按天分组DatetimeIndex与Pandas

Question

我已经将我的数据帧索引到一个日期时间索引，如下所示：

timstamp                 _id
2018-05-09 16:56:40.940  somedata1
2018-05-09 16:54:03.959  somedata2
2018-05-10 16:53:42.975  somedata3
2018-05-11 16:52:44.897  somedata4
2018-05-11 16:46:35.902  somedata5

我想展示每个日期的频率，如下所示：

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提前谢谢！在

Answer 1

str.split+groupby+count

df.groupby(df['timstamp'].str.split().str[0])._id.count().reset_index()

     timstamp  _id
0  2018-05-09    2
1  2018-05-10    1
2  2018-05-11    2

---

to_datetime+groupby+count

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或者

df['timstamp'] = pd.to_datetime(df['timstamp']).dt.floor('D')
df.groupby('timstamp', as_index=False)._id.count()

    timstamp  _id
0 2018-05-09    2
1 2018-05-10    1
2 2018-05-11    2

Answer 2

这是一种方法。在

# convert to datetime
df['timestamp'] = pd.to_datetime(df['timestamp'])

# normalize, count values, convert to dataframe
res = df['timestamp'].dt.normalize()\
                     .value_counts()\
                     .to_frame().reset_index()

# rename columns
res.columns = ['timestamp', 'count']

结果：

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