DOC: add new example

Author: jklymak

doc/users/faq/howto_faq.rst

@@ -23,68 +23,34 @@ as "categorical" variables
 puts one tick per "category", and plots them in the order in which they are
 supplied.
 
-In the example below, the upper row plots are plotted using strings for *x*;
-note that each string gets a tick, and they are in the order of the list passed
-to Matplotlib.  If this is not desired, we need to change *x* to an array of
-numbers.
-
 .. plot::
     :include-source:
     :align: center
 
-    fig, ax = plt.subplots(1, 2, constrained_layout=True, figsize=(6, 2.5))
-    x = ['1', '5', '2', '3']
-    y = [1, 4, 2, 3]
-    ax[0].plot(x, y, 'd')
-    ax[0].tick_params(axis='x', color='r', labelcolor='r')
-    ax[0].set_xlabel('Categories')
-    ax[0].set_title('Ticks seem out of order / misplaced')
-
-    # convert to numbers:
-    x = np.asarray(x, dtype='float')
-    ax[1].plot(x, y, 'd')
-    ax[1].set_xlabel('Floats')
-    ax[1].set_title('Ticks as expected')
+    import matplotlib.pyplot as plt
+    import numpy as np
 
-If *x* has 100 elements, all strings, then we would have 100 (unreadable)
-ticks, and again the solution is to convert the strings to floats:
+    fig, ax = plt.subplots(1, 2, constrained_layout=True, figsize=(6, 2))
 
-.. plot::
-    :include-source:
-    :align: center
+    ax[0].set_title('Ticks seem out of order / misplaced')
+    x = ['5', '20', '1', '9']  # strings
+    y = [5, 20, 1, 9]
+    ax[0].plot(x, y, 'd')
+    ax[0].tick_params(axis='x', labelcolor='red', labelsize=14)
 
-    fig, ax = plt.subplots(1, 2, figsize=(6, 2.5))
-    x = [f'{xx}' for xx in np.arange(100)]
+    ax[1].set_title('Many ticks')
+    x = [str(xx) for xx in np.arange(100)]  # strings
     y = np.arange(100)
-    ax[0].plot(x, y)
-    ax[0].tick_params(axis='x', color='r', labelcolor='r')
-    ax[0].set_title('Too many ticks')
-    ax[0].set_xlabel('Categories')
-
-    ax[1].plot(np.asarray(x, float), y)
-    ax[1].set_title('x converted to numbers')
-    ax[1].set_xlabel('Floats')
+    ax[1].plot(x, y)
+    ax[1].tick_params(axis='x', labelcolor='red', labelsize=14)
 
-A common case is when dates are read from a CSV file, they need to be
-converted from strings to datetime objects to get the proper date locators
-and formatters.
+The solution is to convert the list of strings to numbers or
+datetime objects (often ``np.asarray(['2', '5', '1'], dtype='float')`` or::
 
-.. plot::
-    :include-source:
-    :align: center
+    np.asarray(['2021-10-01', '2021-11-02', '2021-12-03'],
+               dtype='datetime64[s]')
 
-    fig, ax = plt.subplots(1, 2, constrained_layout=True, figsize=(6, 3.5))
-    x = ['2021-10-01', '2021-11-02', '2021-12-03', '2021-10-04']
-    y = [0, 2, 3, 1]
-    ax[0].plot(x, y, 'd')
-    ax[0].tick_params(axis='x', labelrotation=90, color='r', labelcolor='r')
-    ax[0].set_title('Dates out of order')
-
-    # convert to datetime64
-    x = np.asarray(x, dtype='datetime64[s]')
-    ax[1].plot(x, y, 'd')
-    ax[1].tick_params(axis='x', labelrotation=90)
-    ax[1].set_title('x converted to datetimes')
+For more information see :doc:`/gallery/ticks/ticks_too_many`.
 
 .. _howto-determine-artist-extent:
 

examples/ticks/ticks_too_many.py

@@ -0,0 +1,68 @@
+"""
+=====================
+Fixing too many ticks
+=====================
+
+One common cause for unexpected tick behavior is passing a list of strings
+instead of numbers or datetime objects. This can easily happen without notice
+when reading in a comma-delimited text file. Matplotlib treats lists of strings
+as "categorical" variables
+(:doc:`/gallery/lines_bars_and_markers/categorical_variables`), and by default
+puts one tick per "category", and plots them in the order in which they are
+supplied.  If this is not desired, the solution is to convert the strings to
+a numeric type as in the following examples.
+
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+fig, ax = plt.subplots(1, 2, constrained_layout=True, figsize=(6, 2.5))
+x = ['1', '5', '2', '3']
+y = [1, 4, 2, 3]
+ax[0].plot(x, y, 'd')
+ax[0].tick_params(axis='x', color='r', labelcolor='r')
+ax[0].set_xlabel('Categories')
+ax[0].set_title('Ticks seem out of order / misplaced')
+
+# convert to numbers:
+x = np.asarray(x, dtype='float')
+ax[1].plot(x, y, 'd')
+ax[1].set_xlabel('Floats')
+ax[1].set_title('Ticks as expected')
+
+############################################################################
+# If *x* has 100 elements, all strings, then we would have 100 (unreadable)
+# ticks, and again the solution is to convert the strings to floats:
+
+fig, ax = plt.subplots(1, 2, figsize=(6, 2.5))
+x = [f'{xx}' for xx in np.arange(100)]
+y = np.arange(100)
+ax[0].plot(x, y)
+ax[0].tick_params(axis='x', color='r', labelcolor='r')
+ax[0].set_title('Too many ticks')
+ax[0].set_xlabel('Categories')
+
+ax[1].plot(np.asarray(x, float), y)
+ax[1].set_title('x converted to numbers')
+ax[1].set_xlabel('Floats')
+
+############################################################################
+# A common case is when dates are read from a CSV file, they need to be
+# converted from strings to datetime objects to get the proper date locators
+# and formatters.
+
+fig, ax = plt.subplots(1, 2, constrained_layout=True, figsize=(6, 2.75))
+x = ['2021-10-01', '2021-11-02', '2021-12-03', '2021-09-01']
+y = [0, 2, 3, 1]
+ax[0].plot(x, y, 'd')
+ax[0].tick_params(axis='x', labelrotation=90, color='r', labelcolor='r')
+ax[0].set_title('Dates out of order')
+
+# convert to datetime64
+x = np.asarray(x, dtype='datetime64[s]')
+ax[1].plot(x, y, 'd')
+ax[1].tick_params(axis='x', labelrotation=90)
+ax[1].set_title('x converted to datetimes')
+
+plt.show()