. free apps on the app store. @@ -56,7 +59,7 @@ First things first, let's set up our parameters in a new cell. We want to look at two main categories: whether or not an app is free or paid. -Enter the following into a new cell: +Enter the following into a new cell: ```py BINS = [-np.inf, 0.00, np.inf] @@ -64,37 +67,37 @@ LABELS = ["FREE", "PAID"] colors = ['lightcoral', 'yellowgreen'] ``` -When we look back at the data, our data contains numerical price data. +When we look back at the data, our data contains numerical price data. Let's run a quick script to add a column containing free vs. paid price attributes for ease of use. -Enter the following into a new cell: +Enter the following into a new cell: ```py df["price_categories"] = pd.cut(df["price"], BINS, include_lowest=True, labels=LABELS) ``` -Now, let's initialize our plotting space in MatPlotLib. +Now, let's initialize our plotting space in MatPlotLib. -You'll often see this syntax `fig, ax` being used. +You'll often see this syntax `fig, ax` being used. ```py fig, axs = plt.subplots(figsize=(10, 5)) ``` -For easier reference, we'll slice our DataFrame to easily grab the data we want to look at. +For easier reference, we'll slice our DataFrame to easily grab the data we want to look at. You can do this with ANY column or feature in your data. -Enter the following into a new cell: +Enter the following into a new cell: ```py price_df = df["price_categories"].value_counts() ``` -Finally, we'll generate a nice looking doughnut plot in MatPlotLib by plotting our data using a standard pie chart, then modifying the chart with a superimposed white circle to give the visualization some pizzazz. +Finally, we'll generate a nice looking doughnut plot in MatPlotLib by plotting our data using a standard pie chart, then modifying the chart with a superimposed white circle to give the visualization some pizzazz. -Enter all this into a single cell: +Enter all this into a single cell: ```py plt.pie(price_df.values, labels=LABELS, colors=colors, autopct='%1.1f%%', shadow=True) @@ -106,13 +109,15 @@ fig.gca().add_artist(centre_circle) plt.axis('equal') ``` -A little bit of convoluted code, but if you followed it carefully in the same cell, you should see the following: +A little bit of convoluted code, but if you followed it carefully in the same cell, you should see the following: ![screenshot09_donutgraph](../media/screenshot09_donutgraph.png?raw=true) -Awesome! More paid apps than I expected, at least. +>NOTE: It works well but I don't understand anything I pasted in. + +Awesome! More paid apps than I expected, at least. -### Let's keep moving with this idea and check out the highest rated free and paid apps. +### Let's keep moving with this idea and check out the highest rated free and paid apps. First things first: let's slice our categorical price data into two objects based on category in a new cell. @@ -128,9 +133,9 @@ free_apps_rated = free_apps.sort_values(by=["rating_count_tot"], ascending=False paid_apps_rated = paid_apps.sort_values(by=["rating_count_tot"], ascending=False) ``` -Now that we have our rated free and rated paid apps objects, let's visualize them one at a time. +Now that we have our rated free and rated paid apps objects, let's visualize them one at a time. -For the sake of clarity, we'll only visualize the top ten highest rated apps in each category. +For the sake of clarity, we'll only visualize the top ten highest rated apps in each category. First, let's look at free apps. (Feel free to put this in a new cell.) @@ -142,9 +147,9 @@ You should see the following output: ![screenshot10_sidebargraph](../media/screenshot10_sidebargraph.png?raw=true) -Interesting. Personally, I'm shocked that Clash of Clans is more