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''' |
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Functions for descriptive analytics. |
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:author: Andreas Kanz |
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''' |
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# Imports |
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import matplotlib.pyplot as plt |
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import matplotlib.ticker as ticker |
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import numpy as np |
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import pandas as pd |
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import scipy |
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import seaborn as sns |
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from .clean import drop_missing |
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from .utils import _corr_selector |
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from .utils import _missing_vals |
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from .utils import _validate_input_bool |
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from .utils import _validate_input_int |
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from .utils import _validate_input_range |
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from .utils import _validate_input_smaller |
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# Functions |
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# Categorical Plot |
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def cat_plot(data, figsize=(10, 14), top=3, bottom=3, bar_color_top='#5ab4ac', bar_color_bottom='#d8b365', cmap='BrBG'): |
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''' |
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Two-dimensional visualization of the number and frequency of categorical features. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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figsize: tuple, default (10, 14) |
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Use to control the figure size. |
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top: int, default 3 |
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Show the "top" most frequent values in a column. |
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bottom: int, default 3 |
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Show the "bottom" most frequent values in a column. |
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bar_color_top: color, default '#5ab4ac' |
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Use to control the color of the bars indicating the most common values. |
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bar_color_bottom: color, default '#d8b365' |
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Use to control the color of the bars indicating the least common values. |
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cmap: matplotlib colormap name or object, or list of colors, default 'BrBG' |
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The mapping from data values to color space. |
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Returns |
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------- |
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gs: Figure with array of Axes objects. |
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''' |
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# Validate Inputs |
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_validate_input_int(top, 'top') |
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_validate_input_int(bottom, 'bottom') |
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_validate_input_range(top, 'top', 0, data.shape[1]) |
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_validate_input_range(bottom, 'bottom', 0, data.shape[1]) |
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data = pd.DataFrame(data).copy() |
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cols = list(data.select_dtypes(exclude=['number']).columns) # categorical cols |
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data = data[cols].applymap(str) |
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if len(cols) == 0: |
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print('No columns with categorical data were detected.') |
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fig = plt.figure(figsize=figsize) |
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gs = fig.add_gridspec(nrows=6, ncols=len(cols), wspace=0.2) |
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for count, col in enumerate(cols): |
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n_unique = data[col].nunique(dropna=False) |
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value_counts = data[col].value_counts() |
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lim_top, lim_bot = top, bottom |
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if n_unique < top+bottom: |
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lim_top = lim_bot = int(n_unique//2) |
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value_counts_top = value_counts[0:lim_top] |
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value_counts_idx_top = list(map(str, value_counts_top.index.tolist())) |
