Introduce orientation to bar plot (ResidentMario#94)

missingno/missingno.py

@@ -203,13 +203,13 @@ def matrix(df,
         return ax0
 
 
-def bar(df, figsize=(24, 10), fontsize=16, labels=None, log=False, color='dimgray', inline=False,
-        filter=None, n=0, p=0, sort=None, ax=None):
+def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray', inline=False,
+        filter=None, n=0, p=0, sort=None, ax=None, orientation=None):
     """
     A bar chart visualization of the nullity of the given DataFrame.
 
     :param df: The input DataFrame.
-    :param log: Whether or not to display a logorithmic plot. Defaults to False (linear).
+    :param log: Whether or not to display a logarithmic plot. Defaults to False (linear).
     :param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
     :param n: The cap on the number of columns to include in the filtered DataFrame.
     :param p: The cap on the percentage fill of the columns in the filtered DataFrame.
@@ -219,21 +219,36 @@ def bar(df, figsize=(24, 10), fontsize=16, labels=None, log=False, color='dimgra
     :param labels: Whether or not to display the column names. Would need to be turned off on particularly large
     displays. Defaults to True.
     :param color: The color of the filled columns. Default to the RGB multiple `(0.25, 0.25, 0.25)`.
+    :param orientation: The way the bar plot is oriented. Defaults to vertical if there are less than or equal to 50
+    columns and horizontal if there are more.
     :return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing.
     """
     df = nullity_filter(df, filter=filter, n=n, p=p)
     df = nullity_sort(df, sort=sort, axis='rows')
     nullity_counts = len(df) - df.isnull().sum()
 
+    if orientation is None:
+        if len(df.columns) > 50:
+            orientation = 'left'
+        else:
+            orientation = 'bottom'
+
     if ax is None:
         ax1 = plt.gca()
+        if figsize is None:
+            if len(df.columns) <= 50 or orientation == 'top' or orientation == 'bottom':
+                figsize = (25, 10)
+            else:
+                figsize = (25, (25 + len(df.columns) - 50) * 0.5)
     else:
         ax1 = ax
         figsize = None  # for behavioral consistency with other plot types, re-use the given size
 
-    (nullity_counts / len(df)).plot.bar(
-        figsize=figsize, fontsize=fontsize, log=log, color=color, ax=ax1
-    )
+    plot_args = {'figsize': figsize, 'fontsize': fontsize, 'log': log, 'color': color, 'ax': ax1}
+    if orientation == 'bottom':
+        (nullity_counts / len(df)).plot.bar(**plot_args)
+    else:
+        (nullity_counts / len(df)).plot.barh(**plot_args)                      
 
     axes = [ax1]
 
@@ -247,23 +262,61 @@ def bar(df, figsize=(24, 10), fontsize=16, labels=None, log=False, color='dimgra
         if not log:
             ax1.set_ylim([0, 1])
             ax2.set_yticks(ax1.get_yticks())
-            ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
         else:
             # For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
             # appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
             # is used, we have to make it match the `ax1` layout ourselves.
             ax2.set_yscale('log')
             ax2.set_ylim(ax1.get_ylim())
-            ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
+        ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
+
+        # Create the third axis, which displays columnar totals above the rest of the plot.
+        ax3 = ax1.twiny()
+        axes.append(ax3)
+        ax3.set_xticks(ax1.get_xticks())
+        ax3.set_xlim(ax1.get_xlim())
+        ax3.set_xticklabels(nullity_counts.values, fontsize=fontsize, rotation=45, ha='left')
     else:
-        ax1.set_xticks([])
-
-    # Create the third axis, which displays columnar totals above the rest of the plot.
-    ax3 = ax1.twiny()
-    axes.append(ax3)
-    ax3.set_xticks(ax1.get_xticks())
-    ax3.set_xlim(ax1.get_xlim())
-    ax3.set_xticklabels(nullity_counts.values, fontsize=fontsize, rotation=45, ha='left')
+        # Create the numerical ticks.
+        ax2 = ax1.twinx()
+
+        axes.append(ax2)
+        if not log:
+            # Width
+            ax1.set_xlim([0, 1])
+
+            # Bottom
+            ax2.set_xticks(ax1.get_xticks())
+            ax2.set_xticklabels([int(n*len(df)) for n in ax1.get_xticks()], fontsize=fontsize)
+
+            # Right
+            ax2.set_yticks(ax1.get_yticks())
+            ax2.set_yticklabels(nullity_counts.values, fontsize=fontsize, ha='left')
+        else:
+            # For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
+            # appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
+            # is used, we have to make it match the `ax1` layout ourselves.
+            ax1.set_xscale('log')
+            ax1.set_xlim(ax1.get_xlim())
+
+            # Bottom
+            ax2.set_xticks(ax1.get_xticks())
+            ax2.set_xticklabels([int(n*len(df)) for n in ax1.get_xticks()], fontsize=fontsize)
+
+            # Right
+            ax2.set_yticks(ax1.get_yticks())
+            ax2.set_yticklabels(nullity_counts.values, fontsize=fontsize, ha='left')
+
+        # Create the third axis, which displays columnar totals above the rest of the plot.
+        ax3 = ax1.twiny()
+
+        axes.append(ax3)
+        ax3.set_yticks(ax1.get_yticks())
+        if log:
+            ax3.set_xscale('log')
+            ax3.set_xlim(ax1.get_xlim())
+        ax3.set_ylim(ax1.get_ylim())
+
     ax3.grid(False)
 
     for ax in axes: