Feb 9, 2018 · Feb 6, 2018 · Feb 6, 2018 · Feb 6, 2018 · Feb 9, 2018 · Feb 9, 2018
diff --git a/examples/axes_grid1/demo_new_colorbar.py b/examples/axes_grid1/demo_new_colorbar.py
diff --git a/examples/axes_grid1/make_room_for_ylabel_using_axesgrid.py b/examples/axes_grid1/make_room_for_ylabel_using_axesgrid.py
        divider = make_axes_locatable(ax1)

        ax2 = divider.new_horizontal("100%", pad=0.3, sharey=ax1)
        ax2.tick_params(labelleft="off")
        ax2.tick_params(labelleft=False)
        fig.add_axes(ax2)

        divider.add_auto_adjustable_area(use_axes=[ax1], pad=0.1,
diff --git a/examples/images_contours_and_fields/image_transparency_blend.py b/examples/images_contours_and_fields/image_transparency_blend.py
 weights = weights_high + weights_low

 # We'll also create a grey background into which the pixels will fade
 greys = np.ones(weights.shape + (3,)) * 70
 greys = np.empty(weights.shape + (3,), dtype=np.uint8)
 greys.fill(70)

 # First we'll plot these blobs using only ``imshow``.
 vmax = np.abs(weights).max()
diff --git a/examples/showcase/bachelors_degrees_by_gender.py b/examples/showcase/bachelors_degrees_by_gender.py
 as an alternative to a conventional legend.
 """

 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.mlab import csv2rec
 from matplotlib.cbook import get_sample_data

 with get_sample_data('percent_bachelors_degrees_women_usa.csv') as fname:
    gender_degree_data = csv2rec(fname)

 fname = get_sample_data('percent_bachelors_degrees_women_usa.csv',
                        asfileobj=False)
 gender_degree_data = np.genfromtxt(fname, delimiter=',', names=True)

 # These are the colors that will be used in the plot
 color_sequence = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',

 # Remove the tick marks; they are unnecessary with the tick lines we just
 # plotted.
 plt.tick_params(axis='both', which='both', bottom='off', top='off',
                labelbottom='on', left='off', right='off', labelleft='on')
 plt.tick_params(axis='both', which='both', bottom=False, top=False,
                labelbottom=True, left=False, right=False, labelleft=True)

 # Now that the plot is prepared, it's time to actually plot the data!
 # Note that I plotted the majors in order of the highest % in the final year.

 for rank, column in enumerate(majors):
    # Plot each line separately with its own color.
    column_rec_name = column.replace('\n', '_').replace(' ', '_').lower()
    column_rec_name = column.replace('\n', '_').replace(' ', '_')

    line = plt.plot(gender_degree_data.year,
    line = plt.plot(gender_degree_data['Year'],
                    gender_degree_data[column_rec_name],
                    lw=2.5,
                    color=color_sequence[rank])
diff --git a/examples/statistics/hist.py b/examples/statistics/hist.py
    thispatch.set_facecolor(color)

 # We can also normalize our inputs by the total number of counts
 axs[1].hist(x, bins=n_bins,normed=True)
 axs[1].hist(x, bins=n_bins,density=True)

 # Now we format the y-axis to display percentage
 axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
diff --git a/examples/subplots_axes_and_figures/broken_axis.py b/examples/subplots_axes_and_figures/broken_axis.py
 ax.spines['bottom'].set_visible(False)
 ax2.spines['top'].set_visible(False)
 ax.xaxis.tick_top()
 ax.tick_params(labeltop='off')  # don't put tick labels at the top
 ax.tick_params(labeltop=False)  # don't put tick labels at the top
 ax2.xaxis.tick_bottom()

