1 | initial version |
One efficient way to complete missing rows in a pandas dataframe is to use the .reindex()
method. This method allows you to specify a new index and will add any missing rows with NaN values.
For example, if you have a dataframe with a datetime index and there are missing dates, you can complete the missing rows with NaN values using the following code:
import pandas as pd
# create a sample dataframe
dates = pd.date_range('2021-01-01', '2021-01-10', freq='D')
data = {'col1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'col2': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']}
df = pd.DataFrame(data, index=dates)
# create a new index with all dates
new_index = pd.date_range('2021-01-01', '2021-01-15', freq='D')
# reindex the dataframe with the new index
df = df.reindex(new_index)
print(df)
The output will be a dataframe with 5 missing rows, filled with NaN values:
col1 col2
2021-01-01 1.0 a
2021-01-02 2.0 b
2021-01-03 3.0 c
2021-01-04 4.0 d
2021-01-05 5.0 e
2021-01-06 6.0 f
2021-01-07 7.0 g
2021-01-08 8.0 h
2021-01-09 9.0 i
2021-01-10 10.0 j
2021-01-11 NaN NaN
2021-01-12 NaN NaN
2021-01-13 NaN NaN
2021-01-14 NaN NaN
2021-01-15 NaN NaN