How to use ffill() in Pandas

How to use ffill() in Pandas

The ffill method in Pandas is used to forward-fill missing values in a Series or DataFrame.

This method replaces missing values with the last non-null value in the Series or DataFrame, and is commonly used to fill in missing data in time series data.

For example, consider the following Series object:

import pandas as pd

s = pd.Series([1, 2, None, 4, 5, None, 7, 8])

This Series has eight elements, with the third and sixth elements being None (i.e. missing values).

To forward-fill the missing values in this Series, you could do the following:

import pandas as pd

s = pd.Series([1, 2, None, 4, 5, None, 7, 8])

# Forward-fill the missing values
s_filled = s.ffill()

# Print the resulting Series
print(s_filled)

0    1.0
1    2.0
2    2.0
3    4.0
4    5.0
5    5.0
6    7.0
7    8.0
dtype: float64

In the code above, the ffill method is applied to the Series, which replaces the missing values with the last non-null value in the Series.

The result is a new Series object with the missing values filled in. In this case, the resulting Series has the values 1, 2, 2, 4, 5, 5, 7, 8.

You can also use the ffill method on a DataFrame object to forward-fill missing values in multiple columns.

For example, consider the following DataFrame:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, None, 4, 5, None, 7, 8],
    'B': [10, 20, 30, None, 50, 60, None, 80],
    'C': [100, 200, 300, 400, None, 600, 700, 800]
})

This DataFrame has three columns A, B, and C, with eight rows of data. Some of the elements in the DataFrame are missing values, indicated by None.

To forward-fill the missing values in this DataFrame, you could do the following:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, None, 4, 5, None, 7, 8],
    'B': [10, 20, 30, None, 50, 60, None, 80],
    'C': [100, 200, 300, 400, None, 600, 700, 800]
})

# Forward-fill the missing values
df_filled = df.ffill()

# Print the resulting DataFrame
print(df_filled)

In the code above, the ffill method is applied to the DataFrame, which replaces the missing values with the last non-null value in each column.

The result is a new DataFrame object with the missing values filled in:

     A     B      C
0  1.0  10.0  100.0
1  2.0  20.0  200.0
2  2.0  30.0  300.0
3  4.0  30.0  400.0
4  5.0  50.0  400.0
5  5.0  60.0  600.0
6  7.0  60.0  700.0
7  8.0  80.0  800.0