To find the means of the columns in a DataFrame, or the average value of a Series in pandas, the easiest way is to use the pandas **mean()** function.

`df.mean()`

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

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

When working with data, many times we want to calculate summary statistics to understand our data better. One such statistic is the mean, or the average of a number.

Finding the mean of a column, or the mean for all columns or rows in a DataFrame using pandas is easy. We can use the pandas **mean()** function to find the average value 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 means for all columns, we can call the pandas **mean()** function.

```
print(df.mean())
# Output:
Age 45.833333
Test_Score 88.000000
dtype: float64
```

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

```
print(df["Test_Score"].mean())
# Output:
88.0
```

## Using numpy mean to Calculate Averages in pandas DataFrame

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

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

```
print(np.mean(df["Test_Score"]))
# Output:
88.0
```

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

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

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