As you’ve probably guessed, this is where Seaborn comes in. Seaborn isn’t a third-party library, so you can get started without creating user accounts or worrying about API limits, etc. Seaborn is also built on top of Matplotlib, making it the logical next step up for anybody wanting some firepower from their charts. We’ll explore Seaborn by charting some data ourselves.
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 simply presenting data in a graphical or pictorial form which makes the information easy to understand. It helps to explain facts and determine courses of action.
In this article, I’m going to introduce you to the world of data visualization and interpretation using Python.
Python has numerous visualization libraries that come packed with lots of different features. Most of these libraries are general-purpose while some are specific to a task.
Some popular Python libraries for visualization are:
In this article, we’ll be using the Seaborn library for visualization of different datasets, and I’ll be showing you how to interpret them.
Seaborn is built on top of Python’s core visualization library Matplotlib. Seaborn comes with some very important features that make it easy to use. Some of these features are:
Note: The knowledge of Matplotlib is recommended to tweak Seaborn’s default plots.
If you have Python and Anaconda installed on your computer, you can use any of the methods below to install seaborn:
"pip install seaborn"
" conda install seaborn"
"pip install git+https://github.com/mwaskom/seaborn.git"
Seaborn comes pre-packaged with a couple of data sets, and we’ll be using most of them depending on the task. First, let’s import the library and dataset:
import seaborn as sb import numpy as np import matplotlib.pyplot as plt import pandas as pd #Print the list of data sets available in seaborn tips_df = sb.load_dataset('tips') titanic_df = sb.load_dataset('titanic') flights_df = sb.load_dataset('flights')
Take a peek at the data sets:
First 5 rows of the Tips dataset
Univariate plots show the distribution of a feature (single feature). For univariate plots, you can make plots like Bar Graphs and Histograms. In seaborn, you can use the distplot function:
sb.distplot(tips_df['total_bill'], color='r') plt.title("Distribution of total bills") plt.show()
The distribution of total_bills shows that the bills are normally distributed and centred around 10–30.
sb.distplot(titanic_df['fare'], color='g') plt.title("Distribution of fare in titanic") plt.show()
The distribution plot of fare in titanic shows that the fare prices is right-skewed as a majority of the prices are within 0–50. This means that there were cheaper fare tickets than expensive ones.
sb.countplot(tips_df['time']) plt.title("Count of Time") plt.show()
Bivariate Plots are used when we want to compare two variables together. Bivariate plots show the relationship between two variables.
sb.scatterplot(x='total_bill', y='tip', data=tips_df) plt.title("Scatterplot of Total_bill vs. Tips") plt.show()
sb.scatterplot(x='age', y='fare', data=titanic_df) plt.title("Scatterplot of Age vs. Fare") plt.show()
sb.jointplot(x='total_bill', y='tip', data=tips_df) plt.show()
When you have sparse data, the hex or kde plot is better than scatterplot.
sb.jointplot(x='total_bill', y='tip', data=tips_df, kind='hex') plt.show()
Multivariate plots can show the relationship between three or more features. In seaborn, the popular hue parameter can be used to separate features in multiple dimensions.
sb.scatterplot(x='total_bill', y='tip', data=tips_df, hue='sex') plt.show()
Now let’s use another dataset
sb.scatterplot(x=‘age’, y=‘fare’, data=titanic_df, hue=‘class’) plt.title(“Scatterplot of Age vs. Fare”)
sb.barplot(x=‘sex’, y=‘fare’, data=titanic_df, hue=‘class’)
!(https://miro.medium.com/max/422/1*HHiGHDA1lh_qeYAqMfsWKA.png) ## PairWise Plots Pairwise plots show the distributions of multiple features in a dataset. In seaborn, you can use the pair plot() function. This shows the relationship between features in a DataFrame as a matrix of plots and the diagonal plots are the univariate plots.
sb.pairplot(tips_df, hue=‘sex’, diag_kind=‘hist’)
!(https://miro.medium.com/max/676/1*hEATdcp-8PheT525w5ieZA.png) # Numerical features against categorical features Numerical features are features with continuous data points. We can use two popular plot to observe the distribution and variability of these features.
#data-science #data-analysis #data-visualization #seaborn #data analysis
A quick walkthrough using pandas.Series.apply() to create original scale columns to visualize with a count plot graph using sns.countplot().
Displaying results in a way that is easy to understand is a great skill to have when working with datasets. A common way to display information is by using a graph. This post will go through some easy steps using Python to convert your data into easy to understand data and then display that data in a graph.
For this post, we will use some simple data (that I made up*) that has been turned into a DataFrame. There are four variables in this data set; Name, Gender, Age, Miles. The Miles variable refers to how many miles the person walks in a day. From this dataset, we will create a count graph to visually answer the question “ Which age group is the least active?”.
#seaborn #data-science #pandas #python #data-visualization