Generate different random numbers to multiple rows in a column


I got a columns with integer values(n rows). I want to generate random numbers that range from a normal distribution on values that meet certain condition. I tried with code below but they are too slow.

df_members['bd'] = df_members.bd.apply(lambda x: np.random.normal(bd_mean, bd_sd) if float(x)==-99999 else x )

I tried with code below but it will only assign one random value to all the rows.

bd_mean = 29.2223808862 bd_std = 10.4168850957 df_members[df_members['bd'] == -99999] = np.random.normal(bd_mean, bd_sd)

Example Data:

msno city bd gender registered_via 0 URiXrfYPzHAlk+7+n7BOMl9G+T7g8JmrSnT/BU8GmEo= 1 -99999 NaN 9 1 U1q0qCqK/lDMTD2kN8G9OXMtfuvLCey20OAIPOvXXGQ= 1 26 NaN 4 2 W6M2H2kAoN9ahfDYKo3J6tmsJRAeuFc9wl1cau5VL1Q= 1 -99999 NaN 4 3 1qE5+cN7CUyC+KFH6gBZzMWmM1QpIVW6A43BEm98I/w= 5 17 female 4 4 SeAnaZPI+tFdAt+r3lZt/B8PgTp7bcG/1os39u4pLxs= 1 -99999 NaN 4


I guess that generating 3425689(rows) random numbers will take a long time. I will stick to the first way at this moment.


You're missing the <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.normal.html#numpy.random.normal" rel="nofollow">"size" argument</a> that will give the shape of the random values to be generated.

df_members[df_members['bd'] == -99999] = np.random.normal(bd_mean, bd_sd,len(df_members[df_members['bd'] == -99999]))

will give you what you want


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