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Reconstruct a categorical variable from dummies in pandas
(7 answers)
Closed 4 years ago.
df:
category A B C D
x 0 1 0 0
y 1 0 0 0
z 1 0 0 0
l 0 0 0 1
m 0 1 0 0
n 0 0 1 0
how to get df like below
Category Sub-category
x B
y A
z A
l D
m B
n C
I tried:
df['sector'] = df.apply(lambda x: df.columns[x.argmax()], axis = 1)
but getting TypeError: ("reduction operation 'argmax' not allowed for this dtype", 'occurred at index 1')
Just do
df['sub_category'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)
category A B C D sub_category
0 x 0 1 0 0 B
1 y 1 0 0 0 A
2 z 1 0 0 0 A
3 l 0 0 0 1 D
4 m 0 1 0 0 B
5 n 0 0 1 0 C
Of course you may select only the columns you want
df[['category', 'sub_category']]
category sub_category
0 x B
1 y A
2 z A
3 l D
4 m B
5 n C
Related
I am trying to convert a data frame into a 1,0 matrix format
data = pd.DataFrame({'Val1':['A','B','B'],
'Val2':['C','A','D'],
'Val3':['E','F','C'],
'Comb':['Comb1','Comb2','Comb3']})
data:
Val1 Val2 Val3 Comb
0 A C E Comb1
1 B A F Comb2
2 B D C Comb3
What I need is to convert to below data frame
Comb A C D E B F
0 Comb1 1 1 0 1 0 0
1 Comb2 1 0 0 0 1 1
2 Comb3 0 1 1 0 1 0
I was able to do it with a FOR loop but as my dataframe increases, the processing time increases. Is there a better way to do it?
header = set(data[['Val1','Val2','Val3']].values.ravel())
matrix = pd.DataFrame(columns=header)
for i in range(data.shape[0]):
temp_dict = {data["Val1"].iloc[i]:1, data["Val2"].iloc[i]:1, data["Val3"].iloc[i]:1}
matrix = matrix.append(temp_dict, ignore_index=True)
matrix = matrix.loc[:, matrix.columns.notnull()]
matrix = matrix.fillna(0)
matrix = pd.merge(data[["Comb"]],matrix, left_index=True, right_index=True, how= 'outer')
Thanks!
There may be a better solution, but this is what came to my mind: convert each raw to a dictionary of "present" letters, build a Series from the dictionary, and combine the Series into a dataframe.
data.loc[:, 'Val1':'Val3'].apply(lambda row:
pd.Series({letter: 1 for letter in row}), axis=1)\
.fillna(0).astype(int).join(data.Comb)
# A B C D E F Comb
#0 1 0 1 0 1 0 Comb1
#1 1 1 0 0 0 1 Comb2
#2 0 1 1 1 0 0 Comb3
There are propably multiple ways to solve this, I used pd.crosstab for it:
import pandas as pd
data = pd.DataFrame({'Val1':['A','B','B'],
'Val2':['C','A','D'],
'Val3':['E','F','C'],
'Comb':['Comb1','Comb2','Comb3']})
data["lst"] = data[['Val1', 'Val2', 'Val3']].values.tolist()
data = data.explode("lst")
print(pd.crosstab(data["Comb"], data["lst"]))
Out[20]:
lst A B C D E F
Comb
Comb1 1 0 1 0 1 0
Comb2 1 1 0 0 0 1
Comb3 0 1 1 1 0 0
I guess this will work. Please let me know if it works
pd.get_dummies(data, columns=['Val1','Val2','Val3'],prefix="",prefix_sep="").groupby(axis=1,level=0).sum()
Here's another way:
data.melt('Comb').set_index('Comb')['value'].str.get_dummies().sum(level=0).reset_index()
Output:
Comb A B C D E F
0 Comb1 1 0 1 0 1 0
1 Comb2 1 1 0 0 0 1
2 Comb3 0 1 1 1 0 0
I have a pandas.DataFrame that looks like this:
A B C D E F
0 0 1 0 0 0
1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 0 1 0
4 0 0 1 0 0
There are several rows that share a 1 in their columns and in each row there is only one 1 present. I want to merge the rows with each other so the resulting dataFrame would onyl consist of one row, that combines all the 1s of the dataframe, like this:
A B C D E F
0 1 1 1 1 0
Is there a smart, easy way to do this with pandas?
Use DataFrame.sum, then compare for greater or equal by Series.ge and last convert to 0,1 by Series.view:
s = df.sum().ge(1).view('i1')
Another idea if 0,1 values only is use DataFrame.any with convert mask to 0,1:
s = df.any().view('i1')
print (s)
A 1
B 1
C 1
D 1
E 1
F 0
dtype: int8
We can do
df.sum().ge(1).astype(int)
Out[316]:
A 1
B 1
C 1
D 1
E 1
F 0
dtype: int32
Hope there is anybody who feels good with PYEDA.
