summing the number of occurrences per day pandas
dataframe, pandas, python
Solution
If your `timestamp` index is a `DatetimeIndex`:
import io
import pandas as pd
content = '''\
timestamp score
2013-06-29 00:52:28+00:00 -0.420070
2013-06-29 00:51:53+00:00 -0.445720
2013-06-28 16:40:43+00:00 0.508161
2013-06-28 15:10:30+00:00 0.921474
2013-06-28 15:10:17+00:00 0.876710
'''
df = pd.read_table(io.BytesIO(content), sep='\s{2,}', parse_dates=[0], index_col=[0])
print(df)
so `df` looks like this:
score
timestamp
2013-06-29 00:52:28 -0.420070
2013-06-29 00:51:53 -0.445720
2013-06-28 16:40:43 0.508161
2013-06-28 15:10:30 0.921474
2013-06-28 15:10:17 0.876710
print(df.index)
# <class 'pandas.tseries.index.DatetimeIndex'>
You can use:
print(df.groupby(df.index.date).count())
which yields
score
2013-06-28 3
2013-06-29 2
Note the importance of the `parse_dates` parameter. Without it, the index would just be a `pandas.core.index.Index` object. In which case you could not use `df.index.date`.
So the answer depends on the `type(df.index)`, which you have not shown...
Problem
I have a data set like so in a pandas dataframe: ``` score timestamp 2013-06-29 00:52:28+00:00 -0.420070 2013-06-29 00:51:53+00:00 -0.445720 2013-06-28 16:40:43+00:00 0.508161 2013-06-28 15:10:30+00:00 0.921474 2013-06-28 15:10:17+00:00 0.876710 ``` I need to get counts for the number of measurements, that occur so I am looking for something like this: ``` count timestamp 2013-06-29 2 2013-06-28 3 ``` I do not care about the sentiment column I want the count of the occurrences per day.