 # This looks pretty good, and was fairly painless, but you can get that
diff --git a/examples/ticks_and_spines/date_index_formatter.py b/examples/ticks_and_spines/date_index_formatter.py
 """

 from __future__ import print_function

 import numpy as np
 from matplotlib.mlab import csv2rec

 import matplotlib.pyplot as plt
 import matplotlib.cbook as cbook
 from matplotlib.dates import bytespdate2num, num2date
 from matplotlib.ticker import Formatter


 datafile = cbook.get_sample_data('msft.csv', asfileobj=False)
 print('loading %s' % datafile)
 r = csv2rec(datafile)[-40:]
 msft_data = np.genfromtxt(datafile, delimiter=',', names=True,
                          converters={0: bytespdate2num('%d-%b-%y')})[-40:]


 class MyFormatter(Formatter):
        if ind >= len(self.dates) or ind < 0:
            return ''

        return self.dates[ind].strftime(self.fmt)
        returnnum2date(self.dates[ind]).strftime(self.fmt)

 formatter = MyFormatter(r.date)
 formatter = MyFormatter(msft_data['Date'])

 fig, ax = plt.subplots()
 ax.xaxis.set_major_formatter(formatter)
 ax.plot(np.arange(len(r)),r.close, 'o-')
 ax.plot(np.arange(len(msft_data)),msft_data['Close'], 'o-')
 fig.autofmt_xdate()
 plt.show()
diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py

    if plotfuncs is None:
        plotfuncs = dict()
    r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows,
                     checkrows=checkrows, delimiter=delimiter, names=names)
    from matplotlib.cbook import mplDeprecation
    with warnings.catch_warnings():
        warnings.simplefilter('ignore', mplDeprecation)
        r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows,
                         checkrows=checkrows, delimiter=delimiter, names=names)

    def getname_val(identifier):
        'return the name and column data for identifier'
diff --git a/tutorials/introductory/pyplot.py b/tutorials/introductory/pyplot.py
 x = mu + sigma * np.random.randn(10000)

 # the histogram of the data
 n, bins, patches = plt.hist(x, 50,normed=1, facecolor='g', alpha=0.75)
 n, bins, patches = plt.hist(x, 50,density=1, facecolor='g', alpha=0.75)


 plt.xlabel('Smarts')
Original file line number	Diff line number	Diff line change
Expand Up		@@ -44,7 +44,7 @@ def ex3():
		divider = make_axes_locatable(ax1)

		ax2 = divider.new_horizontal("100%", pad=0.3, sharey=ax1)
		ax2.tick_params(labelleft="off")
		ax2.tick_params(labelleft=False)
		fig.add_axes(ax2)

		divider.add_auto_adjustable_area(use_axes=[ax1], pad=0.1,
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -48,7 +48,8 @@ def normal_pdf(x, mean, var):
		weights = weights_high + weights_low

		# We'll also create a grey background into which the pixels will fade
		greys = np.ones(weights.shape + (3,)) * 70
		greys = np.empty(weights.shape + (3,), dtype=np.uint8)
		greys.fill(70)

		# First we'll plot these blobs using only ``imshow``.
		vmax = np.abs(weights).max()
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -10,12 +10,14 @@
		as an alternative to a conventional legend.
		"""

		import numpy as np
		import matplotlib.pyplot as plt
		from matplotlib.mlab import csv2rec
		from matplotlib.cbook import get_sample_data

		with get_sample_data('percent_bachelors_degrees_women_usa.csv') as fname:
		gender_degree_data = csv2rec(fname)

		fname = get_sample_data('percent_bachelors_degrees_women_usa.csv',
		asfileobj=False)
		gender_degree_data = np.genfromtxt(fname, delimiter=',', names=True)

		# These are the colors that will be used in the plot
		color_sequence = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
Expand DownExpand Up		@@ -59,8 +61,8 @@

		# Remove the tick marks; they are unnecessary with the tick lines we just
		# plotted.
		plt.tick_params(axis='both', which='both', bottom='off', top='off',
		labelbottom='on', left='off', right='off', labelleft='on')
		plt.tick_params(axis='both', which='both', bottom=False, top=False,
		labelbottom=True, left=False, right=False, labelleft=True)

