diff --git a/galleries/examples/lines_bars_and_markers/fill_between_alpha.py b/galleries/examples/lines_bars_and_markers/fill_between_alpha.py index 66822aaff0c5..31c2affe5703 100644 --- 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 index 0a9eee4f2b53..258a9e518841 100644 --- 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 index 4e0f3e61037d..f086dd850f2a 100644 --- 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 index 21c81e43d1f2..4894c3559c3e 100644 --- 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 index 4c48ac5ace7a..3c0fbff39740 100644 --- 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 index b6e02041afa0..067e79f7d75a 100644 --- 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 index cf2d5850e6e5..37d685a649d2 100644 --- 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. pFad - Phonifier reborn

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