popular than Temple Run. +Interesting. Personally, I'm shocked that Clash of Clans is more popular than Temple Run. -Next, let's take a look at paid apps. Put it in a new cell. +Next, let's take a look at paid apps. Put it in a new cell. ```py sns.barplot(x=paid_apps_rated["rating_count_tot"][:10], y=paid_apps_rated["track_name"][:10]) @@ -156,25 +161,25 @@ You should see the following (looks familiar?): Looks like as much as I like Minecraft, it clearly doesn't hold a sword up to Fruit Ninja Classic. -### By the way, what if we just want to know the highest rated apps of all time, regardless of price? +### By the way, what if we just want to know the highest rated apps of all time, regardless of price? -Not much setup to do here, so let's just dive into it. +Not much setup to do here, so let's just dive into it. -First, let's initialize our plotting space. New cell! +First, let's initialize our plotting space. New cell! ```py plt.subplots(figsize=(20, 20)) ``` -Next, let's grab our data sorted by ratings as an object. In a new cell. +Next, let's grab our data sorted by ratings as an object. In a new cell. ```py ratings = df.sort_values(by=["rating_count_tot"], ascending=False) ``` -Finally, let's plot it. +Finally, let's plot it. -For the sake of clarity, we'll plot the top thirty apps. And guess what? You'll put it in a new cell! +For the sake of clarity, we'll plot the top thirty apps. And guess what? You'll put it in a new cell! ```py sns.barplot(x=ratings["rating_count_tot"][:30], y=ratings["track_name"][:30]) @@ -184,10 +189,10 @@ After all this, you should see the following: ![screenshot12_sidebargraph](../media/screenshot12_sidebargraph.png?raw=true) -I don't think anyone's truly surprised that Mark Zuckerberg commands the mobile app market. +I don't think anyone's truly surprised that Mark Zuckerberg commands the mobile app market. Think of any simple visualization-based questions you have from the basic data. **How would you go about slicing the DataFrame and creating a visualization based on that?** -Ponder these questions and others you may have regarding the newly explored data as we move on to our *third and final section of this tutorial*. \ No newline at end of file +Ponder these questions and others you may have regarding the newly explored data as we move on to our *third and final section of this tutorial*. diff --git a/notebooks/.ipynb_checkpoints/James Review-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/James Review-checkpoint.ipynb new file mode 100644 index 0000000..a67c6d6 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/James Review-checkpoint.ipynb @@ -0,0 +1,3282 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Pandas is a library for basic data analysis\n", + "import pandas as pd\n", + "\n", + "# NumPy is a library for advanced mathematical computation\n", + "import numpy as np\n", + "\n", + "# MatPlotLib is a library for basic data visualization\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# SeaBorn is a library for advanced data visualization\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "sns.set(style=\"white\", context=\"notebook\", palette=\"deep\")\n", + "\n", + "COLOR_COLUMNS = [\"#66C2FF\", \"#5CD6D6\", \"#00CC99\", \"#85E085\", \"#FFD966\", \"#FFB366\", \"#FFB3B3\", \"#DAB3FF\", \"#C2C2D6\"]\n", + "\n", + "sns.set_palette(palette=COLOR_COLUMNS, n_colors=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5282935706Bible0.0098592053204.55.07.5.14+Reference37545192.77
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15284862767FreeCell4.9963406684.54.54.0.34+Games3852155.15
16284876795TripAdvisor Hotels Flights Restaurants0.0056194874.03.521.14+Travel371261207.91
17284882215Facebook0.0029746762123.53.5954+Social Networking371291389.88
