Mixing
Cross addition in pandas
up vote
0
down vote
favorite
How to apply cross addition (OR) in my pandas dataframe like below.
Input:
A B C D
0 0 1 0 1
Output:
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
So far I can achieve using this,
cols=df.columns
n=len(cols)
df1=pd.concat([df]*n,ignore_index=True).eq(1)
df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
df2.columns=cols
df2=df2.reset_index(drop=True)
print (df1|df2).astype(int)
I think there is much more simpler way to handle this case.
python pandas
asked yesterday
Mohamed Thasin ah
3,13031236
add a comment |
up vote
0
down vote
favorite
How to apply cross addition (OR) in my pandas dataframe like below.
Input:
A B C D
0 0 1 0 1
Output:
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
So far I can achieve using this,
cols=df.columns
n=len(cols)
df1=pd.concat([df]*n,ignore_index=True).eq(1)
df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
df2.columns=cols
df2=df2.reset_index(drop=True)
print (df1|df2).astype(int)
I think there is much more simpler way to handle this case.
python pandas
asked yesterday
Mohamed Thasin ah
3,13031236
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
How to apply cross addition (OR) in my pandas dataframe like below.
Input:
A B C D
0 0 1 0 1
Output:
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
So far I can achieve using this,
cols=df.columns
n=len(cols)
df1=pd.concat([df]*n,ignore_index=True).eq(1)
df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
df2.columns=cols
df2=df2.reset_index(drop=True)
print (df1|df2).astype(int)
I think there is much more simpler way to handle this case.
python pandas
asked yesterday
Mohamed Thasin ah
3,13031236
How to apply cross addition (OR) in my pandas dataframe like below.
Input:
A B C D
0 0 1 0 1
Output:
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
So far I can achieve using this,
cols=df.columns
n=len(cols)
df1=pd.concat([df]*n,ignore_index=True).eq(1)
df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
df2.columns=cols
df2=df2.reset_index(drop=True)
print (df1|df2).astype(int)
I think there is much more simpler way to handle this case.
python pandas
python pandas
asked yesterday
Mohamed Thasin ah
3,13031236
asked yesterday
Mohamed Thasin ah
3,13031236
asked yesterday
Mohamed Thasin ah
3,13031236
asked yesterday
Mohamed Thasin ah
3,13031236
asked yesterday
Mohamed Thasin ah
3,13031236
3,13031236
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
up vote
3
down vote
accepted
You can use numpy | operation with broadcast as:
data = df.values
df = pd.DataFrame((data.T | data), columns=df.columns)
Or using np.logical_or as:
df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)
print(df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
Sandeep Kadapa
5,341426
add a comment |
up vote
1
down vote
Numpy solution:
First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:
a = df.iloc[0].values
df = pd.DataFrame(a | a[:, None], columns=df.columns)
print (df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
jezrael
307k20243317
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
3
down vote
accepted
You can use numpy | operation with broadcast as:
data = df.values
df = pd.DataFrame((data.T | data), columns=df.columns)
Or using np.logical_or as:
df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)
print(df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
Sandeep Kadapa
5,341426
add a comment |
up vote
3
down vote
accepted
You can use numpy | operation with broadcast as:
data = df.values
df = pd.DataFrame((data.T | data), columns=df.columns)
Or using np.logical_or as:
df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)
print(df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
Sandeep Kadapa
5,341426
add a comment |
up vote
3
down vote
accepted
up vote
3
down vote
accepted
You can use numpy | operation with broadcast as:
data = df.values
df = pd.DataFrame((data.T | data), columns=df.columns)
Or using np.logical_or as:
df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)
print(df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
Sandeep Kadapa
5,341426
You can use numpy | operation with broadcast as:
data = df.values
df = pd.DataFrame((data.T | data), columns=df.columns)
Or using np.logical_or as:
df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)
print(df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
Sandeep Kadapa
5,341426
answered yesterday
Sandeep Kadapa
5,341426
answered yesterday
Sandeep Kadapa
5,341426
answered yesterday
Sandeep Kadapa
5,341426
5,341426
add a comment |
add a comment |
up vote
1
down vote
Numpy solution:
First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:
a = df.iloc[0].values
df = pd.DataFrame(a | a[:, None], columns=df.columns)
print (df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
jezrael
307k20243317
add a comment |
up vote
1
down vote
Numpy solution:
First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:
a = df.iloc[0].values
df = pd.DataFrame(a | a[:, None], columns=df.columns)
print (df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
jezrael
307k20243317
add a comment |
up vote
1
down vote
up vote
1
down vote
Numpy solution:
First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:
a = df.iloc[0].values
df = pd.DataFrame(a | a[:, None], columns=df.columns)
print (df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
jezrael
307k20243317
Numpy solution:
First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:
a = df.iloc[0].values
df = pd.DataFrame(a | a[:, None], columns=df.columns)
print (df)
A B C D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1
answered yesterday
jezrael
307k20243317
edited yesterday
answered yesterday
jezrael
307k20243317
answered yesterday
jezrael
307k20243317
answered yesterday
jezrael
307k20243317
307k20243317
add a comment |
add a comment |
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