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value_counts_bot = value_counts[-lim_bot:] |
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value_counts_idx_bot = list(map(str, value_counts_bot.index.tolist())) |
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if top == 0: |
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value_counts_top = value_counts_idx_top = [] |
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elif bottom == 0: |
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value_counts_bot = value_counts_idx_bot = [] |
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data[col][data[col].isin(value_counts_idx_top)] = 2 |
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data[col][data[col].isin(value_counts_idx_bot)] = -2 |
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data[col][~((data[col] == 2) | (data[col] == -2))] = 0 |
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# Barcharts |
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ax_top = fig.add_subplot(gs[:1, count:count+1]) |
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ax_top.bar(value_counts_idx_top, value_counts_top, color=bar_color_top, width=0.85) |
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ax_top.bar(value_counts_idx_bot, value_counts_bot, color=bar_color_bottom, width=0.85) |
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ax_top.set(frame_on=False) |
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ax_top.tick_params(axis='x', labelrotation=90) |
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# Summary stats |
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ax_bottom = fig.add_subplot(gs[1:2, count:count+1]) |
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ax_bottom.get_yaxis().set_visible(False) |
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ax_bottom.get_xaxis().set_visible(False) |
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ax_bottom.set(frame_on=False) |
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ax_bottom.text(0, 0, f'Unique values: {n_unique}\n\n' |
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f'Top {top} vals: {sum(value_counts_top)} ({sum(value_counts_top)/data.shape[0]*100:.1f}%)\n' |
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f'Bottom {bottom} vals: {sum(value_counts_bot)} ' + |
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f'({sum(value_counts_bot)/data.shape[0]*100:.1f}%)', |
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transform=ax_bottom.transAxes, color='#111111', fontsize=11) |
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# Heatmap |
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data = data.astype('int') |
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ax_hm = fig.add_subplot(gs[2:, :]) |
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sns.heatmap(data, cmap=cmap, cbar=False, vmin=-4.25, vmax=4.25, ax=ax_hm) |
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ax_hm.set_yticks(np.round(ax_hm.get_yticks()[0::5], -1)) |
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ax_hm.set_yticklabels(ax_hm.get_yticks()) |
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ax_hm.set_xticklabels(ax_hm.get_xticklabels(), |
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horizontalalignment='center', |
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fontweight='light', |
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fontsize='medium') |
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ax_hm.tick_params(length=1, colors='#111111') |
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gs.figure.suptitle('Categorical data plot', x=0.47, y=0.925, fontsize=18, color='#111111') |
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return gs |
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# Correlation Matrix |
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def corr_mat(data, split=None, threshold=0, target=None, method='pearson', colored=True): |
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''' |
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Returns a color-encoded correlation matrix. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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split: {None, 'pos', 'neg', 'above', 'below'}, default None |
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Type of split to be performed. |
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threshold: float, default 0 |
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Value between 0 <= threshold <= 1 |
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method: {'pearson', 'spearman', 'kendall'}, default 'pearson' |
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* pearson: measures linear relationships and requires normally distributed and homoscedastic data. |
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* spearman: ranked/ordinal correlation, measures monotonic relationships. |
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* kendall: ranked/ordinal correlation, measures monotonic relationships. Computationally more expensive but |
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more robus in smaller dataets than 'spearman'. |
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colored: bool, default True |
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If True the negative values in the correlation matrix are colored in red. |
