Clever Geek Handbook

Add lists inside dictionary to DataFrame as new columns

Let's say I have the following pandas DataFrame:

df = pd.DataFrame({'x': [0, 1, 2], 'y': [3, 4, 5], 'z': [6, 7, 8]})
     x   y   z
 0   0   3   6
 1   1   4   7
 2   2   5   8

And the following dictionary:

d = {'a': [10, 10, 10], 'b': [100, 100, 100]}

What's the best way to add a dictionary to the DataFrame to get the following:

     x   y   z    a    b
 0   0   3   6   10  100
 1   1   4   7   10  100
 2   2   5   8   10  100

This is what I have come up with so far, but I feel like there must be a better way:

df_bigger = pd.concat([df, pd.DataFrame(d)], axis=1)
+3


source to share


4 answers


Use assign

with dictionary unpacking

df.assign(**d)

    x   y   z   a   b
0   0   3   6   10  100
1   1   4   7   10  100
2   2   5   8   10  100


Note that if assign

, if the length of the lists is consistent with the dataframe, then the indexes take care of that.

+3


source


You can use join (). And like @piRsquared mentioned in the comment, pass the index like this.



df = df.join(pd.DataFrame(d, index = df.index))

    x   y   z   a   b
0   0   3   6   10  100
1   1   4   7   10  100
2   2   5   8   10  100
+3


source


One way to do it:

dataframe_dict = pd.DataFrame.to_dict(orient='dict')
d = {'a': [10, 10, 10], 'b': [100, 100, 100]}

new_dict = dict(dataframe_dict.items() + d.items())

By the way, I have never used DataFrames, but here: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_dict.html , it says you can convert dataframe to dict, so I just converted it and created a new dict with other elements included.

+1


source 


#merge existing data with the new dict and re-construct a DF.
pd.DataFrame(dict(df.to_dict(orient='list'),**d))
Out[186]: 
    a    b  x  y  z
0  10  100  0  3  6
1  10  100  1  4  7
2  10  100  2  5  8
0


source






More articles:


All Articles