Geek Answers Handbook

How to call pandas.rolling.apply with parameters from several columns?

I have a dataset:

Open High Low Close 0 132.960 133.340 132.940 133.105 1 133.110 133.255 132.710 132.755 2 132.755 132.985 132.640 132.735 3 132.730 132.790 132.575 132.685 4 132.685 132.785 132.625 132.755

I am trying to use the roll.apply function for all lines, for example:

 df['new_col']= df[['Open']].rolling(2).apply(AccumulativeSwingIndex(df['High'],df['Low'],df['Close']))
  • shows an error

or

 df['new_col']= df[['Open', 'High', 'Low', 'Close']].rolling(2).apply(AccumulativeSwingIndex)
  • pass only the parameter from the "Open" column

Can someone help me?

+20
python pandas
source share
3 answers

Define your own roll

We can create a function that takes a window size argument w and any other key arguments. We use this to create a new DataFrame in which we will call groupby when passing the keyword arguments through kwargs .

Note: I did not need to use stride_tricks.as_strided , but it is concise and, in my opinion, appropriate.
 from numpy.lib.stride_tricks import as_strided as stride import pandas as pd def roll(df, w, **kwargs): v = df.values d0, d1 = v.shape s0, s1 = v.strides a = stride(v, (d0 - (w - 1), w, d1), (s0, s0, s1)) rolled_df = pd.concat({ row: pd.DataFrame(values, columns=df.columns) for row, values in zip(df.index, a) }) return rolled_df.groupby(level=0, **kwargs) roll(df, 2).mean() Open High Low Close 0 133.0350 133.2975 132.8250 132.930 1 132.9325 133.1200 132.6750 132.745 2 132.7425 132.8875 132.6075 132.710 3 132.7075 132.7875 132.6000 132.720

We can also use the pandas.DataFrame.pipe method for the same effect:

 df.pipe(roll, w=2).mean()

OLD RESPONSE

Panel deprecated. See above for an updated answer.

see fooobar.com/questions/456684 / ...

define our own roll

 def roll(df, w, **kwargs): roll_array = np.dstack([df.values[i:i+w, :] for i in range(len(df.index) - w + 1)]).T panel = pd.Panel(roll_array, items=df.index[w-1:], major_axis=df.columns, minor_axis=pd.Index(range(w), name='roll')) return panel.to_frame().unstack().T.groupby(level=0, **kwargs)

You must be able to:

 roll(df, 2).apply(your_function)

Using mean

 roll(df, 2).mean() major Open High Low Close 1 133.0350 133.2975 132.8250 132.930 2 132.9325 133.1200 132.6750 132.745 3 132.7425 132.8875 132.6075 132.710 4 132.7075 132.7875 132.6000 132.720 
 f = lambda df: df.sum(1) roll(df, 2, group_keys=False).apply(f) roll 1 0 532.345 1 531.830 2 0 531.830 1 531.115 3 0 531.115 1 530.780 4 0 530.780 1 530.850 dtype: float64
+12
source share

Since your sliding window is not too large, I think that you can also put them in the same data frame, and then use the apply function to reduce it.

For example, with a df dataset as follows

  Open High Low Close Date 2017-11-07 258.97 259.3500 258.09 258.67 2017-11-08 258.47 259.2200 258.15 259.11 2017-11-09 257.73 258.3900 256.36 258.17 2017-11-10 257.73 258.2926 257.37 258.09 2017-11-13 257.31 258.5900 257.27 258.33

You can simply add moving data to this data frame with

 window = 2 df1 = pd.DataFrame(index=df.index) for i in range(window): df_shifted = df.shift(i).copy() df_shifted.columns = ["{}-{}".format(s, i) for s in df.columns] df1 = df1.join(df_shifted) df1 Open-0 High-0 Low-0 Close-0 Open-1 High-1 Low-1 Close-1 Date 2017-11-07 258.97 259.3500 258.09 258.67 NaN NaN NaN NaN 2017-11-08 258.47 259.2200 258.15 259.11 258.97 259.3500 258.09 258.67 2017-11-09 257.73 258.3900 256.36 258.17 258.47 259.2200 258.15 259.11 2017-11-10 257.73 258.2926 257.37 258.09 257.73 258.3900 256.36 258.17 2017-11-13 257.31 258.5900 257.27 258.33 257.73 258.2926 257.37 258.09

Then you can easily apply with all the necessary data.

 df1.apply(AccumulativeSwingIndex, axis=1)
+2
source share

Try this to pass multiple columns to apply

 df['new_column'] = df.apply(lambda x: your_function(x['High'],x['Low'],x['Close']), axis=1)
-3
source share

More articles:


All Articles