python pandas dataframe：当默认值是可迭代的时，使用默认值创建新列

Question

I have a pandas dataframe as below:

In  [1]: import pandas as pd
In  [2]: df = pd.DataFrame([[1,2],[3,4],[5,6]], columns=['a','b'])
In  [3]: print df
Out [3]: 
   a  b
0  1  2
1  3  4
2  5  6

Now I want to add a new column 'c' with a default value as a dictionary. The resulting dataframe should look like this:

   a  b             c
0  1  2  {1: 2, 3: 4}
1  3  4  {1: 2, 3: 4}
2  5  6  {1: 2, 3: 4}

I tried the following:

df.at[:, 'c'] = {1: 2, 3: 4}
ValueError: Length of values does not match length of index

and

df['c'] = {1: 2, 3: 4}
ValueError: Must have equal len keys and value when setting with an iterable

This one works for me

df['c'] = df.apply(lambda x: {1:2, 3:4}, axis=1)

but looks like a dirty approach. Is there a cleaner way to do this?

Answer 1

You have three rows in your DF and only two elements in your dict, do:

c = {0:1,1:1,2:2}
df['c'] = c

Output:

 a  b  c
0  1  2  0
1  3  4  1
2  5  6  2

To have the same dictionary repeated along your dataframe you need to create a list of such dicts

c = {1:2,3:4}
c = [c]*3
df['c'] = c

Output

 a  b             c
0  1  2  {1: 2, 3: 4}
1  3  4  {1: 2, 3: 4}
2  5  6  {1: 2, 3: 4}

Answer 2

It is possible, but not recommended store dicts in column of DataFrame , because all vectorized pandas functions cannot be used:

df['c'] = [{1: 2, 3: 4} for x in np.arange(len(df))]
print (df)
   a  b             c
0  1  2  {1: 2, 3: 4}
1  3  4  {1: 2, 3: 4}
2  5  6  {1: 2, 3: 4}