matplotlib · mtsokol · Aug 31, 2023
diff --git a/galleries/examples/lines_bars_and_markers/fill_between_alpha.py b/galleries/examples/lines_bars_and_markers/fill_between_alpha.py
@@ -18,7 +18,7 @@
 import matplotlib.cbook as cbook
 
 # load up some sample financial data
-r = cbook.get_sample_data('goog.npz')['price_data'].view(np.recarray)
+r = cbook.get_sample_data('goog.npz')['price_data'].view(np.rec.recarray)
 # create two subplots with the shared x and y axes
 fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
 

diff --git a/galleries/examples/lines_bars_and_markers/scatter_demo2.py b/galleries/examples/lines_bars_and_markers/scatter_demo2.py
@@ -14,7 +14,9 @@
 # low, close, volume, adj_close from the mpl-data/sample_data directory. The
 # record array stores the date as an np.datetime64 with a day unit ('D') in
 # the date column.
-price_data = cbook.get_sample_data('goog.npz')['price_data'].view(np.recarray)
+price_data = cbook.get_sample_data('goog.npz')['price_data'].view(
+    np.rec.recarray
+)
 price_data = price_data[-250:]  # get the most recent 250 trading days
 
 delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]

diff --git a/galleries/examples/misc/keyword_plotting.py b/galleries/examples/misc/keyword_plotting.py
@@ -5,7 +5,7 @@
 
 There are some instances where you have data in a format that lets you
 access particular variables with strings: for example, with
-`numpy.recarray` or `pandas.DataFrame`.
+`numpy.rec.recarray` or `pandas.DataFrame`.
 
 Matplotlib allows you to provide such an object with the ``data`` keyword
 argument. If provided, you may generate plots with the strings

diff --git a/galleries/examples/ticks/centered_ticklabels.py b/galleries/examples/ticks/centered_ticklabels.py
@@ -25,7 +25,7 @@
 import matplotlib.ticker as ticker
 
 # Load some financial data; Google's stock price
-r = cbook.get_sample_data('goog.npz')['price_data'].view(np.recarray)
+r = cbook.get_sample_data('goog.npz')['price_data'].view(np.rec.recarray)
 r = r[-250:]  # get the last 250 days
 
 fig, ax = plt.subplots()

diff --git a/galleries/examples/ticks/date_index_formatter.py b/galleries/examples/ticks/date_index_formatter.py
@@ -25,7 +25,7 @@
 # low, close, volume, adj_close from the mpl-data/sample_data directory. The
 # record array stores the date as an np.datetime64 with a day unit ('D') in
 # the date column (``r.date``).
-r = cbook.get_sample_data('goog.npz')['price_data'].view(np.recarray)
+r = cbook.get_sample_data('goog.npz')['price_data'].view(np.rec.recarray)
 r = r[:9]  # get the first 9 days
 
 fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 6), layout='constrained')

diff --git a/galleries/tutorials/pyplot.py b/galleries/tutorials/pyplot.py
@@ -107,7 +107,7 @@
 #
 # There are some instances where you have data in a format that lets you
 # access particular variables with strings. For example, with
-# `numpy.recarray` or `pandas.DataFrame`.
+# `numpy.rec.recarray` or `pandas.DataFrame`.
 #
 # Matplotlib allows you to provide such an object with
 # the ``data`` keyword argument. If provided, then you may generate plots with

diff --git a/galleries/users_explain/quick_start.py b/galleries/users_explain/quick_start.py
@@ -127,7 +127,7 @@
 #   b_asarray = np.asarray(b)
 #
 # Most methods will also parse an addressable object like a *dict*, a
-# `numpy.recarray`, or a `pandas.DataFrame`.  Matplotlib allows you to
+# `numpy.rec.recarray`, or a `pandas.DataFrame`.  Matplotlib allows you to
 # provide the ``data`` keyword argument and generate plots passing the
 # strings corresponding to the *x* and *y* variables.
 np.random.seed(19680801)  # seed the random number generator.