# Pandas Rolling Sum

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• Post category:Pandas / Python

Pandas `DataFrame.rolling(n).sum()` function is used to get the sum of rolling windows over a DataFrame. Using this function we can get the rolling sum for single or multiple columns of a given Pandas DataFrame. Where `n `is the size of window. `series.rolling(n).sum()` function also is used to calculate the rolling sum for Pandas Series. In this article, I will explain how to calculate the rolling sum of pandas DataFrame and series with examples.

## 1. Quick Examples of Pandas Rolling Sum

If you are in a hurry, below are some quick examples of pandas rolling sum of DataFrame and Series.

``````
# Example 1: Rolling sum of pandas series
ser2 = ser.rolling(3).sum()

# Example 2: Use series.rolling().sum() function
ser2 = ser.rolling(3, center=True).sum()

# Example 3: Rolling sum of single columns
df2 = df['A'].rolling(3).sum()

# Example 4: Use DataFrame.rolling().sum()
# Rolling Sum of multiple columns
df2 = df.rolling(2).sum()

# Example 5: Rolling Sum of multiple columns
df2=df.rolling(window=2).sum()
``````

## 2. Rolling Sum of Pandas Series

Pandas Series is a one-dimensional, Index-labeled data structure available in the Pandas library. It can store all the datatypes such as strings, integers, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.

Now, let’s create pandas series using a list of values.

``````
import pandas as pd
import numpy as np
# Initialize pandas series
ser = pd.Series([0, 3, 6, 9, 8, 12])
print(ser)
``````

Yields below output.

``````
# Output:
0     0
1     3
2     6
3     9
4     8
5    12
dtype: int64
``````

### 2.1 Using Series.rolling().sum() Example

You can use `Series.rolling()` function to get a rolling window over a pandas series and then apply the `sum()` function to get the rolling sum over the window. Here, `3 `is the size of the window you want to use it will return a rolling sum for all the numerical columns in the Series.

``````
# Rolling sum of pandas series
ser2 = ser.rolling(3).sum()
print(ser2)
``````

Yields below output.

``````
# Output:
0     NaN
1     NaN
2     9.0
3    18.0
4    23.0
5    29.0
dtype: float64
``````

Now, let’s do the rolling sum series with `window=3`. By default, the result is set to the right edge of the window. You can change this to the center of the window by setting `center=True`.

``````
# Use series.rolling().sum() function
ser2 = ser.rolling(3, center=True).sum()
print(ser2)
``````

Yields below output.

``````
# Output:
0     NaN
1     9.0
2    18.0
3    23.0
4    29.0
5     NaN
dtype: float64
``````

## 3. Rolling Sum of Pandas DataFrame

Now, Let’s create Pandas DataFrame using data from a Python dictionary, where the columns are `A` and `B`.

``````
import pandas as pd
import numpy as np
# Initialize pandas dataframe
df = pd.DataFrame({'A': [0,2,4,5,8,10,12],
'B': [0,1,3,7,9,np.nan,15]})
print(df)
``````

Yields below output.

``````
# Output:
A     B
0   0   0.0
1   2   1.0
2   4   3.0
3   5   7.0
4   8   9.0
5  10   NaN
6  12  15.0
``````

### 3.1 Use DataFrame.rolling().sum()Example

You can get the three rolling sums of the `"A"` column, you apply the `rolling()` function with a window size of three and then apply the `sum()` function. You get NaN values in the first two rows because you cannot calculate the rolling sum as there are no preceding values to make the three windows complete. The third row is `6.0` which is the sum of `"A"` over the three windows containing `0`, `2`, and `4`.

``````
# Rolling sum of single columns
df2 = df['A'].rolling(3).sum()
print(df2)
``````

Yields below output.

``````
# Output:
0     NaN
1     NaN
2     6.0
3    11.0
4    17.0
5    23.0
6    30.0
Name: A, dtype: float64
``````

### 3.2 Rolling Sum of Multiple Columns

Similarly, You can use `DataFrame.rolling()` function to get the two windows rolling sum of multiple columns and then apply the `sum()` function to get the rolling sum over the window. It will calculate the rolling sum for all the numerical columns in the DataFrame. For example, let’s get the two rolling sums of multiple columns in DataFrame.

``````
# Use DataFrame.rolling().sum()
# Rolling Sum of multiple columns
df2 = df.rolling(2).sum()
print(df2)

# Rolling Sum of multiple columns
df2=df.rolling(window=2).sum()
print(df2)
``````

Yields below output.

``````
# Output:
A     B
0   NaN   NaN
1   2.0   1.0
2   6.0   4.0
3   9.0  10.0
4  13.0  16.0
5  18.0   NaN
6  22.0   NaN
``````

## 4. Complete Example For Rolling Sum

``````
import pandas as pd
import numpy as np
# Initialize pandas dataframe
df = pd.DataFrame({'A': [0,2,4,5,8,10,12],
'B': [0,1,3,7,9,np.nan,15]})
print(df)

# Rolling sum of single columns
df2 = df['A'].rolling(3).sum()
print(df2)

# Use DataFrame.rolling().sum()
# Rolling Sum of multiple columns
df2 = df.rolling(2).sum()
print(df2)
``````

## 5. Conclusion

In this article, I have explained how to get pandas rolling sum of Series and DataFrame by using `series.rolling().sum()`, and `DataFrame.rolling().sum()` functions respectively.

Happy Learning !!

## References

### Malli

I am Mallikarjuna an experienced technical writer with a passion for translating complex Python concepts into clear, concise, and user-friendly documentation. Over the years, I have written hundreds of articles in Pandas, NumPy, Python, and I take pride in my ability to bridge the gap between technical experts and end-users by delivering well-structured, accessible, and informative content.