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Pandas resample by Date and select 2nd smallest value

By : Huadan Song
Date : September 15 2020, 07:00 AM
should help you out Use Resampler.aggregate with custom function for return some value e.g. NaN if not exist second smallest val:
code :
def f(x):
    v= x.nsmallest(2)
        return v.iat[1]
        return np.nan

df = df.resample('D', on='DateTime').agg(f)
def f(x):
    v = np.sort(np.unique(x))
        return v[1]
        return np.nan

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How to resample date column loaded from CSV with pandas?

By : Elena
Date : March 29 2020, 07:55 AM
This might help you I have this data loaded from CSV.
code :
df.index = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S.%f')
df = df.drop('Date', axis=1)
df = df.resample('resamplestring', how='mean')

Resample by Date in Pandas — messes up a date in index

By : Aseel Khalied
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I have a multi-index dataFrame in Pandas, with data indexed by building, and then by date. The different columns represent different kinds of energy, and the values represent how much energy was used for a given month. Image of the dataframe's head is here. I'd like to turn this into yearly data. I currently have the line , Use 'AS-JUL':
code :

Pandas Resample on Date Columns

By : Russ King
Date : March 29 2020, 07:55 AM
it should still fix some issue I have a dataframe with dates as columns. I'd like to average the values from daily to a monthly level. I've tried with Time Grouper and Resample, but it doesn't like the columns names are strings and I can seem to figure out how to make columns into something like a DatetimeIndex. , You can using resample
code :
df.columns = pd.to_datetime(df.columns)
   2013-01-31  2013-02-28
A         1.5         3.5
B         5.5         7.5
   2013-01  2013-02
A      1.5      3.5
B      5.5      7.5

Resample in pandas without changing date

By : emil henriksen
Date : March 29 2020, 07:55 AM
wish of those help If we choose to move missing dates such as 2004-01-19 back to the last date within the resampling time period (e.g. 2004-01-16), then that could be accomplished by applying the resampling not only to the closing prices, but also to the Date itself:

Pandas group_by date and resample

By : J.DOE
Date : March 29 2020, 07:55 AM
may help you . resample is in some sense just a special case of groupby - rather than grouping on distinct values, which is what grouppy('date') would do, it groups a time-based transformation of the index, which is why you need to set the index. Alternatively, you could do:
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