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!

EdChum · Accepted Answer · 2017-01-20 12:59:53Z

11

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.

answered Jan 20, 2017 at 12:59

EdChum

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Ted Petrou · Accepted Answer · 2017-01-20 13:30:14Z

2

You can use .loc very similarly to reindex

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

answered Jan 20, 2017 at 13:30

Ted Petrou

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Comments

piRSquared · Accepted Answer · 2017-01-20 14:18:13Z

0

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

answered Jan 20, 2017 at 14:18

piRSquared

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2 Comments

sikisis Over a year ago

Why is name 'missing' is not defined

piRSquared Over a year ago

@sikisis because you didn't define it. See OP...it has it defined as missing = ['d', 'e']

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