I want to add fictious variables to function
Let me have f=x1, but how can I get truthtable for this function , which will have x2 too
Like truthtable for f(x1)=x1 is:
x1 f
0 0
1 1
But for f(x1,x2)=x1 is:
x1 x2 f
0 0 0
0 1 0
1 0 1
1 1 1
But I will get first table, pyeda will simplify x1&(x2|~x2) to x1 automatically. How can I add this x2?
def calcFunction(function, i):
#here is is point with dimension-size 4
function=function.restrict({x4:i[3]})
function = function.restrict({x3:i[2]})
function = function.restrict({x2:i[1]})
function = function.restrict({x1:i[0]})
if function.satisfy_one() is not None:
return 1
return 0
Here is my algo to fix it, I am calculating func in each point manually, where function can containt 1-4 variables and I am calculating for all point and combinations of x1...x4.
I'm not sure I understand the question as asked, but you might want to try the expression simplify method.
For example:
In [1]: f = (X[1] & X[2]) | (X[3] | X[4] | ~X[3])
In [2]: expr2truthtable(f)
Out[2]:
x[4] x[3] x[2] x[1]
0 0 0 0 : 1
0 0 0 1 : 1
0 0 1 0 : 1
0 0 1 1 : 1
0 1 0 0 : 1
0 1 0 1 : 1
0 1 1 0 : 1
0 1 1 1 : 1
1 0 0 0 : 1
1 0 0 1 : 1
1 0 1 0 : 1
1 0 1 1 : 1
1 1 0 0 : 1
1 1 0 1 : 1
1 1 1 0 : 1
1 1 1 1 : 1
In [3]: f = f.simplify()
In [4]: f
Out[4]: 1
In [5]: expr2truthtable(f)
Out[5]: 1
I have a dataset 'df' that looks something like this:
MEMBER seen_1 seen_2 seen_3 seen_4 seen_5 seen_6
A 1 0 0 1 0 1
B 1 1 0 0 1 0
C 1 1 1 0 0 1
D 0 0 1 0 0 1
As you can see there are several rows of ones and zeros. Can anyone suggest me a code in python such that I am able to count the number of times '1' occurs continuously before the first occurrence of a 1, 0 and 0 in order. For example, for member A, the first double zero event occurs at seen_2 and seen_3, so the event will be 1. Similarly for the member B, the first double zero event occurs at seen_3 and seen_4 so there are two 1s that occur before this. The resultant table should have a new column 'event' something like this:
MEMBER seen_1 seen_2 seen_3 seen_4 seen_5 seen_6 event
A 1 0 0 1 0 1 1
B 1 1 0 0 1 0 2
C 1 1 1 0 0 1 3
D 0 0 1 0 0 1 1
My approach:
df = df.set_index('MEMBER')
# count 1 on each rows since the last 0
s = (df.stack()
.groupby(['MEMBER', df.eq(0).cumsum(1).stack()])
.cumsum().unstack()
)
# mask of the zeros:
u = s.eq(0)
# look for the first 1 0 0
idx = (~u &
u.shift(-1, axis=1, fill_value=False) &
u.shift(-2, axis=1, fill_value=False) ).idxmax(1)
# look up
df['event'] = s.lookup(idx.index, idx)
Test data:
MEMBER seen_1 seen_2 seen_3 seen_4 seen_5 seen_6
0 A 1 0 1 0 0 1
1 B 1 1 0 0 1 0
2 C 1 1 1 0 0 1
3 D 0 0 1 0 0 1
4 E 1 0 1 1 0 0
Output:
MEMBER seen_1 seen_2 seen_3 seen_4 seen_5 seen_6 event
0 A 1 0 1 0 0 1 1
1 B 1 1 0 0 1 0 2
2 C 1 1 1 0 0 1 3
3 D 0 0 1 0 0 1 1
4 E 1 0 1 1 0 0 2
I have following dataframe
A | B | C | D
1 0 2 1
0 1 1 0
0 0 0 1
I want to add the new column have any value of row in the column greater than zero along with column name
A | B | C | D | New
1 0 2 1 A-1, C-2, D-1
0 1 1 0 B-1, C-1
0 0 0 1 D-1
We can use mask and stack
s=df.mask(df==0).stack().\
astype(int).astype(str).\
reset_index(level=1).apply('-'.join,1).add(',').sum(level=0).str[:-1]
df['New']=s
df
Out[170]:
A B C D New
0 1 0 2 1 A-1,C-2,D-1
1 0 1 1 0 B-1,C-1
2 0 0 0 1 D-1
Combine the column names with the df values that are not zero and then filter out the None values.
df = pd.read_clipboard()
arrays = np.where(df!=0, df.columns.values + '-' + df.values.astype('str'), None)
new = []
for array in arrays:
new.append(list(filter(None, array)))
df['New'] = new
df
Out[1]:
A B C D New
0 1 0 2 1 [A-1, C-2, D-1]
1 0 1 1 0 [B-1, C-1]
2 0 0 0 1 [D-1]