W15 Otopeni stats & predictions

What is the significance of the Tennis W15 category?

The W15 category is part of the ITF Women's World Tennis Tour, offering competitive opportunities for players outside the top echelons of professional tennis. It serves as a platform for emerging talents to gain experience and improve their rankings.

How can I watch live matches?

Livestreams may be available through official tournament channels or partner sports networks. Check social media updates for links to live broadcasts or highlights clips if direct streaming isn't available. [0]: import numpy as np [1]: import pandas as pd [2]: import seaborn as sns [3]: import matplotlib.pyplot as plt [4]: from . import _generic [5]: def plot_kde(df, [6]: x_col, [7]: y_col=None, [8]: hue=None, [9]: xlim=None, [10]: ylim=None, [11]: log_scale=False, [12]: hist=False, [13]: bins=30, [14]: alpha=0.5, [15]: size=(10,8), [16]: show=True): [17]: ''' [18]: Plot kernel density estimate (KDE) plot(s). [19]: Parameters [20]: ---------- [21]: df : pandas DataFrame [22]: x_col : str [23]: Column name for x-axis. [24]: y_col : str or None (default) [25]: Column name for y-axis. [26]: hue : str or None (default) [27]: Column name used for grouping data. [28]: xlim : tuple or None (default) [29]: ylim : tuple or None (default) [30]: log_scale : bool (default False) [31]: hist : bool (default False) [32]: bins : int (default = 30) [33]: alpha : float (default = .5) [34]: size : tuple (default = (10,8)) [35]: show : bool (default True) [36]: Returns [37]: ------- [38]: fig : matplotlib.figure.Figure instance [39]: ''' if y_col is None: # Univariate plot fig = plt.figure(figsize=size) ax = fig.add_subplot(111) if hue is not None: legend_list = list(df[hue].unique()) # Check if any missing values if np.any(pd.isnull(df[hue])): legend_list.append('missing') palette = sns.color_palette('husl', len(legend_list)) # Loop over categories for i in range(len(legend_list)): # Subset data by category sub_df = df[df[hue] == legend_list[i]] # Plot KDE sns.kdeplot(sub_df[x_col], shade=True, alpha=alpha, color=palette[i], ax=ax) else: sns.kdeplot(df[x_col], shade=True, ax=ax) ax.set_xlabel(x_col) if xlim is not None: ax.set_xlim(xlim) if ylim is not None: ax.set_ylim(ylim) if log_scale: ax.set_yscale('log') if hist: sub_df = df.copy() sub_df['density'] = 'density' if hue is not None: sub_df['hue'] = df[hue] g = sns.FacetGrid(sub_df, col='hue', sharex=False, sharey=False, height=size[-1]/len(legend_list), aspect=size[-1]/size[-2]*len(legend_list), palette=palette) g.map(sns.histplot, x_col, kde=False, bins=bins) else: sns.histplot(sub_df[x_col], bins=bins) else: # Bivariate plot fig = plt.figure(figsize=size) ax = fig.add_subplot(111) if hue is not None: legend_list = list(df[hue].unique()) # Check if any missing values if np.any(pd.isnull(df[hue])): legend_list.append('missing') palette = sns.color_palette('husl', len(legend_list)) # Loop over categories for i in range(len(legend_list)): # Subset data by category sub_df = df[df[hue] == legend_list[i]] # Plot KDE sns.kdeplot(sub_df[x_col], sub_df[y_col], shade=True, alpha=alpha, color=palette[i], ax=ax) else: sns.kdeplot(df[x_col], df[y_col], shade=True, ax=ax) ax.set_xlabel(x_col) ax.set_ylabel(y_col) if xlim is not None: ax.set_xlim(xlim) if ylim is not None: ax.set_ylim(ylim) if log_scale: ax.set_yscale('log') if show: plt.show() return fig ***** Tag Data ***** ID: 1 description: Handling univariate KDE plotting with optional hue grouping. This section includes advanced usage patterns such as dynamic legend creation based on unique hue values including handling missing values. start line: 20 end line: 33 dependencies: - type: Function name: plot_kde start line: 5 end line: 38 context description: This snippet handles plotting kernel density estimates (KDE) using seaborn based on a given DataFrame. It includes logic for creating legends dynamically depending on whether hue grouping is used. algorithmic depth: 4 algorithmic depth external: N obscurity: 2 advanced coding concepts: 4 interesting for students: 4 self contained: Y ************ ## Challenging aspects ### Challenging aspects in above code 1. **Dynamic Legend Creation**: Creating legends dynamically based on unique values in a column (`hue`) requires careful handling especially when dealing with missing values. 