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orbitfoldwesm
orbitfold
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wesm
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Changes to plotting scatter matrix diagonals
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‎pandas/tools/plotting.py

+68-54
Original file line numberDiff line numberDiff line change
@@ -3,6 +3,7 @@
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from itertools import izip
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import numpy as np
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from scipy import stats
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from pandas.util.decorators import cache_readonly
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import pandas.core.common as com
@@ -12,7 +13,7 @@
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from pandas.tseries.offsets import DateOffset
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def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
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**kwds):
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diagonal='hist', **kwds):
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"""
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Draw a matrix of scatter plots.
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@@ -36,64 +37,77 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
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for i, a in zip(range(n), df.columns):
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for j, b in zip(range(n), df.columns):
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axes[i, j].scatter(df[b], df[a], alpha=alpha, **kwds)
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axes[i, j].set_xlabel('')
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axes[i, j].set_ylabel('')
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axes[i, j].set_xticklabels([])
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axes[i, j].set_yticklabels([])
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ticks = df.index
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is_datetype = ticks.inferred_type in ('datetime', 'date',
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if i == j:
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# Deal with the diagonal by drawing a histogram there.
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if diagonal == 'hist':
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axes[i, j].hist(df[a])
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elif diagonal == 'kde':
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y = df[a]
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gkde = stats.gaussian_kde(y)
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ind = np.linspace(min(y), max(y), 1000)
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axes[i, j].plot(ind, gkde.evaluate(ind), **kwds)
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axes[i, j].yaxis.set_visible(False)
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axes[i, j].xaxis.set_visible(False)
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if i == 0 and j == 0:
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axes[i, j].yaxis.set_ticks_position('left')
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axes[i, j].yaxis.set_label_position('left')
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axes[i, j].yaxis.set_visible(True)
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if i == n - 1 and j == n - 1:
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axes[i, j].yaxis.set_ticks_position('right')
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axes[i, j].yaxis.set_label_position('right')
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axes[i, j].yaxis.set_visible(True)
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else:
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axes[i, j].scatter(df[b], df[a], alpha=alpha, **kwds)
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axes[i, j].set_xlabel('')
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axes[i, j].set_ylabel('')
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axes[i, j].set_xticklabels([])
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axes[i, j].set_yticklabels([])
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ticks = df.index
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is_datetype = ticks.inferred_type in ('datetime', 'date',
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'datetime64')
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if ticks.is_numeric() or is_datetype:
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"""
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Matplotlib supports numeric values or datetime objects as
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xaxis values. Taking LBYL approach here, by the time
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matplotlib raises exception when using non numeric/datetime
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values for xaxis, several actions are already taken by plt.
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"""
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ticks = ticks._mpl_repr()
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# setup labels
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if i == 0 and j % 2 == 1:
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axes[i, j].set_xlabel(b, visible=True)
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#axes[i, j].xaxis.set_visible(True)
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axes[i, j].set_xlabel(b)
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axes[i, j].set_xticklabels(ticks)
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axes[i, j].xaxis.set_ticks_position('top')
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axes[i, j].xaxis.set_label_position('top')
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if i == n - 1 and j % 2 == 0:
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axes[i, j].set_xlabel(b, visible=True)
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#axes[i, j].xaxis.set_visible(True)
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axes[i, j].set_xlabel(b)
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axes[i, j].set_xticklabels(ticks)
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axes[i, j].xaxis.set_ticks_position('bottom')
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axes[i, j].xaxis.set_label_position('bottom')
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if j == 0 and i % 2 == 0:
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axes[i, j].set_ylabel(a, visible=True)
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#axes[i, j].yaxis.set_visible(True)
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axes[i, j].set_ylabel(a)
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axes[i, j].set_yticklabels(ticks)
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axes[i, j].yaxis.set_ticks_position('left')
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axes[i, j].yaxis.set_label_position('left')
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if j == n - 1 and i % 2 == 1:
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axes[i, j].set_ylabel(a, visible=True)
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#axes[i, j].yaxis.set_visible(True)
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axes[i, j].set_ylabel(a)
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axes[i, j].set_yticklabels(ticks)
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axes[i, j].yaxis.set_ticks_position('right')
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axes[i, j].yaxis.set_label_position('right')
70+
if ticks.is_numeric() or is_datetype:
71+
"""
72+
Matplotlib supports numeric values or datetime objects as
73+
xaxis values. Taking LBYL approach here, by the time
74+
matplotlib raises exception when using non numeric/datetime
75+
values for xaxis, several actions are already taken by plt.
76+
"""
77+
ticks = ticks._mpl_repr()
78+
79+
# setup labels
80+
if i == 0 and j % 2 == 1:
81+
axes[i, j].set_xlabel(b, visible=True)
82+
#axes[i, j].xaxis.set_visible(True)
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axes[i, j].set_xlabel(b)
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axes[i, j].set_xticklabels(ticks)
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axes[i, j].xaxis.set_ticks_position('top')
86+
axes[i, j].xaxis.set_label_position('top')
87+
if i == n - 1 and j % 2 == 0:
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axes[i, j].set_xlabel(b, visible=True)
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#axes[i, j].xaxis.set_visible(True)
90+
axes[i, j].set_xlabel(b)
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axes[i, j].set_xticklabels(ticks)
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axes[i, j].xaxis.set_ticks_position('bottom')
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axes[i, j].xaxis.set_label_position('bottom')
94+
if j == 0 and i % 2 == 0:
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axes[i, j].set_ylabel(a, visible=True)
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#axes[i, j].yaxis.set_visible(True)
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axes[i, j].set_ylabel(a)
98+
axes[i, j].set_yticklabels(ticks)
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axes[i, j].yaxis.set_ticks_position('left')
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axes[i, j].yaxis.set_label_position('left')
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if j == n - 1 and i % 2 == 1:
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axes[i, j].set_ylabel(a, visible=True)
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#axes[i, j].yaxis.set_visible(True)
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axes[i, j].set_ylabel(a)
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axes[i, j].set_yticklabels(ticks)
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axes[i, j].yaxis.set_ticks_position('right')
107+
axes[i, j].yaxis.set_label_position('right')
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88109
axes[i, j].grid(b=grid)
89110

90-
# ensure {x,y}lim off diagonal are the same as diagonal
91-
for i in range(n):
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for j in range(n):
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if i != j:
94-
axes[i, j].set_xlim(axes[j, j].get_xlim())
95-
axes[i, j].set_ylim(axes[i, i].get_ylim())
96-
97111
return axes
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def _gca():

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