adviz - npm Package Compare versions

Comparing version 0.0.24 to 0.0.25

adviz.egg-info/PKG-INFO

 Metadata-Version: 2.4
 Name: adviz
-Version: 0.0.24
+Version: 0.0.25
 Summary: advertools visualizations
@@ -5,0 +5,0 @@ Home-page: https://github.com/eliasdabbas/adviz

adviz/__init__.py

@@ -1,2 +0,2 @@
-__version__ = "0.0.24"
+__version__ = "0.0.25"
 
@@ -3,0 +3,0 @@ from dash_bootstrap_templates import load_figure_template

adviz/racing_chart.py

@@ -11,3 +11,3 @@ """Get the top `n` values per period, and create a "racing" bar chart across periods."""
 import plotly.express as px
-from plotly.subplots import make_subplots
+
 import adviz
@@ -20,7 +20,10 @@
     title="Racing Chart",
+    subtitle=None,
     frame_duration=500,
-    theme='none',
+    template="none",
     width=None,
     height=600,
-    font_size=12):
+    font_size=12,
+    **kwargs,
+):
     """
@@ -32,19 +35,21 @@ Create a racing bar chart showing the top `n` values for each period.
     df : pandas.DataFrame
-      A DataFrame containing three columns for entity, metric, and period. The
-      names can be anything bu they have to be in this specific order.
+        A DataFrame containing three columns for entity, metric, and period. The
+        names can be anything bu they have to be in this specific order.
     n : int
-      The number of top items to display for each period.
+        The number of top items to display for each period.
     title : str
-      The title of the chart.
+        The title of the chart.
+    subtitle : str
+        The subtitle of the chart.
     frame_duration : int
-      The duration of each frame during animation, before transitioning to the
-      following frame, in milliseconds.
-    theme : str
-      The theme of the chart. Any of the following values can be used:
+        The duration of each frame during animation, before transitioning to the
+        following frame, in milliseconds.
+    template : str
+        The template of the chart.
     width : int
-      The width of the chart in pixels.
+        The width of the chart in pixels.
     height : int
-      The height of the chart in pixels.
+        The height of the chart in pixels.
     font_size : int
-      The size of the fonts in the chart.
+        The size of the fonts in the chart.
 
@@ -57,7 +62,7 @@ Returns
     # df[f'{df.columns[1]} %'] = df.groupby(df.columns[2])[df.columns[1]].transform(lambda x: x / x.sum())
-    top_n_df = (df
-        .sort_values([df.columns[2], df.columns[1]],
-                     ascending=[True, False])
+    top_n_df = (
+        df.sort_values([df.columns[2], df.columns[1]], ascending=[True, False])
         .groupby(df.columns[2])
-        .head(n))
+        .head(n)
+    )
     fig = px.bar(
@@ -73,15 +78,20 @@ top_n_df,
         title=title,
-        template=theme,
-        range_x=[0,top_n_df[df.columns[1]].max() * 1.05],
+        template=template,
+        range_x=[0, top_n_df[df.columns[1]].max() * 1.05],
+        **kwargs,
     )
-    fig.layout.yaxis.autorange = 'reversed'
+    fig.layout.yaxis.autorange = "reversed"
     fig.layout.font.size = font_size
-    fig.layout.sliders[0].currentvalue.xanchor = 'right'
-    fig.layout.sliders[0].currentvalue.prefix = fig.layout.sliders[0].currentvalue.prefix.replace('=', ': ')
+    fig.layout.sliders[0].currentvalue.xanchor = "right"
+    fig.layout.sliders[0].currentvalue.prefix = fig.layout.sliders[
+        0
+    ].currentvalue.prefix.replace("=", ": ")
     fig.layout.sliders[0].currentvalue.font.size = font_size * 1.5
-    fig.layout.sliders[0].bgcolor = '#D5D5D5'
+    fig.layout.sliders[0].bgcolor = "#D5D5D5"
     fig.layout.sliders[0].borderwidth = 0
-    fig.layout.updatemenus[0]['buttons'][0]['args'][1]['frame']['duration'] = frame_duration
+    fig.layout.updatemenus[0]["buttons"][0]["args"][1]["frame"]["duration"] = (
+        frame_duration
+    )
     for step, total in zip(fig.layout.sliders[0].steps, period_totals.iloc[:, 1]):
-        step['label'] = f"{step['label']}<br>Total: {total:,}"
+        step["label"] = f"{step['label']}<br>Total: {total:,}"
     return fig

adviz/serp_heatmap.py

@@ -11,2 +11,3 @@ """Using multiple SERP datasets, create a heatmap for each domain, showing how often it appeared on each position of the SERPs, with some summary statistics."""
 import pandas as pd
+
 pd.options.display.max_columns = None
@@ -16,3 +17,3 @@
 def _concat(text):
-    return '<br>'.join(sorted(text))
+    return "<br>".join(sorted(text))
 
