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

`df.median()`

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

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

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

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

```
print(df.median())
# Output:
Age 46.0
Test_Score 88.5
dtype: float64
```

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

```
print(df["Test_Score"].median())
# Output:
88.5
```

This is the same output as if we called the pandas quantile() function for the 50th percentile:

```
print(df["Test_Score"].quantile(0.5))
# Output:
88.5
```

## Using numpy median to Calculate Medians in pandas DataFrame

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

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

```
print(np.median(df["Test_Score"]))
# Output:
88.5
```

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

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

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