To calculate the cumulative sum over columns in a DataFrame, or the cumulative sum of the values of a Series in pandas, the easiest way is to use the pandas **cumsum()** function.

```
df.cumsum() # Calculate cumulative sum for all columns
df["Column"].cumsum() #calculate cumulative sum for 1 column
```

You can also use the numpy cumsum() function to calculate the cumulative sum for a column or Series.

`np.cumsum(df["Column"])`

When working with data, many times we want to calculate summary statistics to understand our data better. One such statistic is the cumulative sum, or the additive total of a list of numbers after each element of the list.

Finding the cumulative sum of a column, or the cumulative sum for all columns or rows in a DataFrame using pandas is easy. We can use the pandas **cumsum()** function to find the cumulative sum of a column of numbers, or for all columns or rows in a DataFrame.

Let’s say we have the following DataFrame.

```
df = pd.DataFrame({'Age': [43,23,71,49,52,37],
'Test_Score':[90,87,92,96,84,79]})
print(df)
# Output:
Age Test_Score
0 43 90
1 23 87
2 71 92
3 49 96
4 52 84
5 37 79
```

To get the cumulative sum for all columns, we can call the pandas **cumsum()** function.

```
print(df.cumsum())
# Output:
Age Test_Score
0 43 90
1 66 177
2 137 269
3 186 365
4 238 449
5 275 528
```

If we only want to get the cumulative sum of just one column, we can do this using the pandas **cumsum()** function in the following Python code:

```
print(df["Test_Score"].cumsum())
# Output:
0 90
1 177
2 269
3 365
4 449
5 528
Name: Test_Score, dtype: int64
```

## Calculating the Cumulative Sum by Row in pandas DataFrame

We can also calculate the cumulative sum by row by pass “axis=1” to the **cumsum()** function.

Below is how to find the cumulative sum across the rows of a pandas DataFrame using the same DataFrame from above.

```
print(df.cumsum(axis=1))
# Output:
Age Test_Score
0 43 133
1 23 110
2 71 163
3 49 145
4 52 136
5 37 116
```

## Calculating the Cumulative Sum of a Column With Missing Values

As many of us know, when working with data, sometimes we have to work with messy data or data with missing values. Let’s take our DataFrame from above and add a few NaN values.

```
df = pd.DataFrame({'Age': [43,np.NaN,71,49,np.NaN,37],
'Test_Score':[90,87,92,np.NaN,84,79]})
print(df)
# Output:
Age Test_Score
0 43.0 90.0
1 NaN 87.0
2 71.0 92.0
3 49.0 NaN
4 NaN 84.0
5 37.0 79.0
```

If you want to calculate the cumulative sum of a column with missing values, by default, the **cumsum()** function will ignore those missing values.

```
print(df.cumsum())
# Output:
Age Test_Score
0 43.0 90.0
1 NaN 177.0
2 114.0 269.0
3 163.0 NaN
4 NaN 353.0
5 200.0 432.0
```

If you want the **cumsum()** function to include the NaN values in the sum operation, you can pass “skipna=False”.

```
print(df.cumsum(skipna=False))
# Output:
Age Test_Score
0 43.0 90.0
1 NaN 177.0
2 NaN 269.0
3 NaN NaN
4 NaN NaN
5 NaN NaN
```

## Using the numpy cumsum() Function to Calculate Cumulative Sum of a Column

We can also use the numpy **cumsum()** function to calculate the cumulative sum of columns in a pandas DataFrame.

Let’s say we have the same dataset from above.

To get the cumulative sum of the numbers in the column “Test_Score”, we can use the numpy **cumsum()** function in the following Python code:

```
print(np.cumsum(df["Test_Score"]))
# Output:
0 90
1 177
2 269
3 365
4 449
5 528
Name: Test_Score, dtype: int64
```

As you can see above, this is the same value we received from the pandas **cumsum()** function.

Hopefully this article has been helpful for you to understand how to find the cumulative sum of numbers in a Series or columns in a DataFrame in pandas.