pandas 如何在 Python 中绘制由另一列着色的一列的直方图？
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How to plot a histogram of one column colored by another in Python?
提问by Kristada673
I have a dataset that contains, among other columns, 3 columns titled Gender(either Mor F), House(either Aor Bor C), and Indicator(either 0 or 1). I want to plot the histogram of House Acolored by Gender. This is my code to do this:
我有一个数据集，其中包含 3 列，标题为Gender（M或F）、House（A或B或C）和Indicator（0 或 1）。我想绘制A按性别着色的 House 的直方图。这是我执行此操作的代码：
import pandas as pd
df = pd.read_csv('dataset.csv', usecols=['House','Gender','Indicator')
A = df[df['House']=='A']
A = pd.DataFrame(A, columns=['Indicator', 'Gender'])
This imports the values of House A for the respective genders correctly, as shown by its contents:
这将正确导入各个性别的 House A 的值，如其内容所示：
print(A)
Indicator Gender
0 1 Male
1 1 Male
2 1 Male
4 1 Female
7 1 Male
8 1 Male
11 1 Male
14 1 Male
17 1 Male
18 1 Female
19 1 Female
20 1 Female
21 1 Male
24 1 Male
26 1 Female
27 1 Male
... ... ...
Now when I want to plot the histogram of A colored by gender the way I did in MATLAB, it gives an error:
现在，当我想按照我在 MATLAB 中所做的方式绘制按性别着色的 A 的直方图时，它给出了一个错误：
import matplotlib.pyplot as plt
plt.hist(A)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-130-81c3aef1748b> in <module>()
----> 1 plt.hist(A)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\pyplot.py in hist(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, hold, data, **kwargs)
3130 histtype=histtype, align=align, orientation=orientation,
3131 rwidth=rwidth, log=log, color=color, label=label,
-> 3132 stacked=stacked, normed=normed, data=data, **kwargs)
3133 finally:
3134 ax._hold = washold
~\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\__init__.py in inner(ax, *args, **kwargs)
1853 "the Matplotlib list!)" % (label_namer, func.__name__),
1854 RuntimeWarning, stacklevel=2)
-> 1855 return func(ax, *args, **kwargs)
1856
1857 inner.__doc__ = _add_data_doc(inner.__doc__,
~\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\axes\_axes.py in hist(***failed resolving arguments***)
6512 for xi in x:
6513 if len(xi) > 0:
-> 6514 xmin = min(xmin, xi.min())
6515 xmax = max(xmax, xi.max())
6516 bin_range = (xmin, xmax)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\_methods.py in _amin(a, axis, out, keepdims)
27
28 def _amin(a, axis=None, out=None, keepdims=False):
---> 29 return umr_minimum(a, axis, None, out, keepdims)
30
31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
TypeError: '<=' not supported between instances of 'int' and 'str'
It seems we need to specify the exact column we want to make histogram of. It can't automatically understand (unlike MATLAB) that it needs to color according to the other column. So, doing the following plots the histogram, but with no color indicating the Gender:
似乎我们需要指定我们想要制作直方图的确切列。它无法自动理解（与 MATLAB 不同）它需要根据另一列进行着色。因此，执行以下操作绘制直方图，但没有指示性别的颜色：
plt.hist(A['Indicator'])
So, how do I make either a stacked histogram, or a side-by-side one colored by gender? Something like this, except there'll be only 2 bars for each Indicator, at x=0 and x=1:
那么，我如何制作堆叠直方图或按性别着色的并排直方图？像这样，除了每个指标只有 2 个柱，在 x=0 和 x=1：
x = np.random.randn(1000, 2)
colors = ['red', 'green']
plt.hist(x, color=colors)
plt.legend(['Male', 'Female'])
plt.title('Male and Female indicator by gender')
I have tried to imitate the above by copying the 2 dataframe columns into 2 columns of a list, and then trying to plot the histogram:
我试图通过将 2 个数据框列复制到列表的 2 列中来模仿上述内容，然后尝试绘制直方图：
y=[]
y[0] = A[A['Gender'=='M']].tolist()
y[1] = A[A['Gender'=='F']].tolist()
plt.hist(y)
But this gives the following error:
但这会产生以下错误：
KeyError Traceback (most recent call last)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3062 try:
-> 3063 return self._engine.get_loc(key)
3064 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: False
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-152-138cb74b6e00> in <module>()
2 A= pd.DataFrame(A, columns=['Indicator', 'Gender'])
3 y=[]
----> 4 y[0] = A[A['Gender'=='M']].tolist()
5 y[1] = A[A['Gender'=='F']].tolist()
6 plt.hist(y)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2683 return self._getitem_multilevel(key)
2684 else:
-> 2685 return self._getitem_column(key)
2686
2687 def _getitem_column(self, key):
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
2690 # get column
2691 if self.columns.is_unique:
-> 2692 return self._get_item_cache(key)
2693
2694 # duplicate columns & possible reduce dimensionality
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
2484 res = cache.get(item)
2485 if res is None:
-> 2486 values = self._data.get(item)
2487 res = self._box_item_values(item, values)
2488 cache[item] = res
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
4113
4114 if not isna(item):
-> 4115 loc = self.items.get_loc(item)
4116 else:
4117 indexer = np.arange(len(self.items))[isna(self.items)]
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3063 return self._engine.get_loc(key)
3064 except KeyError:
-> 3065 return self._engine.get_loc(self._maybe_cast_indexer(key))
3066
3067 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: False
回答by piman314
The following should work, not tested with your data though.
以下应该有效，但未使用您的数据进行测试。
genders = A.Gender.unique()
plt.hist([A.loc[A.Gender == x, 'Indicator'] for x in genders], label=genders)
Your code fails on A[A['Gender'=='M']]because it should be A[A['Gender'] == 'M']to get the Male elements, but you also need to select the column that you want.
您的代码失败，A[A['Gender'=='M']]因为它应该是A[A['Gender'] == 'M']获取男性元素，但您还需要选择所需的列。