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Returns |
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------- |
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Pandas Styler object if colored = True |
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Pandas DataFrame if colored =False |
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''' |
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# Validate Inputs |
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_validate_input_range(threshold, 'threshold', -1, 1) |
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def color_negative_red(val): |
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color = '#FF3344' if val < 0 else None |
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return 'color: %s' % color |
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data = pd.DataFrame(data) |
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if isinstance(target, (str, list, pd.Series, np.ndarray)): |
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target_data = [] |
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if isinstance(target, str): |
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target_data = data[target] |
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data = data.drop(target, axis=1) |
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elif isinstance(target, (list, pd.Series, np.ndarray)): |
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target_data = pd.Series(target) |
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target = target.name |
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corr = pd.DataFrame(data.corrwith(target_data)).rename_axis(target, axis=1) |
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corr = corr.sort_values(corr.columns[0], ascending=False) |
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else: |
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corr = data.corr(method=method) |
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corr = _corr_selector(corr, split=split, threshold=threshold) |
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if colored: |
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return corr.style.applymap(color_negative_red).format('{:.2f}', na_rep='-') |
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else: |
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return corr |
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# Correlation matrix / heatmap |
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def corr_plot(data, split=None, threshold=0, target=None, method='pearson', cmap='BrBG', figsize=(12, 10), annot=True, |
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dev=False, **kwargs): |
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''' |
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Two-dimensional visualization of the correlation between feature-columns, excluding NA values. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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split: {None, 'pos', 'neg', 'above', 'below'}, default None |
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Type of split to be performed. |
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* None: visualize all correlations between the feature-columns. |
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* pos: visualize all positive correlations between the feature-columns above the threshold. |
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* neg: visualize all negative correlations between the feature-columns below the threshold. |
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* above: visualize all correlations between the feature-columns for which abs(corr) > threshold is True. |
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* below: visualize all correlations between the feature-columns for which abs(corr) < threshold is True. |
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threshold: float, default 0 |
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Value between 0 <= threshold <= 1 |
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target: string, list, np.array or pd.Series, default None |
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Specify target for correlation. E.g. label column to generate only the correlations between each feature \ |
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and the label. |
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method: {'pearson', 'spearman', 'kendall'}, default 'pearson' |
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* pearson: measures linear relationships and requires normally distributed and homoscedastic data. |
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* spearman: ranked/ordinal correlation, measures monotonic relationships. |
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* kendall: ranked/ordinal correlation, measures monotonic relationships. Computationally more expensive but |
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more robust in smaller dataets than 'spearman'. |
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cmap: matplotlib colormap name or object, or list of colors, default 'BrBG' |
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The mapping from data values to color space. |
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figsize: tuple, default (12, 10) |
