如何从 pandas 数据帧中的逗号分隔值计算以特定 substring 开头的字符串的出现次数？

Question

I am new to Python. I am working with a dataframe (360000 rows and 2 columns) that looks something like this: business_id date

P01         2019-07-6 , 2018-06-05, 2019-07-06...
P02         2016-03-6 , 2019-04-10
P03         2019-01-02

The date column has dates separated by comma and dates from year 2010-2019. I am trying to count only the dates for each month that are in year 2019 for each business id. Specifically, I am looking for the output:

Can anyone please help me? Thanks.

Answer 1

You can do as follows

1. first use str.split to separate the dates in each cell to a list,
2. then explode to flatten the lists
3. convert to datetime with pd.to_datetime and extract the month
4. finally use pd.crosstab to pivot/count the months and join.

Altogether:

s = pd.to_datetime(df['date'].str.split('\s*,\s*').explode()).dt.to_period('M')

out = pd.crosstab(s.index,s )

# this gives the expected output
df.join(out)

Output ( out ):

date   2016-03  2018-06  2019-01  2019-04  2019-07
row_0                                             
0            0        1        0        0        2
1            1        0        0        1        0
2            0        0        1        0        0

Answer 2

If they are not datetime objects yet, you may want to start by converting the column (series) to datetime: pd.to_datetime() Note: the format parameter.

Then you can access the datetime attributes through .dt

ie df[df.COLUMN_NAME.dt.month == 5]