<p>您可以使用:</p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
#sample data
start = pd.to_datetime('2016-01-15')
rng = pd.date_range(start, periods=100)
data_df = pd.DataFrame({'date': rng, 'value': range(100)})
data_df.value = data_df.value * 15 / data_df.date.dt.day
print (data_df)
date value
0 2016-01-15 0.000000
1 2016-01-16 0.937500
2 2016-01-17 1.764706
3 2016-01-18 2.500000
4 2016-01-19 3.157895
5 2016-01-20 3.750000
6 2016-01-21 4.285714
7 2016-01-22 4.772727
8 2016-01-23 5.217391
9 2016-01-24 5.625000
10 2016-01-25 6.000000
...
...
</code></pre>
<p>如有必要,将列<code>date</code>转换为<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html" rel="noreferrer">^{<cd2>}</a>,然后将<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html" rel="noreferrer">^{<cd3>}</a>从<code>date</code>转换为:</p>
^{pr2}$
<p>按<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html" rel="noreferrer">^{<cd7>}</a>绘制<code>Series</code><code>data_df['value']</code>,然后设置<code>x</code>轴的格式:</p>
<pre><code>ax = data_df['value'].plot()
ticklabels = data_df.index.strftime('%Y-%m-%d')
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.show()
</code></pre>
<p>{a4}</p>