I have a data frame with many binary variables and I would like to create a new variable with categorical values based on many of these binary variables
My dataframe looks like this
gov_winner corp_winner in part
1 0 0
0 1 0
0 0 1
I variable I would like to create is called winning_party and would look like this
gov_winner corp_winner in part winning_party
1 0 0 gov
0 1 0 corp
0 0 1 in part
I started trying the following code but haven't had success yet:
harrington_citations = harrington_citations.assign(winning_party=lambda x: x['gov_winner']
== 1 then x = 'gov' else x == 0)
Using anky_91's answer I get the following error:
TypeError: can't multiply sequence by non-int of type 'str'
You can use a dot product:
df.assign(Winner_Party=df.dot(df.columns))
#df.assign(Winner_Party=df # df.columns)
gov_winner corp_winner in_part Winner_Party
0 1 0 0 gov_winner
1 0 1 0 corp_winner
2 0 0 1 in_part
How about idxmax, notice this will only select the first max , you have multiple cell equal to 1 per row, you may want to try Jez's solution
df['Winner_Party']=df.eq(1).idxmax(1)
If there is always only one 1 per rows use DataFrame.dot, also you can filter only 1 and 0 columns before:
df1 = df.loc[:, df.isin([0,1,'0','1']).all()].astype(int)
df['Winner_Party'] = df1.dot(df1.columns)
But if there is multiple 1 per rows and need all matched values add separator and then remove it :
df['Winner_Party'] = df1.dot(df1.columns + ',').str.rstrip(',')
print (df)
gov_winner corp_winner in part Winner_Party
0 1 0 0 gov_winner
1 0 1 0 corp_winner
2 0 0 1 in part
Related
In my dataframe, I have a categorical variable that I'd like to convert into dummy variables. This column however has multiple values separated by commas:
0 'a'
1 'a,b,c'
2 'a,b,d'
3 'd'
4 'c,d'
Ultimately, I'd want to have binary columns for each possible discrete value; in other words, final column count equals number of unique values in the original column. I imagine I'd have to use split() to get each separate value but not sure what to do afterwards. Any hint much appreciated!
Edit: Additional twist. Column has null values. And in response to comment, the following is the desired output. Thanks!
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Use str.get_dummies
df['col'].str.get_dummies(sep=',')
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Edit: Updating the answer to address some questions.
Qn 1: Why is it that the series method get_dummies does not accept the argument prefix=... while pandas.get_dummies() does accept it
Series.str.get_dummies is a series level method (as the name suggests!). We are one hot encoding values in one Series (or a DataFrame column) and hence there is no need to use prefix. Pandas.get_dummies on the other hand can one hot encode multiple columns. In which case, the prefix parameter works as an identifier of the original column.
If you want to apply prefix to str.get_dummies, you can always use DataFrame.add_prefix
df['col'].str.get_dummies(sep=',').add_prefix('col_')
Qn 2: If you have more than one column to begin with, how do you merge the dummies back into the original frame?
You can use DataFrame.concat to merge one hot encoded columns with the rest of the columns in dataframe.
df = pd.DataFrame({'other':['x','y','x','x','q'],'col':['a','a,b,c','a,b,d','d','c,d']})
df = pd.concat([df, df['col'].str.get_dummies(sep=',')], axis = 1).drop('col', 1)
other a b c d
0 x 1 0 0 0
1 y 1 1 1 0
2 x 1 1 0 1
3 x 0 0 0 1
4 q 0 0 1 1
The str.get_dummies function does not accept prefix parameter, but you can rename the column names of the returned dummy DataFrame:
data['col'].str.get_dummies(sep=',').rename(lambda x: 'col_' + x, axis='columns')
In my dataframe, I have a categorical variable that I'd like to convert into dummy variables. This column however has multiple values separated by commas:
0 'a'
1 'a,b,c'
2 'a,b,d'
3 'd'
4 'c,d'
Ultimately, I'd want to have binary columns for each possible discrete value; in other words, final column count equals number of unique values in the original column. I imagine I'd have to use split() to get each separate value but not sure what to do afterwards. Any hint much appreciated!
Edit: Additional twist. Column has null values. And in response to comment, the following is the desired output. Thanks!
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Use str.get_dummies
df['col'].str.get_dummies(sep=',')
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Edit: Updating the answer to address some questions.
Qn 1: Why is it that the series method get_dummies does not accept the argument prefix=... while pandas.get_dummies() does accept it
Series.str.get_dummies is a series level method (as the name suggests!). We are one hot encoding values in one Series (or a DataFrame column) and hence there is no need to use prefix. Pandas.get_dummies on the other hand can one hot encode multiple columns. In which case, the prefix parameter works as an identifier of the original column.
If you want to apply prefix to str.get_dummies, you can always use DataFrame.add_prefix
df['col'].str.get_dummies(sep=',').add_prefix('col_')
Qn 2: If you have more than one column to begin with, how do you merge the dummies back into the original frame?
You can use DataFrame.concat to merge one hot encoded columns with the rest of the columns in dataframe.
df = pd.DataFrame({'other':['x','y','x','x','q'],'col':['a','a,b,c','a,b,d','d','c,d']})
df = pd.concat([df, df['col'].str.get_dummies(sep=',')], axis = 1).drop('col', 1)
other a b c d
0 x 1 0 0 0
1 y 1 1 1 0
2 x 1 1 0 1
3 x 0 0 0 1
4 q 0 0 1 1
The str.get_dummies function does not accept prefix parameter, but you can rename the column names of the returned dummy DataFrame:
data['col'].str.get_dummies(sep=',').rename(lambda x: 'col_' + x, axis='columns')
In my dataframe, I have a categorical variable that I'd like to convert into dummy variables. This column however has multiple values separated by commas:
0 'a'
1 'a,b,c'
2 'a,b,d'
3 'd'
4 'c,d'
Ultimately, I'd want to have binary columns for each possible discrete value; in other words, final column count equals number of unique values in the original column. I imagine I'd have to use split() to get each separate value but not sure what to do afterwards. Any hint much appreciated!
