Python Pandas每日平均值

Question

I'm having problems getting the daily average in a Pandas database. I've checked here Calculating daily average from irregular time series using pandas and it doesn't help. csv files look like this:

Date/Time,Value
12/08/13 12:00:01,5.553
12/08/13 12:30:01,2.604
12/08/13 13:00:01,2.604
12/08/13 13:30:01,2.604
12/08/13 14:00:01,2.101
12/08/13 14:30:01,2.666

and so on. My code looks like this:

# Import iButton temperatures
flistloc = '../data/iButtons/Readings/edit'
flist = os.listdir(flistloc)
# Create empty dictionary to store db for each file
pdib = {}
for file in flist:
    file = os.path.join(flistloc,file)
    # Calls function to return only name
    fname,_,_,_= namer(file)
    # Read each file to db
    pdib[fname] = pd.read_csv(file, parse_dates=0, dayfirst=True, index_col=0)
pdibkeys = sorted(pdib.keys())
#
# Calculate daily average for each iButton
for name in pdibkeys:
    pdib[name]['daily'] = pdib[name].resample('D', how = 'mean')```

The database seems ok but the averaging doesn't work. Here is what one looks like in iPython:

'2B5DE4': <class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1601 entries, 2013-08-12 12:00:01 to 2013-09-14 20:00:01
Data columns (total 2 columns):
Value    1601  non-null values
daily    0  non-null values
dtypes: float64(2)}

Anyone know what's going on?

Answer 1

The question is somewhat old, but i want to contribute anyway since i had to deal with this over and over again (and i think it's not really pythonic...).

The best solution, i have come up so far is to use the original index to create a new dataframe with mostly NA and fill it up at the end.

davg = df.resample('D', how='mean')
davg_NA = davg.loc[df.index]
davg_daily = davg_NA.fillna(method='ffill')

One can even cramp this in one line

df.resample('D', how='mean').loc[df.index].fillna(method='ffill')

Answer 2

When you call resample on your 1 column dataframe, the output is going to be a 1 column dataframe with a different index -- with each date as its own index entry. So when you try and assign it to a column in your original dataframe, I don't know what you expect to happen.

Three possible approaches (where df is your original dataframe):

1. Do you actually need the average values in your original dataframe? If not:

   davg = df.resample('D', how='mean')

2. If you do, a different solution is to merge the two dataframes on the date, after making sure that both have a column (not the index) with the date.

'

davg = df.resample('D', how='mean')
df['day'] = df.index.apply(lambda x: x.date()) 
davg.reset_index('Date/Time', inplace=True)
df = pandas.merge(df, davg, left_on='day',right_on='Date/Time')

3. An alternate to 2 (no intuition about whether it's faster) is to simply groupby the date.

    def compute_avg_val(df): df['daily average'] = df['Value'].mean() return df df['day'] = df.index.apply(lambda x: x.date()) grouped = df.groupby('day') df = grouped.apply(compute_avg_val) 

Answer 3

You can't resample at a lower frequency and then assign the resampled DataFrame or Series back into the one you resampled from, because the indices don't match:

In [49]: df = pd.read_csv(StringIO("""Date/Time,Value
12/08/13 12:00:01,5.553
12/08/13 12:30:01,2.604
12/08/13 13:00:01,2.604
12/08/13 13:30:01,2.604
12/08/13 14:00:01,2.101
12/08/13 14:30:01,2.666"""), parse_dates=0, dayfirst=True, index_col=0)

In [50]: df.resample('D')
Out[50]:
            Value
Date/Time
2013-08-12  3.022

[1 rows x 1 columns]

In [51]: df['daily'] = df.resample('D')

In [52]: df
Out[52]:
                     Value  daily
Date/Time
2013-08-12 12:00:01  5.553    NaN
2013-08-12 12:30:01  2.604    NaN
2013-08-12 13:00:01  2.604    NaN
2013-08-12 13:30:01  2.604    NaN
2013-08-12 14:00:01  2.101    NaN
2013-08-12 14:30:01  2.666    NaN

[6 rows x 2 columns]

One option is to take advantage of partial time indexing on the rows:

davg = df.resample('D', how='mean')
df.loc[str(davg.index.date[0]), 'daily'] = davg.values

which looks like this, when you expand the str(davg.index.date[0]) line:

df.loc['2013-08-12', 'daily'] = davg.values

This is a bit of hack, there might be a better way to do it.