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DataSimple Data Analysis Tips

Proficiency in a diverse array of data science tools and libraries is essential for data analysts. Among the standout assets in their toolkit are Pandas, Seaborn, Yellowbrick, Plotly, WordCloud, and Shap. These Python libraries facilitate data manipulation, visualization, and model evaluation, empowering analysts to glean crucial insights from datasets and present them in visually captivating formats. At the core of data analysis lies Pandas, which enables efficient data cleaning, transformation, and structuring, providing the foundation for advanced exploratory and analytical tasks.

In the realm of data visualization, Seaborn emerges as a powerful ally for data analysts. Leveraging its high-level interface, Seaborn simplifies the creation of aesthetically pleasing and informative statistical plots, facilitating the exploration of data patterns and relationships. Collaborating with machine learning engineers is integral to the data analysis process, and Yellowbrick proves invaluable in this context. By seamlessly integrating with Scikit-learn, Yellowbrick empowers analysts to evaluate and visualize machine learning models, offering essential insights for engineers to fine-tune and optimize these models.

Plotly takes data visualization to the next level by enabling interactive charting and enhancing the effectiveness of data storytelling and presentation. Additionally, WordCloud offers an insightful way to represent textual data, visualizing the most frequently occurring words and aiding analysts in extracting meaningful patterns and trends from large text corpora. Lastly, Shap plays a pivotal role in model interpretation, providing explanations for complex machine learning models through the Shapley Additive exPlanations approach. This allows data analysts to gain a deeper understanding of individual feature contributions to model predictions, leading to more comprehensive insights that can be effectively communicated to business partners.

General Python Analysis Tips

General Python Analysis Tips
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Use Pandas to plot the numerical and categorical distributions in your DataFrame together in one For Loop.

Use Seaborn to plot the numerical and categorical distributions in your DataFrame together in one For Loop matplotlib.pyplot as plt.

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Sweetviz, a powerful Python library that serves as a valuable tool for data analysis in the realm of data science. Sweetviz, which stands for "Sweet Visualization," is an open-source Python library designed to help data scientists, analysts, and engineers perform comprehensive exploratory data analysis (EDA) on their datasets. It offers an array of features and visualizations that can assist you in gaining insights into your data, understanding the distribution of your data, and identifying relationships between various features. Sweetviz truly simplifies the process of understanding your data.

Seaborn in Python Data Analysis Tips

Take a deep dive into data analysis with Seaborn.  The Python library make beautiful plots but also enhances the ability to extract insights from your data analysis.  We'll go over tips from beginner to advanced on how to get the most from your Python Data Analysis in seaborn.

  Univariate Analysis

Univariate Analysis
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Use Seaborn's barplot with categorical and numeric features.  Change outline of bars in barplot.

Seaborn's histogram understand how to control the level of generalization

Seaborn's kdeplot can help us understand the outlier of a distribution which can provide valuable insights.

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Learn you to use Seaborn's boxplot to highlight the outliers your concerned about.

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Humans are great at notice simitry or lack of.  Learn how to get the most from Seabron's violinplot.

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Seaborn's boxenplot, is a hybrid boxplot and histogram.  Controlling the boxes offers valuable insights.

Seaborn's countplot is a univariate analysis for object or category data type, important for understanding if there is an imbalance in your classes.

Seaborn's swarmplot allows for granular level detail to be examined and lets you find insights that only such a detailed plot can see.

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In Python with Seaborn learn how to make an anomaly detection plot.  Stock prices are prone to high volatility and as a portfolio manager, it can be helpful having the ability to detect anomalous movements.

To make an anomaly detection plot we will we use several Seaborn plots together.  In this we use 3 different types of plots.  Seaborn's lineplot, Seaborn's scatterplot and matplotlib.pyplot's axhlineplot to make our anomaly detection plot.