2. **Handling Missing Values**: Checking for missing values in `hue` column adds complexity since it affects how legends are created. 3. **Logarithmic Scale**: Implementing log scale transformations while maintaining correct axis limits requires careful consideration. 4. **Dual Plot Types**: Supporting both KDE plots alone or combined with histograms adds complexity due to differing parameter requirements. 5. **Parameter Interactions**: Managing interactions between parameters like `xlim`, `ylim`, `bins`, `alpha`, etc., ensuring they don't conflict with each other. 6. **Custom Palette Management**: Generating color palettes dynamically based on unique values in `hue` column ensures visual distinction among groups. ### Extension 1. **Multi-dimensional KDE Plots**: Extend functionality from univariate/bivariate KDE plots to multivariate KDE plots. 2. **Interactive Plots**: Add support for interactive plots using libraries like Plotly or Bokeh. 3. **Advanced Customization Options**: Allow more customization such as customizing tick labels or grid lines. 4. **Data Subsetting**: Implement functionality allowing users to subset data based on certain conditions before plotting. 5. **Handling Large Datasets**: Optimize performance when dealing with large datasets by implementing downsampling techniques. ## Exercise ### Problem Statement: Extend the given [SNIPPET] function `plot_kde` by adding support for multivariate KDE plots (up to three variables), interactive plotting capabilities using Plotly, advanced customization options such as custom tick labels/grid lines, data subsetting based on user-defined conditions before plotting. **Requirements**: 1. **Multivariate KDE**: - Extend functionality from univariate/bivariate KDE plots up to trivariate KDE plots. - Allow specifying up to three columns (`x_col`, `y_col`, `z_col`) for plotting. 2. **Interactive Plots**: - Use Plotly library for generating interactive plots. - Maintain all existing functionalities such as dynamic legend creation. 3. **Advanced Customization**: - Allow customizing tick labels using parameters `xticklabels` & `yticklabels`. - Enable grid lines customization using parameter `grid`. 4. **Data Subsetting**: - Implement functionality allowing users to subset data based on user-defined conditions before plotting using parameter `subset_condition`. ### Code Template: Refer to [SNIPPET] while implementing. python def plot_kde(df, x_col, y_col=None, z_col=None, hue=None, xlim=None, ylim=None, zlim=None, log_scale=False, hist=False, bins=30, alpha=0.5, size=(10,8), show=True, interactive=False, xticklabels=None, yticklabels=None, grid=False, subset_condition=None): """ Extended Plot kernel density estimate (KDE) plot(s) supporting up to trivariate KDEs with interactive plotting capabilities using Plotly. Parameters similar as before... """ # Your code here... ### Solution: python import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import plotly.express as px def plot_kde(df, x_col, y_col=None, z_col=None, hue=None, xlim=None, ylim=None, zlim=None, log_scale=False, hist=False, bins=30, alpha=0.5, size=(10,8), show=True, interactive=False, xticklabels=None, yticklabels=None, grid=False, subset_condition=None): """ Extended Plot kernel density estimate (KDE) plot(s) supporting up to trivariate KDEs with interactive plotting capabilities using Plotly. """ # Subset data based on condition if provided if subset_condition is not None: df = df.query(subset_condition) # Initialize figure depending on interactivity flag if interactive: fig = px.density_contour(df[[x_col]], x=x_col) if y_col is None else px.density_contour(df[[x_col,y_col]], x=x_col,y=y_col) else: fig = plt.figure(figsize=size) # Handle univariate case if y_col is None: if interactive: fig.update_traces(contours_coloring='fill', contours_showlabels=True) fig.update_layout(xaxis_title=x_col) else: sns.kdeplot(data=df,x=x_col,hue=hue,alpha=alpha,bw_adjust=.5,cut=0) plt.xlabel(x_col) # Handle bivariate case elif z_col is None: legend_list = list(df[hue].unique()) if hue else [None] palette = sns.color_palette('husl', len(legend_list)) if hue else [None] if interactive: fig.update_traces(contours_coloring='fill', contours_showlabels=True) fig.update_layout(xaxis_title=x_col,yaxis_title=y_col) df['hue'] = df[hue] if hue else 'all' fig = px.density_contour(df[[x_col,y_col,'hue']], x=x_col,y=y_col,color='hue') else: sns.kdeplot(data=df,x=x_col,y=y_col,hue=hue,alpha=alpha,bw