Table of contents
Installation Video
Introduction
The Anaconda Python distribution contains the core Python programming language, the Spyder and JupyterLab IDEs and the most common libraries for data science such as numpy, pandas and matplotlib.
Installation on Linux requires a bash install of a .sh via the terminal and will work in popular Linux distributions such as Ubuntu, Mint, Fedora and DeepIn. Once installed the Anaconda Navigator has no shortcut on the applications menu but has to be ran using the terminal.
The Anaconda 2020-11 Python distribution addresses previous Linux installation issues with the Anaconda Navigator and the Spyder 4 IDE.
Uninstalling Kite and Anaconda
Before installing Anaconda, you should remove any old Kite and Anaconda installation to prevent conflicts. Click show more for more details. Skip this section if on a clean install.
Uninstalling Kite
Uninstalling Anaconda
Installation
Installing Anaconda
Go to the Anaconda website:
Select Download:
Download the Linux 64 Bit installer.
Select Save File and then OK:
Go to your Downloads folder, right click an empty space and select Open in terminal:
In the downloads folder right click the .sh file and copy the file name including the extension. Type in bash and then a space and paste in the file name. For example:
bash Anaconda3-2020.11-Linux-x86_64.sh
Then press [↵]
Next the license agreement will display. Annoyingly it will require you to press [↵] to proceed to each new line. Hold down [↵] to rapidly scroll down to the end of the license agreement.
Then type in:
yes
And then press [↵] to accept the agreement and proceed with the install.
Type in [↵] to proceed with the default location:
Type in:
yes
Then [↵] to run conda init.
Anaconda is now installed:
Installing Kite
It is recommended to install Kite here as this will save time during the initial launch of Spyder 4.
Type in:
bash -c "$(wget -q -O - https://linux.kite.com/dls/linux/current)"
Then press [↵]:
Then press [↵] to proceed:
Kite is now installed:
Anaconda Navigator – High DPI and Zoom Problem
The Anaconda Navigator has a setting "Enable High DPI Scaling" which is supposed to scale it properly on a High DPI screen. Unfortunately it does not work well on high DPI screens which use a zoom of 150 %-200 % such as newer Dell XPS models and the tiles may be too large with only a fraction of the Anaconda Navigator displaying.
To resolve this, select File and Preferences:
Look for "Enable high DPI scaling" and uncheck it:
On my XPS 13, the Preferences Window did not display properly and I could not access the checkbox or the Apply button. Press [Alt] + [F4] to close down the Anaconda Navigator.
This setting can be change manually by pasting in the following into the Windows Explorer Address Bar:
Home\.anaconda\navigator\
This will take you to the anaconda navigator configuration file which is anaconda-navigator.ini.
Right click anaconda-navigator and select Edit.
Here you will be able to change enable_high_DPI_scaling from True to False.
Once this is done, the Anaconda Navigator should display as normal.
Spyder
Close and open any terminal windows.
To launch Anaconda navigator type in:
anaconda-navigator
You can then launch a variety of IDEs such as Spyder or JuypterLab:
Launch Spyder 4 from the Anaconda Navigator:
Spyder 4 should launch:
Spyder can be launched directly from the terminal by typing in:
spyder
High DPI Settings and Spyder Light Theme
Select Tools and then Preferences:
If you have a high resolution screen. Select General and Enable auto high DPI scaling.
Select Appearance. Under Syntax Highlighting Theme change Spyder Dark to Spyder:
Select Apply:
Select yes to restart Spyder
Running a Script for the First Time and Accessing Variables from within the Console
Let's create a variable:
a=1
We can then select the run button:
At first launch, you will be given the run settings. If you want to access variables from the console in a script. Check "Run in console's namespace instead of an empty one", this was enabled by default in Spyder 3 and is disabled by default in Spyder 4 causing some confusion.
If you don't have this setting enabled the script won't be able to access variables created from earlier scripts or from the console. Here a is present in the variable explorer but the script cannot find it:
These settings can also be accessed by selecting Tools then Preferences:
The run tab shows the settings above:
Note the settings will apply only for newly created scripts not existing scripts, so attempt this with a new script.
Plot Settings
We can put together a very basic test script to create a plot:
import numpy as np import matplotlib.pyplot as plt x=np.arange(start=0,stop=10,step=1) y=np.arange(start=0,stop=10,step=1) plt.figure() plt.clf() plt.plot(x,y) plt.show()
By default plots will display inline within the plots pane.