@@ -25,4 +26,5 @@ # %% ../nbs/06_serp_heatmap.ipynb 7
     width=None,
-    title='SERP Heatmap',
-    theme='none'):
+    title="SERP Heatmap",
+    template="none",
+):
     """Create a heatmap for visualizing domain positions on SERPs.
@@ -44,4 +46,4 @@
       The title of the chart.
-    theme : str
-      The theme to apply to the chart.
+    template : str
+      The template to apply to the chart.
 
@@ -52,81 +54,96 @@ Returns
     """
-    df = serp_df[['searchTerms', 'rank', 'displayLink']]
-    top_domains = df['displayLink'].value_counts()[:num_domains].index.tolist()
-    top_df = df[df['displayLink'].isin(top_domains) & df['displayLink'].ne('')]
-    top_df_counts_means = (
-        top_df
-        .groupby('displayLink', as_index=False)
-        .agg({'rank': ['count', 'mean']}))
-    top_df_counts_means.columns = ['displayLink', 'rank_count', 'avg_pos']
-    top_df = (pd.merge(top_df, top_df_counts_means)
-              .sort_values(['rank_count', 'avg_pos'],
-                           ascending=[False, True]))
-    hovertxt_df = (top_df
-                   .groupby(['rank', 'displayLink'], as_index=False)
-                   .agg({'searchTerms': _concat}))
-    rank_counts = (top_df
-                   .groupby(['displayLink', 'rank'])
-                   .agg({'rank': ['count']})
-                   .reset_index())
-    rank_counts.columns = ['displayLink', 'rank', 'count']
+    df = serp_df[["searchTerms", "rank", "displayLink"]]
+    top_domains = df["displayLink"].value_counts()[:num_domains].index.tolist()
+    top_df = df[df["displayLink"].isin(top_domains) & df["displayLink"].ne("")]
+    top_df_counts_means = top_df.groupby("displayLink", as_index=False).agg(
+        {"rank": ["count", "mean"]}
+    )
+    top_df_counts_means.columns = ["displayLink", "rank_count", "avg_pos"]
+    top_df = pd.merge(top_df, top_df_counts_means).sort_values(
+        ["rank_count", "avg_pos"], ascending=[False, True]
+    )
+    hovertxt_df = top_df.groupby(["rank", "displayLink"], as_index=False).agg(
+        {"searchTerms": _concat}
+    )
+    rank_counts = (
+        top_df.groupby(["displayLink", "rank"]).agg({"rank": ["count"]}).reset_index()
+    )
+    rank_counts.columns = ["displayLink", "rank", "count"]
     summary = (
-        df
-        .groupby(['displayLink'], as_index=False)
-        .agg({'rank': ['count', 'mean']})
-        .sort_values(('rank', 'count'), ascending=False)
-        .assign(coverage=lambda df: (df[('rank', 'count')]
-                                     .div(df[('rank', 'count')]
-                                     .sum()))))
-    summary.columns = ['displayLink', 'count', 'avg_pos', 'coverage']
-    summary['displayLink'] = summary['displayLink'].str.replace('www.', '', regex=True)
-    summary['avg_pos'] = summary['avg_pos'].round(1)
-    summary['coverage'] = (summary['coverage'].mul(100)
-                           .round(1).astype(str).add('%'))
-    num_queries = df['searchTerms'].nunique()
+        df.groupby(["displayLink"], as_index=False)
+        .agg({"rank": ["count", "mean"]})
+        .sort_values(("rank", "count"), ascending=False)
+        .assign(
+            coverage=lambda df: (df[("rank", "count")].div(df[("rank", "count")].sum()))
+        )
+    )
+    summary.columns = ["displayLink", "count", "avg_pos", "coverage"]
+    summary["displayLink"] = summary["displayLink"].str.replace("www.", "", regex=True)
+    summary["avg_pos"] = summary["avg_pos"].round(1)
+    summary["coverage"] = summary["coverage"].mul(100).round(1).astype(str).add("%")
+    num_queries = df["searchTerms"].nunique()
     fig = go.Figure()
     fig.add_scatter(
-        x=top_df['displayLink'].str.replace('www\.', '', regex=True),
-        y=top_df['rank'],
-        mode='markers',
-        hovertext=top_df['searchTerms'],
-        marker={'size': 30, 'opacity': 1/rank_counts['count'].max()})
+        x=top_df["displayLink"].str.replace(r"www\.", "", regex=True),
+        y=top_df["rank"],
+        mode="markers",
+        hovertext=top_df["searchTerms"],
+        marker={"size": 30, "opacity": 1 / rank_counts["count"].max()},
+    )
     fig.add_scatter(
-        x=rank_counts['displayLink'].str.replace('www\.', '', regex=True),
-        y=rank_counts['rank'], mode='text',
