如何计算两列中唯一字符串的数量？

Question

I have a DataFrame with two columns containing strings, like:

col1 --- col2
Ernst --- Jim
Peter --- Ernst
Bill --- NaN
NaN --- Doug
Jim --- Jake

Now I want to create a new DataFrame with a list of unique strings in the first column and in the second column the number of occurrences of each string in the 2 original columns, like:

str --- occurences
Ernst --- 2
Peter --- 1
Bill --- 1
Jim --- 2
Jake --- 1
Doug --- 1

How do I do that in the most efficient way? Thanks!

Answer 1

First combine your original two columns into one:

In [127]: s = pd.concat([df.col1, df.col2], ignore_index=True)

In [128]: s
Out[128]: 
0    Ernst
1    Peter
2     Bill
3      NaN
4      Jim
5      Jim
6    Ernst
7      NaN
8     Doug
9     Jake
dtype: object

and then use value_counts :

In [129]: s.value_counts()
Out[129]: 
Ernst    2
Jim      2
Bill     1
Doug     1
Jake     1
Peter    1
dtype: int64

Answer 2

I'd do that way (assuming you taking the data from a file your_file.txt and you want to print out the result):

from collections import Counter;

separator = ' --- '
with open('your_file.txt') as f:
    content = f.readlines()  # here you got a list of elements corresponding to the lines
    people = separator.join(content).split(separator) # here you got a list of all elements
    people_count = Counter(people) # you got here a dict-like object with key=name value=count
    for name, val in people_count.iteritems():
        # print the column the way you want
        print '{name}{separator}{value}'.format(name=name, separator=separator, value=val)

The example use the Counter object which allows you to efficiently count element from an iterable. the rest of the code is only string manipulation.

Answer 3

Try this:

df = pd.DataFrame({"col1" : ["Ernst", "Peter","Bill",np.nan,"Jim"],
 "col2" : ["Jim","Ernst",np.nan,"Doug","Jake"]})
print df
df1 = df.groupby("col1")["col1"].count()
df2 = df.groupby("col2")["col2"].count()
print df1.add(df2,fill_value=0)