Select non-null rows from a specific column in a DataFrame and take a sub-selection of other columns

Question

I have a dataframe which has several columns, so I chose some of its columns to create a variable like this.

xtrain = df[['Age', 'Fare', 'Group_Size', 'deck', 'Pclass', 'Title']]

I want to drop from these columns all rows where the Survive column in the main dataframe is nan.

Answer 1

You can pass a boolean mask to your df based on notnull() of 'Survive' column and select the cols of interest:

In [2]:
# make some data
df = pd.DataFrame(np.random.randn(5,7), columns= ['Survive', 'Age','Fare', 'Group_Size','deck', 'Pclass', 'Title' ])
df['Survive'].iloc[2] = np.NaN
df
Out[2]:
    Survive       Age      Fare  Group_Size      deck    Pclass     Title
0  1.174206 -0.056846  0.454437    0.496695  1.401509 -2.078731 -1.024832
1  0.036843  1.060134  0.770625   -0.114912  0.118991 -0.317909  0.061022
2       NaN -0.132394 -0.236904   -0.324087  0.570660  0.758084 -0.176421
3 -2.145934 -0.020003 -0.777785    0.835467  1.498284 -1.371325  0.661991
4 -0.197144 -0.089806 -0.706548    1.621260  1.754292  0.725897  0.860482

Now pass a mask to loc to take only non NaN rows:

In [3]:
xtrain = df.loc[df['Survive'].notnull(), ['Age','Fare', 'Group_Size','deck', 'Pclass', 'Title' ]]
xtrain

Out[3]:
        Age      Fare  Group_Size      deck    Pclass     Title
0 -0.056846  0.454437    0.496695  1.401509 -2.078731 -1.024832
1  1.060134  0.770625   -0.114912  0.118991 -0.317909  0.061022
3 -0.020003 -0.777785    0.835467  1.498284 -1.371325  0.661991
4 -0.089806 -0.706548    1.621260  1.754292  0.725897  0.860482

Answer 2

Two alternatives because... well why not?
Both drop nan prior to column slicing. That's two call rather than EdChum's one call.

one

df.dropna(subset=['Survive'])[
    ['Age','Fare', 'Group_Size','deck', 'Pclass', 'Title' ]]

two

df.query('Survive == Survive')[
    ['Age','Fare', 'Group_Size','deck', 'Pclass', 'Title' ]]

Answer 3

It might be more readable if you assign the subset of the columns to a variable and filter.

notna_msk = df['Survive'].notna()
cols = ['Age', 'Fare', 'Group_Size', 'deck', 'Pclass', 'Title', 'Survive']
new_df = df.loc[notna_msk, cols]

Also, in case you already created xtrain from df as in the OP, then you can still filter this dataframe with the mask, even if it doesn't have Survive column; just the index is enough.

new_df = xtrain.loc[df['Survive'].notna()]