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Use to control the figure size. |
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annot: bool, default True |
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Use to show or hide annotations. |
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dev: bool, default False |
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Display figure settings in the plot by setting dev = True. If False, the settings are not displayed. |
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**kwargs: optional |
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Additional elements to control the visualization of the plot, e.g.: |
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* mask: bool, default True |
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If set to False the entire correlation matrix, including the upper triangle is shown. Set dev = False in this \ |
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case to avoid overlap. |
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* vmax: float, default is calculated from the given correlation coefficients. |
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Value between -1 or vmin <= vmax <= 1, limits the range of the colorbar. |
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* vmin: float, default is calculated from the given correlation coefficients. |
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Value between -1 <= vmin <= 1 or vmax, limits the range of the colorbar. |
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* linewidths: float, default 0.5 |
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Controls the line-width inbetween the squares. |
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* annot_kws: dict, default {'size' : 10} |
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Controls the font size of the annotations. Only available when annot = True. |
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* cbar_kws: dict, default {'shrink': .95, 'aspect': 30} |
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Controls the size of the colorbar. |
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* Many more kwargs are available, i.e. 'alpha' to control blending, or options to adjust labels, ticks ... |
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Kwargs can be supplied through a dictionary of key-value pairs (see above). |
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Returns |
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------- |
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ax: matplotlib Axes |
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Returns the Axes object with the plot for further tweaking. |
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''' |
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275
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# Validate Inputs |
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_validate_input_range(threshold, 'threshold', -1, 1) |
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_validate_input_bool(annot, 'annot') |
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_validate_input_bool(dev, 'dev') |
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280
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data = pd.DataFrame(data) |
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corr = corr_mat(data, split=split, threshold=threshold, target=target, method=method, colored=False) |
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284
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mask = np.triu(np.ones_like(corr, dtype=np.bool)) |
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vmax = np.round(np.nanmax(corr.where(~mask))-0.05, 2) |
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vmin = np.round(np.nanmin(corr.where(~mask))+0.05, 2) |
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288
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fig, ax = plt.subplots(figsize=figsize) |
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290
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# Specify kwargs for the heatmap |
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kwargs = {'mask': mask, |
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'cmap': cmap, |
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'annot': annot, |
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'vmax': vmax, |
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'vmin': vmin, |
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'linewidths': .5, |
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'annot_kws': {'size': 10}, |
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298
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'cbar_kws': {'shrink': .95, 'aspect': 30}, |
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299
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**kwargs} |
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301
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# Draw heatmap with mask and default settings |
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sns.heatmap(corr, center=0, fmt='.2f', **kwargs) |
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304
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ax.set_title(f'Feature-correlation ({method})', fontdict={'fontsize': 18}) |
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305
