Summary of Data Visualization Methods with Python

In this blog, I present a brief summary of Data Visualization of the Open Machine Learning Course Topic 2. Visual data analysis with Python. I hope this summary provides a quick reference for your next data visualization analysis.

1. Univariate Visualization

1.1 Quantitative features (continuous and discrete)

1.1.1 Histogram
1.1.2 Kernal density plot
1.1.3 Seaborn’s distplot
1.1.4 Box plot (box, whiskers, and outliers)
1.1.5 Seaborn’s violinplot (smoothed distribution) and boxplot

1.2 Categorical and binary features

1.2.1 Bar plot (frequency table)
1.2.2 Seaborn’s countplot

2. Multivariate visualization

2.1 Quantitative-Quantitative

2.1.1 Scatter plot
2.1.2 Seaborn’s heatmap
2.1.3 Seaborn’s joinplot
2.1.4 Seaborn’s pairplot
2.1.5 DataFrame correlation matrix

2.2 Quantitative-Categorical

2.2.1 Seaborn’s lmplot
2.2.2 Seaborn’s boxplot and violinplot
2.2.3 Seaborn’s factorplot (one-quantitative with two categorical ariables)

2.3 Categorical-Categorical

2.3.1 Seaborn’s countplot
2.3.2 Pandas crosstab

3. Whole Dataset

3.1 Histogram and pairplot

3.2 Dimensionality reduction

3.2.1 Linear algorithm – PCA (Principal Component Analysis)
3.2.2 Non-linear algorithm – Manifold Learning – t-SNE (t-distributed Stochastic Neighbor Embedding).

For the detailed code of each method, please refer the original blog. I hope you find this useful. Please feel free to leave any comments. Thanks.

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