In this tutorial, you'll learn how to visualize data with the Python Matplotlib Library. Matplotlib is a very powerful Python library that can visualize your data the way you want.
#datavisualization #matplotlib #python
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
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
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
Python offers multiple graphics libraries, with which you can create interactive, live or highly customizable plots with the given data.
To get a little overview here are a few popular plotting libraries:
In this article, we will learn about creating a different type of plots using the Matplotlib library.
Matplotlib is the most popular plotting library for python, which was designed to have a similar feel to MATLAB’s graphical plotting. It gives you control over every aspect of a plot.
Matplotlib allows you to create reproducible figures using a few lines of code. Let’s learn how to use it! I also encourage you to explore: http://matplotlib.org/.
Install it with pip or conda at your command line or the terminal with:-
pip install matplotlib OR conda install matplotlib
To quickly get started with Matplotlib without installing anything on your local machine, check out Google Colab. It provides Jupyter Notebooks hosted on the cloud for free which are associated with your Google Drive account and it comes with all the important packages pre-installed.
pyplot is a module of Matplotlib that makes this library work like MATLAB. Import the
matplotlib.pyplot module under the name
plt (the tidy way):
import matplotlib.pyplot as plt import numpy as np # for working with arrays
We pass two NumPy arrays(x and y) and ‘r’ as arguments to Pyplot’s
plot() function. Here ‘r’ is for red colour, x elements will appear on x-axis and y elements will appear on the y-axis.
import matplotlib.pyplot as plt import numpy as np x = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5]) y = x ** 2 # y is now a list with elements of x to the power 2 plt.plot(x, y, 'r') plt.xlabel('X Axis Title Here') plt.ylabel('Y Axis Title Here') plt.title('String Title Here') plt.show() # The plot below is the output of this program.
Creating Multiple Plots on The Same Canvas
subplot(): a method of pyplot, divides the canvas into
ncols parts and using
plot_number argument you can choose the plot.
subplot(nrows, ncols, plot_number)
In the below example, using
plt.plot(x, y, 'r--’)we plot a red coloured graph with line style ‘- -’ between x and y at plot_number=1.
import matplotlib.pyplot as plt import numpy as np x = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5]) y = x ** 2 plt.subplot(1,2,1) # subplot(nrows, ncols, plot_number) plt.plot(x, y, 'r--') # r-- meaning colour red with -- pattern plt.subplot(1,2,2) plt.plot(y, x, 'g*-') # g*- meaning colour green with *- pattern # The plot below is the output of this program.
For making it more simple
subplots() method can be used instead of
subplot(). You will see its example in “Creating Multiple plots on The Same Canvas” under “Matplotlib Object-Oriented Method”.
#data-science #matplotlib #data-visualization #python #plotting-data #data analysis
So firstly let’s understand the visualization first .
Human mostly understand things by seeing instead of reading. “A Picture depicts thousand of words. A Large amount of Data which cannot be depict through large set of texts and tabular data can easily be understand through visual.Data visualization derives insights from the data.”
There are many ways to use to Matplotlib. In this article I used the Pyplot interface.
#Import the matplotlib.pyplot submodule and name it plt import matplotlib.pyplot as plt #Create a Figure and an Axes with plt.subplots fig, ax = plt.subplots() # Call the show function plt.show()
The plt.subplots() function when invoked without the inputs , it create two different objects:
When you hit this code you can see the figure with the empty axis as shown in fig (1).
Let’s see it with the data . So we can use any basic datasets to know about the plots, I used my own so its your choice to take any data for the practice purpose.
So here I have datasets of the weather , in which its shows the precipitation according to the months from JAN to DEC.
#Create a Figure and an Axes with plt.subplots fig, ax = plt.subplots() # Plot PRECIPITATION_NML from north_weather against the MONTH ax.plot(north_weather[“MONTH”], north_weather[‘PRECIPITATION_NML’]) # Plot PRECIPITATION_NML from south_weather against MONTH ax.plot(south_weather[“MONTH”], south_weather[‘PRECIPITATION_NML’]) #Call the show function plt.show()
So here I used two different plot in same figure and output would be two line plot in single graph. We also use the single plot axis, I used two axis here. Its your choice, how you want to visualize data. Let hit the show() function and output will be like as shown in fig(2).
Let’s we customize plot according to our needs. We can do many customization in plots like change the color, lines etc.
2.1 Coloring and Markers :
# Plot North data ax.plot(north_weather[“MONTH”], north_weather[“PRECIPITATION_NML”], color = ‘b’,marker = ‘o’) # Plot South data ax.plot(south_weather[“MONTH”], south_weather[“PRECIPITATION_NML”],color = ‘r’,marker = ‘v’ ) # Call show to display the resulting plot plt.show()
In this code snippet we changed the color with color argument and marker . We can see the output in the fig(3).
2.2 Labeling :
To make our plot more easily understandable we will use the the set__xlabel_() and set_ylabel() function and will also give the title to our plot with help of _set_title() _function. Let us have a look on code snippet. The fig(4) shows the output of the following snippet.
#data-analysis #data-visualization #pyplot #data-science #matplotlib #data analysis