如何在熊猫表的一列中计算逗号分隔值？

I have the following code:

我有以下代码:

businessdata = ['Name of Location','Address','City','Zip Code','Website','Yelp',
'# Reviews', 'Yelp Rating Stars','BarRestStore','Category',
'Price Range','Alcohol','Ambience','Latitude','Longitude']

business = pd.read_table('FL_Yelp_Data_v2.csv', sep=',', header=1, names=businessdata)
print '\n\nBusiness\n'
print business[:6]

It reads my file and creates a Panda table I can work with. What I need is to count how many categories are in each line of the 'Category' variable and store this number in a new column named '# Categories'. Here is the target column sample:

它读取我的文件并创建一个我可以使用的Panda表。我需要的是计算“类别”变量的每一行中有多少类别,并将此数字存储在名为“#Categories”的新列中。以下是目标列示例:

Category                                         
French                                               
Adult Entertainment , Lounges , Music Venues         
American (New) , Steakhouses                        
American (New) , Beer, Wine & Spirits , Gastropubs 
Chicken Wings , Sports Bars , American (New)         
Japanese

Desired output:

Category                                        # Categories  
French                                               1           
Adult Entertainment , Lounges , Music Venues         3         
American (New) , Steakhouses                         2        
American (New) , Beer, Wine & Spirits , Gastropubs   4         
Chicken Wings , Sports Bars , American (New)         3         
Japanese                                             1

EDIT 1:

Raw input = CSV file. Target column: "Category" I can't post screenshots yet. I don't think the values to be counted are lists.

原始输入= CSV文件。目标栏:“类别”我无法发布截图。我不认为要计算的值是列表。

This is my code:

这是我的代码:

business = pd.read_table('FL_Yelp_Data_v2.csv', sep=',', header=1, names=businessdata, skip_blank_lines=True)
#business = pd.read_csv('FL_Yelp_Data_v2.csv')

business['Category'].str.split(',').apply(len)
#not sure where to declare the df part in the suggestions that use it.

print business[:6]

but I keep getting the following error:

但我一直收到以下错误:

TypeError: object of type 'float' has no len()

EDIT 2:

I GIVE UP. Thanks for all your help, but I'll have to figure something else.

我放弃。谢谢你的帮助,但我必须要想出别的东西。

5 个解决方案

#1

Assuming that Category is actually a list, you can use apply (per @EdChum's suggestion):

假设Category实际上是一个列表,你可以使用apply(per @ EdChum的建议):

business['# Categories'] = business.Category.apply(len)

If not, you first need to parse it and convert it into a list.

如果没有,您首先需要解析它并将其转换为列表。

df['Category'] = df.Category.map(lambda x: [i.strip() for i in x.split(",")])

Can you show some sample output of EXACTLY what this column looks like (including correct quotations)?

您能否显示一些样本输出完全符合此列的含义(包括正确的引用)?

P.S. @EdChum Thank you for your suggestions. I appreciate them. I believe the list comprehension method may be faster, per a sample of some text data I tested with 30k+ rows of data:

附: @EdChum感谢您的建议。我很感激他们。我相信列表理解方法可能更快,根据我用30k +行数据测试的一些文本数据的样本:

%%timeit
df.Category.str.strip().str.split(',').apply(len)
10 loops, best of 3: 44.8 ms per loop

%%timeit
df.Category.map(lambda x: [i.strip() for i in x.split(",")])
10 loops, best of 3: 28.4 ms per loop

Even accounting for the len function call:

甚至考虑len函数调用:

%%timeit
df.Category.map(lambda x: len([i.strip() for i in x.split(",")]))
10 loops, best of 3: 30.3 ms per loop

#2

This works:

business['# Categories'] = business['Category'].apply(lambda x: len(x.split(',')))

If you need to handle NA, etc, you can pass a more elaborate function instead of the lambda.

如果你需要处理NA等,你可以传递更复杂的函数而不是lambda。

#3

Use pd.read_csv to make the input easier:

使用pd.read_csv可以更轻松地输入:

business = pd.read_csv('FL_Yelp_Data_v2.csv')

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html

Once this is created, you can create a function to split the categories column by the "," and count the length of the resulting list. Use lambda and apply.

创建完成后,您可以创建一个函数,将“类别”列拆分为“,”,并计算结果列表的长度。使用lambda并申请。

#4

You can do this...

你可以这样做...

for i in business['Category'].tolist():
    business.loc[i, '#Categories'] = len(i.split(","))

#5

I had a similar doubt. I had count number of comma-separated words in each row . I resolved it in the following manner:

我有类似的疑问。我计算了每行中以逗号分隔的单词数。我通过以下方式解决了这个问题:

data['Number_of_Categories'] = data['Category'].apply(lambda x : len(str(x).split(',')))

data ['Number_of_Categories'] = data ['Category']。apply(lambda x:len(str(x).split(',')))

Basically I am first converting each row to string since Python is recognizing it as a float and then performing the 'len' function. Hope this helps

基本上我首先将每一行转换为字符串,因为Python将其识别为float,然后执行'len'函数。希望这可以帮助

#1