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Update docstring
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datasist/visualizations.py

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@@ -556,7 +556,7 @@ def plot_confusion_matrix(y_true, y_pred, classes,
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def plot_auc(labels, predictions):
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'''
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Compute the false positive rate, true positive rate and threshold along with the AUC
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Computes and plot the false positive rate, true positive rate and threshold along with the AUC
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Parameters:
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--------------------
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@@ -592,6 +592,7 @@ def plot_auc(labels, predictions):
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def plot_scatter_shape(data = None, cols = None, shape_col = '', col_y = '', alpha = 0.2):
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'''
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Makes a scatter plot of data using shape_col as seperation.
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Parameter:
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data: Dataframe
@@ -633,6 +634,7 @@ def plot_scatter_shape(data = None, cols = None, shape_col = '', col_y = '', alp
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def autoviz(data):
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'''
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Automatically visualize a data set. If dataset is large, autoViz uses a statistically valid sample for plotting.
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Parameter:
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--------------------
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data: Dataframe

docs/visualizations.html

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@@ -619,6 +619,7 @@ <h1 class="title">Module <code>datasist.visualizations</code></h1>
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def plot_scatter_shape(data = None, cols = None, shape_col = &#39;&#39;, col_y = &#39;&#39;, alpha = 0.2):
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&#39;&#39;&#39;
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Makes a scatter plot of data using shape_col as seperation.
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Parameter:
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data: Dataframe
@@ -660,6 +661,7 @@ <h1 class="title">Module <code>datasist.visualizations</code></h1>
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def autoviz(data):
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&#39;&#39;&#39;
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Automatically visualize a data set. If dataset is large, autoViz uses a statistically valid sample for plotting.
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Parameter:
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data: Dataframe
@@ -697,6 +699,9 @@ <h2 class="section-title" id="header-functions">Functions</h2>
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<span>def <span class="ident">autoviz</span></span>(<span>data)</span>
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</code></dt>
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<dd>
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<section class="desc">
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<p> Automatically visualize a data set. If dataset is large, autoViz uses a statistically valid sample for plotting.
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</p>
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<section class="desc"><h2 id="parameter">Parameter:</h2>
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<pre><code>data: Dataframe
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The data to plot

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