Matplotlib 散点图不在 x 轴上取字符串？
Matplotlib scatter plot doesn't take strings on x-axis?
我有这种格式的数据。我能够从中得到条形图但不是散点图，因为 x 轴有字符串。我查看了其他几个帖子，从那里我无法收集到太多信息。
import pandas as pd
import numpy as np
df = pd.DataFrame({"id":["ssa", "ssb", "ssc", "xxa", "xxb", "xxc"], "mean":[1.3,1.5,5.2,3.1,2.1,3.2], "sd":[0.9,0.5,0.3,0.1,0.2,0.3]})
df
我使用以下命令得到了一个带有误差条的条形图：
import matplotlib.pyplot as plt
ax = plt.figure()
ax = df.plot(kind='bar',x='id', y='mean',figsize=[15,6], yerr='sd')
ax.set_xlabel("id")
ax.set_ylabel("mean")
ax = plt.tight_layout()
ax = plt.show()
但是当我尝试绘制相同 df 的散点图时出现错误。
ax = plt.figure()
ax = df.plot(kind='scatter',x='id', y='mean',figsize=[15,6], yerr='sd')
ax.set_xlabel("id")
ax.set_ylabel("mean")
ax = plt.tight_layout()
ax = plt.show()
错误回溯：
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-10-b3ab7237d4f1> in <module>()
1 ax = plt.figure()
----> 2 ax = df.plot(kind='scatter',x='id', y='mean',figsize=[15,6], yerr='sd', style='.')
3 ax.set_xlabel("id")
4 ax.set_ylabel("mean")
5 ax = plt.tight_layout()
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
2618 fontsize=fontsize, colormap=colormap, table=table,
2619 yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 2620 sort_columns=sort_columns, **kwds)
2621 __call__.__doc__ = plot_frame.__doc__
2622
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
1855 yerr=yerr, xerr=xerr,
1856 secondary_y=secondary_y, sort_columns=sort_columns,
-> 1857 **kwds)
1858
1859
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
1680 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
1681
-> 1682 plot_obj.generate()
1683 plot_obj.draw()
1684 return plot_obj.result
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in generate(self)
236 self._compute_plot_data()
237 self._setup_subplots()
--> 238 self._make_plot()
239 self._add_table()
240 self._make_legend()
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in _make_plot(self)
829 else:
830 label = None
--> 831 scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
832 label=label, cmap=cmap, **self.kwds)
833 if cb:
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
2060 return self._getitem_multilevel(key)
2061 else:
-> 2062 return self._getitem_column(key)
2063
2064 def _getitem_column(self, key):
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.pyc in _getitem_column(self, key)
2067 # get column
2068 if self.columns.is_unique:
-> 2069 return self._get_item_cache(key)
2070
2071 # duplicate columns & possible reduce dimensionality
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
1532 res = cache.get(item)
1533 if res is None:
-> 1534 values = self._data.get(item)
1535 res = self._box_item_values(item, values)
1536 cache[item] = res
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\internals.pyc in get(self, item, fastpath)
3588
3589 if not isnull(item):
-> 3590 loc = self.items.get_loc(item)
3591 else:
3592 indexer = np.arange(len(self.items))[isnull(self.items)]
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexes\base.pyc in get_loc(self, key, method, tolerance)
2393 return self._engine.get_loc(key)
2394 except KeyError:
-> 2395 return self._engine.get_loc(self._maybe_cast_indexer(key))
