Splitting Column Lists in Pandas DataFrame

问题

I'm looking for an good way to solve the following problem. My current fix is not particularly clean, and I'm hoping to learn from your insight.

Suppose I have a Panda DataFrame, whose entries look like this:

>>> df=pd.DataFrame(index=[1,2,3],columns=['Color','Texture','IsGlass'])

>>> df['Color']=[np.nan,['Red','Blue'],['Blue', 'Green', 'Purple']]
>>> df['Texture']=[['Rough'],np.nan,['Silky', 'Shiny', 'Fuzzy']]
>>> df['IsGlass']=[1,0,1]

>>> df
                            Color                   Texture   IsGlass
    1                         NaN                  ['Rough']        1
    2              ['Red', 'Blue']                       NaN        0 
    3  ['Blue', 'Green', 'Purple']  ['Silky','Shiny','Fuzzy']       1

So each observation in the index corresponds to something I measured about its color, texture, and whether it's glass or not. What I'd like to do is turn this into a new "indicator" DataFrame, by creating a column for each observed value, and changing the corresponding entry to a one if I observed it, and NaN if I have no information.

>>> df
         Red Blue Green Purple Rough Silky Shiny Fuzzy Is Glass               
    1    Nan  Nan  Nan   Nan    1     NaN   Nan   Nan     1        
    2     1    1   Nan   Nan    Nan   Nan   Nan   Nan     0 
    3    Nan   1    1     1     Nan    1     1     1      1

I have solution that loops over each column, looks at its values, and through a series of Try/Excepts for non-Nan values splits the lists, creates a new column, etc., and concatenates.

This is my first post to StackOverflow - I hope this post conforms to the posting guidelines. Thanks.

回答1:

Stacking Hacks!

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

df = df.stack().unstack(fill_value=[])

def b(c):
    d = mlb.fit_transform(c)
    return pd.DataFrame(d, c.index, mlb.classes_)

pd.concat([b(df[c]) for c in ['Color', 'Texture']], axis=1).join(df.IsGlass)

   Blue  Green  Purple  Red  Fuzzy  Rough  Shiny  Silky IsGlass
1     0      0       0    0      0      1      0      0       1
2     1      0       0    1      0      0      0      0       0
3     1      1       1    0      1      0      1      1       1

回答2:

I am just using pandas, get_dummies

l=[pd.get_dummies(df[x].apply(pd.Series).stack(dropna=False)).sum(level=0) for x in ['Color','Texture']]
pd.concat(l,axis=1).assign(IsGlass=df.IsGlass)
Out[662]: 
   Blue  Green  Purple  Red  Fuzzy  Rough  Shiny  Silky  IsGlass
1     0      0       0    0      0      1      0      0        1
2     1      0       0    1      0      0      0      0        0
3     1      1       1    0      1      0      1      1        1

回答3:

For each texture/color in each row, I check if the value is null. If not, we add that value as a column = 1 for that row.

import numpy as np
import pandas as pd

df=pd.DataFrame(index=[1,2,3],columns=['Color','Texture','IsGlass'])

df['Color']=[np.nan,['Red','Blue'],['Blue', 'Green', 'Purple']]
df['Texture']=[['Rough'],np.nan,['Silky', 'Shiny', 'Fuzzy']]
df['IsGlass']=[1,0,1]

for row in df.itertuples():

    if not np.all(pd.isnull(row.Color)):
        for val in row.Color:
            df.loc[row.Index,val] = 1     

    if not np.all(pd.isnull(row.Texture)):
        for val in row.Texture:
            df.loc[row.Index,val] = 1

来源：https://stackoverflow.com/questions/46942847/splitting-column-lists-in-pandas-dataframe

标签

python-3.x

pandas

dataframe

pandas-groupby