Press "Enter" to skip to content

Tag: seaborn

How to Make a Scatter Plot in Python using Seaborn

Data visualization is a big part of the process of data analysis. In this post, we will learn how make a scatter plot using Python and the package Seaborn. In detail, we will learn how to use the Seaborn methods scatterplot, regplot, lmplot, and pairplot to create scatter plots in Python.

More specifically, we will learn how to make scatter plots, change the size of the dots, change the markers, the colors, and change the number of ticks.  Furthermore, we will learn how to plot a regression line, add text, plot a distribution on a scatter plot, among other things. Finally, we will also learn how to save Seaborn plots in high resolution. That is, we learn how to make print-ready plots.

How to Read and Write JSON Files using Python and Pandas

In this post we will learn how to read and write JSON files using Python. In the first, part we are going to use the Python package json to create a JSON file and write a JSON file. In the next part we are going to use Pandas json method to load JSON files into Pandas dataframe. Here, we will learn how to read from a JSON file locally and from an URL as well as how to read a nested JSON file using Pandas.

Finally, as a bonus, we will also learn how to manipulate data in Pandas dataframes, rename columns, and plot the data using Seaborn.

9 Data Visualization Techniques You Should Learn in Python

With ever increasing volume of data, it is impossible to tell stories without visualizations. Data visualization is an art of how to turn numbers into useful knowledge. Using Python we can learn how to create data visualizations and present data in Python using the Seaborn package.

In this post we are going to learn how to use the following 9 plots:

  1. Scatter Plot
  2. Histogram
  3. Bar Plot
  4. Time Series Plot
  5. Box Plot
  6. Heat Map
  7. Correlogram
  8. Violin Plot
  9. Raincloud Plot

Python Data Visualization Tutorial: Seaborn

As previously mentioned in this Python Data Visualization tutorial we are mainly going to use Seaborn but also Pandas,  and Numpy. However, to create the Raincloud Plot we are going to have to use the Python package ptitprince.

Python Raincloud Plot using the ptitprince package

Exploratory Data Analysis in Python Using Pandas, SciPy, and Seaborn

In this post we are going to learn how to explore data using Python, Pandas, and Seaborn. The data we are going to explore is data from a Wikipedia article. In this post we are actually going to learn how to parse data from a URL using Python Pandas. Furthermore, we are going to explore the scraped data by grouping it and by Python data visualization. More specifically, we will learn how to count missing values, group data to calculate the mean, and then visualize relationships between two variables, among other things.

In previous posts we have used Pandas to import data from Excel and CSV files. In this post, however, we are going to use Pandas read_html, because it has support for reading data from HTML from URLs (https or http). To read HTML Pandas use one of the Python libraries LXML, Html5Lib, or BeautifulSoup4. This means that you have to make sure that at least one of these libraries are installed. In the specific Pandas read_html example here, we use BeautifulSoup4 to parse the html tables from the Wikipedia article.