How can I fill a dataframe on specific column names of another dataframe

Question

I have constructed a pretty basic dataframe where the column names are years:

import pandas as pd
column_names = [x for x in range(2000,2005)]
df = pd.DataFrame(columns=column_names)

Which of course gives me a dataframe where the column names are years and currently has no entries.

2000    2001    2002    2003    2004

I also have a different dataframe where one column has specific dates and the second column has the corresponding year. I call this dataframe set0 because I will continously add more sets which are then numbered (set1,set2,etc.).

data = {'Date': ['2001-06-08', '2002-05-23', '2002-05-24', '2003-06-23'],
        'Year': [2001, 2002, 2002, 2003]}
df2 = pd.DataFrame(data)

Date    Year
0   2001-06-08  2001
1   2002-05-23  2002
2   2002-05-24  2002
3   2003-06-23  2003

Now what I want to do is to create something like this: It takes the first dataframe, adds a first column which has a name of a certain dataset, in this case set0. I will then group this dataset by years and if I have an entry for a year I can create this:

set_name 2000   2001    2002    2003    2004
set0        0      1       2       1       0

I have found nothing similar on the web. I have done the grouping but then wasn't able to add the entries in the corresponding columns. Any help or hint is much appreciated!

Answer 1

Does this answer your question?

import pandas as pd

column_names = [x for x in range(2000, 2005)]

df = pd.DataFrame(index=column_names)

data = {
    'Date': ['2001-06-08', '2002-05-23', '2002-05-24', '2003-06-23'],
    'Year': [2001, 2002, 2002, 2003]
}

df2 = pd.DataFrame(data)
df2_grouped = df2.groupby('Year').count()['Date']

df['set0'] = df2_grouped
df = df.fillna(0).reset_index(names='set_name').pivot_table(columns='set_name')

print(df)

Result:

set_name  2000  2001  2002  2003  2004
set0       0.0   1.0   2.0   1.0   0.0