Fix pandas deprecation warnings

hailiangliu89 · Aug 23, 2018 · ca2198b · ca2198b
1 parent b310303
commit ca2198b
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Showing 5 changed files with 17 additions and 16 deletions.
diff --git a/pypsa/components.py b/pypsa/components.py
@@ -746,15 +746,15 @@ def __getitem__(self, key):
     #presence of links without s_nom_extendable
     def branches(self):
         return pd.concat((self.df(c) for c in self.branch_components),
-                         keys=self.branch_components)
+                         keys=self.branch_components, sort=False)
 
     def passive_branches(self):
         return pd.concat((self.df(c) for c in self.passive_branch_components),
-                         keys=self.passive_branch_components)
+                         keys=self.passive_branch_components, sort=False)
 
     def controllable_branches(self):
         return pd.concat((self.df(c) for c in self.controllable_branch_components),
-                         keys=self.controllable_branch_components)
+                         keys=self.controllable_branch_components, sort=False)
 
     def determine_network_topology(self):
         """

diff --git a/pypsa/descriptors.py b/pypsa/descriptors.py
@@ -195,7 +195,7 @@ def get_switchable_as_dense(network, component, attr, snapshots=None, inds=None)
         pd.DataFrame(np.repeat([df.loc[fixed_i, attr].values], len(snapshots), axis=0),
                      index=snapshots, columns=fixed_i),
         pnl[attr].loc[snapshots, varying_i]
-    ], axis=1).reindex(columns=index))
+    ], axis=1, sort=False).reindex(columns=index))
 
 def get_switchable_as_iter(network, component, attr, snapshots, inds=None):
     """

diff --git a/pypsa/io.py b/pypsa/io.py
@@ -666,7 +666,7 @@ def import_components_from_dataframe(network, dataframe, cls_name):
     old_df = network.df(cls_name)
     new_df = dataframe.drop(non_static_attrs_in_df, axis=1)
     if not old_df.empty:
-        new_df = pd.concat((old_df, new_df))
+        new_df = pd.concat((old_df, new_df), sort=False)
 
     if not new_df.index.is_unique:
         logger.error("Error, new components for {} are not unique".format(cls_name))
@@ -712,14 +712,14 @@ def import_series_from_dataframe(network, dataframe, cls_name, attr):
     if len(diff) > 0:
         logger.warning("Components {} for attribute {} of {} are not in main components dataframe {}".format(diff,attr,cls_name,list_name))
 
-    diff = network.snapshots.difference(dataframe.index)
-    if len(diff):
-        logger.warning("Snapshots {} are missing from {} of {}".format(diff,attr,cls_name))
-
-
     attr_series = network.components[cls_name]["attrs"].loc[attr]
     columns = dataframe.columns
 
+    diff = network.snapshots.difference(dataframe.index)
+    if len(diff):
+        logger.warning("Snapshots {} are missing from {} of {}. Filling with default value '{}'".format(diff,attr,cls_name,attr_series["default"]))
+        dataframe = dataframe.reindex(network.snapshots, fill_value=attr_series["default"])
+
     if not attr_series.static:
         pnl[attr] = pnl[attr].reindex(columns=df.index|columns, fill_value=attr_series.default)
     else:

diff --git a/pypsa/networkclustering.py b/pypsa/networkclustering.py
@@ -81,7 +81,7 @@ def aggregategenerators(network, busmap, with_time=True, carriers=None):
 
     new_df = pd.concat([new_df,
                         network.generators.loc[~gens_agg_b]
-                        .assign(bus=lambda df: df.bus.map(busmap))], axis=0)
+                        .assign(bus=lambda df: df.bus.map(busmap))], axis=0, sort=False)
 
     new_pnl = dict()
     if with_time:
@@ -93,7 +93,7 @@ def aggregategenerators(network, busmap, with_time=True, carriers=None):
                     df_agg = df_agg.multiply(weighting.loc[df_agg.columns], axis=1)
                 pnl_df = df_agg.groupby(grouper, axis=1).sum()
                 pnl_df.columns = _flatten_multiindex(pnl_df.columns).rename("name")
-                new_pnl[attr] = pd.concat([df.loc[:, ~pnl_gens_agg_b], pnl_df], axis=1)
+                new_pnl[attr] = pd.concat([df.loc[:, ~pnl_gens_agg_b], pnl_df], axis=1, sort=False)
 
     return new_df, new_pnl
 
@@ -145,7 +145,7 @@ def aggregatelines(network, buses, interlines, line_length_factor=1.0):
     positive_order = interlines.bus0_s < interlines.bus1_s
     interlines_p = interlines[positive_order]
     interlines_n = interlines[~ positive_order].rename(columns={"bus0_s":"bus1_s", "bus1_s":"bus0_s"})
-    interlines_c = pd.concat((interlines_p,interlines_n))
+    interlines_c = pd.concat((interlines_p,interlines_n), sort=False)
 
     attrs = network.components["Line"]["attrs"]
     columns = set(attrs.index[attrs.static & attrs.status.str.startswith('Input')]).difference(('name', 'bus0', 'bus1'))
@@ -199,7 +199,7 @@ def normed(s):
 
     linemap_p = interlines_p.join(lines['name'], on=['bus0_s', 'bus1_s'])['name']
     linemap_n = interlines_n.join(lines['name'], on=['bus0_s', 'bus1_s'])['name']
-    linemap = pd.concat((linemap_p,linemap_n))
+    linemap = pd.concat((linemap_p,linemap_n), sort=False)
 
     return lines, linemap_p, linemap_n, linemap
 

diff --git a/pypsa/opf.py b/pypsa/opf.py
@@ -872,7 +872,7 @@ def define_passive_branch_constraints(network,snapshots):
 
 
     s_max_pu = pd.concat({c : get_switchable_as_dense(network, c, 's_max_pu', snapshots)
-                          for c in network.passive_branch_components}, axis=1)
+                          for c in network.passive_branch_components}, axis=1, sort=False)
 
     flow_upper = {(b[0],b[1],sn) : [[(1,network.model.passive_branch_p[b[0],b[1],sn])],
                                     "<=", s_max_pu.at[sn,b]*fixed_branches.at[b,"s_nom"]]
@@ -1204,7 +1204,8 @@ def get_shadows(constraint, multiind=True):
             pd.concat({c.name:
                        c.pnl.p.loc[snapshots].multiply(c.df.sign, axis=1)
                        .groupby(c.df.bus, axis=1).sum()
-                       for c in network.iterate_components(network.controllable_one_port_components)}) \
+                       for c in network.iterate_components(network.controllable_one_port_components)},
+                      sort=False) \
               .sum(level=1) \
               .reindex(columns=network.buses_t.p.columns, fill_value=0.)