How to use ggplot in Python to Build Data Visualizations with Plotnine

In this tutorial, you’ll learn how to use ggplot in Python to create data visualizations using a grammar of graphics. A grammar of graphics is a high-level tool that allows you to create data plots in an efficient and consistent way. It abstracts most low-level details, letting you focus on creating meaningful and beautiful visualizations for your data.

There are several Python packages that provide a grammar of graphics. This tutorial focuses on plotnine since it’s one of the most mature ones. plotnine is based on ggplot2 from the R programming language, so if you have a background in R, then you can consider plotnine as the equivalent of ggplot2 in Python.

In this tutorial, you’ll learn how to:

• Install plotnine and Jupyter Notebook
• Combine the different elements of the grammar of graphics
• Use plotnine to create visualizations in an efficient and consistent way
• Export your data visualizations to files

Setting Up Your Environment

In this section, you’ll learn how to set up your environment. You’ll cover the following topics:

1. Creating a virtual environment
2. Installing plotnine
3. Installing Juptyer Notebook

Virtual environments enable you to install packages in isolated environments. They’re very useful when you want to try some packages or projects without messing with your system-wide installation.

Run the following commands to create a directory named data-visualization and a virtual environment inside it:

$ mkdir data-visualization
$ cd data-visualization
$ python3 -m venv venv

After running the above commands, you’ll find your virtual environment inside the data-visualization directory. Run the following command to activate the virtual environment and start using it:

$ source ./venv/bin/activate

When you activate a virtual environment, any package that you install will be installed inside the environment without affecting your system-wide installation.

Next, you’ll install plotnine inside the virtual environment using the pip package installer.

Install plotnine by running this command:

$ pip install plotnine

Executing the above command makes the plotnine package available in your virtual environment.

Finally, you’ll install Jupyter Notebook. While this isn’t strictly necessary for using plotnine, you’ll find Jupyter Notebook really useful when working with data and building visualizations.

To install Jupyter Notebook, use the following command:

$ pip install jupyter

Congratulations, you now have a virtual environment with plotnine and Jupyter Notebook installed! With this setup, you’ll be able to run all the code samples presented through this tutorial.

Building Your First Plot With ggplot and Python

In this section, you’ll learn how to build your first data visualization using ggplot in Python. You’ll also learn how to inspect and use the example datasets included with plotnine.

The example datasets are really convenient when you’re getting familiar with plotnine’s features. Each dataset is provided as a pandas DataFrame, a two-dimensional tabular data structure designed to hold data.

You’ll work with the following datasets in this tutorial:

• **economics**: A time series of US economic data
• **mpg**: Fuel economy data for a range of vehicles
• **huron**: The level of Lake Huron between the years 1875 and 1972

You can find the full list of example datasets in the plotnine reference.

You can use Jupyter Notebook to inspect any dataset. Launch Jupyter Notebook with the following commands:

$ source ./venv/bin/activate
$ jupyter-notebook

Then, once inside Jupyter Notebook, run the following code to see the raw data in the economics dataset:

from plotnine.data import economics

economics

The code imports the economics dataset from plotnine.data and shows it in a table:

      date      pce      pop   psavert uempmed  unemploy
0   1967-07-01  507.4   198712  12.5    4.5     2944
1   1967-08-01  510.5   198911  12.5    4.7     2945
... ...         ...     ...     ...     ...     ...
572 2015-03-01  12161.5 320707  5.2     12.2    8575
573 2015-04-01  12158.9 320887  5.6     11.7    8549

As you can see, the dataset includes economics information for each month between the years 1967 and 2015. Each row has the following fields:

• **date**: The month when the data was collected
• **pce**: Personal consumption expenditures (in billions of dollars)
• **pop**: The total population (in thousands)
• **psavert**: The personal savings rate
• **uempmed**: The median duration of unemployment (in weeks)
• **unemploy**: The number of unemployed (in thousands)

Now, using plotnine, you can create a plot to show the evolution of the population through the years:

from plotnine.data import economics
from plotnine import ggplot, aes, geom_line

(
    ggplot(economics)  ## What data to use
    + aes(x="date", y="pop")  ## What variable to use
    + geom_line()  ## Geometric object to use for drawing
)

This short code example creates a plot from the economics dataset. Here’s a quick breakdown:

1. Line 1: You import the economics dataset.
2. Line 2: You import the ggplot() class as well as some useful functions from plotnine, aes() and geom_line().
3. Line 5: You create a plot object using ggplot(), passing the economics DataFrame to the constructor.
4. Line 6: You add aes() to set the variable to use for each axis, in this case date and pop.
5. Line 7: You add geom_line() to specify that the chart should be drawn as a line graph.

Running the above code yields the following output:

You’ve just created a plot showing the evolution of the population over time!

In this section, you saw the three required components that you need to specify when using the grammar of graphics:

1. The data that you want to plot
2. The variables to use on each axis
3. The geometric object to use for drawing

You also saw that different components are combined using the **+** operator.

In the following sections, you’ll take a more in-depth look at grammars of graphics and how to create data visualizations using plotnine.

#python #r #data-science #programming #developer

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Buddha Community

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation