![](\"img/f2.png\")
\n",
- "\n",
- "\n",
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Controlling Aesthetics of Seaborn Plots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the biggest advantages of Seaborn over Matplotlib is that its default aesthetics are visually far more appealing. Undoubtedly Matplotlib is highly customizable, but sometimes it may get difficult to know exact settings to tweak in order to achieve an attractive plot. Whereas, Seaborn comes with a number of customized themes and a high-level interface for controlling the look of same matplotlib figures.\n",
+ "\n",
+ "Seaborn splits Matplotlib parameters into two independent groups: *First* group sets the aesthetic style of the plot; and *second* scales various elements of the figure to get easily incorporated into different contexts."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Seaborn doesn’t take away any of Matplotlib credits, but rather adds some nice default aesthetics and built-in plots that complement and sometimes replace the complicated Matplotlib code professionals needed to write. *Facet plots* and *Regression plots* are an example of that. In this lecture I shall show how easy it is to build a *Regression plot* using Seaborn and then let us compare it by building something similar in Matplotlib."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ">**Note** (for non-native English speakers): The term *Aesthetics* just refers to the appearance of the figure/plot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-07-16T14:23:04.768850Z",
+ "start_time": "2021-07-16T14:23:02.634110Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Importing intrinsic libraries:\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, once we have imported necessary libraries, let us begin talking about aesthetics with those 2 special lines of code we had added in our previous lecture. Our first line of code was:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-07-16T14:23:11.678443Z",
+ "start_time": "2021-07-16T14:23:11.656963Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**`%matplotlib`** is one of the *magic function* of Jupyter Notebook that along with **`inline`** **[sets the backend of matplotlib](http://ipython.readthedocs.io/en/stable/interactive/plotting.html)** to the *inline* backend within Jupyter notebook. If we look into relevant documentation, it says:\n",
+ "\n",
+ "IPython has a set of predefined **‘magic functions’** that you can call with a command line style syntax. There are two kinds of magics: line-oriented and cell-oriented. \n",
+ "\n",
+ "1. Line magics are prefixed with the **`%`** character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes. Lines magics can return results and can be used in the right hand side of an assignment. \n",
+ "\n",
+ "2. Cell magics are prefixed with a double **`%%`**, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument.\n",
+ "\n",
+ "With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document.\n",
+ "\n",
+ "Furthermore, if we want interactivity as well, we can use **`nbagg`** backend with **`%matplotlib`** notebook (in IPython 3.x). Though we won't be getting into that extremely advanced level of Matplotlib specific presentation here, but if that interest you, please check **[here](https://matplotlib.org/users/whats_new.html#the-nbagg-backend)**.\n",
+ "\n",
+ "Let us now move on to our second line of code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-07-16T14:25:25.755122Z",
+ "start_time": "2021-07-16T14:25:25.737547Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "sns.set()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Seaborn runs on top of Matplotlib and with that as a default comes a *White* background for each of our plot, that looks pretty dull. With this line of code, we switch our plot to Seaborn default using **`.set()`** function. Let us look into what it yields, using one of the built-in dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-07-16T14:25:37.203243Z",
+ "start_time": "2021-07-16T14:25:36.597297Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "
+
+
![](\"img/anaconda_prompt.png\")
\n",
+ "
+
+
+
+
+
+
+
+
+
+
+
+
-[002_Python_Function_Global_Local_Nonlocal](https://github.com/milaan9/04_Python_Functions/blob/main/002_Python_Function_Global_Local_Nonlocal.ipynb)
-
-
-[003_Python_Function_global_Keywords](https://github.com/milaan9/04_Python_Functions/blob/main/003_Python_Function_global_Keywords.ipynb)
-
-
-[004_Python_Function_Arguments](https://github.com/milaan9/04_Python_Functions/blob/main/004_Python_Function_Arguments.ipynb)
-
-
-[005_Python_Function_Recursion](https://github.com/milaan9/04_Python_Functions/blob/main/005_Python_Function_Recursion.ipynb)
-
-
-[006_Python_Function_Anonymous](https://github.com/milaan9/04_Python_Functions/blob/main/006_Python_Function_Anonymous.ipynb)
-
-
-[007_Python_Function_Module](https://github.com/milaan9/04_Python_Functions/blob/main/007_Python_Function_Module.ipynb)
-
-
-[008_Python_Function_random_Module](https://github.com/milaan9/04_Python_Functions/blob/main/008_Python_Function_random_Module.ipynb)
-
-
-[009_Python_Function_math_Module.ipynb](https://github.com/milaan9/04_Python_Functions/blob/main/009_Python_Function_math_Module.ipynb.ipynb)
-
-
-[010_Python_Function_Package](https://github.com/milaan9/04_Python_Functions/blob/main/010_Python_Function_Package.ipynb)
-
-
-[Python_Docstrings](https://github.com/milaan9/04_Python_Functions/blob/main/Python_Docstrings.ipynb)
-
-
-These are online read-only versions.
-
+---
## Frequently asked questions ❔
### How can I thank you for writing and sharing this tutorial? 🌷
-You can ⭐ star this tutorial. Starring is free for you, but it tells me and other people that it was helpful and you like this tutorial.
+You can 
and 
Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.
-Go [here](https://github.com/milaan9/04_Python_Functions) if you aren't here already and click the "⭐ Star" button in the top right corner. You will be asked to create a GitHub account if you don't already have one.
+Go [**`here`**](https://github.com/milaan9/04_Python_Functions) if you aren't here already and click ➞ **`✰ Star`** and **`ⵖ Fork`** button in the top right corner. You'll be asked to create a GitHub account if you don't already have one.
-### How can I read this tutorial without an Internet connection? 🤔
+---
-1. Go [here](https://github.com/milaan9/04_Python_Functions) if you aren't here already.
-
-2. Click the big green "Clone or download" button in the top right of the page, then click "[Download ZIP](https://github.com/milaan9/04_Python_Functions/archive/refs/heads/main.zip)".
+### How can I read this tutorial without an Internet connection? 
+
+1. Go [**`here`**](https://github.com/milaan9/04_Python_Functions) and click the big green ➞ **`Code`** button in the top right of the page, then click ➞ [**`Download ZIP`**](https://github.com/milaan9/04_Python_Functions/archive/refs/heads/main.zip).
-3. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.
+2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.
-4. Launch ipython notebook from the folder which contains the notebooks. Open each one of them
+3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them
- `Cell > All Output > Clear`
+ **`Kernel > Restart & Clear Output`**
This will clear all the outputs and now you can understand each statement and learn interactively.
If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.
+---
## Authors ✍️
@@ -85,6 +92,7 @@ If you have trouble with this tutorial please tell me about it by [Create an iss
If you like this tutorial, please [give it a ⭐ star](https://github.com/milaan9/04_Python_Functions).
+---
## Licence 📜
diff --git a/img/anaconda_prompt.png b/img/anaconda_prompt.png
new file mode 100644
index 0000000..8ba6673
Binary files /dev/null and b/img/anaconda_prompt.png differ
diff --git a/script.py b/script.py
new file mode 100644
index 0000000..38ebc2b
--- /dev/null
+++ b/script.py
@@ -0,0 +1,7 @@
+import sys
+#print(sys.argv[0], argv[1],sys.argv[2]) # this line would print out: filename argument1 argument2
+print('Welcome {}. Enjoy {} for Data Science ^_^'.format(sys.argv[1], sys.argv[2]))
+
+
+
+