Merge pull request #109 from MarinManuel/master

- Fixed bug preventing non-string columns to be used
- Modified the way data is accessed from pivoted_plot_data to avoid problems with NaN in some lines (from PR #110

Author: Jacobluke-

dabest/_classes.py

@@ -127,18 +127,21 @@ def __init__(self, data, idx, x, y, paired, id_col, ci,
 
             else:
                 x1_level = data_in[x[0]].unique()    
+        elif experiment is not None:
+            experiment_label = data_in[experiment].unique()
+            x1_level = data_in[x[0]].unique() 
         self.__experiment_label = experiment_label
         self.__x1_level         = x1_level
 
 
-        # Check if idx is specified
-        if delta2 is False and not idx:
-            err = '`idx` is not a column in `data`. Please check.'
-            raise IndexError(err)
+        # # Check if idx is specified
+        # if delta2 is False and not idx:
+        #     err = '`idx` is not a column in `data`. Please check.'
+        #     raise IndexError(err)
 
 
         # create new x & idx and record the second variable if this is a valid 2x2 ANOVA case
-        if delta2 is True:
+        if idx is None and x is not None and y is not None:
             # add a new column which is a combination of experiment and the first variable
             new_col_name = experiment+x[0]
             while new_col_name in data_in.columns:
@@ -165,7 +168,7 @@ def __init__(self, data, idx, x, y, paired, id_col, ci,
 
 
         # Determine the kind of estimation plot we need to produce.
-        if all([isinstance(i, str) for i in idx]):
+        if all([isinstance(i, (str, int, float)) for i in idx]):
             # flatten out idx.
             all_plot_groups = pd.unique([t for t in idx]).tolist()
             if len(idx) > len(all_plot_groups):

dabest/plotter.py

@@ -400,35 +400,24 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
         # Plot the raw data as a slopegraph.
         # Pivot the long (melted) data.
             if color_col is None:
-                pivot_values = yvar
+                pivot_values = [yvar]
             else:
                 pivot_values = [yvar, color_col]
             pivoted_plot_data = pd.pivot(data=plot_data, index=dabest_obj.id_col,
-                                         columns=xvar, values=pivot_values)
+                                            columns=xvar, values=pivot_values)
             x_start = 0
             for ii, current_tuple in enumerate(temp_idx):
-                if len(temp_idx) > 1:
-                    # Select only the data for the current tuple.
-                    if color_col is None:
-                        current_pair = pivoted_plot_data.reindex(columns=current_tuple)
-                    else:
-                        current_pair = pivoted_plot_data[yvar].reindex(columns=current_tuple)
-                else:
-                    if color_col is None:
-                        current_pair = pivoted_plot_data
-                    else:
-                        current_pair = pivoted_plot_data[yvar]
+                current_pair = pivoted_plot_data.loc[:, pd.MultiIndex.from_product([pivot_values, current_tuple])].dropna()
                 grp_count = len(current_tuple)
                 # Iterate through the data for the current tuple.
                 for ID, observation in current_pair.iterrows():
                     x_points = [t for t in range(x_start, x_start + grp_count)]
-                    y_points = observation.tolist()
+                    y_points = observation[yvar].tolist()
 
                     if color_col is None:
                         slopegraph_kwargs['color'] = ytick_color
                     else:
-                        color_key = pivoted_plot_data[color_col,
-                                                      current_tuple[0]].loc[ID]
+                        color_key = observation[color_col][0]
                         if isinstance(color_key, str) == True:
                             slopegraph_kwargs['color'] = plot_palette_raw[color_key]
                             slopegraph_kwargs['label'] = color_key