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Seaborn is a popular Python data visualization library that offers a range of statistical plots and aesthetics. The Conditional Kernel Density Estimate (CKDE) is a valuable tool within Seaborn's toolkit as it allows for the visualization and analysis of conditional distributions. By leveraging the CKDE in Seaborn, users can gain insights into the relationship between variables while considering the influence of other factors.

The cube_helix palette generator in Seaborn is a powerful tool for creating visually appealing color palettes. It produces a sequence of colors that smoothly transitions from dark to light, with a unique helical shape. This palette is particularly useful when visualizing continuous data or creating gradient-filled plots, as it provides a visually pleasing and perceptually uniform color scheme.

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In data analysis understanding your distribution is usually the first step to understanding your data.  In Machine Learning and Deep Learning understanding the distribution of each feature is often more important than understanding what the data means in real life.  


With Python using Seaborn we make a detailed distribution that allows you to see many aspects of your distribution together.  The boxenplot in Seaborn allows us to see the quartiles, and the overlayed stripplot adds texture.


Below we use Seaborn to plot a histplot, calling the hue argument in the plot to give further insight into how this other feature affects the distribution.  We highlight the histogram with the axvline plot to show case using the IQR formula where the outliers are under this classical definition of outliers.

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In this guide, we'll dive into the intricacies of Python classes, tailoring them to meet Seaborn's specific needs. By designing default settings for various plot styles, we can expedite the process of generating visually compelling visualizations, saving us valuable time and effort in our analysis projects. This skill will undoubtedly prove invaluable for both aspiring data analysts and seasoned analysts, enhancing our ability to communicate insights effectively and make data-driven decisions with ease.

Bivariate Analysis

Seaborn Bivariate Analysis
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learn to use seaborn's heatmap with seaborn's color palette generators to highlight specific insights in your heatmap

Learn how to control the sub-plotting functions histplot and scattplot in seaborn the make up the jointplot

learn to use seaborn's lineplot.  this is a simple plot but valuable in time series analysis

      Figure Level Plots

Seaborn Figure Level Plot
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learn to use seaborn's catplot figure level ploting function that allows you to seperate and groupby categories multiple times to get the most from you categorical analysis

learn to use seaborn's FacetGrid a figure level plotting function that allows you to separate and groupby categories and analyze patterns by sub groups

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Seaborn's lmplot() helps us to examine linear relationships.  Lmplot, linear model plot, performs a linear regression between x and y.  We can also use the row and col argument to groupby a categorical variable and plot the linear correlation between two variables in the subgroups. 

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Use Seaborn's Ridge Plot to gain extra insights on the distributions you plot.  Although a histogram is an important plot and using the hue arguement gives you amazing insights.  The perspective given with the ridgeplot in Seaborn can yield unique insights and it looks amazing so great for a business presentation.

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Seaborn's PairGrid gives us a lot of flexibility to plot many variates and univariate plots in our data analyses. Why PairGrid better than Seaborn pairplot.

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In Python with Seaborn we use the stripplot and pointplot with the Facetgrid in Seaborn.  Using this combination of 3 Seaborn plots we make the StripPointPlot.  This custom plot looks beautiful but also gives an ability to analyze our data from a diferent perspective.  Also learn how to plot different scales together with StandardScaler.

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The figure-level plotting tools, relplot, displot, catplot, provide powerful functionalities for visualizing data relationships, distributions, and categorical variables in a concise and intuitive manner.

Powerful figure-level plotting tools that provide efficient and flexible ways to explore relationships, distributions, and categorical variables

Seaborn Color Palette and Style Custimization

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learn to use Seaborn's diverging palette generator with Python.  the Diverging palette is great for highlighting positive and negative correlations in Seaborn's heatmap.

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Seaborn has the ability to control with a lot of flexible the color mix either diverging or sequential palettes the problem is you have so much control it takes a lot of time change find the right balance as you're generating your plot.  Seaborn's choose diverging palette tool lets you play with the various arguments until you've found your perfect unique color palette.  

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The sns.set_style function in the Seaborn library allows for easy customization of visual styles for data visualization. With sns set_style, you can modify the overall aesthetics of your plots by specifying a particular style.   Additionally, you can set the font scale and context to adjust the size and style of the text within your plots. By utilizing sns.set_style, you can effortlessly tailor the visual appearance of your Seaborn plots to suit your preferences and enhance the presentation of your data.

Pandas in Python Data Analysis Tips

Take a deep dive into data analysis with Pandas.  In Python Pandas is a necessity for working with data but it also has some quick and easy plots and allow for analysis that aren't available elsewhere. We'll go over tips from beginner to advanced on how to get the most from your Python Data Analysis.

Pandas Univariate Analysis

Univariate Analysis
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The area plot in Pandas is a specialized plot that allows us to view how to data usually over a time series flow together.  Pandas' areaplot allows us to notice when and how relationships move in different patterns across the rows.  This can be a valuable plot in time series analysis or notice how the balance between features changes from observation to observation.

Pandas DataFrame is fundamental in data science but Pandas also has a great plotting tool.


What pandas plot lacks in statistic power when compared to other visualization libraries like Seaborn. Pandas plotting makes up for this in its ease to use and it a typical data analysis workflow a data scientist will you both pandas plot and seaborn depending on the need of the analysis.


A very cool feature of pandas plotting is that we can a back-end option to allow us to use pandas plot to plot interactive plots like can with Plotly.

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Pandas plot is a fast and easy-to-use plotting engine in Python.  Pandas plot is handy because you'll likely already be using a pandas DataFrame and so you will have quick and easy access to the plotting function available in pandas.

But what if I told you, you could turn that accessibility into interactivity?

Using a backdoor in Pandas we can change the base plotting engine from Matplotlib to Plotly and gain the interactivity we have available in the Python library Plotly.  

Pandas Bivariate Analysis

Pandas Bivariate Data Analysis
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Learn to use Pandas' Pie plot function.  When using a pie plot we need to pay special attention to how the proportions of each section are clear.  We do this by adding the percentages to each section or wedge and using the explode argument to highligh sections

Pandas Figure Level Plots

Pandas Figure Level Plots Python
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The main advantage of using Pandas Plot, over other Python visualization libraries is that is it very quick and easy to use.  

The main issue with Pandas is that it is not quite as beautiful as Seaborn so I normally use plt style use and choose a different style palette which tends to make Pandas look quite nice.  Here I am using the Solarize_Light2 in matplotlib style use function.

We often have variables of different scales that we want to plot alongside each other to look for patterns in the relationship between these two variables.  Pandas plot function has a handy solution with secondary_y argument and setting it to True on a second call of the plotting function will plot the second feature on the right y-axis making it a dual y-axis plot. 

Here we have plt style use set to seaborn style.

Plotly in Python Data Analysis Tips

Take a deep dive into interactive data analysis with Plotly.  Plotly is one of the newer Python visualization libraries.  One of the defining characteristics of Ploty is the interactivy you get and allows you to spend more time inspecting each univariate or bivariate analysis in more depth. 

Plotly Univariate Analysis

Plotly Univariate Analysis
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In data science understanding the distribution of each feature becomes very important.  Plotly's histogram plot has some amazing data analysis features built into the histogram.  Changing the color by category, adding a histogram above or adding a count of value all easy options to use in the Plotly histogram.  Plus the interactivy of the Ploty allows you to take your data analysis to the next level.  

Plotly Multivariate Analysis

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I was so excited to try the 3D scatter plot in Plotly the first time because wow cool no?  But I was also pleasantly surprised and the extra insights I was able to extract from this plot.   Something about actually going inside your data and exploring the data in 3D yields amazing insights.  

The 3D Scatter plot in Plotly is an amazing data analysis tool.  The 3D scatter plot allows us to inspect 3 continuous features side by side by side.  Better yet in this 3D environment, we can zoom in and it almost feels like we are able to jump inside of our data and explore this tri variate relationship in detail.

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