how to collapse columns in pandas on null values?

问题

Suppose I have the following dataframe:

pd.DataFrame({'col1':    ["a", "a", np.nan, np.nan, np.nan],
            'override1': ["b", np.nan, "b", np.nan, np.nan],
            'override2': ["c", np.nan, np.nan, "c", np.nan]})


    col1    override1   override2
0     a        b          c
1     a       NaN        NaN
2     NaN      b         NaN
3     NaN     NaN         c
4     NaN     NaN         NaN

Is there a way to collapse the 3 columns into one column, where override2 overrides override1, which overrides col1, however, in case there is NaN, then the values bofore is to be kept? Also, I am mainly looking for a way where I would not have to make an additional column. I am really looking for a built-in pandas solution.

This is the output I am looking for:

 collapsed
0  c
1  a
2  b
3  c
4  NaN

回答1:

using ffill

df.ffill(1).iloc[:,-1]

回答2:

Performance NOT in mind but rather beauty and elegance (-:

df.stack().groupby(level=0).last().reindex(df.index)

0      c
1      a
2      b
3      c
4    NaN
dtype: object

回答3:

A straightforward solution involves forward filling and picking off the last column. This was mentioned in the comments.

df.ffill(1).iloc[:,-1].to_frame(name='collapsed')

  collapsed
0         c
1         a
2         b
3         c
4       NaN

If you're interested in performance, we can use a modified version of Divakar's justify function:

pd.DataFrame({'collapsed': justify(
    df.values, invalid_val=np.nan, axis=1, side='right')[:,-1]
})

  collapsed
0         c
1         a
2         b
3         c
4       NaN

Reference.

def justify(a, invalid_val=0, axis=1, side='left'):    
    """
    Justifies a 2D array

    Parameters
    ----------
    A : ndarray
        Input array to be justified
    axis : int
        Axis along which justification is to be made
    side : str
        Direction of justification. It could be 'left', 'right', 'up', 'down'
        It should be 'left' or 'right' for axis=1 and 'up' or 'down' for axis=0.

    """

    if invalid_val is np.nan:
        mask = pd.notna(a)   # modified for strings
    else:
        mask = a!=invalid_val
    justified_mask = np.sort(mask,axis=axis)
    if (side=='up') | (side=='left'):
        justified_mask = np.flip(justified_mask,axis=axis)
    out = np.full(a.shape, invalid_val, dtype=a.dtype) 
    if axis==1:
        out[justified_mask] = a[mask]
    else:
        out.T[justified_mask.T] = a.T[mask.T]
    return out

回答4:

With focus on performance, here's one with NumPy -

In [106]: idx = df.shape[1] - 1 - df.notnull().to_numpy()[:,::-1].argmax(1)

In [107]: pd.Series(df.to_numpy()[np.arange(len(df)),idx])
Out[107]: 
0      c
1      a
2      b
3      c
4    NaN
dtype: object

回答5:

Here's one approach:

df.lookup(df.index , df.notna().cumsum(1).idxmax(1))
# array(['c', 'a', 'b', 'c', nan], dtype=object)

Or equivalently working with the underlying numpy arrays, and changing idxmax with ndarray.argmax:

df.values[df.index, df.notna().cumsum(1).values.argmax(1)]
# array(['c', 'a', 'b', 'c', nan], dtype=object)

回答6:

import pandas as pd
import numpy as np
df=pd.DataFrame({'col1':    ["a", "a", np.nan, np.nan, np.nan],
            'override1': ["b", np.nan, "b", np.nan, np.nan],
            'override2': ["c", np.nan, np.nan, "c", np.nan]})

print(df)
df=df['col1'].fillna('') + df['override1'].fillna('')+ df['override2'].fillna('')
print(df)

来源：https://stackoverflow.com/questions/56583174/how-to-collapse-columns-in-pandas-on-null-values

标签

python

pandas

numpy

concatenation

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