Pandas 绘图错误：缺少 StrCategoryConverter 的类别信息；这可能是由于无意中混合了分类数据和数字数据造成的

Question

我试图使用 pandas 绘图方法绘制线图，如果我使用 matplotlib 方法，则相同的数据和完全相同的方法运行良好，但是如果我使用

df.plot

然后注释会给我错误

ValueError: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data

假设我有一个数据框，

data = {'Unit': {0: 'Admin ', 1: 'C-Level', 2: 'Engineering', 3: 'IT', 4: 'Manufacturing', 5: 'Sales'}, 'Mean': {0: 4.642857142857143, 1: 4.83, 2: 4.048, 3: 4.237317073170732, 4: 4.184319526627219, 5: 3.9904545454545453}}
result=pd.DataFrame(data)

使用 matplotlib 时

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(18,9))                                  
ax.plot(results['Unit'],results['Mean'])

for i, val in enumerate(zip(results['Unit'],results['Mean'])):
    label = str(results.loc[i, 'Mean'])
    ax.annotate(label, val, ha='center')
                             
plt.show()

上面的代码工作得很好。

使用 pandas 绘图函数（这给了我错误）

results.plot(x = 'Unit', y = 'Mean', marker = 'o', figsize=(8,5))
ax = plt.gca()
for i, val in enumerate(zip(results['Unit'],results['Mean'])):
    label = str(results.loc[i, 'Mean'])
    ax.annotate(label, val, ha='center')
plt.show()

这给了我错误：

Traceback (most recent call last):
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\axis.py", line 1506, in convert_units
    ret = self.converter.convert(x, self.units, self)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\category.py", line 49, in convert
    raise ValueError(
ValueError: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\backends\backend_qt.py", line 477, in _draw_idle
    self.draw()
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\backends\backend_agg.py", line 436, in draw
    self.figure.draw(self.renderer)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\artist.py", line 73, in draw_wrapper
    result = draw(artist, renderer, *args, **kwargs)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\artist.py", line 50, in draw_wrapper
    return draw(artist, renderer)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\figure.py", line 2837, in draw
    mimage._draw_list_compositing_images(
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\image.py", line 132, in _draw_list_compositing_images
    a.draw(renderer)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\artist.py", line 50, in draw_wrapper
    return draw(artist, renderer)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\axes\_base.py", line 3091, in draw
    mimage._draw_list_compositing_images(
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\image.py", line 132, in _draw_list_compositing_images
    a.draw(renderer)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\artist.py", line 50, in draw_wrapper
    return draw(artist, renderer)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\text.py", line 1969, in draw
    if not self.get_visible() or not self._check_xy(renderer):
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\text.py", line 1559, in _check_xy
    xy_pixel = self._get_position_xy(renderer)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\text.py", line 1552, in _get_position_xy
    return self._get_xy(renderer, x, y, self.xycoords)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\text.py", line 1419, in _get_xy
    x = float(self.convert_xunits(x))
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\artist.py", line 252, in convert_xunits
    return ax.xaxis.convert_units(x)
  File "C:\Users\hpoddar\AppData\Local\Programs\Python\Python310\lib\site-packages\matplotlib\axis.py", line 1508, in convert_units
    raise munits.ConversionError('Failed to convert value(s) to axis '
matplotlib.units.ConversionError: Failed to convert value(s) to axis units: 'Admin '

预期输出：

带注释的图表

为什么在使用 pandas 绘图时会出现错误，以及如何解决该错误

Answer 1

matplotlib.axes.Axes.annotate()

采用参数

xy

如下：

xy（浮动，浮动）

要注释的点 (x, y)。坐标系由xycoords确定。

因此，要解决此问题，您可以创建一个具有

(i, val[1])

的元组，而不是传递包含字符串类型的

val

。

ax = results.plot(x = 'Unit', y = 'Mean', marker = 'o', figsize=(8,5))
for i, val in enumerate(zip(results['Unit'],results['Mean'])):
    label = str(results.loc[i, 'Mean'])
    ax.annotate(text=label, xy=(i, val[1]), ha='center')

另一种选择是使用

matplotlib.axes.Axes.text()

:

ax = results.plot(x = 'Unit', y = 'Mean', marker = 'o', figsize=(8,5))
for i, val in enumerate(zip(results['Unit'],results['Mean'])):
    label = str(results.loc[i, 'Mean'])
    ax.text(x=i, y=val[1], s=label, ha='center')

---

正如您所指出的，当我们在

annotate()

之后调用

matplotlib.axes.Axes.plot

时，就没有

ConversionError

。但如果我们在

annotate()

之后调用

pandas.DataFrame.plot

，就会有

ConversionError

。

我最好的猜测是，使用 matplotlib 绘图后，注释上将不会出现 conversion 。但对于 pandas 情节，将尝试进行转换。这可能是由于两种情况下 x 刻度的表示不同。

为了演示，如果我们尝试以下代码，将会抛出相同的错误：

fig, ax = plt.subplots(figsize=(18,9))
# Call annotate without plotting! Throws ConversionError
# ax.plot(results['Unit'],results['Mean'])

for i, val in enumerate(zip(results['Unit'],results['Mean'])):
    label = str(results.loc[i, 'Mean'])
    ax.annotate(label, val, ha='center')

如果我们执行以下操作，则不会抛出错误：

fig, ax = plt.subplots(figsize=(18,9))
# Call annotate without plotting! No error
# ax.plot(results['Unit'],results['Mean'])

for i, val in enumerate(zip(results['Unit'],results['Mean'])):
    label = str(results.loc[i, 'Mean'])
    ax.annotate(text=label, xy=(i, val[1]), ha='center')

Answer 2

我在提取方法时收到此错误消息，这些方法对于许多图表来说都是相同的。

当数据点的绘制 (

plt.plot(x_values, y_values)

) 不再集成在活动代码中时，会出现该消息。