问题描述
我有一个熊猫数据框.
df = pd.DataFrame(['Donald Dump','Make America Great Again!','Donald Shrimp'],
columns=['text'])
我想在Dataframe中找到另一列，该列的'text'列中包含字符串的长度.
对于上面的示例，将是
text text_length
0 Donald Dump 11
1 Make America Great Again! 25
2 Donald Shrimp 13
我知道我可以遍历它并获取长度，但是有什么方法可以向量化此操作?我有几百万行.
我认为最简单的方法是使用DataFrame的apply方法.使用这种方法，您可以根据需要任意操作数据.您可以执行以下操作:
df['text_ength'] = df['text'].apply(len)
使用所需的数据创建一个新列.
编辑:看到@jezrael回答后，我很好奇，决定下定时间.我创建了一个带有lorem ipsum句子(101000行)的DataFrame，两者之间的差别很小.对我来说，我得到了:
In [59]: %timeit df['text_length'] = (df.text.str.len())
10 loops, best of 3: 20.6 ms per loop
In [60]: %timeit df['text_length'] = df['text'].apply(len)
100 loops, best of 3: 17.6 ms per loop
I have a pandas dataframe.
df = pd.DataFrame(['Donald Dump','Make America Great Again!','Donald Shrimp'],
columns=['text'])
What I like to have is another column in Dataframe which has the length of the strings in the 'text' column.
For above example, it would be
text text_length
0 Donald Dump 11
1 Make America Great Again! 25
2 Donald Shrimp 13
I know I can loop through it and get the length but is there any way to vectorize this operation? I have few million rows.
I think the easiest way is to use the apply method of the DataFrame.With this method you can manipulate the data any way you want.
You could do something like:
df['text_ength'] = df['text'].apply(len)
to create a new column with the data you want.
Edit After seeing @jezrael answer I was curious and decided to timeit.I created a DataFrame full with lorem ipsum sentences (101000 rows) and the difference is quite small. For me I got:
In [59]: %timeit df['text_length'] = (df.text.str.len())
10 loops, best of 3: 20.6 ms per loop
In [60]: %timeit df['text_length'] = df['text'].apply(len)
100 loops, best of 3: 17.6 ms per loop
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