What I learned by casually studying Python for ten days

How difficult is Python

Looking back to what my generation considers “programming”, a term later changed to “development”, we see a gradual shift of programming languages, from tools that help us talk to a machine in its native language (which is the instruction set of its processor(s)), to a toolset that comes ever closer to understanding more business terms, and needs less delving into the binary reality of a processor. What has remained the same? The need to implement certain functionality, whether it be displaying a scatter plot on screen, or calculating a standard deviation, or handling files, anything that may be required.

From that point of view, programming has become “easier”, while development has become “harder”. This is not a controversial statement. It is easier by now, to create an array that will hold data, for example. Less and less complexity has to be dealt with, whether this is working with files, memory, or parallelizing. At the same time, the plethora of available tools, and the complexity of the modern IT ecosystem, along with the simplicity of tools to a degree, center the developer to direct a fully sized orchestra. Knowledge of procedural programming, of the libraries/tools relevant to the business, intimacy and “instinct” for the data at hand, are all considered necessary assets.

This is where Python, and similar solutions, like R, stand. Working with files, memory, writing your program logic is not very unlikely from using a previous generation language. Many features, like the argv[0] to get the executable path/name, is very similar to ANSI C. Where their true strength, and complexity lies, is for one to be skilled with the data and the available functions at hand. This may take much more time to learn, than just going through files and printing the infamous “Hello World”. Back to the original question, and keeping in mind the title of this article: Python is easy to “program”, yet can be infinitely hard to “develop”

Who should learn Python

Python is an analytical programming tool commonly associated with Machine Learning, Artificial Intelligence and Big Data. If engineering in those domains sounds interesting to you, it is probably time to start. While it may or may not be part of every solution, it is a very common tool to use, along with R

Simple things to get you started

First, install the environment. The packages are located here: . If there is any request to my site i can do a step by step installation

If you plan to use graphics, choose a website that offers graphics functions, and become familiar. I started to using this I seem to think I discovered through my Google feed

How to get inspired

Site offers online courses, to get one start using the language (i am not affiliated to them, but i did find their content useful). It is highly recommended, to really work on the exercises than scroll through the code. it took me a while to realize that tabs can indicate nested operations, for example. So going through the simplest examples and working your way up, is highly recommended.

Also, if I can create a plot from a CSV, and host the result online, then so can you! Have a look at my article here: Make a bar-chart from a CSV in Python


Make a bar-chart from a CSV in Python CSV bar plot bar chart using a CSV

Test Case was implemented in Python 3.6.5 running on a Ubuntu Linux 18.04 64-bit virtual machine. In order to carry out this test-case you will need to create an account in and create the credentials file on the host you will be running Python from. All instructions are on their web site

Step 1:
Suppose a CSV which has a first row we want to define as the X-Axis of our plot, and two further rows which we want as the data in the Y-Axis. It could be something like this:

~$ cat /home/nikolas/categories.csv
SciFi-Fantasy , 31.550787 , 68.449219
Spirituality , 83.411890 , 16.588112
Home-Improvement , 47.082787 , 52.917217
Gaming , 2.256584 , 97.743423
Mountain-Bike-Touring , 40.905171 , 59.094826
Korean-Culture , 71.040140 , 28.959862
Health-Safety , 32.872467 , 67.127533
Religion , 37.452973 , 62.547028
Fashion , 98.597282 , 1.402729

Step 2:
Load the CSV into a data frame using library Pandas using function read_csv and display the data of each row, using function iloc:

Python 3.6.5 (default, Apr 1 2018, 05:46:30)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> mycatg=pd.read_csv('/home/nikolas/categories.csv',sep=',',header=None)
>>> mycatg.iloc[:,0]
0 SciFi-Fantasy
1 Spirituality
2 Home-Improvement
3 Gaming
4 Mountain-Bike-Touring
5 Korean-Culture
6 Health-Safety
7 Religion
8 Fashion
Name: 0, dtype: object
>>> mycatg.iloc[:,1]
0 31.550787
1 83.411890
2 47.082787
3 2.256584
4 40.905171
5 71.040140
6 32.872467
7 37.452973
8 98.597282
Name: 1, dtype: float64
>>> mycatg.iloc[:,2]
0 68.449219
1 16.588112
2 52.917217
3 97.743423
4 59.094826
5 28.959862
6 67.127533
7 62.547028
8 1.402729
Name: 2, dtype: float64

Step 3:

Include the libraries

import plotly.plotly as py
import plotly.graph_objs as go

then define the axis data, using the above way to address the rows in the data frame. so:

yaxis1 = go.Bar(
name='Category A'
yaxis2 = go.Bar(
name='Category B'

data = [yaxis1, yaxis2]
layout = go.Layout(

then perform the plot itself.

fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='barplot in,')

Step 4:

This is it. The plot is created as a SVG file in In this case the graph was created here: