Replace pandas column values with array

Question

I have an array:

([ 137.55021238,  125.30017675,  130.20181675,  109.47348838])

I need the array values to replace the b column, with the index number remaining the same:

Index    a         b         
0       0.671399 Nan
35      0.446172 Nan
63      0.614758 Nan
72      0.634448 Nan

I tried to use replace but it didn't work. Is there another way of doing this without turning array into a dataframe and merging?

df['b'] = [ 137.55021238, 125.30017675, 130.20181675, 109.47348838] why won't this work? — cs95
– cs95, Commented Oct 15, 2017 at 21:43
oh shoot. I overestimated the problem that had such a simple solution. Thank you @coldspeed. Should I erase this question that I posted? — Adam Schroeder
– Adam Schroeder, Commented Oct 15, 2017 at 21:47
I think this might be a duplicate, but it's okay to leave it be. — cs95
– cs95, Commented Oct 15, 2017 at 21:48

cs95 · Accepted Answer · 2017-10-15 21:59:27Z

8

vals = [137.55021238, 125.30017675, 130.20181675, 109.47348838]

Option 1
Direct assignment.

df['b'] = vals
print(df)
              a           b
Index
0      0.671399  137.550212
35     0.446172  125.300177
63     0.614758  130.201817
72     0.634448  109.473488

Option 2
df.assign

df = df.assign(b=vals)
print(df)
              a           b
Index
0      0.671399  137.550212
35     0.446172  125.300177
63     0.614758  130.201817
72     0.634448  109.473488

Option 3
df.fillna

df.b = df.b.fillna(pd.Series(vals, index=df.index))
print(df)
              a           b
Index
0      0.671399  137.550212
35     0.446172  125.300177
63     0.614758  130.201817
72     0.634448  109.473488

If your values are Nan (string) instead of NaN (float), you can convert it, using df.replace:

df = df.replace('Nan', np.nan)

edited Oct 15, 2017 at 21:59

answered Oct 15, 2017 at 21:46

cs95

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2 Comments

Adam Schroeder Over a year ago

thank you. I'll accept your answer in 10 minutes @coldspeed

cs95 Over a year ago

@piRSquared Thought of you when writing the assign :)

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