使用另一个数据框在数据框中创建子列 [英] Create a sub columns in the dataframe using a another dataframe
问题描述
我是python和pandas的新手.在这里，我有一个以下数据框.
I am new to the python and pandas. Here, I have a following dataframe .
did features offset word JAPE_feature manual_feature
0 200 0 aa 200 200
0 200 11 bf 200 200
0 200 12 vf 100 100
0 100 13 rw 2200 2200
0 100 14 asd 2600 100
0 2200 16 dsdd 2200 2200
0 2600 18 wd 2200 2600
0 2600 20 wsw 2600 2600
0 4600 21 sd 4600 4600
现在，我有一个数组，其中包含可以为该ID显示的所有特征值.
Now , I have an array which has all the feature values which can appear for that id.
feat = [100,200,2200,2600,156,162,4600,100]
现在，我正在尝试创建一个看起来像这样的数据框，
Now, I am trying to create a dataframe whic will look like,
id Features
100 200 2200 2600 156 162 4600 100
0 0 1 0 0 0 0 0 0
1 0 1 0 0 0 0 0 0
2 0 1 0 0 0 0 0 0
3 0 1 0 0 0 0 0 0
4 1 0 0 0 0 0 0 0
5 1 0 0 0 0 0 0 0
7 0 0 1 0 0 0 0 0
8 0 0 0 1 0 0 0 0
9 0 0 0 1 0 0 0 0
10 0 0 0 0 0 0 1 0
所以，在进行比较时，
feature_manual
1
1
0
0
1
1
1
1
1
Here compairing the features and the manual_feature columns. if values are same then 1 or else 0. so 200 and 200 for 0 is same in both so 1
因此，这是预期的输出.在这里，我正在尝试在新的csv中为该功能添加值1，并为其他0添加值.
So, this is the expected output. Here I am trying to add the value 1 for that feature in the new csv and for other 0.
So, it is by row by row.
因此，如果我们在第一行中检查该特征为200，则200处为1，其他为0.
So, If we check in the first row the feature is 200 so there is 1 at 200 and others are 0.
有人可以帮助我吗?
我尝试过的是
mux = pd.MultiIndex.from_product([['features'],feat)
df = pd.DataFrame(data, columns=mux)
SO，此处创建子列，但删除所有其他值.有人可以帮我吗?
SO, Here creatig subcolumns but removing all other values . can any one help me ?
推荐答案
使用 get_dummies 与如果需要MultiIndex，则仅将mux传递给reindex，还将id列转换为index:
If need MultiIndex only pass mux to reindex, but also convert id column to index:
feat = [100,200,2200,2600,156,162,4600,100]
mux = pd.MultiIndex.from_product([['features'],feat])
df = pd.get_dummies(df.set_index('id')['features']).reindex(mux, axis=1, fill_value=0)
print (df)
features
100 200 2200 2600 156 162 4600 100
id
0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0
cols = ['features', 'JAPE_feature', 'manual_feature']
df = pd.get_dummies(df, columns=cols)
df.columns = df.columns.str.rsplit('_',1, expand=True)
print (df)
did offset word features JAPE_feature \
NaN NaN NaN 100 200 2200 2600 4600 100 200 2200 2600
0 0 0 aa 0 1 0 0 0 0 1 0 0
1 0 11 bf 0 1 0 0 0 0 1 0 0
2 0 12 vf 0 1 0 0 0 1 0 0 0
3 0 13 rw 1 0 0 0 0 0 0 1 0
4 0 14 asd 1 0 0 0 0 0 0 0 1
5 0 16 dsdd 0 0 1 0 0 0 0 1 0
6 0 18 wd 0 0 0 1 0 0 0 1 0
7 0 20 wsw 0 0 0 1 0 0 0 0 1
8 0 21 sd 0 0 0 0 1 0 0 0 0
manual_feature
4600 100 200 2200 2600 4600
0 0 0 1 0 0 0
1 0 0 1 0 0 0
2 0 1 0 0 0 0
3 0 0 0 1 0 0
4 0 1 0 0 0 0
5 0 0 0 1 0 0
6 0 0 0 0 1 0
7 0 0 0 0 1 0
8 1 0 0 0 0 1
如果要避免没有MultiIndex的列的列中MultIndex的值丢失:
If want avoid missing values in MultIndex in columns for columns with no MultiIndex:
cols = ['features', 'JAPE_feature', 'manual_feature']
df = df.set_index(df.columns.difference(cols).tolist())
df = pd.get_dummies(df, columns=cols)
df.columns = df.columns.str.rsplit('_',1, expand=True)
print (df)
features JAPE_feature \
100 200 2200 2600 4600 100 200 2200 2600 4600
did offset word
0 0 aa 0 1 0 0 0 0 1 0 0 0
11 bf 0 1 0 0 0 0 1 0 0 0
12 vf 0 1 0 0 0 1 0 0 0 0
13 rw 1 0 0 0 0 0 0 1 0 0
14 asd 1 0 0 0 0 0 0 0 1 0
16 dsdd 0 0 1 0 0 0 0 1 0 0
18 wd 0 0 0 1 0 0 0 1 0 0
20 wsw 0 0 0 1 0 0 0 0 1 0
21 sd 0 0 0 0 1 0 0 0 0 1
manual_feature
100 200 2200 2600 4600
did offset word
0 0 aa 0 1 0 0 0
11 bf 0 1 0 0 0
12 vf 1 0 0 0 0
13 rw 0 0 1 0 0
14 asd 1 0 0 0 0
16 dsdd 0 0 1 0 0
18 wd 0 0 0 1 0
20 wsw 0 0 0 1 0
21 sd 0 0 0 0 1
如果要通过manual_feature列比较列表中的某些列，请使用 DataFrame.eq 转换为整数:
If want compare some column from list by manual_feature column use DataFrame.eq with converting to integers:
cols = ['JAPE_feature', 'features']
df1 = df[cols].eq(df['manual_feature'], axis=0).astype(int)
print (df1)
JAPE_feature features
0 1 1
1 1 1
2 1 0
3 1 0
4 0 1
5 1 1
6 0 1
7 1 1
8 1 1
这篇关于使用另一个数据框在数据框中创建子列的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