2396
2397 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5239)()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5085)()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20405)()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20359)()
KeyError: 'id'
因此，我改用 seaborn 进行绘图，而且绘图非常完美。
但是，我不确定如何通过 "sd" 列来绘制其中的误差线。
fig, ax = plt.subplots(figsize=(5,3))
ax = sns.pointplot(x="id", y="mean", data=df, join=False)
ax = plt.xticks(rotation=90)
ax = plt.tight_layout()
ax = plt.show()
fig, ax = plt.subplots(figsize=(25,5))
ax = sns.pointplot(x="id", y="mean", data=df, join=False)
ax.map(plt.errorbar, "id", "mean", "sd", marker="o")
ax = plt.xticks(rotation=90)
ax = plt.tight_layout()
ax = plt.show()
以上代码抛出以下错误：
AttributeError Traceback (most recent call last)
<ipython-input-21-18652e3e8b12> in <module>()
1 fig, ax = plt.subplots(figsize=(25,5))
2 ax = sns.pointplot(x="id", y="mean", data=df, join=False)
----> 3 ax.map(plt.errorbar, "id", "mean", "sd", marker="o")
4 ax = plt.xticks(rotation=90)
5 ax = plt.tight_layout()
AttributeError: 'AxesSubplot' object has no attribute 'map'
理想情况下，我想要的是一个类似于点图的图，但每个点都有不同的大小（由其相应的 sd 指定）或每个点都有一个误差条（由 sd 给出）。
谁能告诉我怎么做？
只需使用 matplotlib.axes.Axes.errorbar()，如 example 所示，添加以下行：
ax.errorbar(np.arange(len(df['id'])), df['mean'], yerr=df['sd'], ls='None')
但我认为你不必使用 seaborn:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame({"id":["ssa", "ssb", "ssc", "xxa", "xxb", "xxc"],
"mean":[1.3,1.5,5.2,3.1,2.1,3.2],
"sd":[0.9,0.5,0.3,0.1,0.2,0.3]})
plt.errorbar(np.arange(len(df['id'])), df['mean'], yerr=df['sd'], ls='None', marker='o')
ax = plt.gca()
ax.xaxis.set_ticks(np.arange(len(df['id'])))
ax.xaxis.set_ticklabels(df['id'], rotation=90)
plt.xlabel("id")
plt.ylabel("mean")
plt.show()
编辑：OP 现在想要“删除误差线并绘制与 sd 成比例的点”。
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame({"id":["ssa", "ssb", "ssc", "xxa", "xxb", "xxc"],
"mean":[1.3,1.5,5.2,3.1,2.1,3.2],
"sd":[0.9,0.5,0.3,0.1,0.2,0.3]})
fig, ax = plt.subplots()
size_scaler = 300 # Your points will be too small if you just use sd
ax.scatter(np.arange(len(df['id'])), df['mean'], s=df['sd']*size_scaler, marker='o')
ax.xaxis.set_ticks(np.arange(len(df['id'])))
ax.xaxis.set_ticklabels(df['id'], rotation=90)
plt.xlabel("id")
plt.ylabel("mean")
plt.show()
我有这种格式的数据。我能够从中得到条形图但不是散点图，因为 x 轴有字符串。我查看了其他几个帖子，从那里我无法收集到太多信息。
import pandas as pd
import numpy as np
df = pd.DataFrame({"id":["ssa", "ssb", "ssc", "xxa", "xxb", "xxc"], "mean":[1.3,1.5,5.2,3.1,2.1,3.2], "sd":[0.9,0.5,0.3,0.1,0.2,0.3]})
df
我使用以下命令得到了一个带有误差条的条形图：
import matplotlib.pyplot as plt
ax = plt.figure()
ax = df.plot(kind='bar',x='id', y='mean',figsize=[15,6], yerr='sd')
ax.set_xlabel("id")
ax.set_ylabel("mean")
ax = plt.tight_layout()
ax = plt.show()
但是当我尝试绘制相同 df 的散点图时出现错误。
ax = plt.figure()
ax = df.plot(kind='scatter',x='id', y='mean',figsize=[15,6], yerr='sd')
ax.set_xlabel("id")
ax.set_ylabel("mean")
ax = plt.tight_layout()
ax = plt.show()
错误回溯：
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-10-b3ab7237d4f1> in <module>()
1 ax = plt.figure()
----> 2 ax = df.plot(kind='scatter',x='id', y='mean',figsize=[15,6], yerr='sd', style='.')
