Skip to content

Data analysis and visualization with PyData ecosystem: Pandas, Matplotlib Numpy, and Seaborn

Notifications You must be signed in to change notification settings

ejaz22/eda

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

54 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Exploratory Data Analysis and Visualization in Python

Python Programming Part 1 & 2

  • Looping
  • Functions
  • Lambda Expressions
  • Methods

SciPy

Matplotlib Part 1 & 2

  • Subplots
  • Customizing plot appearance

Matplotlib - Basics

  • Multiplots
  • Subplots
  • Setting colors

Matplotlib Advanced

  • Log scale
  • Text annotation
  • Axis grid
  • Twin axis
  • 3D figures

FiveThirtyEight Visualizations with Matplotlib

  • Customizing tick marks and axes
  • Adding titles and subtitles
  • Adding a signature bar

NumPy and Pandas for 1D and 2D Data

  • Similarities & differences
  • Pearson's r function

Numpy

  • Creating arrays
  • Indexing and Selection
  • Basic operations

Pandas

  • Subsetting dataframes
  • Working with the Index
  • Missing data
  • Group By
  • Combining dataframes (merge, join, concatenation)
  • Pivot Table

911 Call Exploratory Data Analysis

  • Time Series study with seaborn
  • Heat maps with seaborn

Financial Exploratory Analysis

  • Pairplot with seaborn
  • Moving average line plot
  • Heat maps with seaborn

Python A-Z Profit Analysis

  • For loops
  • Round function

Country Birth Rate Exploratory Analysis

  • Creating dataframes
  • Merging dataframes
  • Scatterplots with seaborn

Domestic Gross Percentage Exploratory Analysis

  • Filtering using isin function
  • Box plot with jitter

Python A-Z Internet Users Exploratory Analysis

  • Subsetting
  • Filtering

Melbourne Housing Exploratory Analysis

  • Re-ordering columns
  • Identifying missing data
  • Converting variable types

SF Salaries Exploratory Analysis

Python A-Z Internet Users Data Visualization

  • Seaborn plots
  • Keyword Arguments in Python

Python A-Z Movie Rating Data Visualization

  • Converting variables
  • Stacked histograms
  • KDE plot with seaborn
  • Subplots
  • Building & styling dashboards

Pandas Data Visualization

  • Hex bin
  • Kernel density plot

Pandas Data Visualization Part 2

  • Area maps

Pandas Built-In Data Visualization

Ploty & Cufflinks

  • 3D surface plots
  • Spread plots
  • Bubble plots
  • Scatter matrix plots

Plotly Geograhical Plotting

  • USA choropleth map
  • Global choropleth map

Plotly Choropleth Maps

Seaborn

  • Distribution plots (histogram, joint plot, rug plot, kde)
  • Categorical plots (bar plot, count plot, box plot, violin plot, strip plot, swarm plot, factorplot))
  • Matrix plots (heat maps, cluster maps)
  • Regression plots (linear model plot, faceted linear model plot)
  • Grids (pairplot, pair grid, facet grid)
  • Customizing style and color

Seaborn Plots

  • Categorical Plots
  • Distribution Plots
  • Seaborn Matrix Plots
  • Seaborn Regression Plots
  • Seaborn Style and Color

About

Data analysis and visualization with PyData ecosystem: Pandas, Matplotlib Numpy, and Seaborn

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%