move late imports to top

pandas/plotting/_matplotlib/boxplot.py

@@ -2,11 +2,14 @@
 import warnings
 
 from matplotlib import pyplot as plt
+from matplotlib.artist import setp
 import numpy as np
 
 from pandas.core.dtypes.generic import ABCSeries
 from pandas.core.dtypes.missing import remove_na_arraylike
 
+import pandas as pd
+
 from pandas.io.formats.printing import pprint_thing
 from pandas.plotting._matplotlib.core import LinePlot, MPLPlot
 from pandas.plotting._matplotlib.style import _get_standard_colors
@@ -105,16 +108,14 @@ def maybe_color_bp(self, bp):
             medians = self.color or self._medians_c
             caps = self.color or self._caps_c
 
-        from matplotlib.artist import setp
         setp(bp['boxes'], color=boxes, alpha=1)
         setp(bp['whiskers'], color=whiskers, alpha=1)
         setp(bp['medians'], color=medians, alpha=1)
         setp(bp['caps'], color=caps, alpha=1)
 
     def _make_plot(self):
         if self.subplots:
-            from pandas.core.series import Series
-            self._return_obj = Series()
+            self._return_obj = pd.Series()
 
             for i, (label, y) in enumerate(self._iter_data()):
                 ax = self._get_ax(i)
@@ -197,8 +198,7 @@ def _grouped_plot_by_column(plotf, data, columns=None, by=None,
         ax_values.append(re_plotf)
         ax.grid(grid)
 
-    from pandas.core.series import Series
-    result = Series(ax_values, index=columns)
+    result = pd.Series(ax_values, index=columns)
 
     # Return axes in multiplot case, maybe revisit later # 985
     if return_type is None:
@@ -230,7 +230,6 @@ def _get_colors():
 
     def maybe_color_bp(bp):
         if 'color' not in kwds:
-            from matplotlib.artist import setp
             setp(bp['boxes'], color=colors[0], alpha=1)
             setp(bp['whiskers'], color=colors[0], alpha=1)
             setp(bp['medians'], color=colors[2], alpha=1)
@@ -314,8 +313,7 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
                               figsize=figsize, layout=layout)
         axes = _flatten(axes)
 
-        from pandas.core.series import Series
-        ret = Series()
+        ret = pd.Series()
         for (key, group), ax in zip(grouped, axes):
             d = group.boxplot(ax=ax, column=column, fontsize=fontsize,
                               rot=rot, grid=grid, **kwds)
@@ -324,10 +322,9 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
         fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1,
                             right=0.9, wspace=0.2)
     else:
-        from pandas.core.reshape.concat import concat
         keys, frames = zip(*grouped)
         if grouped.axis == 0:
-            df = concat(frames, keys=keys, axis=1)
+            df = pd.concat(frames, keys=keys, axis=1)
         else:
             if len(frames) > 1:
                 df = frames[0].join(frames[1::])

pandas/plotting/_matplotlib/misc.py

@@ -1,4 +1,7 @@
-# being a bit too dynamic
+import random
+
+import matplotlib.lines as mlines
+import matplotlib.patches as patches
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -96,14 +99,12 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
 
 
 def _get_marker_compat(marker):
-    import matplotlib.lines as mlines
     if marker not in mlines.lineMarkers:
         return 'o'
     return marker
 
 
 def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
-    import matplotlib.patches as patches
 
     def normalize(series):
         a = min(series)
@@ -168,12 +169,11 @@ def normalize(series):
 
 def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
                    colormap=None, **kwds):
-    from math import sqrt, pi
 
     def function(amplitudes):
         def f(t):
             x1 = amplitudes[0]
-            result = x1 / sqrt(2.0)
+            result = x1 / np.sqrt(2.0)
 
             # Take the rest of the coefficients and resize them
             # appropriately. Take a copy of amplitudes as otherwise numpy
@@ -196,15 +196,15 @@ def f(t):
     class_col = frame[class_column]
     classes = frame[class_column].drop_duplicates()
     df = frame.drop(class_column, axis=1)
-    t = np.linspace(-pi, pi, samples)
+    t = np.linspace(-np.pi, np.pi, samples)
     used_legends = set()
 
     color_values = _get_standard_colors(num_colors=len(classes),
                                         colormap=colormap, color_type='random',
                                         color=color)
     colors = dict(zip(classes, color_values))
     if ax is None:
-        ax = plt.gca(xlim=(-pi, pi))
+        ax = plt.gca(xlim=(-np.pi, np.pi))
     for i in range(n):
         row = df.iloc[i].values
         f = function(row)
@@ -223,7 +223,6 @@ def f(t):
 
 
 def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
-    import random
 
     # random.sample(ndarray, int) fails on python 3.3, sigh
     data = list(series.values)

pandas/plotting/_matplotlib/style.py

@@ -1,17 +1,20 @@
 # being a bit too dynamic
 import warnings
 
+import matplotlib.cm as cm
+import matplotlib.colors
 import matplotlib.pyplot as plt
 import numpy as np
 
 from pandas.core.dtypes.common import is_list_like
 
+import pandas.core.common as com
+
 
 def _get_standard_colors(num_colors=None, colormap=None, color_type='default',
                          color=None):
     if color is None and colormap is not None:
         if isinstance(colormap, str):
-            import matplotlib.cm as cm
             cmap = colormap
             colormap = cm.get_cmap(colormap)
             if colormap is None:
@@ -37,7 +40,6 @@ def _get_standard_colors(num_colors=None, colormap=None, color_type='default',
 
             colors = colors[0:num_colors]
         elif color_type == 'random':
-            import pandas.core.common as com
 
             def random_color(column):
                 """ Returns a random color represented as a list of length 3"""
@@ -50,7 +52,6 @@ def random_color(column):
             raise ValueError("color_type must be either 'default' or 'random'")
 
     if isinstance(colors, str):
-        import matplotlib.colors
         conv = matplotlib.colors.ColorConverter()
 
         def _maybe_valid_colors(colors):

pandas/plotting/_matplotlib/timeseries.py

@@ -1,6 +1,7 @@
 # TODO: Use the fact that axis can have units to simplify the process
 
 import functools
+import warnings
 
 from matplotlib import pylab
 import matplotlib.pyplot as plt
@@ -25,7 +26,6 @@
 
 
 def tsplot(series, plotf, ax=None, **kwargs):
-    import warnings
     """
     Plots a Series on the given Matplotlib axes or the current axes
 

pandas/plotting/_matplotlib/tools.py

@@ -3,6 +3,8 @@
 import warnings
 
 import matplotlib.pyplot as plt
+import matplotlib.table
+import matplotlib.ticker as ticker
 import numpy as np
 
 from pandas.core.dtypes.common import is_list_like
@@ -37,7 +39,6 @@ def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
 
     cellText = data.values
 
-    import matplotlib.table
     table = matplotlib.table.table(ax, cellText=cellText,
                                    rowLabels=rowLabels,
                                    colLabels=colLabels, **kwargs)
@@ -260,7 +261,6 @@ def _remove_labels_from_axis(axis):
     try:
         # set_visible will not be effective if
         # minor axis has NullLocator and NullFormattor (default)
-        import matplotlib.ticker as ticker
         if isinstance(axis.get_minor_locator(), ticker.NullLocator):
             axis.set_minor_locator(ticker.AutoLocator())
         if isinstance(axis.get_minor_formatter(), ticker.NullFormatter):