3 ax.set_xlabel("id")
4 ax.set_ylabel("mean")
5 ax = plt.tight_layout()
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
2618 fontsize=fontsize, colormap=colormap, table=table,
2619 yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 2620 sort_columns=sort_columns, **kwds)
2621 __call__.__doc__ = plot_frame.__doc__
2622
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
1855 yerr=yerr, xerr=xerr,
1856 secondary_y=secondary_y, sort_columns=sort_columns,
-> 1857 **kwds)
1858
1859
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
1680 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
1681
-> 1682 plot_obj.generate()
1683 plot_obj.draw()
1684 return plot_obj.result
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in generate(self)
236 self._compute_plot_data()
237 self._setup_subplots()
--> 238 self._make_plot()
239 self._add_table()
240 self._make_legend()
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.pyc in _make_plot(self)
829 else:
830 label = None
--> 831 scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
832 label=label, cmap=cmap, **self.kwds)
833 if cb:
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
2060 return self._getitem_multilevel(key)
2061 else:
-> 2062 return self._getitem_column(key)
2063
2064 def _getitem_column(self, key):
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.pyc in _getitem_column(self, key)
2067 # get column
2068 if self.columns.is_unique:
-> 2069 return self._get_item_cache(key)
2070
2071 # duplicate columns & possible reduce dimensionality
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
1532 res = cache.get(item)
1533 if res is None:
-> 1534 values = self._data.get(item)
1535 res = self._box_item_values(item, values)
1536 cache[item] = res
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\internals.pyc in get(self, item, fastpath)
3588
3589 if not isnull(item):
-> 3590 loc = self.items.get_loc(item)
3591 else:
3592 indexer = np.arange(len(self.items))[isnull(self.items)]
C:\Users\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexes\base.pyc in get_loc(self, key, method, tolerance)
2393 return self._engine.get_loc(key)
2394 except KeyError:
-> 2395 return self._engine.get_loc(self._maybe_cast_indexer(key))
2396
2397 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5239)()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5085)()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20405)()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20359)()
KeyError: 'id'
因此，我改用 seaborn 进行绘图，而且绘图非常完美。 但是，我不确定如何通过 "sd" 列来绘制其中的误差线。
fig, ax = plt.subplots(figsize=(5,3))
ax = sns.pointplot(x="id", y="mean", data=df, join=False)
ax = plt.xticks(rotation=90)
ax = plt.tight_layout()
ax = plt.show()
fig, ax = plt.subplots(figsize=(25,5))
ax = sns.pointplot(x="id", y="mean", data=df, join=False)
ax.map(plt.errorbar, "id", "mean", "sd", marker="o")
ax = plt.xticks(rotation=90)
ax = plt.tight_layout()
ax = plt.show()
以上代码抛出以下错误：
AttributeError Traceback (most recent call last)
<ipython-input-21-18652e3e8b12> in <module>()
1 fig, ax = plt.subplots(figsize=(25,5))
2 ax = sns.pointplot(x="id", y="mean", data=df, join=False)
----> 3 ax.map(plt.errorbar, "id", "mean", "sd", marker="o")
4 ax = plt.xticks(rotation=90)
5 ax = plt.tight_layout()
AttributeError: 'AxesSubplot' object has no attribute 'map'
理想情况下，我想要的是一个类似于点图的图，但每个点都有不同的大小（由其相应的 sd 指定）或每个点都有一个误差条（由 sd 给出）。 谁能告诉我怎么做？
只需使用 matplotlib.axes.Axes.errorbar()，如 example 所示，添加以下行：
ax.errorbar(np.arange(len(df['id'])), df['mean'], yerr=df['sd'], ls='None')
但我认为你不必使用 seaborn:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame({"id":["ssa", "ssb", "ssc", "xxa", "xxb", "xxc"],
"mean":[1.3,1.5,5.2,3.1,2.1,3.2],
"sd":[0.9,0.5,0.3,0.1,0.2,0.3]})
plt.errorbar(np.arange(len(df['id'])), df['mean'], yerr=df['sd'], ls='None', marker='o')
ax = plt.gca()
ax.xaxis.set_ticks(np.arange(len(df['id'])))
ax.xaxis.set_ticklabels(df['id'], rotation=90)
plt.xlabel("id")
plt.ylabel("mean")
plt.show()
编辑：OP 现在想要“删除误差线并绘制与 sd 成比例的点”。
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame({"id":["ssa", "ssb", "ssc", "xxa", "xxb", "xxc"],
"mean":[1.3,1.5,5.2,3.1,2.1,3.2],
"sd":[0.9,0.5,0.3,0.1,0.2,0.3]})
fig, ax = plt.subplots()
size_scaler = 300 # Your points will be too small if you just use sd
ax.scatter(np.arange(len(df['id'])), df['mean'], s=df['sd']*size_scaler, marker='o')
ax.xaxis.set_ticks(np.arange(len(df['id'])))
ax.xaxis.set_ticklabels(df['id'], rotation=90)
plt.xlabel("id")
plt.ylabel("mean")
plt.show()