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

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

You can also use the numpy **sum()** function.

`np.sum(df["Column"]) #calculate sum for 1 column`

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

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

```
print(df.sum())
# Output:
Age 275
Test_Score 528
dtype: int64
```

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

```
print(df["Test_Score"].sum())
# Output:
528
```

If you want to see how the sum is calculated step by step, you can use the pandas cumsum() function and return a Series for each column with the cumulative sum at each point.

## Using numpy sum to Calculate a Sum in pandas DataFrame

We can also use the numpy **sum()** function to calculate the sum of the numbers in a column in a pandas DataFrame.

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

```
print(np.sum(df["Test_Score"]))
# Output:
528
```

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

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

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