		# Now that the plot is prepared, it's time to actually plot the data!
		# Note that I plotted the majors in order of the highest % in the final year.
Expand All		@@ -80,9 +82,9 @@

		for rank, column in enumerate(majors):
		# Plot each line separately with its own color.
		column_rec_name = column.replace('\n', '_').replace(' ', '_').lower()
		column_rec_name = column.replace('\n', '_').replace(' ', '_')

		line = plt.plot(gender_degree_data.year,
		line = plt.plot(gender_degree_data['Year'],
		gender_degree_data[column_rec_name],
		lw=2.5,
		color=color_sequence[rank])
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -63,7 +63,7 @@
		thispatch.set_facecolor(color)

		# We can also normalize our inputs by the total number of counts
		axs[1].hist(x, bins=n_bins,normed=True)
		axs[1].hist(x, bins=n_bins,density=True)

		# Now we format the y-axis to display percentage
		axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -36,7 +36,7 @@
		ax.spines['bottom'].set_visible(False)
		ax2.spines['top'].set_visible(False)
		ax.xaxis.tick_top()
		ax.tick_params(labeltop='off') # don't put tick labels at the top
		ax.tick_params(labeltop=False) # don't put tick labels at the top
		ax2.xaxis.tick_bottom()

		# This looks pretty good, and was fairly painless, but you can get that
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -12,15 +12,19 @@
		"""

		from __future__ import print_function

		import numpy as np
		from matplotlib.mlab import csv2rec

		import matplotlib.pyplot as plt
		import matplotlib.cbook as cbook
		from matplotlib.dates import bytespdate2num, num2date
		from matplotlib.ticker import Formatter


		datafile = cbook.get_sample_data('msft.csv', asfileobj=False)
		print('loading %s' % datafile)
		r = csv2rec(datafile)[-40:]
		msft_data = np.genfromtxt(datafile, delimiter=',', names=True,
		converters={0: bytespdate2num('%d-%b-%y')})[-40:]


		class MyFormatter(Formatter):
Expand All		@@ -34,12 +38,12 @@ def __call__(self, x, pos=0):
		if ind >= len(self.dates) or ind < 0:
		return ''

		return self.dates[ind].strftime(self.fmt)
		returnnum2date(self.dates[ind]).strftime(self.fmt)

		formatter = MyFormatter(r.date)
		formatter = MyFormatter(msft_data['Date'])

		fig, ax = plt.subplots()
		ax.xaxis.set_major_formatter(formatter)
		ax.plot(np.arange(len(r)),r.close, 'o-')
		ax.plot(np.arange(len(msft_data)),msft_data['Close'], 'o-')
		fig.autofmt_xdate()
		plt.show()
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2411,8 +2411,11 @@ def plotfile(fname, cols=(0,), plotfuncs=None,

		if plotfuncs is None:
		plotfuncs = dict()
		r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows,
		checkrows=checkrows, delimiter=delimiter, names=names)
		from matplotlib.cbook import mplDeprecation
Copy link Member dstansbyFeb 9, 2018 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. Should this whole method be deprecated since it depends on`csv2rec`? Copy link Member tacaswellFeb 9, 2018 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. Yes, and point people at pandas. Copy link MemberAuthor QuLogicFeb 9, 2018 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. You want to deprecate`plt.plotfile` for Pandas? Probably should be an independent PR. dstansby and tacaswell reacted with thumbs up emoji
		with warnings.catch_warnings():
		warnings.simplefilter('ignore', mplDeprecation)
		r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows,
		checkrows=checkrows, delimiter=delimiter, names=names)

		def getname_val(identifier):
		'return the name and column data for identifier'
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -333,7 +333,7 @@ def f(t):
		x = mu + sigma * np.random.randn(10000)

		# the histogram of the data
		n, bins, patches = plt.hist(x, 50,normed=1, facecolor='g', alpha=0.75)
		n, bins, patches = plt.hist(x, 50,density=1, facecolor='g', alpha=0.75)


		plt.xlabel('Smarts')
Expand Down