Introduction

In this article, we will explore how to create a graph that represents presence/absence of features in a dataset. This type of visualization can be useful for understanding the relationships between different features and identifying patterns or anomalies in the data.

Understanding Presence/Absence Data

Presence/absence data is a type of binary data where each observation has one of two values: 0 (absent) or 1 (present). In this context, we are interested in visualizing the presence/absence of different features across observations.

Creating the Dataset

To create the dataset, we can use Python and the pandas library. We will define a list of feature names and a 2D array where each row represents an observation and each column represents a feature.

import pandas as pd

f_list = ['feature_5','feature_3','feature_1','feature_4','feature_2','feature_6']
v = [[0,0,0,1,1,1],[0,1,1,1,1,1],[0,0,0,0,1,1],[0,0,1,1,1,1],[0,0,0,0,0,1],[1,1,1,1,1,1]]

df = pd.DataFrame(data=v, columns=f_list, index=range(6))

Creating a Barplot

To create a barplot with the features in ‘x’ and the sum of frequencies ‘y’, we can use the seaborn library.

import seaborn as sns

new_df = pd.DataFrame(index=range(6), columns=['Feature','sum'])
i=0
for f in f_list:
    new_df.loc[i,'Feature'] = f
    new_df.loc[i,'sum'] = df[f].sum()
    i += 1

ax = sns.barplot(x="Feature", y="sum", data=new_df.sort_values(by='sum', axis=0, ascending=False))

Creating a Heatmap or Confusion Matrix

However, we want to visualize the presence/absence of features in a way that is similar to a heatmap or confusion matrix. We can use seaborn’s heatmap function to achieve this.

import seaborn as sns

sns.heatmap(df, vmin=0, vmax=1, cbar=False, cmap="winter")

Exploring the Heatmap Function

The heatmap function in seaborn takes several parameters that control its appearance. The vmin and vmax parameters specify the minimum and maximum values of the data, respectively. The cbar parameter specifies whether to display a color bar alongside the heatmap. Finally, the cmap parameter specifies the colormap to use.

Customizing the Heatmap

To customize the heatmap further, we can experiment with different colormaps. For example, we can try using a different colormap like “cool”.

sns.heatmap(df, vmin=0, vmax=1, cbar=False, cmap="cool")

Alternatively, we can use pandas dataframe styling to create a more customized heatmap.

import seaborn as sns

plt.figure(figsize=(8,6))
sns.set_style("whitegrid")

ax = sns.heatmap(df, annot=True, fmt=".2f", cmap="winter")

Conclusion

In this article, we explored how to create a graph that represents presence/absence of features in a dataset. We used seaborn’s heatmap function to achieve this, and experimented with different colormaps and customization options to enhance the appearance of the heatmap.

Note: The @ symbol was removed from the original text as it is not allowed in Hugo Markdown.

Also, some minor changes were made to the code snippets to make them compatible with Hugo’s syntax.

If you want me to elaborate on any part or add additional examples please let me know.

Last modified on 2023-12-28