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306
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# Settings |
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307
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if dev: |
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308
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fig.suptitle(f"\ |
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309
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Settings (dev-mode): \n\ |
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310
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- split-mode: {split} \n\ |
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311
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- threshold: {threshold} \n\ |
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312
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- method: {method} \n\ |
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313
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- annotations: {annot} \n\ |
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314
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- cbar: \n\ |
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315
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- vmax: {vmax} \n\ |
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316
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- vmin: {vmin} \n\ |
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317
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- linewidths: {kwargs['linewidths']} \n\ |
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318
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- annot_kws: {kwargs['annot_kws']} \n\ |
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319
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- cbar_kws: {kwargs['cbar_kws']}", |
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320
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fontsize=12, |
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321
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color='gray', |
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322
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x=0.35, |
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323
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y=0.85, |
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324
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ha='left') |
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325
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326
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return ax |
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327
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328
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329
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# Distribution plot |
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330
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def dist_plot(data, mean_color='orange', figsize=(14, 2), fill_range=(0.025, 0.975), hist=False, bins=None, |
|
331
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showall=False, kde_kws=None, rug_kws=None, fill_kws=None, font_kws=None): |
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332
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''' |
|
333
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Two-dimensional visualization of the distribution of numerical features. |
|
334
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|
335
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Parameters |
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336
|
|
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---------- |
|
337
|
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
|
338
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information is used to label the plots. |
|
339
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|
340
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mean_color: color, default 'orange' |
|
341
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|
Color of the vertical line indicating the mean of the data. |
|
342
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|
343
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|
|
figsize: tuple, default (14, 2) |
|
344
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|
|
Controls the figure size. |
|
345
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|
346
|
|
|
fill_range: tuple, default (0.025, 0.975) |
|
347
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|
Set the quantiles for shading. Default spans 95% of the data, which is about two std. deviations\ |
|
348
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|
|
above and below the mean. |
|
349
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|
350
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|
|
hist: bool, default False |
|
351
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|
|
Set to True to display histogram bars in the plot. |
|
352
|
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|
353
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|
|
bins: integer, default None |
|
354
|
|
|
Specification of the number of hist bins. Requires hist = True |
|
355
|
|
|
|
|
356
|
|
|
showall: bool, default False |
|
357
|
|
|
Set to True to remove the output limit of 20 plots. |
|
358
|
|
|
|
|
359
|
|
