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How to resample a pandas data frame every half second with data every second?


By : Chau Nguyen
Date : September 15 2020, 12:00 AM
around this issue I have a pandas DataFrame like this with time in the index and column A: , Let try resample interpolate
code :
df=df.resample('500ms').interpolate()
df
                           A
2018-01-24 00:06:00.000  3.0
2018-01-24 00:06:00.500  3.5
2018-01-24 00:06:01.000  4.0
2018-01-24 00:06:01.500  6.0
2018-01-24 00:06:02.000  8.0
2018-01-24 00:06:02.500  6.0
2018-01-24 00:06:03.000  4.0
2018-01-24 00:06:03.500  3.0
2018-01-24 00:06:04.000  2.0


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How to resample a Pandas multi-index data frame via methods depending on the column name


By : user1999107
Date : March 29 2020, 07:55 AM
wish helps you If you start with f, you can use a groupby with a TimeGrouper to do the resample "manually":
code :
In [11]: grp = f.groupby(pd.TimeGrouper('6H', level=0))

In [12]: grp['A'].sum()
Out[12]:
0
2001-01-01   -1.805954
Freq: 6H, Name: A, dtype: float64

In [13]: grp['B'].mean()
Out[13]:
0
2001-01-01   -0.461053
Freq: 6H, Name: B, dtype: float64
In [21]: grp2 = f.groupby([pd.TimeGrouper('6H', level=0),
                             f.index.get_level_values('extra')])

In [22]: grp2['A'].sum()
Out[22]:
0           extra
2001-01-01  0        2.030321
            1       -3.836275
Name: A, dtype: float64

In [23]: grp2['B'].mean()
Out[23]:
0           extra
2001-01-01  0       -0.554839
            1       -0.367267
Name: B, dtype: float64
In [31]: f2 = g.stack(level=1)  # Note: use stack to get f from g
In [32]: pd.DataFrame({'A': grp['A'].sum(), 'B': grp['B'].mean()})
Out[32]:
                         A         B
0          extra
2001-01-01 0     -2.762064 -0.269427
           1     -2.006839 -0.026213

In [33]: _.unstack(level=1)
Out[33]:
                   A                   B
extra              0         1         0         1
0
2001-01-01 -2.762064 -2.006839 -0.269427 -0.026213
In [41]: dict(zip(g.columns,
                  map({'A': 'sum', 'B': 'mean'}.get,
                      [x[0] for x in g.columns])))
Out[41]: {('A', 0): 'sum', ('A', 1): 'sum', ('B', 0): 'mean', ('B', 1): 'mean'}

In [42]: g.resample('6H', _)
Out[42]:
                   A         B         A         B
                   1         0         0         1
time
2001-01-01 -3.836275 -0.554839  2.030321 -0.367267

Pandas data frame: resample with linear interpolation


By : user2247568
Date : March 29 2020, 07:55 AM
Hope that helps try creating two separate dataframes, reset_index them (so they have the same numerical index), fillna on them, and then just do the math on df1 and df2. e.g:
code :
df1 = df.resample(rule='5Min',how='last',closed='left').reset_index().fillna(method='ffill')
df2 = df.resample(rule='5Min',how='first',closed='left').reset_index().fillna(method='ffill')

dt = df1.datetime - df2.datetime
px_fld = df1.price + ...

pandas resample when cumulative function returns data frame


By : pooya parsa
Date : March 29 2020, 07:55 AM
I wish this help you Just define your function f with a last_row parameter. You can default it to False so that it returns the entire dataframe. When True it returns the last row
code :
def f(data, last_row=False):
    df = ((1+data).cumprod(axis=0)-1)
    if last_row:
        return df.iloc[-1]
    return df
df.resample('BM').apply(f, last_row=True)

                    0           1          2
2017-01-31   0.185662   -0.580058  -1.004879
2017-02-28  -1.004035   -0.999878  17.059846
2017-03-31  -0.995280   -1.000001  -1.000507
2017-04-28  -1.000656 -240.369487  -1.002645
2017-05-31  47.646827  -72.042190  -1.000016
....
df.resample('BM').apply(f)

Unable to resample then plot a Pandas Data Frame


By : Kevin Jump
Date : March 29 2020, 07:55 AM
it should still fix some issue I have been trying to plot a simple resampled data that is coming from a Pandas dataframe. Here is my initial code: , date is not column, but index, so need:
code :
x = df_sampled.index
y = df_sampled['value']
df_sampled = df.set_index('date').resample('5D').mean().reset_index()
#alternative
#df_sampled = df.resample('5D', on='date').mean().reset_index()

x = df_sampled['date']
y = df_sampled['value']

Resample (unsampling) yields unpredictable errors on Pandas data frame


By : Alexey Egoshin
Date : March 29 2020, 07:55 AM
This might help you My index contained duplicates, and was a multi-level index. Instead of using pandas parse_date, I just read and reassemble columns into dates, then set it back as an index. take a look
code :
import pandas as pd

climat = pd.read_csv('data_export_2019-02-16 (1).csv', delimiter=';', na_values='--')            #, parse_dates=True, index_col = [2,1,0], 
idx = pd.to_datetime(climat.Year.astype(str) + '-' + climat.Month.astype(str) + '-' + climat.Day.astype(str) + ' ' + climat.Hour.astype(str))
climat.set_index(idx, inplace=True)
climat.drop(['Year','Day', 'Month', 'Hour'], axis=1, inplace=True)

climat_ = climat.drop_duplicates()
upsampled = climat_[['TMP','WNDCHILL','DPT','WNDSPD','WNDGUST','HR','PRMSL','VISIBILITY','TCDC']].resample('15Min')
interpolated = upsampled.interpolate(method='spline', order=2)
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