Coding Ref

The `cumsum()`

function in Pandas is used to compute the cumulative sum of the values in a column or series. The cumulative sum is the sum of all the values in a series up to that point, including the current value.

For example, if the series is [1, 2, 3, 4, 5], the cumulative sum at each point would be [1, 3, 6, 10, 15].

Here's an example of using the `cumsum()`

function in Pandas:

main.py

```
import pandas as pd
# create a sample series
s = pd.Series([1, 2, 3, 4, 5])
# compute the cumulative sum of the series
s_cumsum = s.cumsum()
# display the result
print(s_cumsum)
```

This will compute the cumulative sum of the series and return a new series with the same index as the original series.

The output will be:

output

```
0 1
1 3
2 6
3 10
4 15
dtype: int64
```

You can also use the `cumsum()`

function on a dataframe to compute the cumulative sum of the values in one or more columns. For example:

main.py

```
# create a sample dataframe
df = pd.DataFrame({"A": [1, 2, 3, 4, 5],
"B": [2, 3, 4, 5, 6],
"C": [3, 4, 5, 6, 7]})
# compute the cumulative sum of the A and B columns
df_cumsum = df[["A", "B"]].cumsum()
# display the result
print(df_cumsum)
```

This will compute the cumulative sum of the `A`

and `B`

columns in the dataframe and return a new dataframe with the same index as the original dataframe. The output will be:

outupt

```
A B
0 1 2
1 3 5
2 6 9
3 10 14
4 15 20
```

As you can see, the `cumsum()`

function is useful for computing the cumulative sum of a series or dataframe, which can be useful for a variety of analysis and visualization tasks.

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