CODEWITHSUNDEEP
pythonpandasdataframe
Aman Singh
Aman Singh

Reputation: 1241

faster way of creating pandas dataframe from another dataframe

I have a dataframe with over 41500 records and 3 fields: ID,start_date and end_date.

I want to create a separate dataframe out of it with just 2 fields as: ID and active_years which will contain records having each identifiers against all the possible years that exists between the start_year and end_year range (inclusive of end year in the range).

This is what I'm doing right now, but for 41500 rows it takes more than 2 hours to finish.

df = pd.DataFrame(columns=['id', 'active_years'])
ix = 0

for _, row in raw_dataset.iterrows():

    st_yr = int(row['start_date'].split('-')[0]) # because dates are in the format yyyy-mm-dd
    end_yr = int(row['end_date'].split('-')[0])

    for year in range(st_yr, end_yr+1):

        df.loc[ix, 'id'] = row['ID']
        df.loc[ix, 'active_years'] = year
        ix = ix + 1

So is there any faster way to achieve this?

[EDIT] some examples to try and work around,

raw_dataset = pd.DataFrame({'ID':['a121','b142','cd3'],'start_date':['2019-10-09','2017-02-06','2012-12-05'],'end_date':['2020-01-30','2019-08-23','2016-06-18']})

print(raw_dataset)
     ID  start_date    end_date
0  a121  2019-10-09  2020-01-30
1  b142  2017-02-06  2019-08-23
2   cd3  2012-12-05  2016-06-18

# the desired dataframe should look like this
print(desired_df)
     id  active_years
0  a121  2019
1  a121  2020
2  b142  2017
3  b142  2018
4  b142  2019
5   cd3  2012
6   cd3  2013
7   cd3  2014
8   cd3  2015
9   cd3  2016

Upvotes: 0

Views: 646

Answers (1)

Xukrao
Xukrao

Reputation: 8634

Dynamically growing python lists is much faster than dynamically growing numpy arrays (which are the underlying data structure of pandas dataframes). See here for a brief explanation. With that in mind:

import pandas as pd

# Initialize input dataframe
raw_dataset = pd.DataFrame({
    'ID':['a121','b142','cd3'],
    'start_date':['2019-10-09','2017-02-06','2012-12-05'],
    'end_date':['2020-01-30','2019-08-23','2016-06-18'],
})

# Create integer columns for start year and end year
raw_dataset['start_year'] = pd.to_datetime(raw_dataset['start_date']).dt.year
raw_dataset['end_year'] = pd.to_datetime(raw_dataset['end_date']).dt.year

# Iterate over input dataframe rows and individual years
id_list = []
active_years_list = []
for row in raw_dataset.itertuples():
    for year in range(row.start_year, row.end_year+1):
        id_list.append(row.ID)
        active_years_list.append(year)

# Create result dataframe from lists
desired_df = pd.DataFrame({
    'id': id_list,
    'active_years': active_years_list,
})

print(desired_df)
# Output:
#     id  active_years
# 0  a121          2019
# 1  a121          2020
# 2  b142          2017
# 3  b142          2018
# 4  b142          2019
# 5   cd3          2012
# 6   cd3          2013
# 7   cd3          2014
# 8   cd3          2015
# 9   cd3          2016

Upvotes: 2

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