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20284993459Shazam - Discover music, artists, videos & lyrics0.004029251364.04.511.0.312+Music373161147.09
21285005463Crash Bandicoot Nitro Kart 3D2.993145641784.03.51.0.04+Games4701110.74
22285946052iQuran1.9929299664.54.53.34+Reference4302170.71
23285994151:) Sudoku +2.99114477815.05.05.2.64+Games405116.17
24286058814Yahoo Sports - Teams, Scores, News & Highlights0.001379511314.04.56.94+Sports37261130.58
25286070473Mileage Log | Fahrtenbuch5.99804.50.09.0.54+Business3753171.20
27286799607Cleartune - Chromatic Tuner3.9932412974.04.02.1.34+Music43210111.42
28286906691Lifesum – Inspiring healthy lifestyle app0.005795123.54.08.4.14+Health & Fitness375111188.02
29286911400Hangman.0.00423162483.03.52.0.69+Games380114.77
31288113403iTranslate - Language Translator & Dictionary0.00123215253.55.010.5.44+Productivity375231287.93
32288120394TouchOSC4.9978274.03.51.9.84+Music431114.26
33288419283RadarScope9.993449234.04.53.4.14+Weather37511172.77
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109951182265441脱出ゲーム　わたしをみつけて　-おじいさんとわたしの物語-0.00111.01.01.0.14+Games38411177.50
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110101183234072CTFxCmoji0.003945.05.01.39+Social Networking3711126.08
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7197 rows × 15 columns

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Coaster World - Virtual Reality 0.00 \n", + "11097 1188375727 Escape the Sweet Shop Series 0.00 \n", + "\n", + " rating_count_tot rating_count_ver user_rating user_rating_ver \\\n", + "1 21292 26 4.0 4.5 \n", + "2 161065 26 4.0 3.5 \n", + "3 188583 2822 3.5 4.5 \n", + "4 262241 649 4.0 4.5 \n", + "5 985920 5320 4.5 5.0 \n", + "6 8253 5516 4.0 4.0 \n", + "7 119487 879 4.0 4.5 \n", + "8 1126879 3594 4.0 4.5 \n", + "9 1117 4 4.5 5.0 \n", + "10 7885 40 4.0 4.0 \n", + "11 76720 4017 4.5 4.5 \n", + "12 105776 166 3.5 2.5 \n", + "13 479440 203 3.5 4.0 \n", + "14 119773 2336 3.5 4.5 \n", + "15 6340 668 4.5 4.5 \n", + "16 56194 87 4.0 3.5 \n", + "17 2974676 212 3.5 3.5 \n", + "18 223885 3726 4.0 4.5 \n", + "20 402925 136 4.0 4.5 \n", + "21 31456 4178 4.0 3.5 \n", + "22 2929 966 4.5 4.5 \n", + "23 11447 781 5.0 5.0 \n", + "24 137951 131 4.0 4.5 \n", + "25 8 0 4.5 0.0 \n", + "27 3241 297 4.0 4.0 \n", + "28 5795 12 3.5 4.0 \n", + "29 42316 248 3.0 3.5 \n", + "31 123215 25 3.5 5.0 \n", + "32 782 7 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\n", + "3 5.0.0 4+ Weather 37 \n", + "4 5.10.0 12+ Shopping 37 \n", + "5 7.5.1 4+ Reference 37 \n", + "6 1.8 4+ Games 47 \n", + "7 6.12.0 4+ Finance 37 \n", + "8 8.4.1 12+ Music 37 \n", + "9 3.6.6 4+ Utilities 37 \n", + "10 4.0.4 4+ Games 38 \n", + "11 4.10.1 4+ Games 38 \n", + "12 5.19.0 4+ Games 37 \n", + "13 27 17+ Utilities 37 \n", + "14 7.3.8 4+ Finance 37 \n", + "15 4.0.3 4+ Games 38 \n", + "16 21.1 4+ Travel 37 \n", + "17 95 4+ Social Networking 37 \n", + "18 11.15.0 12+ Travel 37 \n", + "20 11.0.3 12+ Music 37 \n", + "21 1.0.0 4+ Games 47 \n", + "22 3.3 4+ Reference 43 \n", + "23 5.2.6 4+ Games 40 \n", + "24 6.9 4+ Sports 37 \n", + "25 9.0.5 4+ Business 37 \n", + "27 2.1.3 4+ Music 43 \n", + "28 8.4.1 4+ Health & Fitness 37 \n", + "29 2.0.6 9+ Games 38 \n", + "31 10.5.4 4+ Productivity 37 \n", + "32 1.9.8 4+ Music 43 \n", + "33 3.4.1 4+ Weather 37 \n", + "... ... ... ... ... \n", + "10995 1.0.1 4+ Games 38 \n", + "10998 1.0.1 4+ Games 38 \n", + "11002 1.0.1 4+ Entertainment 37 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100.52 \n", + "4 5 9 1 128.51 \n", + "5 5 45 1 92.77 \n", + "6 5 1 1 10.49 \n", + "7 0 19 1 227.80 \n", + "8 4 1 1 130.24 \n", + "9 5 1 1 49.25 \n", + "10 0 10 1 70.02 \n", + "11 4 11 1 49.62 \n", + "12 0 6 1 227.55 \n", + "13 