# How to get values at start and end of each day in a time series

## Question:

I have a time series with 2 columns and ~10k rows:

Time Value
2022-01-01 09:53:34 1.9342
2022-01-01 19:03:21 2.3213
2022-01-02 10:14:32 3.4332
2022-01-02 11:31:51 0.387

I want to summarize the data by day and get the start, end, min and max for each day:

Date Start Min Max End
2022-01-01 1.9342 1.9342 2.3213 2.3213
2022-01-02 3.4332 0.387 3.4332 0.387

I could probably do this with a bunch of nested for loops iterating through the initial dataframe but looking for a more elegant solution.

So far, I can get the min and max for each day by doing the following:

``````# convert date time to two columns to separate the date and times
df["date_only"] = df.time.dt.date
df["time_only"] = df.time.dt.time
df.drop(columns="time", inplace=True)

# group by date, temporarily drop the time, and get the min and max values for each day
min_df = df.drop(columns="time_only").groupby(["date_only"]).min()
max_df = df.drop(columns="time_only").groupby(["date_only"]).max()

#concat the columns afterwards
``````

I’m struggling to find a way to get the `start` and `end` values for each day though. If I group by both the `date_only` and `time_only` columns, I can get the time but can’t seem to reference the `value` at that time.

I could get the `start` and `end` for each `date` and go back to the initial `df` to `.loc` with for loops or am I missing a much more obvious and elegant solution?

You can use the agg method with a dictionary as below

``````     df["date"] = df["Time"].dt.date
df.set_index("date", inplace=True)

summary = df.groupby(df.index).agg({"Value": ["first", "min", "max", "last"]})
summary.columns = ["Start", "Min", "Max", "End"]
``````

Here is a possible approach using `df.groupby()` and `pandas.DataFrame.agg`

``````df['Date'] = pd.to_datetime(df['Time'])
df = df.groupby(df['Date'].dt.date).agg(
Start=('Value', 'first'),
Min=('Value', 'min'),
Max=('Value', 'max'),
End=('Value', 'last')
).reset_index()
print(df)
``````

``````         Date   Start     Min     Max     End
0  2022-01-01  1.9342  1.9342  2.3213  2.3213
1  2022-01-02  3.4332  0.3870  3.4332  0.3870
``````
``````import pandas as pd

df = pd.DataFrame(
{'Time': ['2022-01-01 09:53:34', '2022-01-01 19:03:21',
'2022-01-02 10:14:32', '2022-01-02 11:31:51'],
'Value': [1.9342, 2.3213, 3.4332, 0.387]}
)

df['Time'] = pd.to_datetime(df['Time'] , infer_datetime_format=True)
df.set_index('Time', inplace=True)

Start = df.resample("D").agg({'Value':'first'}).rename(columns={'Value':'Start'})
Min = df.resample("D").agg({'Value':'min'}).rename(columns={'Value':'Min'})
Max = df.resample("D").agg({'Value':'max'}).rename(columns={'Value':'Max'})
End = df.resample("D").agg({'Value':'last'}).rename(columns={'Value':'End'})

print( pd.concat([Start, Min, Max, End], axis=1) )

#              Start     Min     Max     End
# Time
# 2022-01-01  1.9342  1.9342  2.3213  2.3213
# 2022-01-02  3.4332  0.3870  3.4332  0.3870
``````
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