answer:Yes, Ángel is a character from the video game series The King of Fighters. She is a female fighter who makes her first appearance in The King of Fighters '99. She is often associated with the team composed of her, Leona, and Kula, known as the "KOF girls team".

answer:This code first creates a crosstab of two variables ('Analysis' and 'Vader_Analysis') from a dataset ('df_english_copy') using the pandas 'crosstab' function. The resulting crosstab is normalized by setting the 'normalize' parameter to "index". Then, the seaborn library's 'heatmap' function is used to plot the crosstab as a heatmap with the percentage values inside the map. The heatmap is colored with a green-blue color palette ('YlGnBu'). Finally, the 'plt.show()' function is called to show the plot. This code snippet generates a visual representation of the relationship between the two variables, with the heatmap making it easier to compare values across the two variables.

answer:To arrange the three heatmaps side-by-side, you can use matplotlib's 'subplots' function with one row and three columns, like this: ``` fig, axs = plt.subplots(1, 3, figsize=(12,4)) # plot the crosstab as a heatmap with percentage values inside the map sns.heatmap(crosstab1, annot=True, fmt=".2%", cmap="YlGnBu", ax=axs[0]) sns.heatmap(crosstab2, annot=True, fmt=".2%", cmap="YlGnBu", ax=axs[1]) sns.heatmap(crosstab3, annot=True, fmt=".2%", cmap="YlGnBu", ax=axs[2]) # add titles axs[0].set_title('Analysis vs. Vader Analysis') axs[1].set_title('Analysis vs. SWN Analysis') axs[2].set_title('Vader Analysis vs. SWN Analysis') plt.tight_layout() plt.show() ``` In this example, 'fig, axs = plt.subplots(1, 3, figsize=(12,4))' creates a figure with one row and three columns of subplots, and sets the size of the figure to (12,4). The 'axs' object represents the array of AxesSubplot objects for each subplot. Then, each heatmap is plotted by specifying the appropriate 'ax' parameter (e.g., 'ax=axs[0]' for the first heatmap), and given a title using the 'set_title' method. The 'tight_layout' function adjusts the subplots to fit in the figure area without overlapping. Finally, the combined plot is displayed using 'plt.show()'.

answer:To create a count plot for three variables, you can use the 'sns.catplot' function with 'kind' set to 'count', like this: ``` g = sns.catplot(data=df_english_copy, kind='count', x='Analysis', hue='Vader_Analysis', col='SWN_analysis') g.set_axis_labels('Analysis', 'Count') plt.show() ``` In this example, the 'sns.catplot' function creates a grid of plots, with one row for each unique value of 'SWN_analysis'. The count plot for each value of 'SWN_analysis' is shown in a separate column. The 'x' parameter specifies the variable to use on the x-axis, 'hue' specifies the variable to use for coloring, and 'col' specifies the variable to use for separating the plots. The 'set_axis_labels' method sets the axis labels for each plot. Finally, the combined plot is displayed using 'plt.show()'.