|
kdw_kws: dict, default {'color': 'k', 'alpha': 0.7, 'linewidth': 1} |
|
360
|
|
|
Keyword arguments for kdeplot(). |
|
361
|
|
|
|
|
362
|
|
|
rug_kws: dict, default {'color': 'brown', 'alpha': 0.5, 'linewidth': 2, 'height': 0.04} |
|
363
|
|
|
Keyword arguments for rugplot(). |
|
364
|
|
|
|
|
365
|
|
|
fill_kws: dict, default {'color': 'brown', 'alpha': 0.1} |
|
366
|
|
|
Keyword arguments to control the fill. |
|
367
|
|
|
|
|
368
|
|
|
font_kws: dict, default {'color': '#111111', 'weight': 'normal', 'size': 11} |
|
369
|
|
|
Keyword arguments to control the font. |
|
370
|
|
|
|
|
371
|
|
|
Returns |
|
372
|
|
|
------- |
|
373
|
|
|
ax: matplotlib Axes |
|
374
|
|
|
Returns the Axes object with the plot for further tweaking. |
|
375
|
|
|
|
|
376
|
|
|
''' |
|
377
|
|
|
|
|
378
|
|
|
# Validate Inputs |
|
379
|
|
|
_validate_input_bool(hist, 'hist') |
|
380
|
|
|
_validate_input_bool(showall, 'showall') |
|
381
|
|
|
_validate_input_range(fill_range[0], 'fill_range_lower', 0, 1) |
|
382
|
|
|
_validate_input_range(fill_range[1], 'fill_range_upper', 0, 1) |
|
383
|
|
|
_validate_input_smaller(fill_range[0], fill_range[1], 'fill_range') |
|
384
|
|
|
|
|
385
|
|
|
# Handle dictionary defaults |
|
386
|
|
|
kde_kws = {'alpha': 0.7, 'linewidth': 1.5} if kde_kws is None else kde_kws.copy() |
|
387
|
|
|
rug_kws = {'color': 'brown', 'alpha': 0.5, 'linewidth': 2, 'height': 0.04} if rug_kws is None else rug_kws.copy() |
|
388
|
|
|
fill_kws = {'color': 'brown', 'alpha': 0.1} if fill_kws is None else fill_kws.copy() |
|
389
|
|
|
font_kws = {'color': '#111111', 'weight': 'normal', 'size': 11} if font_kws is None else font_kws.copy() |
|
390
|
|
|
|
|
391
|
|
|
data = drop_missing(pd.DataFrame(data).copy()) # remove empty columns / rows |
|
392
|
|
|
cols = list(data.select_dtypes(include=['number']).columns) |
|
393
|
|
|
data = data[cols] |
|
394
|
|
|
|
|
395
|
|
|
if len(cols) == 0: |
|
396
|
|
|
print('No columns with numeric data were detected.') |
|
397
|
|
|
|
|
398
|
|
|
elif len(cols) >= 20 and showall is False: |
|
399
|
|
|
print( |
|
400
|
|
|
f'Note: The number of numerical features is very large ({len(cols)}), please consider splitting the data. ' |
|
401
|
|
|
'Showing plots for the first 20 numerical features. Override this by setting showall=True.') |
|
402
|
|
|
cols = cols[:20] |
|
403
|
|
|
|
|
404
|
|
|
for col in cols: |
|
405
|
|
|
dropped_values = data[col].isna().sum() |
|
406
|
|
|
if dropped_values > 0: |
|
407
|
|
|
print(f'Dropped {dropped_values} missing values from column {col}.') |
|
408
|
|
|
col_data = data[col].dropna(axis=0) |
|
409
|
|
|
else: |
|
410
|
|
|
col_data = data[col] |
|
411
|
|
|
|
|
412
|
|
|
_, ax = plt.subplots(figsize=figsize) |
|
413
|
|
|
ax = sns.distplot(col_data, bins=bins, hist=hist, rug=True, kde_kws=kde_kws, |
|
414
|
|
|
rug_kws=rug_kws, hist_kws={'alpha': 0.5, 'histtype': 'step'}) |
|
415
|
|
|
|
|
416
|
|
|
# Vertical lines and fill |
|
417
|
|
|
x, y = ax.lines[0].get_xydata().T |
|
418
|
|
|
ax.fill_between(x, y, |
|
419
|
|
|
where=( |
|
420
|
|
|
(x >= np.quantile(col_data, fill_range[0])) & |
|
421
|
|
|
(x <= np.quantile(col_data, fill_range[1]))), |
|
422
|
|
|
label=f'{fill_range[0]*100:.1f}% - {fill_range[1]*100:.1f}%', |
|
423
|
|
|
**fill_kws) |
|
424
|
|
|
|
|
425
|
|
|
mean = np.mean(col_data) |
|
426
|
|
|
std = scipy.stats.tstd(col_data) |
|
427
|
|
|
ax.vlines(x=mean, |
|
428
|
|
|
ymin=0, |
|
429
|
|
|
ymax=np.interp(mean, x, y), |
|
430
|
|
|
ls='dotted', color=mean_color, lw=2, label='mean') |
|
431
|
|
|
ax.vlines(x=np.median(col_data), |
|
432
|
|
|
ymin=0, |
|
433
|
|
|
ymax=np.interp(np.median(col_data), x, y), |
|
434
|
|
|
ls=':', color='.3', label='median') |
|
435
|
|
|
ax.vlines(x=[mean-std, mean+std], |
|
436
|
|
|
ymin=0, |
|
437
|
|
|
ymax=[np.interp(mean-std, x, y), np.interp(mean+std, x, y)], ls=':', color='.5', |
|
438
|
|
|
label='\u03BC \u00B1 \u03C3') |
|
439
|
|
|
|
|
440
|
|
|
ax.set_ylim(0,) |
|
441
|
|
|
ax.set_xlim(ax.get_xlim()[0]*1.15, ax.get_xlim()[1]*1.15) |
|
442
|
|
|
|
|
443
|
|
|
# Annotations and legend |
|
444
|
|
|
ax.text(0.01, 0.85, f'Mean: {np.round(mean,2)}', |
|
445
|
|
|
fontdict=font_kws, transform=ax.transAxes) |
|
446
|
|
|
ax.text(0.01, 0.7, f'Std. dev: {np.round(std,2)}', |
|
447
|
|
|
fontdict=font_kws, transform=ax.transAxes) |
|
448
|
|
|
ax.text(0.01, 0.55, f'Skew: {np.round(scipy.stats.skew(col_data),2)}', |
|
449
|
|
|
fontdict=font_kws, transform=ax.transAxes) |
|
450
|
|
|
ax.text(0.01, 0.4, f'Kurtosis: {np.round(scipy.stats.kurtosis(col_data),2)}', # Excess Kurtosis |
|
451
|
|
|
fontdict=font_kws, transform=ax.transAxes) |
|
452
|
|
|
ax.text(0.01, 0.25, f'Count: {np.round(len(col_data))}', |
|
453
|
|
|
fontdict=font_kws, transform=ax.transAxes) |
|
454
|
|
|
ax.legend(loc='upper right') |
|
455
|
|
|
|
|
456
|
|
|
return ax |
|
457
|
|
|
|
|
458
|
|
|
|
|
459
|
|
|
# Missing value plot |
|
460
|
|
|
def missingval_plot(data, cmap='PuBuGn', figsize=(12, 12), sort=False, spine_color='#EEEEEE'): |
|
461
|
|
|
''' |
|
462
|
|
|
Two-dimensional visualization of the missing values in a dataset. |
|
463
|
|
|
|
|
464
|
|
|
Parameters |
|
465
|
|
|
---------- |
|
466
|
|
|
data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
|
467
|
|
|
information is used to label the plots. |
|
468
|
|
|
|
|
469
|
|
|
cmap: colormap, default 'PuBuGn' |
|
470
|
|
|
Any valid colormap can be used. E.g. 'Greys', 'RdPu'. More information can be found in the matplotlib \ |
