设置矩形的宽度: In [208]: df = pd.DataFrame(np.random.random((6, 5)) * 10,
index=list('abcdef'), columns=list('ABCDE'))
In [209]: df
Out[209]:
A B C D E
a 4.2 6.7 1.0 7.1 1.4
b 1.3 9.5 5.1 7.3 5.6
c 8.9 5.0 5.0 6.7 3.8
d 5.5 0.5 2.4 8.4 6.4
e 0.3 1.4 4.8 1.7 9.3
f 3.3 0.2 6.9 8.0 6.1
In [210]: ax = df.plot(kind='bar', stacked=True, align='center')
In [211]: for container in ax.containers:
plt.setp(container, width=1)
.....:
In [212]: x0, x1 = ax.get_xlim()
In [213]: ax.set_xlim(x0 -0.5, x1 + 0.25)
Out[213]: (-0.5, 6.5)
In [214]: plt.tight_layout() [Z8nql.png] ... 展开详请
做了一些快速测试,也许毫不奇怪,使用的内置版本dataframe.columns.values.tolist()是最快的:
In [1]: %timeit [column for column in df]
1000 loops, best of 3: 81.6 ?s per loop
In [2]: %timeit df.columns.values.tolist()
10000 loops, best of 3: 16.1 ?s per loop
In [3]: %timeit list(df)
10000 loops, best of 3: 44.9 ?s per loop
In [4]: % timeit list(df.columns.values)
10000 loops, best of 3: 38.4 ?s per loop
(我还是很喜欢这个list(dataframe),)... 展开详请