Add New Rows To A Multiindex Dataframe

Given this MultiIndex Dataframe: arrays = [np.array(['A', 'A', 'B', 'B', 'C', 'C']), np.array(['one', 'two', 'one', 'two', 'one', 'two'])] df = pd.DataFrame(np.random.rand

Solution 1:

MultiIndex.from_product + reindex

a, b = df.index.levelsres= df.reindex(pd.MultiIndex.from_product([a, [*b, 'three']]))
res[res.index.get_level_values(1) == 'three'] = 3

             col1
A one-1.011201
  two    0.376914
  three  3.000000
B one0.465666
  two   -0.634804
  three  3.000000
C one-0.348338
  two    1.295683
  three  3.000000

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An update to this answer to account for your desire to add specific values. Replace the last line with this code snippet:

d = {'A':3, 'B':4, 'C':5}
s = res.index.get_level_values(0).map(d)
res.col1.where(res.col1.notnull(), s.values)

A  one-2.542087
   two      0.966193
   three    3.000000
B  one-0.126671
   two      0.864258
   three    4.000000
C  one0.063544
   two     -0.401936
   three    5.000000
Name: col1, dtype: float64

Solution 2:

Possibly verbose, but you can construct a new dataframe, concatenate, then sort by index:

idx = pd.MultiIndex.from_tuples([(i, 'three') for i in df.index.levels[0]])
df_new = pd.DataFrame(3, index=idx, columns=df.columns)

df = pd.concat([df, df_new]).sort_index()

print(df)

             col1
A one   -0.810362
  three  3.000000
  two    0.014020
B one    0.700392
  three  3.000000
  two    0.189968
C one   -1.214194
  three  3.000000
  two    1.199316

Solution 3:

Using concat

s=pd.Series({'A':3, 'B':4, 'C':5}).to_frame('col1').assign(index='three')
pd.concat([df,s.set_index('index',append=True)]).sort_index(level=0)
Out[205]: 
             col1
A one    0.529647
  three  3.000000
  two   -1.763707
B one   -0.673773
  three  4.000000
  two   -0.706385
C one    1.105963
  three  5.000000
  two    1.291009