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I have the following dataframe:
d = {'col1':['a','b','c','b','a','c','c','c'],'col2':[1,2,3,4,5,6,7,8]}
df = pd.DataFrame(data=d)
I wonder how can I change 'a' to 1, 'b' to 0 and 50% of 'c' to 1 and 50% of the rest to 0 in col1 at random?
So col1 may look like this [1,0,1,0,1,0,0,1]
Answer
Compare values by c for mask by Series.eq, then use Series.map for set values by dictionary and last set 50% of values by Series.sample only filtered values:
m = df['col1'].eq('c')
df['col1'] = df['col1'].map({'a':1, 'b':0, 'c':0})
df.loc[df[m].sample(frac = 0.5).index, 'col1'] = 1
Or you can filter values and add False values by Series.reindex for mask with size like original DataFrame:
m = df['col1'].eq('c')
df['col1'] = df['col1'].map({'a':1, 'b':0, 'c':0})
mask = m[m].sample(frac = 0.5).reindex(df.index, fill_value=False)
df.loc[mask, 'col1'] = 1
print (df)
col1 col2
0 1 1
1 0 2
2 1 3
3 0 4
4 1 5
5 1 6
6 0 7
7 0 8
Numpy solution with numpy.random.choice:
m = df['col1'].eq('c')
df['col1'] = df['col1'].map({'a':1, 'b':0, 'c':0})
df.loc[m, 'col1'] = np.random.choice([0,1], p=[0.5, 0.5], size=m.sum())
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