有效地将新列添加到Pandas DataFrame中，并使用从现有列中处理的值？

Question

I have a string column foo in my DataFrame . I need to create a new column bar , whose values are derived from corresponding foo values by a sequence of string-processing operations - a bunch of str.split() s and str.join() s in this particular case.

What is the most efficient way to do this?

Answer 1

Take a look at the vectorized string methods of pandas dataframes. http://pandas.pydata.org/pandas-docs/dev/text.html#text-string-methods

# You can call whatever vectorized string methods on the RHS
df['bar'] = df['foo']

eg.

df = pd.DataFrame(['a c', 'b d'], columns=['foo'])
df['bar'] = df['foo'].str.split(' ').str.join('-')
print(df)

yields

   foo  bar
0  a c  a-c
1  b d  b-d

Answer 2

Pandas can do this for you. A simple example might look like:

foo = ["this", "is an", "example!"]

df = pd.DataFrame({'foo':foo})
df['upper_bar'] = df.foo.str.upper()
df['lower_bar'] = df.foo.str.lower()
df['split_bar'] = df.foo.str.split('_')
print(df)

which will give you

       foo   upper_bar  lower_bar   split_bar
0      this      THIS      this      [this]
1     is an     IS AN     is an     [is an]
2  example!  EXAMPLE!  example!  [example!]

See the link above from Alex