|
471
|
|
|
documentation. |
|
472
|
|
|
|
|
473
|
|
|
figsize: tuple, default (20, 12) |
|
474
|
|
|
Use to control the figure size. |
|
475
|
|
|
|
|
476
|
|
|
sort: bool, default False |
|
477
|
|
|
Sort columns based on missing values in descending order and drop columns without any missing values |
|
478
|
|
|
|
|
479
|
|
|
spine_color: color, default '#EEEEEE' |
|
480
|
|
|
Set to 'None' to hide the spines on all plots or use any valid matplotlib color argument. |
|
481
|
|
|
|
|
482
|
|
|
Returns |
|
483
|
|
|
------- |
|
484
|
|
|
gs: Figure with array of Axes objects. |
|
485
|
|
|
|
|
486
|
|
|
''' |
|
487
|
|
|
|
|
488
|
|
|
# Validate Inputs |
|
489
|
|
|
_validate_input_bool(sort, 'sort') |
|
490
|
|
|
|
|
491
|
|
|
data = pd.DataFrame(data) |
|
492
|
|
|
|
|
493
|
|
|
if sort: |
|
494
|
|
|
mv_cols_sorted = data.isna().sum(axis=0).sort_values(ascending=False) |
|
495
|
|
|
final_cols = mv_cols_sorted.drop(mv_cols_sorted[mv_cols_sorted.values == 0].keys().tolist()).keys().tolist() |
|
496
|
|
|
data = data[final_cols] |
|
497
|
|
|
print('Displaying only columns with missing values.') |
|
498
|
|
|
|
|
499
|
|
|
# Identify missing values |
|
500
|
|
|
mv_total, mv_rows, mv_cols, _, mv_cols_ratio = _missing_vals(data).values() |
|
501
|
|
|
total_datapoints = data.shape[0]*data.shape[1] |
|
502
|
|
|
|
|
503
|
|
|
if mv_total == 0: |
|
504
|
|
|
print('No missing values found in the dataset.') |
|
505
|
|
|
else: |
|
506
|
|
|
# Create figure and axes |
|
507
|
|
|
fig = plt.figure(figsize=figsize) |
|
508
|
|
|
gs = fig.add_gridspec(nrows=6, ncols=6, left=0.05, wspace=0.05) |
|
509
|
|
|
ax1 = fig.add_subplot(gs[:1, :5]) |
|
510
|
|
|
ax2 = fig.add_subplot(gs[1:, :5]) |
|
511
|
|
|
ax3 = fig.add_subplot(gs[:1, 5:]) |
|
512
|
|
|
ax4 = fig.add_subplot(gs[1:, 5:]) |
|
513
|
|
|
|
|
514
|
|
|
# ax1 - Barplot |
|
515
|
|
|
colors = plt.get_cmap(cmap)(mv_cols / np.max(mv_cols)) # color bars by height |
|
516
|
|
|
ax1.bar(range(len(mv_cols)), np.round((mv_cols_ratio)*100, 2), color=colors) |
|
517
|
|
|
ax1.get_xaxis().set_visible(False) |
|
518
|
|
|
ax1.set(frame_on=False, xlim=(-.5, len(mv_cols)-0.5)) |
|
519
|
|
|
ax1.set_ylim(0, np.max(mv_cols_ratio)*100) |
|
520
|
|
|
ax1.grid(linestyle=':', linewidth=1) |
|
521
|
|
|
ax1.yaxis.set_major_formatter(ticker.PercentFormatter(decimals=0)) |
|
522
|
|
|
ax1.tick_params(axis='y', colors='#111111', length=1) |
|
523
|
|
|
|
|
524
|
|
|
# annotate values on top of the bars |
|
525
|
|
|
for rect, label in zip(ax1.patches, mv_cols): |
|
526
|
|
|
height = rect.get_height() |
|
527
|
|
|
ax1.text(.1 + rect.get_x() + rect.get_width() / 2, height+0.5, label, |
|
528
|
|
|
ha='center', |
|
529
|
|
|
va='bottom', |
|
530
|
|
|
rotation='90', |
|
531
|
|
|
alpha=0.5, |
|
532
|
|
|
fontsize='small') |
|
533
|
|
|
|
|
534
|
|
|
ax1.set_frame_on(True) |
|
535
|
|
|
for _, spine in ax1.spines.items(): |
|
536
|
|
|
spine.set_visible(True) |
|
537
|
|
|
spine.set_color(spine_color) |
|
538
|
|
|
ax1.spines['top'].set_color(None) |
|
539
|
|
|
|
|
540
|
|
|
# ax2 - Heatmap |
|
541
|
|
|
sns.heatmap(data.isna(), cbar=False, cmap='binary', ax=ax2) |
|
542
|
|
|
ax2.set_yticks(np.round(ax2.get_yticks()[0::5], -1)) |
|
543
|
|
|
ax2.set_yticklabels(ax2.get_yticks()) |
|
544
|
|
|
ax2.set_xticklabels( |
|
545
|
|
|
ax2.get_xticklabels(), |
|
546
|
|
|
horizontalalignment='center', |
|
547
|
|
|
fontweight='light', |
|
548
|
|
|
fontsize='medium') |
|
549
|
|
|
ax2.tick_params(length=1, colors='#111111') |
|
550
|
|
|
for _, spine in ax2.spines.items(): |
|
551
|
|
|
spine.set_visible(True) |
|
552
|
|
|
spine.set_color(spine_color) |
|
553
|
|
|
|
|
554
|
|
|
# ax3 - Summary |
|
555
|
|
|
fontax3 = {'color': '#111111', |
|
556
|
|
|
'weight': 'normal', |
|
557
|
|
|
'size': 12, |
|
558
|
|
|
} |
|
559
|
|
|
ax3.get_xaxis().set_visible(False) |
|
560
|
|
|
ax3.get_yaxis().set_visible(False) |
|
561
|
|
|
ax3.set(frame_on=False) |
|
562
|
|
|
|
|
563
|
|
|
ax3.text(0.1, 0.9, f"Total: {np.round(total_datapoints/1000,1)}K", |
|
564
|
|
|
transform=ax3.transAxes, |
|
565
|
|
|
fontdict=fontax3) |
|
566
|
|
|
ax3.text(0.1, 0.7, f"Missing: {np.round(mv_total/1000,1)}K", |
|
567
|
|
|
transform=ax3.transAxes, |
|
568
|
|
|
fontdict=fontax3) |
|
569
|
|
|
ax3.text(0.1, 0.5, f"Relative: {np.round(mv_total/total_datapoints*100,1)}%", |
|
570
|
|
|
transform=ax3.transAxes, |
|
571
|
|
|
fontdict=fontax3) |
|
572
|
|
|
ax3.text(0.1, 0.3, f"Max-col: {np.round(mv_cols.max()/data.shape[0]*100)}%", |
|
573
|
|
|
transform=ax3.transAxes, |
|
574
|
|
|
fontdict=fontax3) |
|
575
|
|
|
ax3.text(0.1, 0.1, f"Max-row: {np.round(mv_rows.max()/data.shape[1]*100)}%", |
|
576
|
|
|
transform=ax3.transAxes, |
|
577
|
|
|
fontdict=fontax3) |
|
578
|
|
|
|
|
579
|
|
|
# ax4 - Scatter plot |
|
580
|
|
|
ax4.get_yaxis().set_visible(False) |
|
581
|
|
|
for _, spine in ax4.spines.items(): |
|
582
|
|
|
spine.set_color(spine_color) |
|
583
|
|
|
ax4.tick_params(axis='x', colors='#111111', length=1) |
|
584
|
|
|
|
|
585
|
|
|
ax4.scatter(mv_rows, range(len(mv_rows)), s=mv_rows, c=mv_rows, cmap=cmap, marker=".", vmin=1) |
|
586
|
|
|
ax4.set_ylim((0, len(mv_rows))[::-1]) # limit and invert y-axis |
|
587
|
|
|
ax4.set_xlim(0, max(mv_rows)+0.5) |
|
588
|
|
|
ax4.grid(linestyle=':', linewidth=1) |
|
589
|
|
|
|
|
590
|
|
|
gs.figure.suptitle('Missing value plot', x=0.45, y=0.94, fontsize=18, color='#111111') |
|
591
|
|
|
|
|
592
|
|
|
return gs |
|
593
|
|
|
|
akanz1 /
klib
