pandas :具有multiIndex数据框的条形图 [英] Pandas: bar plot with multiIndex dataframe

问题描述

我有一个带有TIMESTAMP列的pandas DataFrame(不是索引)，时间戳格式如下:

I have a pandas DataFrame with a TIMESTAMP column (not the index), and the timestamp format is as follows:

2015-03-31 22:56:45.510

我也有称为CLASS和AXLES的列.我想分别为AXLES的每个唯一值(AXLES可以取3-12之间的整数值)分别计算每个月的记录数.

I also have columns called CLASS and AXLES. I would like to compute the count of records for each month separately for each unique value of AXLES (AXLES can take an integer value between 3-12).

我想出了resample和groupby的组合:

resamp = dfWIM.set_index('TIMESTAMP').groupby('AXLES').resample('M', how='count').CLASS

这似乎给了我一个multiIndex数据框对象，如下所示.

This seems to give me a multiIndex dataframe object, as shown below.

In [72]: resamp

Out [72]:

AXLES  TIMESTAMP 
3      2014-07-31      5517
       2014-08-31     31553
       2014-09-30     42816
       2014-10-31     49308
       2014-11-30     44168
       2014-12-31     45518
       2015-01-31     54782
       2015-02-28     52166
       2015-03-31     47929
4      2014-07-31      3147
       2014-08-31     24810
       2014-09-30     39075
       2014-10-31     46857
       2014-11-30     42651
       2014-12-31     48282
       2015-01-31     42708
       2015-02-28     43904
       2015-03-31     50033

在这里，我如何才能访问此multiIndex对象的不同组件，以针对以下条件创建条形图?

From here, how can I access different components of this multiIndex object to create a bar plot for the following conditions?

• 在AXLES = 3时显示数据
• 以月-年"格式显示x刻度(无天，小时，分钟等)

谢谢！

编辑:以下代码为我提供了绘图，但是我无法将xtick格式更改为MM-YY.

EDIT: Following code gives me the plot, but I could not change the xtick formatting to MM-YY.

resamp[3].plot(kind='bar')

EDIT 2 是一个代码片段，可生成与我相似的数据的一小部分样本:

EDIT 2 below is a code snippet that generates a small sample of the data similar to what I have:

dftest = {'TIMESTAMP':['2014-08-31','2014-09-30','2014-10-31'], 'AXLES':[3, 3, 3], 'CLASS':[5,6,7]}
dfTest = pd.DataFrame(dftest)
dfTest.TIMESTAMP = pd.to_datetime(pd.Series(dfTest.TIMESTAMP))
resamp = dfTest.set_index('TIMESTAMP').groupby('AXLES').resample('M', how='count').CLASS
resamp[3].plot(kind='bar')

下面是解决方法:

A.绘制整个重新采样的数据帧(基于@Ako的建议):

A.Plot the whole resampled dataframe (based on @Ako 's suggestion):

df = resamp.unstack(0)
df.index = [ts.strftime('%b 20%y') for ts in df.index]
df.plot(kind='bar', rot=0)

B.从重采样的数据框中绘制单个索引(基于@Alexander的建议):

B.Plot an individual index from the resampled dataframe (based on @Alexander 's suggestion):

df = resamp[3]
df.index = [ts.strftime('%b 20%y') for ts in df.index]
df.plot(kind='bar', rot=0)

推荐答案

以下内容应该可以工作，但是没有一些数据很难进行测试.

The following should work, but it is difficult to test without some data.

首先重置索引以访问TIMESTAMP列.然后使用strftime将其格式化为所需的文本表示形式(例如mm-yy).最后，将索引重置为AXLES和TIMESTAMP.

Start by resetting your index to get access to the TIMESTAMP column. Then use strftime to format it to your desired text representation (e.g. mm-yy). Finally, reset the index back to AXLES and TIMESTAMP.

df = resamp.reset_index()
df['TIMESTAMP'] = [ts.strftime('%m-%y') for ts in df.TIMESTAMP]
df.set_index(['AXLES', 'TIMESTAMP'], inplace=True)
>>> df.xs(3, level=0).plot(kind='bar')

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