Unable to find date in pandas - pandas

Question

I have a dataset in this form:
company_name date
0 global_infotech 2019-06-15
1 global_infotech 2020-03-22
2 global_infotech 2020-08-30
3 global_infotech 2018-06-19
4 global_infotech 2018-06-15
5 global_infotech 2018-02-15
6 global_infotech 2018-11-22
7 global_infotech 2019-01-15
8 global_infotech 2018-12-15
9 global_infotech 2019-06-15
10 global_infotech 2018-12-19
11 global_infotech 2019-12-31
12 global_infotech 2019-02-18
13 global_infotech 2018-06-16
14 global_infotech 2019-02-10
15 global_infotech 2019-03-15
16 Qualcom 2019-07-11
17 Qualcom 2018-01-11
18 Qualcom 2018-05-29
19 Qualcom 2018-10-06
20 Qualcom 2018-11-11
21 Qualcom 2019-08-17
22 Qualcom 2019-02-22
23 Qualcom 2019-10-16
24 Qualcom 2018-06-22
25 Qualcom 2018-06-14
26 Qualcom 2018-06-16
27 Syscin 2018-02-10
28 Syscin 2019-02-16
29 Syscin 2018-04-12
30 Syscin 2018-08-22
31 Syscin 2018-09-16
32 Syscin 2019-04-20
33 Syscin 2018-02-28
34 Syscin 2018-01-19
CONSIDERING TODAY'S DATE AS 1st JANUARY 2020, I WANT TO WRITE A CODE TO FIND THE NUMBER OF TIMES EACH COMPANY NAME IS OCCURING IN LAST 3 MONTHS. For example, suppose from 1st Oct 2019 to 1st Jan 2020, golbal_infotech's name is appearing 5 times, then 5 should appear infront of every global_infotech value like:
company_name date appearance_count_last_3_months
0 global_infotech 2019-06-15 5
1 global_infotech 2020-03-22 5
2 global_infotech 2020-08-30 5
3 global_infotech 2018-06-19 5
4 global_infotech 2018-06-15 5
5 global_infotech 2018-02-15 5
6 global_infotech 2018-11-22 5
7 global_infotech 2019-01-15 5
8 global_infotech 2018-12-15 5
9 global_infotech 2019-06-15 5
10 global_infotech 2018-12-19 5
11 global_infotech 2019-12-31 5
12 global_infotech 2019-02-18 5
13 global_infotech 2018-06-16 5
14 global_infotech 2019-02-10 5
15 global_infotech 2019-03-15 5

Answer 1

IIUC:
you can create a custom function:
def getcount(company,month=3,df=df):
df=df.copy()
df['date']=pd.to_datetime(df['date'],format='%Y-%m-%d',errors='coerce')
df=df[df['company_name'].eq(company)]
val=df.groupby(pd.Grouper(key='date',freq=str(month)+'m')).count().max().get(0)
df['appearance_count_last_3_months']=val
return df
getcount('global_infotech')
#OR
getcount('global_infotech',3)
Update:
since you have 92 different companies so you can use for loop:
lst=[]
for x in df['company_name'].unique():
lst.append(getcount(x))
out=pd.concat(lst)
If you print out then you will get your desired output

Answer 2

You can first filter the data for the last 3 months, and then groupby company name and merge back into the original dataframe.
import pandas as pd
from datetime import datetime
from dateutil.relativedelta import relativedelta
# sample data
df = pd.DataFrame({
'company_name': ['global_infotech', 'global_infotech', 'Qualcom','another_company'],
'date': ['2019-02-18', '2021-07-02', '2021-07-01','2019-02-18']
})
df['date'] = pd.to_datetime(df['date'])
# filter for last 3 months
summary = df[df['date']>=datetime.now()-relativedelta(months=3)]
# groupby then aggregate with desired column name
summary = summary.rename(columns={'date':'appearance_count_last_3_months'})
summary = summary.groupby('company_name')
summary = summary.agg('count')
# merge summary back into original df, filling missing values with 0
df = df.merge(summary, left_on='company_name', right_index=True, how='left')
df['appearance_count_last_3_months'] = df['appearance_count_last_3_months'].fillna(0).astype('int')
# result:
df
company_name date appearance_count_last_3_months
0 global_infotech 2019-02-18 1
1 global_infotech 2021-07-02 1
2 Qualcom 2021-07-01 1
3 another_company 2019-02-18 0