To merge multiple columns into one column and count the repetition of unique values and maintain a separate column for each count in pandas dataframe2019 Community Moderator ElectionSelecting multiple columns in a pandas dataframeAdding new column to existing DataFrame in Python pandasHow can I replace all the NaN values with Zero's in a column of a pandas dataframeDelete column from pandas DataFrame by column nameSet value for particular cell in pandas DataFrame using index“Large data” work flows using pandasHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headersHow to count the NaN values in a column in pandas DataFrame
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To merge multiple columns into one column and count the repetition of unique values and maintain a separate column for each count in pandas dataframe
2019 Community Moderator ElectionSelecting multiple columns in a pandas dataframeAdding new column to existing DataFrame in Python pandasHow can I replace all the NaN values with Zero's in a column of a pandas dataframeDelete column from pandas DataFrame by column nameSet value for particular cell in pandas DataFrame using index“Large data” work flows using pandasHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headersHow to count the NaN values in a column in pandas DataFrame
I have a Pandas data frame like this:
Q1 Q2 Q3 Q4
0 Bachelor Postgrad Postgrad Masters
1 Bachelor Postgrad Postgrad Bachelor
2 Masters Postgrad Postgrad Masters
3 Bachelor Bachelor Bachelor Masters
4 Bachelor NaN NaN Masters Masters
...
I want to add columns like this:
Q1 Q2 Q3 Q4 Bachelor Masters Postgrad
0 Bachelor Postgrad Postgrad Masters 1 1 2
1 Bachelor Postgrad Postgrad Bachelor 2 0 2
2 Masters Postgrad Postgrad Masters 0 2 2
3 Bachelor Bachelor Bachelor Masters 3 1 0
4 Bachelor NaN Masters Masters 1 1 1
...
I tried and able to merge Q1 to Q4 into one column but unable to count the unique values and print these count in the seperate column. Any help to this will be appreciated.
python-3.x pandas
asked Mar 6 at 17:41
vishal_agvishal_ag
203
add a comment |
I have a Pandas data frame like this:
Q1 Q2 Q3 Q4
0 Bachelor Postgrad Postgrad Masters
1 Bachelor Postgrad Postgrad Bachelor
2 Masters Postgrad Postgrad Masters
3 Bachelor Bachelor Bachelor Masters
4 Bachelor NaN NaN Masters Masters
...
I want to add columns like this:
Q1 Q2 Q3 Q4 Bachelor Masters Postgrad
0 Bachelor Postgrad Postgrad Masters 1 1 2
1 Bachelor Postgrad Postgrad Bachelor 2 0 2
2 Masters Postgrad Postgrad Masters 0 2 2
3 Bachelor Bachelor Bachelor Masters 3 1 0
4 Bachelor NaN Masters Masters 1 1 1
...
I tried and able to merge Q1 to Q4 into one column but unable to count the unique values and print these count in the seperate column. Any help to this will be appreciated.
python-3.x pandas
asked Mar 6 at 17:41
vishal_agvishal_ag
203
add a comment |
I have a Pandas data frame like this:
Q1 Q2 Q3 Q4
0 Bachelor Postgrad Postgrad Masters
1 Bachelor Postgrad Postgrad Bachelor
2 Masters Postgrad Postgrad Masters
3 Bachelor Bachelor Bachelor Masters
4 Bachelor NaN NaN Masters Masters
...
I want to add columns like this:
Q1 Q2 Q3 Q4 Bachelor Masters Postgrad
0 Bachelor Postgrad Postgrad Masters 1 1 2
1 Bachelor Postgrad Postgrad Bachelor 2 0 2
2 Masters Postgrad Postgrad Masters 0 2 2
3 Bachelor Bachelor Bachelor Masters 3 1 0
4 Bachelor NaN Masters Masters 1 1 1
...
I tried and able to merge Q1 to Q4 into one column but unable to count the unique values and print these count in the seperate column. Any help to this will be appreciated.
python-3.x pandas
asked Mar 6 at 17:41
vishal_agvishal_ag
203
I have a Pandas data frame like this:
Q1 Q2 Q3 Q4
0 Bachelor Postgrad Postgrad Masters
1 Bachelor Postgrad Postgrad Bachelor
2 Masters Postgrad Postgrad Masters
3 Bachelor Bachelor Bachelor Masters
4 Bachelor NaN NaN Masters Masters
...
I want to add columns like this:
Q1 Q2 Q3 Q4 Bachelor Masters Postgrad
0 Bachelor Postgrad Postgrad Masters 1 1 2
1 Bachelor Postgrad Postgrad Bachelor 2 0 2
2 Masters Postgrad Postgrad Masters 0 2 2
3 Bachelor Bachelor Bachelor Masters 3 1 0
4 Bachelor NaN Masters Masters 1 1 1
...
I tried and able to merge Q1 to Q4 into one column but unable to count the unique values and print these count in the seperate column. Any help to this will be appreciated.
python-3.x pandas
python-3.x pandas
asked Mar 6 at 17:41
vishal_agvishal_ag
203
asked Mar 6 at 17:41
vishal_agvishal_ag
203
asked Mar 6 at 17:41
vishal_agvishal_ag
203
asked Mar 6 at 17:41
vishal_agvishal_ag
203
asked Mar 6 at 17:41
vishal_agvishal_ag
203
203
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You are looking for get_dummies
s=pd.get_dummies(df,prefix='', prefix_sep='').sum(1,level=0)
s
Out[502]:
Bachelor Masters Postgrad
0 1 1 2
1 2 0 2
2 0 2 2
3 3 1 0
4 1 2 0
# then using concat
df=pd.concat([df,s],axis=1)
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You are looking for get_dummies
s=pd.get_dummies(df,prefix='', prefix_sep='').sum(1,level=0)
s
Out[502]:
Bachelor Masters Postgrad
0 1 1 2
1 2 0 2
2 0 2 2
3 3 1 0
4 1 2 0
# then using concat
df=pd.concat([df,s],axis=1)
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
add a comment |
You are looking for get_dummies
s=pd.get_dummies(df,prefix='', prefix_sep='').sum(1,level=0)
s
Out[502]:
Bachelor Masters Postgrad
0 1 1 2
1 2 0 2
2 0 2 2
3 3 1 0
4 1 2 0
# then using concat
df=pd.concat([df,s],axis=1)
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
add a comment |
You are looking for get_dummies
s=pd.get_dummies(df,prefix='', prefix_sep='').sum(1,level=0)
s
Out[502]:
Bachelor Masters Postgrad
0 1 1 2
1 2 0 2
2 0 2 2
3 3 1 0
4 1 2 0
# then using concat
df=pd.concat([df,s],axis=1)
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
You are looking for get_dummies
s=pd.get_dummies(df,prefix='', prefix_sep='').sum(1,level=0)
s
Out[502]:
Bachelor Masters Postgrad
0 1 1 2
1 2 0 2
2 0 2 2
3 3 1 0
4 1 2 0
# then using concat
df=pd.concat([df,s],axis=1)
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
answered Mar 6 at 17:45
Wen-BenWen-Ben
117k83369
117k83369
add a comment |
add a comment |
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