-        text=rank_counts['count'])
-    for domain in rank_counts['displayLink'].unique():
-        rank_counts_subset = rank_counts[rank_counts['displayLink'] == domain]
+        x=rank_counts["displayLink"].str.replace(r"www\.", "", regex=True),
+        y=rank_counts["rank"],
+        mode="text",
+        text=rank_counts["count"],
+    )
+    for domain in rank_counts["displayLink"].unique():
+        rank_counts_subset = rank_counts[rank_counts["displayLink"] == domain]
         fig.add_scatter(
-            x=[domain.replace('www.', '')],
+            x=[domain.replace("www.", "")],
             y=[0],
-            mode='text',
-            marker={'size': 50},
-            text=str(rank_counts_subset['count'].sum()))
+            mode="text",
+            marker={"size": 50},
+            text=str(rank_counts_subset["count"].sum()),
+        )
         fig.add_scatter(
-            x=[domain.replace('www.', '')],
+            x=[domain.replace("www.", "")],
             y=[-1],
-            mode='text',
-            text=format(rank_counts_subset['count'].sum() / num_queries, '.1%'))
+            mode="text",
+            text=format(rank_counts_subset["count"].sum() / num_queries, ".1%"),
+        )
         fig.add_scatter(
-            x=[domain.replace('www.', '')],
+            x=[domain.replace("www.", "")],
             y=[-2],
-            mode='text',
-            marker={'size': 50},
-            text=str(round(rank_counts_subset['rank']
-                           .mul(rank_counts_subset['count'])
-                           .sum() / rank_counts_subset['count']
-                           .sum(), 1)))
+            mode="text",
+            marker={"size": 50},
+            text=str(
+                round(
+                    rank_counts_subset["rank"].mul(rank_counts_subset["count"]).sum()
+                    / rank_counts_subset["count"].sum(),
+                    1,
+                )
+            ),
+        )
     fig.add_scatter(
-        x=hovertxt_df['displayLink'].str.replace('^www\.', '', regex=True),
-        y=hovertxt_df['rank'],
-        name='',
-        hovertemplate='<b>%{x}</b><br>%{hovertext}',
-        hoverlabel={'bgcolor': '#efefef'},
-        hovertext=hovertxt_df['searchTerms'],
-        mode='markers', marker={'opacity': 0, 'size': 30})
-    minrank, maxrank = int(min(top_df['rank'].unique())), int(max(top_df['rank'].unique()))
-    fig.layout.yaxis.tickvals = [-2, -1, 0] + list(range(minrank, maxrank+1))
-    fig.layout.yaxis.ticktext = ['Avg. Pos.', 'Coverage', 'Total<br>appearances'] + list(range(minrank, maxrank+1))
+        x=hovertxt_df["displayLink"].str.replace(r"^www\.", "", regex=True),
+        y=hovertxt_df["rank"],
+        name="",
+        hovertemplate="<b>%{x}</b><br>%{hovertext}",
+        hoverlabel={"bgcolor": "#efefef"},
+        hovertext=hovertxt_df["searchTerms"],
+        mode="markers",
+        marker={"opacity": 0, "size": 30},
+    )
+    minrank, maxrank = (
+        int(min(top_df["rank"].unique())),
+        int(max(top_df["rank"].unique())),
+    )
+    fig.layout.yaxis.tickvals = [-2, -1, 0] + list(range(minrank, maxrank + 1))
+    fig.layout.yaxis.ticktext = [
+        "Avg. Pos.",
+        "Coverage",
+        "Total<br>appearances",
+    ] + list(range(minrank, maxrank + 1))
     fig.layout.height = max([600, 100 + ((maxrank - minrank) * 50)])
-    fig.layout.yaxis.title = 'SERP Rank<br>(number of appearances)'
+    fig.layout.yaxis.title = "SERP Rank<br>(number of appearances)"
     fig.layout.showlegend = False
@@ -136,12 +153,12 @@ fig.layout.margin.r = 2
     fig.layout.margin.pad = 0
-    fig.layout.yaxis.autorange = 'reversed'
+    fig.layout.yaxis.autorange = "reversed"
     fig.layout.yaxis.zeroline = False
-    fig.layout.template = theme
+    fig.layout.template = template
     fig.layout.title = title
     fig.layout.height = height
     fig.layout.width = width
-    fig.layout.xaxis.showgrid  = False
-    fig.layout.yaxis.ticks = 'inside'
-    fig.layout.xaxis.ticks = 'inside'
-    fig.layout.yaxis.griddash = 'dot'
+    fig.layout.xaxis.showgrid = False
+    fig.layout.yaxis.ticks = "inside"
+    fig.layout.xaxis.ticks = "inside"
+    fig.layout.yaxis.griddash = "dot"
     return fig

