# How to Get the Sum of Pandas Column

We will introduce how to get the sum of pandas dataframe `column`. It includes methods like calculating cumulative sum with `groupby`, and dataframe sum of columns based on conditional of other column values.

## Method to Get the Sum of Pandas `DataFrame` Column

First, we create a random array using the `NumPy` library and then get each column’s sum using the `sum()` function.

``````import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randint(0, 10, size=(10, 4)), columns=list("1234"))
print(df)
Total = df["1"].sum()
print("Column 1 sum:", Total)
Total = df["2"].sum()
print("Column 2 sum:", Total)
Total = df["3"].sum()
print("Column 3 sum:", Total)
Total = df["4"].sum()
print("Column 4 sum:", Total)
``````

Pandas DataFrame `sum()` method sums the Pandas column.

If you run this code, you will get the output as follows.

``````   1  2  3  4
0  2  2  3  8
1  9  4  3  1
2  8  5  6  0
3  9  5  7  4
4  2  7  3  7
5  9  4  1  3
6  6  7  7  3
7  0  4  2  8
8  0  6  6  4
9  5  8  7  2
Column 1 sum: 50
Column 2 sum: 52
Column 3 sum: 45
Column 4 sum: 40
``````

## Cumulative Sum With `groupby`

We can get the Pandas cumulative sum by using the `groupby` method. Consider the following `DataFrame` with `Date`, `Fruit`, and `Sale` columns:

``````import pandas as pd

df = pd.DataFrame(
{
"Date": ["08/09/2018", "10/09/2018", "08/09/2018", "10/09/2018"],
"Fruit": ["Apple", "Apple", "Banana", "Banana"],
"Sale": [34, 12, 22, 27],
}
)
``````

If we want to calculate the cumulative sum of Sale per Fruit and for every date, we can do:

``````import pandas as pd

df = pd.DataFrame(
{
"Date": ["08/09/2018", "10/09/2018", "08/09/2018", "10/09/2018"],
"Fruit": ["Apple", "Apple", "Banana", "Banana"],
"Sale": [34, 12, 22, 27],
}
)

print(df.groupby(by=["Fruit", "Date"]).sum().groupby(level=[0]).cumsum())
``````

After running the above codes, we will get the following output, which shows the cumulative sum of `Fruit` for each date:

``````Fruit  Date         Sale
Apple  08/09/2018    34
10/09/2018    46
Banana 08/09/2018    22
10/09/2018    49

``````

## Method to Get the Sum of Columns Based on Conditional of Other Column Values

This method provides functionality to get the sum if the given condition is `True` and replace the sum with given value if the condition is `False`. Consider the following code,

``````import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randn(5, 3), columns=list("xyz"))

df["sum"] = df.loc[df["x"] > 0, ["x", "y"]].sum(axis=1)

df["sum"].fillna(0, inplace=True)
print(df)
``````

In above code, we add new column `sum` to `DataFrame`. `sum` element is the sum of first two columns `['x','y']` if `['x']` is greater than 1, otherwise we replace `sum` with `0`.

After running the code, we will get the following output (values might differ in your case).

``````          x         y         z       sum
0 -1.067619  1.053494  0.179490  0.000000
1 -0.349935  0.531465 -1.350914  0.000000
2 -1.650904  1.534314  1.773287  0.000000
3  2.486195  0.800890 -0.132991  3.287085
4  1.581747 -0.667217 -0.182038  0.914530
``````