[FIXED] Adding a column with one single categorical value to a pandas dataframe

Issue

I have a pandas.DataFrame df and would like to add a new column col with one single value "hello". I would like this column to be of dtype category with the single category "hello". I can do the following.

df["col"] = "hello"
df["col"] = df["col"].astype("catgegory")

1. Do I really need to write df["col"] three times in order to achieve this?
2. After the first line I am worried that the intermediate dataframe df might take up a lot of space before the new column is converted to categorical. (The dataframe is rather large with millions of rows and the value "hello" is actually a much longer string.)

Are there any other straightforward, "short and snappy" ways of achieving this while avoiding the above issues?

An alternative solution is

df["col"] = pd.Categorical(itertools.repeat("hello", len(df)))

but it requires itertools and the use of len(df), and I am not sure how memory usage is under the hood.

Solution

We can explicitly build the Series of the correct size and type instead of implicitly doing so via __setitem__ then converting:

df['col'] = pd.Series('hello', index=df.index, dtype='category')

Sample Program:

import pandas as pd

df = pd.DataFrame({'a': [1, 2, 3]})

df['col'] = pd.Series('hello', index=df.index, dtype='category')

print(df)
print(df.dtypes)
print(df['col'].cat.categories)

   a    col
0  1  hello
1  2  hello
2  3  hello

a         int64
col    category
dtype: object

Index(['hello'], dtype='object')

Answered By - Henry Ecker