adviz/status_codes.py

@@ -22,5 +22,5 @@ """Create a treemap for visualizing status code on two levels: the status category (100, 200, 300, 400, 500) and the status codes' actual values (200, 301, 404, etc.)"""
     width=None,
-    theme="none",
+    template="none",
     title="Status Codes",
-    export_to_html=None,
+    subtitle=None,
 ):
@@ -38,11 +38,11 @@ """Create a treemap to visualize a list of status codes.
         The desired width of the figure in pixels
-    theme : str
-        Name of theme to use for the chart. Available themes:
+    template : str
+        Name of template to use for the chart. Available themes:
             ggplot2, seaborn, simple_white, plotly, plotly_white, plotly_dark,
             presentation, xgridoff, ygridoff, gridon, none
-    title: str
+    title : str
         The title of the figure. You can use/include the following HTML tags in
         the title: `<a>`, `<b>`, `<br>`, `<i>`, `<sub>`, `<sup>`
-    export_to_html: str, optional
-        The path to the HTML file if you want to save it as a shareable file
+    subtitle : str
+        The subtitle of the figure, by default 70% of the font size of the title
 
@@ -68,3 +68,4 @@ Returns
         title=title,
-        template=theme,
+        subtitle=subtitle,
+        template=template,
     )
@@ -75,4 +76,2 @@ status_fig.data[0].marker.line.width = 0.01
     status_fig.data[0]["hovertemplate"] = _texttemplate
-    if export_to_html:
-        status_fig.write_html(export_to_html)
     return status_fig

adviz/url_structure.py

@@ -25,5 +25,6 @@ """Visualizing a website's URL structure with a treemap"""
     width=None,
-    theme="none",
+    template="none",
     domain="example.com",
     title="URL Structure",
+    subtitle=None,
 ):
@@ -44,12 +45,12 @@ """
         The width of the chart in pixels.
-    theme : str
-        Name of theme to use for the chart. Available themes:
-            ggplot2, seaborn, simple_white, plotly, plotly_white, plotly_dark,
-            presentation, xgridoff, ygridoff, gridon, none.
+    template : str
+        Name of template to use for the chart.
     domain : str
         The main domain of the URL list. This will be displayed at the top
         panel in the treemap to display values like a breadcrumb.
-    title: str
+    title : str
         The title of the figure. You can use/include the following HTML tags in
         the title: `<a>`, `<b>`, `<br>`, `<i>`, `<sub>`, `<sup>`
+    subtitle : str
+        The subtitle of the figure.
 
@@ -90,3 +91,4 @@ Returns
         title=title,
-        template=theme,
+        subtitle=subtitle,
+        template=template,
         values="count",
@@ -93,0 +95,0 @@ )

adviz/value_counts.py

@@ -20,6 +20,7 @@ """Count repeated values in a `list`, `pandas.Series`, or `pandas.DataFrame`. This function adds to the pandas `value_counts` method in a few ways. It counts values absolutely, and as a percentage. It provides cumulative counts and cumulative percentages. It also lumps all values beyond the `show_top` values under "Others:". You alsoe get the values colored with the sequential color scale of your choice."""
     title=None,
+    subtitle=None,
     style=True,
     width=900,
     height=650,
-    colorscale='cividis',
+    colorscale="cividis",
 ):
@@ -41,2 +42,4 @@ """
         The title of the chart.
+    subtitle : str
+        The subtitle of the chart.
     style : bool
@@ -63,29 +66,35 @@ Whether or not to style the resulting table with a heatmap.
         others_df = pd.DataFrame(
-            [['Others:'] + ['' for i in range(len(val_counts.columns)-2)] + [val_counts[show_top:]['count'].sum()]],
-            columns=val_counts.columns)
-        val_counts = pd.concat([
-            val_counts[:show_top],
-            others_df
-        ])
+            [
+                ["Others:"]
+                + ["" for i in range(len(val_counts.columns) - 2)]
+                + [val_counts[show_top:]["count"].sum()]
+            ],
+            columns=val_counts.columns,
+        )
+        val_counts = pd.concat([val_counts[:show_top], others_df])
         if sort_others:
-            val_counts = val_counts.sort_values(by=['count'], ascending=False)
+            val_counts = val_counts.sort_values(by=["count"], ascending=False)
 
