Develop Reference

Get Dummies for Column with Multiple Variables [duplicate]

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')

How to generate a sequence of numbers when encountered a value in python pandas dataframe

sample and expected data
The block one is current data and block 2 is the expected data that is, when i encounter 1 i need the next row to be incremented by one and for next country b same should happen

First replace all another values after first 1 to 1, so is possible use GroupBy.cumsum:
df = pd.DataFrame({'c':['a']*3 + ['b']*3+ ['c']*3, 'v':[1,0,0,0,1,0,0,0,1]})
s = df.groupby('c')['v'].cumsum()
df['new'] = s.where(s.eq(0), 1).groupby(df['c']).cumsum()
print (df)
c v new
0 a 1 1
1 a 0 2
2 a 0 3
3 b 0 0
4 b 1 1
5 b 0 2
6 c 0 0
7 c 0 0
8 c 1 1
Another solution is replace all not 1 values to missing values and forward filling 1 per groups, then first missing values are replaced to 0, so cumulative sum also working perfectly:
s = df['v'].where(df['v'].eq(1)).groupby(df['c']).ffill().fillna(0).astype(int)
df['new'] = s.groupby(df['c']).cumsum()

Create new categorical variable based on multiple binary columns

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

Encoding a dataframe of lists [duplicate]

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')

Add columns to pandas dataframe containing max of each row, AND corresponding column name

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