Python：从列表创建2列数据框并在列表上进行计算

Question

I'm taking my first baby steps in python and I'm hoping you can help me with the following:

I have a list

scores = [1,1,1,2,2,2,3,3,3,3,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5]

And I would like to create a dataframe that has scores in column 1 and the frequency of the scores in column 2.

Any help or pointers is appreciated. Thanks!

My first attempt was not very good:

scores = [1,1,1,2,2,2,3,3,3,3,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5]
freq = []
df = {'col1': scores, 'col2': freq}

Answer 1

First off, create a Counter object to count the frequency of each score.

In [1]: scores = [1,1,1,2,2,2,3,3,3,3,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5]

In [2]: from collections import Counter

In [3]: score_counts = Counter(scores)

In [4]: score_counts
Out[4]: Counter({5: 12, 4: 8, 3: 4, 1: 3, 2: 3})

In [5]: import pandas as pd

In [6]: pd.DataFrame.from_dict(score_counts, orient='index')
Out[6]: 

    0
1   3
2   3
3   4
4   8
5  12

[5 rows x 1 columns]

The part that may trip up some users is the pd.DataFrame.from_dict() . The documentation is here: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.from_dict.html

Answer 2

I would use value_counts (eg here for the Series docs). Note that I've changed the data here a little:

>>> import pandas as pd
>>> scores = [1]*3 + [2]*3 + [3]*4 + [4]*1 + [5]*4
>>> pd.value_counts(scores)
5    4
3    4
2    3
1    3
4    1
dtype: int64

And you can change the output as you like:

>>> pd.value_counts(scores, ascending=True)
4    1
1    3
2    3
3    4
5    4
dtype: int64
>>> pd.value_counts(scores).sort_index()
1    3
2    3
3    4
4    1
5    4
dtype: int64
>>> pd.value_counts(scores).sort_index().to_frame()
   0
1  3
2  3
3  4
4  1
5  4

Answer 3

To calculate the frequencies:

freq = {}
for score in scores:
     freq[score] = freq.get(score, 0) + 1

This will give you a dictionary with keys mapping to the frequency of the key values. Then to create two columns you can just create a dictionary such as:

data = {'scores': scores, 'freq': freq}

You could also accomplish this using a list comprehension where the index of a list is equal to your score and the value is the frequency, but if the range of your scores is large this will require a large, sparse array, so you may be better off using a dictionary as above