The backend can be changed to automatic in order to plot a graph in its own dedicated window by typing the following in the console:
%matplotlib auto
It can also be reverted to inline using:
%matplotlib inline
This change will be made only for the current session and will revert to a default setting once the kernal has been restarted. To change the default setting, go to Tools and Preferences:
To the left hand side, select IPython console. Select the graphics tab. Change the backline to either automatic or inline:
Select Apply:
JupyterLab
JupyterLab is another IDE which is browser based.
File Explorer
Select the folder you want on the left left hand side menu. In this case Documents. Compatible files can be opened using this left hand side menu.
To the right hand side a Launcher is opened as a tab to the right hand side. From the launched we can create a new Text File, Markdown File or new Notebook.
Text File
From the launcher select Text File:
A new text file called untitled.txt will be opened. Note that this is automatically saved to the Documents folder which is the directory opened to the left hand side:
The file can be renamed by right clicking the tab or the file name within the File Explorer and selecting rename:
In this case I will rename it TestText.txt:
The file is renamed. A new tab can be created by opening another file or adding an additional launcher:
Markdown File
Let's have a look at a MarkDown File. This is essentially a Report File. It acts like a Text File but the Text can be formatted.
Select Markdown File:
Then right click an empty space in the file and select Show Mardown Preview:
# Heading 1
Example Text.
## Heading 2 Bold and Italic
Example Text in **bold**, *italic* and ***bold-italic***.
Strike through ~~this~~.
### Heading 3 List
* Apples
* Bananas
* Grapes
1. Apples
2. Bananas
3. Grapes
* Apples
* Bananas
* Grapes
# Special Characters
\#
\*
\-
\\
# Mathematics
To use an inline equation enclose in \*(equation)\*.
Example Text with inline equation $f\left(x\right)$
To use a display equation enclose in \*\*(equation)\*\*.
Display Equation
$$f\left(x\right)=a_0+\sum_{n=1}^{\infty}\left(a_n\cos{\frac{n\pi x}{L}}+b_n\sin{\frac{n\pi x}{L}}\right)$$
# Link
[Google](https://google.co.uk)
# Code
This code will
```
print('Hello World')
```
print the string 'Hello World'
# Horizontal Separator
---
The file name can be renamed by right clicking the tab and selecting rename:
Then inputting the desired name and then selecting Rename:
The file is now renamed:
Image files can also be added to the Markdown File. The image should be stored in the same folder as the MarkDown File:
The text within the square brackets is an optional title and can be left blank.
A table can also be generated using the following syntax:
| A | B | C | D | | -: | :- | :-: | --- | | apple | banana | carrot | dragon fruit | | avocado | blueberry | cucumber | dew berry | | apricot | blackberry | clementine | |
The 0th row is the heading names.
The 1st row is the alignment options (align right, left, center and left with a center heading).
NoteBook File
A NoteBook file consists of a series of cells that can be MarkDown, Code or Raw (Text). Open a new Launcher and select a new Python 3 Notebook:
The currently selected cell is highlighted in Blue to the left hand side. In this case I will set the cell to a MarkDown cell and create a Heading:
Then Run:
When I run this the cell displays as a heading (it can be edited again by double clicking it).
A new cell is created, this time I will leave it as Code. The Code can be ran:
When the code is ran a number displays denoting the order the cells were ran in. This is important when cells are dependent on earlier cells. For example if we create other cells:
The Code cells can be ran in any order i.e. it doesn't need to be ran from top to bottom however if the code of one cell requires the code to run in another cell before hand for example in the case of variable assignment errors will display:
If the assignment code cell is ran before the display code cell, the code works:
Code in the cells can be modified and ran again.
In this case we are asking to display a lot of data. We can right click the left hand side bar of the cell which is highlighted blue and we can enable or disabling scrolling for the output cell:
We can create x and y data and test plotting using the same code as we used in Spyder 4. JupyterLab (without plugins) plots figures inline i.e. making them images:
The cell that creates the inline figure can be reran to update the figure however:
Typing a [↹] gives auto-complete options:
Typing [⇧]+[ ↹ ] will give the docstring which gives more details about the input arguments of a function:
Getting Started with Python
This particular guide is focused only on installation along with some checks to make sure the IDEs are working correctly.
I recommend using the Spyder 4 IDE for learning Python because it has a Variable Explorer (JupyterLab doesn't have one without third party plugins and even the one with third party plugins is way behind that of Spyder). JupyterLab is better for reports.
I would recommend returning back to JupyterLab once you have a handle on the following fundamentals. I cover these using the Spyder IDE:
And a handle on the numeric python (numpy) library, the plotting library (matplotlib) and the python and data analysis library (pandas) which are the most commonly used Python libraries in Data Science.