4 33 1 179.98 \n", + "14 0 2 1 160.93 \n", + "15 5 2 1 55.15 \n", + "16 1 26 1 207.91 \n", + "17 1 29 1 389.88 \n", + "18 5 18 1 167.41 \n", + "20 3 16 1 147.09 \n", + "21 0 1 1 10.74 \n", + "22 0 2 1 70.71 \n", + "23 5 1 1 6.17 \n", + "24 2 6 1 130.58 \n", + "25 5 3 1 71.20 \n", + "27 2 10 1 11.42 \n", + "28 5 11 1 188.02 \n", + "29 0 1 1 4.77 \n", + "31 5 23 1 287.93 \n", + "32 1 1 1 4.26 \n", + "33 5 1 1 172.77 \n", + "... ... ... ... ... \n", + "10995 4 1 1 177.50 \n", + "10998 5 1 1 89.19 \n", + "11002 0 31 1 32.69 \n", + "11010 1 1 1 26.08 \n", + "11013 0 1 1 143.35 \n", + "11016 5 1 1 242.51 \n", + "11019 0 1 1 18.16 \n", + "11022 5 1 1 169.54 \n", + "11024 5 25 1 64.24 \n", + "11027 4 1 1 184.32 \n", + "11031 5 1 1 33.95 \n", + "11033 5 1 1 327.73 \n", + "11035 4 1 1 113.38 \n", + "11036 4 1 1 94.01 \n", + "11038 5 0 1 178.16 \n", + "11040 0 1 1 9.36 \n", + "11041 5 1 1 57.35 \n", + "11042 5 0 1 171.94 \n", + "11043 5 1 1 208.03 \n", + "11050 5 1 1 85.58 \n", + "11051 5 2 1 52.34 \n", + "11060 4 1 1 168.77 \n", + "11074 5 1 1 537.46 \n", + "11077 5 3 1 23.00 \n", + "11079 0 1 1 27.85 \n", + "11081 5 1 1 126.64 \n", + "11082 0 1 1 120.76 \n", + "11087 1 1 1 111.32 \n", + "11089 0 2 1 97.24 \n", + "11097 0 2 1 90.90 \n", + "\n", + "[7197 rows x 15 columns]" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def _byte_resizer(data):\n", + " return np.around(data / 1000000, decimals=2)\n", + "\n", + "df[\"size_Mb\"] = df[\"size_bytes\"].apply(_byte_resizer)\n", + "df.drop(\"size_bytes\", axis=\"columns\", inplace=True)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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T03a5UqmkqqoqSdKnT5+sWrUqTU1Nqa2tbTvmnevbOxYA4KNgs1/43qPH/96kubk5/fr1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\ntd51Rx55ZGX58uW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize=(10, 8))\n", + "BINS = [0.00, 10.00, 20.00, 50.00, 100.00, 200.00, 500.00, 1000.00, 2000.00, np.inf]\n", + "LABELS = [\"<10m\", \"10-20m\", \"20-50m\", \"50-100m\", \"100-200m\", \"200-500m\", \"500-1000m\", \"1-2G\", \">2G\"]\n", + "freqs = pd.cut(df[\"size_Mb\"], BINS, include_lowest=True, labels=LABELS)\n", + "sns.barplot(y=freqs.value_counts().values, x=freqs.value_counts().index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "BINS = [-np.inf, 0.00, np.inf]\n", + "LABELS = [\"FREE\", \"PAID\"]\n", + "colors = ['lightcoral', 'yellowgreen']" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.1057396083108322,\n", + " 1.1002733599434824,\n", + " -1.1276675874208923,\n", + " 1.1112839653444184)" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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yWHhIM5xOJyrLywEAvqIKDzcsk5sQuXGZMzykRSw8pBklRUVw2GyAqsJbQOHh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\nPEREROT2/h9KGoVoZf7tIAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[\"price_categories\"] = pd.cut(df[\"price\"], BINS, include_lowest=True, labels=LABELS)\n", + "fig, axs = plt.subplots(figsize=(10, 5))\n", + "price_df = df[\"price_categories\"].value_counts()\n", + "plt.pie(price_df.values, labels=LABELS, colors=colors, autopct='%1.1f%%', shadow=True)\n", + "centre_circle = plt.Circle((0,0),0.75,color='black', fc='white',linewidth=1.25)\n", + "\n", + "fig = plt.gcf()\n", + "fig.gca().add_artist(centre_circle)\n", + "\n", + "plt.axis('equal')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "free_apps = df.loc[df[\"price_categories\"] == \"FREE\"]\n", + "paid_apps = df.loc[df[\"price_categories\"] == \"PAID\"]\n", + "free_apps_rated = free_apps.sort_values(by=[\"rating_count_tot\"], ascending=False)\n", + "paid_apps_rated = paid_apps.sort_values(by=[\"rating_count_tot\"], ascending=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}