Append empty rows to Dataframe in pandas

Question

I want to append empty rows (filled with np.NaN) to a pandas dataframe and currently only know how to do this using loc

T = pd.DataFrame(index=['a', 'b', 'c'], data={'Col0': 0, 'Col1': 1})
T
   Col0  Col1
a     0     1
b     0     1
c     0     1

missing = ['d', 'e']
for m in missing:
    T.loc[m] = np.NaN

  Col0  Col1
a   0.0   1.0
b   0.0   1.0
c   0.0   1.0
d   NaN   NaN
e   NaN   NaN

Do you know of a more elegant way to do this?

Why it is not possible to do something like

T.loc[missing] = np.NaN

thx!

Answer 1

You can reindex by taking the union of the current index and the missing row values:

In [281]:    
T.reindex(T.index.union(missing))

Out[281]:
   Col0  Col1
a   0.0   1.0
b   0.0   1.0
c   0.0   1.0
d   NaN   NaN
e   NaN   NaN

basically loc looks for the passed in labels, unfortunately setting with enlargement only works for a single row label.

It'd be more efficient to do the above, here we take the union of the current index and the missing values and use these to reindex the df, where rows don't exist NaN values are inserted.

Answer 2

你可以使用.loc非常类似于reindex

df.loc[df.index.tolist() + missing]

Answer 3

If you actually have a dataframe, you can use pd.concat

df = pd.DataFrame(index=missing)

pd.concat([T, df])

Or alternatively, you can use pd.DataFrame.append

df = pd.DataFrame(index=missing)

T.append(df)

---

Both yield:

   Col0  Col1
a   0.0   1.0
b   0.0   1.0
c   0.0   1.0
d   NaN   NaN
e   NaN   NaN