Python: create a new column from existing columns

Question

I am trying to create a new column based on both columns. Say I want to create a new column z, and it should be the value of y when it is not missing and be the value of x when y is indeed missing. So in this case, I expect z to be [1, 8, 10, 8] .

   x   y
0  1 NaN
1  2   8
2  4  10
3  8 NaN

Answer 1

The new column 'z' get its values from column 'y' using df['z'] = df['y'] . This brings over the missing values so fill them in using fillna using column 'x' . Chain these two actions:

>>> df['z'] = df['y'].fillna(df['x'])
>>> df
   x   y   z
0  1 NaN   1
1  2   8   8
2  4  10  10
3  8 NaN   8

Answer 2

You can use apply with option axis=1 . Then your solution is pretty concise.

df[z] = df.apply(lambda row: row.y if pd.notnull(row.y) else row.x, axis=1)

Answer 3

Use np.where :

In [3]:

df['z'] = np.where(df['y'].isnull(), df['x'], df['y'])
df
Out[3]:
   x   y   z
0  1 NaN   1
1  2   8   8
2  4  10  10
3  8 NaN   8

Here it uses the boolean condition and if true returns df['x'] else df['y']

Answer 4

Let's say DataFrame is called df . First copy the y column.

df["z"] = df["y"].copy()

Then set the nan locations of z to the locations in x where the nans are in z.

import numpy as np
df.z[np.isnan(df.z)]=df.x[np.isnan(df.z)]


>>> df 
   x   y   z
0  1 NaN   1
1  2   8   8
2  4  10  10
3  8 NaN   8

Answer 5

I'm not sure if I understand the question, but would this be what you're looking for?

"if y[i]" will skip if the value is none.

for i in range(len(x));
    if y[i]:
        z.append(y[i])
    else:
        z.append(x[i])

Answer 6

The update method does almost exactly this. The only caveat is that update will do so in place so you must first create a copy:

df['z'] = df.x.copy()
df.z.update(df.y)

In the above example you start with x and replace each value with the corresponding value from y , as long as the new value is not NaN .