Count Number of Rows within Time Interval in Pandas Dataframe

Say we have this data:

list1, list2, list3 = [1,2,3,4], [1990, 1990, 1990, 1991], [2009, 2009, 2009, 2009]
df = pd.DataFrame(list(zip(list1, list2, list3)), columns = ['Index', 'Y0', 'Y1'])

> df

Index  Y0          Y1
1      1990        2009
2      1990        2009
3      1990        2009
4      1991        2009

I want to count, for each year, how many rows (“index”) fall within each year, but excluding the Y0.

So say we start at the first available year, 1990:

How many rows do we count? 0.

1991:

• Three (row 1, 2, 3)

1992:

• Four (row 1, 2, 3, 4)

…

2009:

• Four (row 1, 2, 3, 4)

So I want to end up with a dataframe that says:

Count  Year
0      1990     
3      1991     
4.     1992
...    ...    
4      2009     

My attempt:

df['Y0'] = pd.to_datetime(df['Y0'], format='%Y')
df['Y1'] = pd.to_datetime(df['Y1'], format='%Y')

# Group by the interval between Y0 and Y1 
df = d.groupby([d['Y0'].dt.year, d['Y1'].dt.year]).agg({'count'})
df.columns = ['count', 'Y0 count', 'Y1 count']

# sum the total
df_sum = pd.DataFrame(df.groupby(df.index)['count'].sum())

But the result doesn’t look right.

Appreciate any help.

Answer

you could do:

min_year = df[['Y0', 'Y1']].values.min()
max_year = df[['Y0', 'Y1']].values.max()
year_range = np.arange(min_year, max_year+1)
counts = ((df[['Y0']].values < year_range) & (year_range<= df[['Y1']].values)).sum(axis=0)
o = pd.DataFrame({"counts": counts, 'year': year_range})

counts  year
0   0   1990
1   3   1991
2   4   1992
3   4   1993
4   4   1994
5   4   1995
6   4   1996
7   4   1997
8   4   1998
9   4   1999
10  4   2000
11  4   2001
12  4   2002
13  4   2003
14  4   2004
15  4   2005
16  4   2006
17  4   2007
18  4   2008
19  4   2009