-    count_df = (val_counts
-                .assign(
-                    cum_count=lambda df: df['count'].cumsum(),
-                    perc=lambda df: df['count'].div(df['count'].sum()),
-                    cum_perc=lambda df: df['perc'].cumsum())
-                )
+    count_df = val_counts.assign(
+        cum_count=lambda df: df["count"].cumsum(),
+        perc=lambda df: df["count"].div(df["count"].sum()),
+        cum_perc=lambda df: df["perc"].cumsum(),
+    )
     if not style:
         return count_df
 
-    count_df.insert(0, 'rank', range(1, len(count_df)+1))
+    count_df.insert(0, "rank", range(1, len(count_df) + 1))
     num_columns = count_df.shape[1]
-    count_df = count_df.rename(columns={'cum_count': 'cum. count', 'perc': '%', 'cum_perc': 'cum. %'})
+    count_df = count_df.rename(
+        columns={"cum_count": "cum. count", "perc": "%", "cum_perc": "cum. %"}
+    )
     subplot_titles = count_df.columns
     fig = make_subplots(
-        cols=num_columns, rows=1,
-        column_widths=[0.05] + [0.65/(num_dims) for d in range(num_dims)] + [.15, .15, .15, .15],
+        cols=num_columns,
+        rows=1,
+        column_widths=[0.05]
+        + [0.65 / (num_dims) for d in range(num_dims)]
+        + [0.15, 0.15, 0.15, 0.15],
         horizontal_spacing=0,
-        subplot_titles=subplot_titles
+        subplot_titles=subplot_titles,
     )
@@ -95,17 +104,27 @@ for i in range(count_df.shape[1]):
         fig.add_heatmap(
-            z=[[100] for i in range(len(tempdf))] if i in range(num_dims+1) else tempdf,
-            col=i+1,
+            z=[[100] for i in range(len(tempdf))]
+            if i in range(num_dims + 1)
+            else tempdf,
+            col=i + 1,
             row=1,
-            hoverinfo='skip',
+            hoverinfo="text" if i == 1 else "skip",
             showscale=False,
             name=subplot_titles[i],
-            colorscale='RdBu' if i in range(num_dims+1) else colorscale,
-            texttemplate="%{text}" if i in range(num_dims+1) else "%{text:,.0f}" if subplot_titles[i].endswith('count') else "%{text:.1%}",
-            textfont={'size': 16},
-            text=tempdf)
+            colorscale="RdBu" if i in range(num_dims + 1) else colorscale,
+            texttemplate="%{text}"
+            if i in range(num_dims + 1)
+            else "%{text:,.0f}"
+            if subplot_titles[i].endswith("count")
+            else "%{text:.1%}",
+            textfont={"size": 16},
+            text=tempdf,
+        )
     fig.layout.width = width
     fig.layout.height = height
     fig.update_xaxes(showticklabels=False, showgrid=False, zeroline=False)
-    fig.update_yaxes(showticklabels=False, showgrid=False, zeroline=False, autorange='reversed')
+    fig.update_yaxes(
+        showticklabels=False, showgrid=False, zeroline=False, autorange="reversed"
+    )
     fig.layout.title = title
+    fig.layout.title.subtitle.text = subtitle
     return fig

PKG-INFO

 Metadata-Version: 2.4
 Name: adviz
-Version: 0.0.24
+Version: 0.0.25
 Summary: advertools visualizations
@@ -5,0 +5,0 @@ Home-page: https://github.com/eliasdabbas/adviz

settings.ini

 [DEFAULT]
 repo = adviz
 lib_name = adviz
-version = 0.0.24
+version = 0.0.25
 min_python = 3.7
@@ -6,0 +6,0 @@ license = apache2

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Improved metrics

Total package byte prevSize

85404

0.25%

Lines of code

1927

2.39%