How to maintain order when selecting rows in pandas dataframe?

Here's a non-intrusive solution using Index.get_indexer that doesn't involve setting the index:

df.iloc[pd.Index(df['items']).get_indexer(['tv','car','phone'])]

   items  quantity
3     tv         5
0    car         1
4  phone         6

Note that if this is going to become a frequent thing (by thing, I mean "indexing" with a list on a column), you're better off turning that column into an index. Bonus points if you sort it.

df2 = df.set_index('items')
df2.loc[['tv','car','phone']]  

       quantity
items          
tv            5
car           1
phone         6

IIUC Categorical

df=df.loc[df['items'].isin(arr)]
df.iloc[pd.Categorical(df['items'],categories=arr,ordered=True).argsort()]
Out[157]: 
   items  quantity
3     tv         5
0    car         1
4  phone         6

Or reindex :Notice only different is this will not save the pervious index and if the original index do matter , you should using Categorical (Mentioned by Andy L, if you have duplicate in items ,reindex will failed )

df.set_index('items').reindex(arr).reset_index()
Out[160]: 
   items  quantity
0     tv         5
1    car         1
2  phone         6

Or loop via the arr

pd.concat([df[df['items']==x] for x in arr])
Out[171]: 
   items  quantity
3     tv         5
0    car         1
4  phone         6

merge to the rescue:

(pd.DataFrame({'items':['tv','car','phone']})
   .merge(df, on='items')
)

Output:

   items  quantity
0     tv         5
1    car         1
2  phone         6