Edit: Additional twist. Column has null values. And in response to comment, the following is the desired output. Thanks!
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Use str.get_dummies
df['col'].str.get_dummies(sep=',')
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Edit: Updating the answer to address some questions.
Qn 1: Why is it that the series method get_dummies does not accept the argument prefix=... while pandas.get_dummies() does accept it
Series.str.get_dummies is a series level method (as the name suggests!). We are one hot encoding values in one Series (or a DataFrame column) and hence there is no need to use prefix. Pandas.get_dummies on the other hand can one hot encode multiple columns. In which case, the prefix parameter works as an identifier of the original column.
If you want to apply prefix to str.get_dummies, you can always use DataFrame.add_prefix
df['col'].str.get_dummies(sep=',').add_prefix('col_')
Qn 2: If you have more than one column to begin with, how do you merge the dummies back into the original frame?
You can use DataFrame.concat to merge one hot encoded columns with the rest of the columns in dataframe.
df = pd.DataFrame({'other':['x','y','x','x','q'],'col':['a','a,b,c','a,b,d','d','c,d']})
df = pd.concat([df, df['col'].str.get_dummies(sep=',')], axis = 1).drop('col', 1)
other a b c d
0 x 1 0 0 0
1 y 1 1 1 0
2 x 1 1 0 1
3 x 0 0 0 1
4 q 0 0 1 1
The str.get_dummies function does not accept prefix parameter, but you can rename the column names of the returned dummy DataFrame:
data['col'].str.get_dummies(sep=',').rename(lambda x: 'col_' + x, axis='columns')
My system
Windows 7, 64 bit
python 3.5.1
The challenge
I've got a pandas dataframe, and I would like to know the maximum value for each row, and append that info as a new column. I would also like to know the name of the column where the maximum value is located. And I would like to add another column to the existing dataframe containing the name of the column where the max value can be found.
A similar question has been asked and answered for R in this post.
Reproducible example
In[1]:
# Make pandas dataframe
df = pd.DataFrame({'a':[1,0,0,1,3], 'b':[0,0,1,0,1], 'c':[0,0,0,0,0]})
# Calculate max
my_series = df.max(numeric_only=True, axis = 1)
my_series.name = "maxval"
# Include maxval in df
df = df.join(my_series)
df
Out[1]:
a b c maxval
0 1 0 0 1
1 0 0 0 0
2 0 1 0 1
3 1 0 0 1
4 3 1 0 3
So far so good. Now for the add another column to the existing dataframe containing the name of the column part:
In[2]:
?
?
?
# This is what I'd like to accomplish:
Out[2]:
a b c maxval maxcol
0 1 0 0 1 a
1 0 0 0 0 a,b,c
2 0 1 0 1 b
3 1 0 0 1 a
4 3 1 0 3 a
Notice that I'd like to return all column names if multiple columns contain the same maximum value. Also please notice that the column maxval is not included in maxcol since that would not make much sense. Thanks in advance if anyone out there finds this interesting.
You can compare the df against maxval using eq with axis=0, then use apply with a lambda to produce a boolean mask to mask the columns and join them:
In [183]:
df['maxcol'] = df.ix[:,:'c'].eq(df['maxval'], axis=0).apply(lambda x: ','.join(df.columns[:3][x==x.max()]),axis=1)
df
Out[183]:
a b c maxval maxcol
0 1 0 0 1 a
1 0 0 0 0 a,b,c
2 0 1 0 1 b
3 1 0 0 1 a
4 3 1 0 3 a
I have a file with 13 columns and I am looking to perform some grouping tasks. The input looks like so:
A B C D E F G H I J K L M
0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 1
Excluding column A, the grouping is to be done as follows producing five new columns, the columns J,K,L,M will be merged into one as it is a special case.
A,B > new column D,E > new colum
B C Result
1 0 1
0 1 1
1 1 1
0 0 0
If either of the two columns has "1" in it or maybe both, I want to count it as 1. Right now I have written this little snippet but I am not sure how to proceed.
from collections import Counter
with open("datagroup.txt") as inFile:
print Counter([" ".join(line.split()[::2]) for line in inFile])
* Edit *
A B&C D&E F&G H&I J,K,L,M
1 1 0 0 1 1
1 1 0 0 0 1
0 1 0 0 1 0
1 0 0 0 0 1
0 1 0 1 1 1
1 0 0 0 0 1
Basically what I want to do is to exclude the first column and then compare every two columns after that until column J, If either column has a "1" present, I want to report that as "1" even if both columns have "1" I would still report that as "1". For the last for columns, namely: J,K,L,M if I see a "1" in either four, it should be reported as "1".
First, you're obviously going to have to iterate over the rows in some way to do something for each row.
Second, I have no idea what what you're trying to do with the [::2], since that will just give you all the even columns, or what the Counter is for in the first place, or why specifically you're trying to count strings made up of a bunch of concatenated columns.
But I think what you want is this:
with open("datagroup.txt") as inFile:
for row in inFile:
columns = row.split()
outcolumns = []
outcolumns.append(columns[0]) # A
for group in zip(columns[1:-4:2], columns[2:-4:2])+columns[-4:]:
outcolumns.append('1' if '1' in group else '0')
print(' '.join(outcolumns))
You can make this a lot more concise with a bit of itertools and comprehensions, but I wanted to keep this verbose and simple so you'd understand it.